DISPARITY STUDY FOR ENVER PUBLIC SCHOOLS RAFT REPORTfile… · In February 2014, the Denver Public Schools Board of Education (DPS) contracted with MGT of America, Inc., (MGT) to

Mr. Reginald Smith, Partner October 17, 2014 3800 Esplanade Way, Suite 210 Tallahassee, Florida 32311 P: 850.386.3191 F: 850.385.4501 [email protected]

DISPARITY STUDY FOR DENVER PUBLIC SCHOOLS

DRAFT REPORT

TABLE OF CONTENTS

PAGE 1.0 INTRODUCTION ............................................................................................................................. 1-1 1.1 Background ........................................................................................................................ 1-1 1.2 Overview of Study Approach ............................................................................................. 1-1 1.3 Report Organization .......................................................................................................... 1-2 2.0 LEGAL FRAMEWORK ..................................................................................................................... 2-1

2.1 Introduction ....................................................................................................................... 2-1 2.2 Standards of Review for Race-Specific and Gender-Specific Programs ............................ 2-2 2.3 To Withstand Strict Scrutiny, an MWBE Program Must Be Based on Thorough Evidence

Showing a Compelling Governmental Interest ................................................................. 2-5 2.4 Sufficiently Strong Evidence of Significant Statistical Disparities Between Qualified

Minorities Available and Minorities Utilized Will Satisfy Strict Scrutiny and Justify a Narrowly Tailored M/WBE Program ............................................... 2-7

2.5 The Governmental Entity or Agency Enacting an M/WBE Program Must Be Shown to Have Actively or Passively Perpetuated the Discrimination ........................... 2-10

2.6 To Withstand Strict Scrutiny, an M/WBE Program Must Be Narrowly Tailored to Remedy Identified Discrimination ................................................................ 2-11 2.7 Small Business Procurement Preferences ....................................................................... 2-15 2.8 Conclusions ...................................................................................................................... 2-16 3.0 RELEVANT MARKET AREA AND UTILIZATION ANALYSES .............................................................. 3-1 3.1 Chapter Definitions ............................................................................................................ 3-1 3.2 Data Collection and Management ..................................................................................... 3-3 3.3 Market Area Analysis ......................................................................................................... 3-2 3.4 Utilization Analysis ............................................................................................................ 3-5 3.5 Conclusion ....................................................................................................................... 3-11 4.0 AVAILABILITY AND DISPARITY ANALYSES ...................................................................................... 4-1 4.1 Chapter Definitions ............................................................................................................ 4-1 4.2 Availability Analysis ........................................................................................................... 4-3 4.3 Disparity Indices, Methodology and Results ..................................................................... 4-8 4.4 Conclusion ....................................................................................................................... 4-15 5.0 PRIVATE SECTOR ANALYSES .......................................................................................................... 5-1

5.1 Methodology ..................................................................................................................... 5-1 5.2 Collection and Management of Data ................................................................................ 5-2 5.3 Private Sector Utilization Analysis, Commercial Permits .................................................. 5-3

5.0 PRIVATE SECTOR ANALYSES (Continued) ...................................................................................... 5-1

5.4 Analysis of Race, Ethnicity, and Gender Effects on Self-Employment .............................. 5-7 5.5 Analysis of Race, Ethnicity, and Gender Effects on Individual Earnings .......................... 5-18 5.6 Conclusion ....................................................................................................................... 5-22

6.0 ANECDOTAL ANALYSIS .................................................................................................................. 6-1 6.1 Methodology ..................................................................................................................... 6-1 6.2 Demographics .................................................................................................................... 6-4 6.3 Barriers to Doing Business with Denver Public Schools .................................................... 6-7 6.4 Prime Contractor Practices .............................................................................................. 6-10

6.5 Access to Capital .............................................................................................................. 6-11 6.6 Disparate Treatment and Discrimination ........................................................................ 6-11 6.7 Other Noteworthy Comments ......................................................................................... 6-13 6.8 Suggested Remedies from Anecdotal Participants ......................................................... 6-14 6.9 Conclusions ...................................................................................................................... 6-14

7.0 FINDINGS ....................................................................................................................................... 7-1 7.1 Introduction ....................................................................................................................... 7-1 7.2 Findings .............................................................................................................................. 7-1

7.3 Commendations and Recommendations .......................................................................... 7-4 APPENDICES

Appendix A: Utilization Analyses

Appendices B - B-3: Custom Census Survey Instruments – Construction and Construction-Related Professional Services

Appendix C: Disparity Study Announcement

Appendices D - D-1: Public Hearing Notices

Appendix E: Survey of Vendors Instrument

Appendix F: Survey of Business Vendors Results

Appendix G: Personal Interview Guide

Appendix H: Private Sector Nexus

Appendix I: Public Use Microdata Sample (PUMS) Regression Analysis

1.0: INTRODUCTION

CHAPTER 1: INTRODUCTION

MGT of America, Inc. is pleased to submit the Denver Public Schools (DPS) Disparity Study to the Denver Public Schools Board of Education. The MGT team that conducted the Disparity Study for DPS is the most experienced team in the disparity study business. Because of this experience, MGT team members know how to navigate the challenges, obstacles, and volatility which can easily derail the most well-planned and executed study. The result is that the DPS Disparity Study delivers on the commitment to provide a study which is accurate, valid, reliable, and legally defensible.

The team of experts that dedicated their time, attention, and expertise to this study include:

Fred Seamon, Ph.D., Partner-In-Charge, MGT of America, Inc. Reggie Smith, Partner and Project Director, MGT of America, Inc. J. Vincent Eagan, Ph.D., J.D., Technical Advisor, MGT of America, Inc. Vernetta Mitchell, Senior Consultant, MGT of America, Inc. Hope Smith, MBA, Senior Consultant, MGT of America, Inc. Melanie Urso, CEO, L.S. Gallegos, MGT Subconsultant. Annaliese Oppenheim, CEO, Oppenheim Research, MGT Subconsultant.

1. BACKGROUND

In February 2014, the Denver Public Schools Board of Education (DPS) contracted with MGT of America, Inc., (MGT) to conduct a Disparity Study. This is the first disparity study conducted for DPS. The study reviewed procurement activity from years 2009 through 2013 for prime contracts and 2008 through 2012 for subcontracts. The purpose of the disparity study is to:

Determine whether DPS, either in the past or currently, engages in discriminatory practices in the solicitation and award of contracts in construction and construction-related professional services, to minority- and women-owned business enterprises (M/WBEs).

Determine if a legally justified need exists for an M/WBE program, in accordance with the guidelines set forth by the Supreme Court and relevant subsequent cases.

Provide recommendations regarding suggested modifications to DPS’s programs, including the consideration of race- and gender-based programs based on the study’s findings.

2. OVERVIEW OF STUDY APPROACH

MGT followed a carefully designed work plan that allowed study team members to fully analyze availability and utilization with regard to the utilization of M/WBEs in the procurement practices of DPS for the study period of 2009 through 2013 for prime contracts and 2008 through 2012 for subcontracts. The Disparity Study analyzed the business categories, defined in Chapter 3 – Market Area and Utilization Analyses, of:

Construction Construction-related Professional Services

Denver Public Schools Draft Report Chapter 1.0 October 2014 1-1

INTRODUCTION

The Disparity Study analyzed contracting opportunities in these business categories in order to identify with particularity whether a statistical disparity exists from which the existence of past or present public or private discrimination may be inferred in the relevant market area. In conducting the DPS’s study, MGT used the following research questions to guide the methodologies. These research questions are embedded in relevant chapters throughout this report:

Is there factual predicate evidence to support a race- and gender-conscious M/WBE program for DPS?

How does case law inform the research methodology in a particular region for a particular client?

Are there disparities between the availability and utilization of M/WBEs? If there are disparities, what are the most relevant causal factors that contribute directly or indirectly to the disparities between the availability and utilization of M/WBEs?

If there is statistical evidence of disparity, what is the cause of the disparity? Is there other evidence that supports and/or explains why there is disparity?

Does DPS passively engage in practices that result in disparities?

Are there statistically significant disparities in the utilization of M/WBEs by prime contractors on projects where there are no M/WBE goals?

Is there qualitative/anecdotal evidence of disparate treatment of M/WBE subcontractors by prime contractors?

The work plan consisted of, but was not limited to, the following major tasks:

Establish data parameters and finalize the work plan. Review the legal framework for disparity studies. Review policies, procedures, and programs. Conduct market area and utilization analyses. Determine the availability of qualified firms. Analyze the utilization and availability of firms for disparity, if any. Analyze the utilization for firms in the private marketplace. Examine the effects of race, ethnicity, and gender on business formation. Conduct a survey of business owners. Collect and analyze anecdotal information. Prepare and present draft and final reports for the study.

3. REPORT ORGANIZATION

In addition to this introductory chapter, this report consists of:

CHAPTER 2 Presents a review of the legal framework for disparity studies. It includes an overview of controlling legal precedents that impact remedial procurement programs inclusive of Tenth Circuit decisions.

CHAPTER 3 Presents the methodology used to determine the DPS relevant market area and statistical analysis of vendor utilization by DPS for procurement of contracting activities.


INTRODUCTION

CHAPTER 4 Provides a discussion of the availability of firms and the levels of disparity for vendors as well as a review of the multivariate analysis for DPS.

CHAPTER 5 Provides an analysis of the presence of disparity, if any, in the private sector and its effect on the ability of firms to win procurement contracts from DPS.

CHAPTER 6 Presents an analysis of anecdotal data collected from the survey of business owners, personal interviews, and public hearings.

CHAPTER 7 A summary of the findings based upon the analyses presented in previous chapters.

APPENDICES Additional analyses and documents used to conduct the study.

MGT recommends reading the report in its entirety to understand the basis for the findings and conclusions presented in Chapter 7 – Findings.


2.0: LEGAL FRAMEWORK

CHAPTER 2.0: LEGAL FRAMEWORK

2.1 INTRODUCTION

This chapter provides legal background for the Denver Public Schools (DPS) Disparity Study. This chapter is the standard MGT chapter for Tenth Circuit decisions on this legal material, reviewed for recent cases. The material that follows does not constitute legal advice to DPS on minority- and woman-owned business (M/WBE) programs, affirmative action, or any other matter. Instead, it provides a context for the statistical and anecdotal analyses that appear in subsequent chapters of this report.

The Supreme Court decisions in City of Richmond v. J.A. Croson Company1 (Croson) and later cases have established and applied the constitutional standards for government contracting affirmative action programs. This chapter identifies and analyzes those decisions, summarizing how courts evaluate the constitutionality of race-specific and gender-specific programs. Decisions of the Tenth Circuit, which includes the city of Denver, offer the most directly binding authority, but where those decisions leave issues unsettled, the review considers decisions from other circuits.

By way of a preliminary outline, the courts have determined that an affirmative action program involving governmental procurement of goods or services must meet the following standards:

A remedial race-conscious program is subject to strict judicial scrutiny under the Equal Protection Clause of the Fourteenth Amendment to the United States Constitution.

- Strict scrutiny has two basic components: a compelling governmental interest in the program and narrow tailoring of the program.

- To survive the strict scrutiny standard, a remedial race-conscious program must be based on a compelling governmental interest.

* “Compelling interest” means the government must prove past or present racial discrimination requiring remedial attention.

* There must be a specific “strong basis in the evidence” for the compelling governmental interest.

* Statistical evidence is preferred and possibly necessary as a practical matter; anecdotal evidence is permissible and can offer substantial support, but it probably cannot stand on its own. A program designed to address the compelling governmental interest must be narrowly tailored to remedy the identified discrimination. “Narrow tailoring” means the remedy must fit the findings.

* The evidence showing compelling interest must guide the tailoring very closely.

* Race-neutral alternatives must be considered first.

1 Richmond v. Croson, 488 U.S. 469 (1989).


LEGAL FRAMEWORK

- A lesser standard, intermediate judicial scrutiny, applies to programs that establish gender preferences.

* To survive the intermediate scrutiny standard, a remedial gender-conscious program must serve important governmental objectives and be substantially related to the achievement of those objectives.

* The evidence does not need to be as strong and the tailoring does not need to be as specific under the lesser standard.

2.2 STANDARDS OF REVIEW FOR RACE-SPECIFIC AND GENDER-SPECIFIC PROGRAMS

2.2 .1 RACE-SPECIF IC PROGRAMS: THE CROSON DECISION

Croson established the framework for testing the validity of programs based on racial discrimination. It held that programs established by local governments to set goals for the participation of minority- and woman-owned companies, must be supported by evidence of past discrimination in the awarding of their contracts.

In 1983, the Richmond City Council adopted a Minority Business Utilization Plan (the Plan) following a public hearing in which seven citizens testified about historical societal discrimination. In adopting the Plan, the council also relied on a study indicating that “while the general population of Richmond was 50 percent African American, only 0.67 percent of the city’s prime construction contracts had been awarded to minority businesses in the five-year period from 1978 to 1983.”2

The evidence before the council also established that a variety of state and local contractor associations had little or no minority business membership. The council relied on statements by a council member whose opinion was that “the general conduct of the construction industry in this area, the state, and around the nation, is one in which race discrimination and exclusion on the basis of race is widespread.”3 There was, however, no direct evidence of race discrimination on the part of the city in its contracting activities, and no evidence that the city’s prime contractors had discriminated against minority-owned subcontractors.4

The Plan required the city’s prime contractors to subcontract at least 30 percent of the dollar amount of each contract to one or more minority-owned business enterprises (MBEs). The Plan did not establish any geographic limits for eligibility. Therefore, an otherwise qualified MBE from anywhere in the United States could benefit from the 30 percent set-aside.

J.A. Croson Company, a non-MBE mechanical plumbing and heating contractor, filed a lawsuit against the city of Richmond alleging that the Plan was unconstitutional because it violated the Equal Protection Clause of the Fourteenth Amendment. After a considerable record of litigation and appeals, the Fourth Circuit struck down the Plan and the Supreme Court affirmed this decision.5 The Supreme Court

2 Croson, at 479-80. 3 Id. at 480. 4 Id. 5 Id. at 511.


LEGAL FRAMEWORK determined that strict scrutiny was the appropriate standard of judicial review for MBE programs, so that a race-conscious program must be based on a compelling governmental interest and be narrowly tailored to achieve its objectives. This standard requires a firm evidentiary basis for concluding that the underutilization of minorities is a product of past discrimination.6

Croson established that a local government could not rely on society-wide discrimination as the basis for a race-based program but, instead, was required to identify discrimination within the company’s jurisdiction,7 such as the local construction market.

2.2 .2 GENDER-SPECIF IC PROGRAMS

The Supreme Court has not addressed the specific issue of a gender-based classification in the context of a woman-owned business enterprise (WBE) program. Croson was limited to the review of an MBE program. In evaluating gender-based classifications, the Court has used what some call “intermediate scrutiny,” a less stringent standard of review than the “strict scrutiny” applied to race-based classifications. Intermediate scrutiny requires that classifying persons on the basis of sex “must carry the burden of showing an exceedingly persuasive justification for the classification.”8 The classification meets this burden “only by showing at least that the classification serves important governmental objectives and that the discriminatory means employed are substantially related to the achievement of those objectives.”9

The Tenth Circuit, on the second appeal in Concrete Works of Colorado v. City and County of Denver (Concrete Works IV),10 approved the constitutionality of a WBE program based on evidence comparable to that supporting an MBE program that the court also upheld in the same decision. Concrete Works IV offered no independent guidance on the level of evidence required to support a WBE program.

2.2 .3 AN OVERVIEW OF THE APPLICABLE CASE LAW

Croson did not find a compelling justification for a complete MBE program. Croson found the city of Richmond’s evidence to be inadequate as a matter of law. Nevertheless, subsequent cases in other federal circuits have addressed applications of the law that were not considered in Croson. Thus, it becomes necessary to look to the decisions of other federal circuits to predict the level of evidence that might be required to establish a government contracting affirmative action program.

The discussion in this review will also attend closely to the most relevant decisions in the area of government contracting. Justice O’Connor, distinguishing her majority opinion on affirmative action in law school admissions from her opinions in government contracting cases, wrote:

Context matters when reviewing race-based governmental action under the Equal Protection Clause. . . . Not every decision influenced by race is equally objectionable and strict scrutiny is designed to provide a framework for carefully examining the importance

6 Id. at 493. 7 Id. at 497 (1989). 8 Miss. Univ. for Women v. Hogan, 458 U.S. 718, 724 (1982) (quoting Kirchberg v. Feenstra, 450 U.S. 455, 461 (1981)); see also United States v. Virginia, 518 U.S. 515, 531 (1996), Nguyen v. U.S., 533 U.S. 53, 60 (2001). 9 Miss. Univ. for Women, 458 U.S. at 724 (quoting Wengler v. Druggists Mut. Ins. Co., 446 U.S. 142, 150 (1980)); see also Virginia, supra, at 533, Nguyen, supra, at 60. 10 321 F.3d 950 (10th Cir. 2003).


LEGAL FRAMEWORK

and the sincerity of the reasons advanced by the governmental decision-maker for the use of race in that particular context.11

Further, some caution must be exercised in relying upon opinions of the federal district courts, which make both findings of fact and holdings of law. As to holdings of law, the district courts are ultimately subject to rulings by their circuit courts. As to matters of fact, their decisions depend heavily on the precise record before them, in these cases frequently including matters such as evaluations of the credibility and expertise of witnesses. Such findings are not binding precedents outside their districts, even if they may indicate the kind of evidence and arguments that might succeed elsewhere.

Finally, the ways in which municipalities participate in national disadvantaged business enterprise (DBE) programs is a specialized issue distinct from that of supporting municipal programs, even if the same kinds of evidence and same levels of review apply. In Adarand Constructors, Inc. v. Peña,12 the Supreme Court decided that federal DBE programs should be examined by the same strict scrutiny standard that Croson mandated for state and local programs. Nevertheless, cases considering national DBE programs have many important distinctions from cases considering municipal programs, particularly when it comes to finding a compelling governmental interest.13 The national DBE cases have somewhat more application in determining whether a local program is narrowly tailored, as discussed in Section 2.6 below.

Thus, the majority of this review will be based on decisions of the federal circuit courts, with an emphasis on Tenth Circuit law, applying Croson to city or county programs designed to increase participation by M/WBEs in government contracting. That is not a large body of case law. While other cases are useful as to particular points, only a handful of circuit court cases have reviewed strictly local M/WBE programs and given clear, specific, and binding guidance about the adequacy of a complete factual record including thorough, local disparity studies with at least some statistical analysis.14

Only two circuit court decisions since Croson have passed definitively on thorough, strictly local disparity studies: Engineering Contractors Association of South Florida, Inc.,15 and Concrete Works IV.16 In Concrete Works IV, the Tenth Circuit, after holding that the district court had used an improper standard for weighing the evidence, went on to evaluate the evidence and determine that it was adequate as a matter of law to establish a compelling justification for Denver’s program. The Supreme Court refused to hear the appeal in Concrete Works IV,17 although the refusal in itself has no precedential effect. The dissent to that denial, written by Justice Scalia with the Chief Justice joining, argues that these cases may mark a split in approach among the circuits that will need to be reconciled. By contrast, in Engineering Contractors, the Eleventh Circuit ultimately upheld the district court finding that Dade County’s (FL)

11 Grutter v. Bollinger, 539 U.S. 306, 327 (2003). 12 Adarand Constructors, Inc. v. Peña, 515 U.S. 200 (1995). 13 See, e.g., Adarand v. Slater, 228 F.3d 1147 (10th Cir. 2000), cert. granted in part sub nom. Adarand Constructors, Inc. v. Mineta, 532 U.S. 941 (2001); cert. dismissed as improvidently granted, 534 U.S. 103 (2001); Sherbrooke Turf v. Minn. Dep’t of Transp., 345 F.3d 964 (8th Cir. 2003). 14 In one of the three directly applicable circuit court cases, the Third Circuit evaded the issue of compelling justification after lengthy discussion, holding that the Philadelphia M/WBE program was unconstitutional because it was not narrowly tailored. See Contractors Ass’n of Eastern Pa. v. City of Phila., 91 F.3d 586, 605 (3rd Cir. 1996). 15 122 F.3d 895 (11th Cir. 1997). 16 321 F.3d 950 (10th Cir. 2003). 17 Concrete Works v. City and County of Denver, Scalia, J. dissenting, 124 S.Ct. 556, 557-60 (2003).


LEGAL FRAMEWORK disparity studies were not adequate to support an M/WBE program, at least in the face of rebuttal evidence.18

2.3 TO WITHSTAND STRICT SCRUTINY, AN M/WBE PROGRAM MUST BE BASED ON THOROUGH EVIDENCE SHOWING A COMPELLING GOVERNMENTAL INTEREST

Croson identified two necessary factors for establishing racial discrimination sufficiently to demonstrate a compelling governmental interest in establishing an M/WBE program. First, there needs to be identified discrimination in the relevant market.19 Second, “the governmental actor enacting the set-aside program must have somehow perpetuated the discrimination to be remedied by the program,”20 either actively or at least passively with “the infusion of tax dollars into a discriminatory industry.”21

Although the Supreme Court in Croson did not specifically define the methodology that should be used to establish the evidentiary basis required by strict scrutiny, the Court did outline governing principles. Lower courts have expanded the Supreme Court’s Croson guidelines and have applied or distinguished these principles when asked to decide the constitutionality of state, county, and city programs that seek to enhance opportunities for minorities and women.

2.3 .1 COMPELLING INTERESTS OTHER THAN REMEDYING DISCRIMINATION

For government contracting programs, courts have yet to find a compelling governmental interest for affirmative action other than remedying discrimination in the relevant marketplace. In other arenas, diversity has served as a compelling governmental interest for affirmative action. For example, the Ninth Circuit upheld race-based admission standards at an experimental elementary school in order to provide a more real world education experience.22 In Petit v. Chicago,23 the Seventh Circuit relied on Grutter v. Bollinger in stating that urban police departments had “an even more compelling need for diversity” than universities and upheld the Chicago program “under the Grutter standards.”24

The holding that other compelling interests may support affirmative action does not yet appear to have any application to public contracting.25 The Tenth Circuit in Concrete Works did not consider any other compelling interests for the M/WBE program outside of remedying discrimination.

18 Compare Cone Corp. v. Hillsborough County, 908 F.2d 908 (11th Cir. 1990), an earlier decision of the Eleventh Circuit reversing summary judgment against an MBE program where more limited statistical evidence was found adequate to require a trial on the merits in the face of a relatively weak challenge. 19 Croson, 488 U.S. at 492, 509-10. 20 Coral Constr., 941 F.2d at 916. 21 Id. at 922. 22 Hunter v. Regents of Univ. of Ca., 190 F.3d 1061 (9th Cir. 1999). 23 Petit v. Chi., 352 F.3d 1111 (7th Cir. 2003). 24 Id. 25 Grutter v. Bollinger, 539 U.S. 306 (2003). For an argument that other bases could serve as a compelling interest in public contracting, see Michael K. Fridkin, “The Permissibility of Non-Remedial Justifications for Racial Preferences in Public Contracting,” 24 N. Ill. U. L. Rev. 509 (Summer 2004).


LEGAL FRAMEWORK 2.3 .2 BURDEN OF PROOF

The Tenth Circuit in Concrete Works IV ruled that the district court in reviewing the evidence should only have asked whether Denver had demonstrated strong evidence from which an inference of past or present discrimination could be drawn.26 Denver was not required to prove the existence of discrimination. The Tenth Circuit went on to state that Denver did not have the “burden of establishing by a preponderance that not only were there inferences to discrimination, but in fact that the inferences were correct.”27 The Tenth Circuit also clarified the burden faced by the plaintiff in these cases, so that “once Denver meets its burden, [the plaintiff] must introduce credible particularized evidence to rebut [the city’s] initial showing of the existence of a compelling interest.”28

2.3 .3 POST-ENACTMENT EVIDENCE

The Supreme Court in Croson found pre-enactment evidence of discrimination insufficient to justify the program. The defendant in Croson did not seek to defend its program based on post-enactment evidence. However, following Croson, a number of circuits did defend the use of post-enactment evidence to support the establishment of a local public affirmative action program.29

The federal circuit courts had been in substantial agreement permissible uses for post-enactment evidence. The Tenth Circuit has ruled that evidence collected after establishment of an M/WBE program, but before the reenactment of the program can be considered in evaluating the program’s constitutionality.30 In addition, the Tenth Circuit held that post-enactment evidence is relevant to determining whether the M/WBE program is narrowly tailored.31

The Supreme Court case of Shaw v. Hunt (Shaw) raised anew the issue of post-enactment evidence in defending local public sector affirmative action programs.32 Shaw involved the use of racial factors in drawing voting districts in North Carolina. In Shaw, the Supreme Court rejected the use of reports providing evidence of discrimination in North Carolina because the reports were not developed before the voting districts were designed. Thus, the critical issue was whether the legislative body believed that discrimination had existed before the districts were drafted.33 Following the Shaw decision, two district courts rejected the use of post-enactment evidence in the evaluation of the constitutionality of local minority business programs.34

26 Concrete Works IV, 321 F.2d at 970 (10th Cir. 2003). 27 Id. 28 Concrete Works IV, 321 F.2d at 959. 29 See, e.g., Eng’g Contractors v. Dade County, 122 F.3d 895 (11th Cir. 1997); Contractors Ass’n v. Phila., 6 F.3d 990, 1009 n. 18 (3rd Cir. 1993); Concrete Works v. City and County of Denver, 36 F.3d 1513 (10th Cir. 1994). 30 Concrete Works II, 36 F.3d 1513, 1521 (10th Cir. 1994). 31 Concrete Works II, 36 F.3d at 1521. 32 Shaw v. Hunt, 517 U.S. 899 (1996). 33 Id. at 910. 34 Associated Util. Contrs. of Md., Inc. v. Mayor of Baltimore, 83 F.Supp.2d 613, 620-22 (D.Md. 2000) (saying that inquiry must be restricted to evidence which the City actually considered before enacting the numerical goals); W. Tenn. Chapter of Associated Builders & Contrs., Inc. v. Me. City Sch., 64 F.Supp.2d 714, 718-21 (W.D. Tenn. 1999) (concluding that admitting post-enactment evidence to show a compelling interest was contrary to federal precedent and, therefore, post-enactment evidence may not be used to demonstrate that the government's interest in remedying prior discrimination was compelling).


LEGAL FRAMEWORK 2.3 .4 RACIAL CLASSIFICATIONS SUBJECT TO STRICT SCRUTINY

In Scott v. Jackson,35 the city argued that its disadvantaged business program was not a racial classification subject to strict scrutiny because (1) it was based upon disadvantage, not race, and (2) it was a goals program and not a quota. The Fifth Circuit disagreed with the claim that the Jackson program was not a racial classification because the city used the federal Section 8(d), which grants a rebuttable presumption of social and economic disadvantage to firms owned by minorities.36 Such a presumption is subject to strict scrutiny. The Fifth Circuit also noted that strict scrutiny applied not simply when race-conscious measures were required, but also when such measures were authorized or encouraged.37

2.4 SUFFICIENTLY STRONG EVIDENCE OF SIGNIFICANT STATISTICAL DISPARITIES BETWEEN QUALIFIED MINORITIES AVAILABLE AND MINORITIES UTILIZED WILL SATISFY STRICT SCRUTINY AND JUSTIFY A NARROWLY TAILORED M/WBE PROGRAM

The Supreme Court in Croson stated that “where gross statistical disparities can be shown, they alone in a proper case may constitute prima facie proof of a pattern or practice of discrimination.”38 But the statistics must go well beyond comparing the rate of minority presence in the general population to the rate of prime construction contracts awarded to MBEs. The Court in Croson objected to such a comparison, indicating that the proper statistical evaluation would compare the percentage of qualified MBEs in the relevant market with the percentage of total municipal construction dollars awarded to them.39

To meet this more precise requirement, courts have accepted the use of a disparity index.40 The Supreme Court in Croson recognized statistical measures of disparity that compared the number of qualified and available M/WBEs with the rate of municipal construction dollars actually awarded to M/WBEs in order to demonstrate discrimination in a local construction industry.41 The Tenth Circuit has used disparity indices in its discussion of cases without opining on the merits of the indices themselves.42

2.4 .1 DETERMINING AVAILABILITY

To perform proper disparity analysis, the government must determine “availability”—the number of qualified minority contractors willing and able to perform a particular service for the municipality. In Croson, the Court stated, “Where there is a significant statistical disparity between the number of qualified minority contractors willing and able to perform a particular service and the number of such contractors actually engaged by the locality or the locality’s prime contractors, an inference of discriminatory exclusion could arise.”43

35 Scott v. City of Jackson, 199 F.3d 206 (5th Cir. 1999). 36 Scott, 199 F.3d at 216-17. 37 Id. at 215 (quoting Bras v. Cal. Pub. Utilities Comm’n., 59 F.3d 869, 875 (9th Cir. 1995)). 38 Croson, 488 U.S. at 501, quoting Hazelwood Sch. Div. v. United States, 433 U.S. 299, 307-308 (1977). 39 Id. at 471. 40 See, e.g., Eng’g. Contractors Ass’n of S. Fla., Inc., 122 F.3d at 914; Concrete Works IV, 321 F.3d at 964-69. 41 Croson, 488 U.S. at 503-504. 42 Concrete Works II, at 1524; Concrete Works IV, at 962. 43 Croson, 488 U.S. at 509 (emphasis added).


LEGAL FRAMEWORK An accurate determination of availability also permits the government to meet the requirement that it “determine the precise scope of the injury it seeks to remedy” by its program.44 Following Croson’s statements on availability, lower courts have considered how legislative bodies may determine the precise scope of the injury sought to be remedied by an MBE program. Nevertheless, the federal courts have not provided clear guidance on the best data sources or techniques for measuring M/WBE availability.

At least one commentator has suggested using bidder data to measure M/WBE availability,45 but Croson does not require the use of bidder data to determine availability. In Concrete Works IV, in the context of plaintiffs’ complaint that the city of Denver had not used bidder data, the Tenth Circuit noted that bid information also has its limits. Firms that bid may not be qualified or able, and firms that do not bid may be qualified and able, to undertake agency contracts.46

2.4 .2 RACIAL CLASSIFICATIONS

In determining availability, choosing the appropriate racial groups to consider becomes an important threshold interest.47 In Croson, the Supreme Court criticized the city of Richmond’s inclusion of “Spanish-speaking, Oriental, Indian, Eskimo, or Aleut persons” in its affirmative action program.48 These groups had not previously participated in city contracting, and “the random inclusion of racial groups that, as a practical matter, may never have suffered from discrimination in the construction industry in Richmond suggests that perhaps the city’s purpose was not in fact to remedy past discrimination.”49 To evaluate availability properly, data must be gathered for each racial group in the marketplace.

2.4 .3 RELEVANT MARKET AREA

Another issue in availability analysis is the definition of the relevant market area. Specifically, the question is whether the relevant market area should be defined as the area from which a specific percentage of purchases are made, the area in which a specific percentage of willing and able contractors may be located, or the area determined by a fixed geopolitical boundary.

The Supreme Court did not establish how the relevant market area should be defined. The Tenth Circuit in Concrete Works II, Concrete Works of Colorado, a non-M/WBE construction company, argued that Croson precluded consideration of discrimination evidence from the six-county Denver Metropolitan Statistical Area (MSA), so that Denver should use data only from within the city and county of Denver. The Tenth Circuit, interpreting Croson, concluded, “The relevant area in which to measure discrimination . . . is the local construction market, but that is not necessarily confined by jurisdictional boundaries.”50 The court further stated, “It is important that the pertinent data closely relate to the jurisdictional area of the municipality whose program we scrutinize, but here Denver’s contracting activity, insofar as construction work is concerned, is closely related to the Denver MSA.”51 The Tenth Circuit ruled that since more than 80 percent of Denver Department of Public Works construction and design contracts were awarded to

44 Id. at 498. 45 G. LaNoue, “Who Counts? Determining the Availability of Minority Businesses for Contracting After Croson,” 21 Harvard Journal of Law and Public Policy 793, 833 (1998). 46 Concrete Works IV, 321 F.3d at 983-84. 47 Racial Groups, as the term is used herein, include both racial and ethnic categories. 48 Croson, 488 U.S. at 506. 49 Id. 50 Concrete Works II, at 1520. 51 Id.


LEGAL FRAMEWORK firms located within the Denver MSA, the appropriate market area should be the Denver MSA, not the city and county of Denver alone.52 Accordingly, data from the Denver MSA were “adequately particularized for strict scrutiny purposes.”53

2.4 .4 FIRM QUALIFICATIONS

Another availability consideration is whether M/WBE firms are qualified to perform the required services. In Croson, the Supreme Court noted that although gross statistical disparities may demonstrate prima facie proof of discrimination, “when special qualifications are required to fill particular jobs, comparisons to the general population (rather than to the smaller group of individuals who possess the necessary qualifications) may have little probative value.”54 The Court, however, did not define the test for determining whether a firm is qualified. The Tenth Circuit did note that bidding on projects “says nothing about whether those firms are qualified.”55

Considering firm qualifications is important not only to assess whether M/WBEs in the relevant market area can provide the goods and services required, but also to ensure proper comparison between the number of qualified M/WBEs and the total number of similarly qualified contractors in the marketplace.56 In short, proper comparisons ensure the required integrity and specificity of the statistical analysis.

2.4 .5 WILLINGNESS

Croson requires that an “available” firm must be not only qualified but also “willing” to provide the required services. In this context, it can be difficult to determine whether a business is willing. Some District courts have approved including businesses in the availability pool that may not be on the government’s certification list.57 Concrete Works IV did not separately discuss willingness and did state that the plaintiff did not introduce evidence that MBEs bid at lower rates than non-MBEs.58

2.4 .6 ABILITY

Another availability consideration is whether the firms being considered are able to perform a particular service. Those who challenge affirmative action often question whether M/WBE firms have the “capacity” to perform particular services.

The Tenth Circuit in Concrete Works II and IV recognized the shortcomings of this treatment of firm size.59 In Concrete Works IV, the court noted that the small size of such firms can itself be a result of discrimination.60 The Tenth Circuit acknowledged the city of Denver’s argument that a small construction

52 Id. 53 Id. 54 Croson, 488 U.S. at 501, quoting Hazelwood Sch. Dist. v. United States, 433 U.S. 299, 308, n.13 (1977). 55 Concrete Works IV, at 983. 56 See Hazelwood Sch. Dist., 433 U.S. at 308; Contractors Ass’n, 91 F.3d at 603. 57 In Concrete Works II, Denver’s availability analysis indicated that while most MBEs and WBEs had never participated in City contracts, “almost all firms contacted indicated that they were interested in municipal work. (Concrete Works II, 36 F.3d at 1529). 58 Concrete Works IV, 321 F.3d at 983. 59 Concrete Works II, 36 F.3d at 1528-29; Concrete Works IV, 321 F.3d at 980-92. 60 Concrete Works IV, 321 F.3d at 980-84.


LEGAL FRAMEWORK firm’s precise capacity can be highly elastic.61 Under this view, the relevance of firm size may be somewhat diminished.

2.4 .7 ANECDOTAL EVIDENCE OF DISCRIMINATION IN DISPARITY STUDIES

Most disparity studies present anecdotal evidence along with statistical data. The Supreme Court in Croson discussed the relevance of anecdotal evidence and explained, “Evidence of a pattern of individual discriminatory acts can, if supported by appropriate statistical proof, lend support to a local government’s determination that broader remedial relief is justified.”62

Croson did not expressly consider the form or level of specificity required for anecdotal evidence. Not only have courts found that a municipality does not have to specifically identify all the discriminatory practices impeding M/WBE utilization, but the Tenth Circuit in Concrete Works IV also held that anecdotal evidence collected by a municipality did not have to present corroborating evidence. The court stated:

There is no merit to the [plaintiff’s] argument that witnesses’ accounts must be verified to provide support for Denver’s burden. Anecdotal evidence is nothing more than a witness’ narrative of an incident told from the witness’ perspective and including the witness’ perceptions/ In this case, the anecdotal evidence was not subject to rigorous cross-examination…Denver was not required to present corroborating evidence and [the plaintiff] was free to present its own witnesses to either refute the incidents described by Denver’s witnesses or to relate their own perceptions on discrimination in the Denver construction industry.63

2.5 THE GOVERNMENTAL ENTITY OR AGENCY ENACTING AN M/WBE PROGRAM MUST BE SHOWN TO HAVE ACTIVELY OR PASSIVELY PERPETUATED THE DISCRIMINATION

In Croson, the Supreme Court stated, “It is beyond dispute that any public entity, state or federal, has a compelling interest in assuring that public dollars, drawn from the tax contributions of all citizens, do not serve to finance the evil of private prejudice.”64 Croson provided that the government “can use its spending powers to remedy private discrimination, if it identifies that discrimination with the particularity required by the Fourteenth Amendment.”65 The government agency’s active or passive participation in discriminatory practices in the marketplace may show the compelling interest. That is, courts mainly seek to ensure that M/WBE programs are based on findings of active or passive discrimination in the government contracting marketplace, and not simply attempts to remedy general societal discrimination. Defining passive participation, Croson stated, “Thus, if the city could show that it had essentially become a “passive participant” in a system of racial exclusion practiced by elements of the local construction industry, we think it clear that the city could take affirmative steps to dismantle such a system.”66

61 Id. at 981. 62 Croson, 488 U.S. at 509. 63 Concrete Works IV, 321 F.3d at 989. 64 Coral Constr., 941 F.2d at 922 (citing Croson, 488 U.S. at 492) (emphasis added). 65 See Croson; see generally I. Ayres and F. Vars, “When Does Private Discrimination Justify Public Affirmative Action?” 98 Columbia Law Review 1577 (1998). 66 Croson, 488 U.S. at 492.


LEGAL FRAMEWORK The Tenth Circuit decision in Adarand v. Slater concluded that evidence of private sector discrimination provided a compelling interest for a DBE program.67 In Concrete Works IV, the Tenth Circuit upheld the relevance of data from the private marketplace to establish a factual predicate for M/WBE programs.68 Later cases have reaffirmed that the government has a compelling interest in avoiding the financing of private discrimination with public dollars.69

Concrete Works IV expressly cited as evidence of discrimination that M/WBE contractors used for business with the city of Denver were not used by the same prime contractors for private sector contracts.70 The Tenth Circuit in Concrete Works IV also found a decline in M/WBE utilization following program termination was evidence that prime contractors were not willing to use M/WBEs in the absence of legal requirements.71

Finally, some courts have been willing to see capital market discrimination as part of the required nexus between private and public contracting discrimination, even if capital market discrimination could arguably be seen as simply part of broader societal discrimination. In Adarand v. Slater, the Tenth Circuit favorably cited evidence of capital market discrimination as relevant in establishing the factual predicate for the federal DBE program.72 The same court, in Concrete Works IV, found that barriers to business formation were relevant insofar as this evidence demonstrated that M/WBEs were “precluded at the outset from competing for public construction contracts.”73 Along related lines, the court also found a regression analysis of census data to be relevant evidence showing barriers to M/WBE formation.74

2.6 TO WITHSTAND STRICT SCRUTINY, AN M/WBE PROGRAM MUST BE NARROWLY TAILORED TO REMEDY IDENTIFIED DISCRIMINATION

The discussion of compelling interest in the court cases has been extensive, but narrowly tailoring may be the more critical issue.75

Many courts have found that even when an entity has a compelling interest to enact an M/WBE program, these programs are not narrowly tailored.76 However, in Concrete Works IV, the court found a compelling interest for a local M/WBE program, but did not consider the issue of narrow tailoring.77 Instead, the Tenth Circuit held that the plaintiffs had waived any challenge to the original district court ruling that the program was narrowly tailored.78

67 Adarand v. Slater, 228 F.3d 1147 (10th Cir. 2000). 68 Concrete Works IV, 321 F.3d at 969. 69 Associated Gen. Contractors of Ohio, Inc. v. Drabik, 214 F.3d 730, 734-35 (6th Cir. 2000). See also Concrete Works II, 36 F.3d at 1529; Coral Constr., 941 F.2d at 916; AGC v. New Haven, 791 F.Supp. 941, 947 (D.Conn. 1992). 70 Concrete Works IV, 321 F.3d at 984-85. 71 Concrete Works IV, 321 F.3d at 985; see also Sherbrooke Turf, 345 F.3d at 973. 72 Adarand v. Slater, 228 F.3d at 1169-70 (10th Cir. 2000). 73 Concrete Works IV, 321 F.2d at 977. The district court had rejected evidence of credit market discrimination as adequate to provide a factual predicate for an M/W program. Concrete Works v. City and County of Denver, 86 F.Supp.2d 1042 (D. Colo. 2000) (Concrete Works I). 74 Id. at 990. 75 See Croson, 488 U.S. at 493 76 See, e.g., Verdi v. DeKalb County Schl. Dist., 135 Fed. App’x 262 (11th Cir. 2005); Eng’g Contractors Ass’n of S. Fla., Inc., 122 F.3d at 926-29; Contractors Ass’n of E. Pa., Inc., 91 F.3d at 605. 77 Concrete Works IV, 321 F.3d at 992. 78 Id.


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LEGAL FRAMEWORK The federal courts have found that the DBE program established pursuant to federal regulations (49 CFR, Part 26) issued under the Transportation Equity Act (TEA-21) (1998) has been narrowly tailored to serve a compelling interest.79 The federal courts had previously ruled that there was a factual predicate for the federal Department of Transportation (DOT) DBE program, but that in its earlier versions the program was not narrowly tailored.80 Subsequent rulings provide some guidance as to what program configurations the courts will judge to be narrowly tailored. Following Supreme Court precedent, the Tenth Circuit has identified the following elements of narrow tailoring: the necessity for the relief and the efficacy of alternative remedies; the flexibility and duration of the relief, including the availability of waiver provisions; the relationship of the numerical goals to the relevant labor market; over/under inclusivensss; and the impact of the relief on the rights of third parties.81

2.6 .1 RACE-NEUTRAL ALTERNATIVES

Concerning race-neutral alternatives, the Supreme Court in Croson concluded that a governmental entity must demonstrate that it has evaluated the use of race-neutral means to increase minority business participation in contracting or purchasing activities. In upholding the narrow tailoring of federal DBE regulations, the Tenth Circuit stated that “if a recipient can meet its overall goal through race-neutral means, it must implement its program without the use of race-conscious contracting measures, and enumerate a list of race-neutral measures.”82 Those measures included “overcome bonding and financing obstacles, providing technical assistance, [and] establishing programs to assist start-up firms.”83

Strict scrutiny does not mandate that every race-neutral measure be considered and found wanting. The Eighth Circuit also affirmed that “narrow tailoring does not require exhaustion of every conceivable race-neutral alternative,” but it does require “serious, good faith consideration of workable race-neutral alternatives.”84

2.6 .2 FLEXIB ILITY AND DURATION OF THE REMEDY

The Tenth Circuit found that “the present version of the regulations have increased the flexibility of the government’s DBE programs.”85 The Eighth Circuit noted that,

A State may obtain waivers or exemptions from any requirement and is not penalized for a good faith failure to meet its overall goal. In addition, the program limits preferences to small businesses falling beneath an earnings threshold, and any individual whose net worth exceeds $750,000 cannot qualify as economically disadvantaged.86

79 Adarand v. Slater, 228 F.3d 1147; Sherbrooke Turf, 345 F.3d 964; W. States Paving v. Washington Dep’t of Transp., 407 F.3d 983 (9th Cir. 2005). 80 In 1998, in Sherbrooke I, the Minnesota district court had ruled that while there was a compelling interest for the DBE program, the program was not narrowly tailored. In 1996, before the new DBE regulations, the district court in Colorado, upon remand from the 1995 U.S. Supreme Court, had made a similar ruling in Adarand v. Peña. 81 Adarand v. Slater, 228 F.3d at 1177 (10th Cir. 2000) (citing U.S. v. Paradise, 480 U.S. 149, 171 (1987)). 82 Adarand v. Slater, 228 F.3d. at 1179. 83 Id. 84 Sherbrooke Turf, at 972 (citing Grutter, 123 S. Ct. at 2344-45). See also Coral Construction, 941 F.2d at 923; AGCC v. Coalition for Economic Equity, 950 F.2d 1401, 1417 (9th Cir 1991). The Tenth Circuit has not opined on this issue. 85 Adarand v. Slater, 228 F.3d at 1181. See also Sherbrooke Turf, at 972. 86 Sherbrooke Turf, at 972 (citing 49 C.F.R. § 26.67(b)).


LEGAL FRAMEWORK DBE and M/WBE programs achieve flexibility by using waivers and variable project goals to avoid merely setting a quota.87 Croson favorably mentioned the contract-by-contract waivers in the federal DOT DBE program.88 Virtually all successful MBE programs have this waiver feature in their enabling legislation. As for project goals, the approved DBE provisions set aspirational, not mandatory, goals; expressly forbid quotas; and use overall goals simply as a framework for setting local contract goals, if any, based on local data. All of these factors have impressed the courts that have upheld the constitutionality of the revised DOT DBE program.89

With respect to program duration, in Adarand v. Peña, the Supreme Court wrote that a program should be “appropriately limited such that it will not last longer than the discriminatory effects it is designed to eliminate.”90 The Tenth Circuit noted the limits in the revised DBE program, in the fact that the DBE program is subject to periodic congressional reauthorization and the DBE graduation provisions.91 Other appellate courts have noted possible mechanisms for limiting program duration: required termination if goals have been met92 and decertification of MBEs who achieve certain levels of success, or mandatory review of MBE certification at regular, relatively brief periods.93 Governments thus have some duty to ensure that they update their evidence of discrimination regularly enough to review the need for their programs and to revise programs by narrowly tailoring them to fit the fresh evidence.94 Whether all of these provisions are necessary in every case remains an open question.

2.6 .3 RELATIONSHIP OF GOALS TO AVAILABILITY

Narrow tailoring under the Croson standard requires that remedial goals be in line with measured availability. Merely setting percentages without a carefully selected basis in statistical studies, as the city of Richmond did in Croson itself, has played a strong part in decisions finding other programs unconstitutional.95

By contrast, the Tenth, Ninth, and Eighth Circuits have approved the goal-setting process for the DOT DBE program, as revised in 1999.96 The approved DOT DBE regulations require that goals be based on one of several methods for measuring DBE availability.97 The Tenth Circuit noted that the DBE goals are tied to the relevant markets and there “is little danger of arbitrariness in the setting of such goals, as was the case in Croson.”98

Moreover, the approved DBE regulations use built-in mechanisms to ensure that DBE goals are not set excessively high relative to DBE availability. For example, the approved DBE goals are to be set aside if the overall goal has been met for two consecutive years by race-neutral means. The approved DBE

87 M/WBE set-asides and bid preferences were not directly addressed in by the Tenth Circuit in Concrete Works IV or Adarand v. Slater. 88 Croson, 488 U.S. at 489. 89 Adarand v. Slater, 228 F.3d at 1181. See also Sherbrooke Turf, at 972. 90 Adarand Constructors v. Peña, 515 U.S. at 238 (internal quotations and citations omitted). 91 Adarand v. Slater, 228 F.3d at 1180-81. 92 Sherbrooke, 345 F.3d at 972. 93 Adarand v. Slater, 228 F.3d at 1179, 1180. 94 Roth v. US Dept of Defense, 262 F.3d 1306, 1331 (Fed Cir 2011) (commenting on the possible staleness of information after 7, 12, and 17 years). 95 See, e.g., Builders Ass’n of Greater Chi., 256 F.3d 642, 647 (7th Cir. 2001). 96 Adarand v. Slater, 228 F.3d at 1182; Sherbrooke Turf, 345 F.3d at 972. W. States, 407 F.3d at 995. 97 49 CFR, Section 26, Part 45. 98 Adarand v. Slater, 228 F.3d at 1182.


LEGAL FRAMEWORK contract goals also must be reduced if overall goals have been exceeded with race-conscious means for two consecutive years.

2.6 .4 BURDEN ON THIRD PARTIES

Narrow tailoring also requires minimizing the burden of the program on third parties. The Eighth Circuit stated the following with respect to the revised DBE program:

Congress and DOT have taken significant steps to minimize the race-based nature of the DBE program. Its benefits are directed at all small businesses owned and controlled by the socially and economically disadvantaged. While TEA21 creates a rebuttable presumption that members of certain racial minorities fall within that class, the presumption is rebuttable, wealthy minority owners and wealthy minority-owned firms are excluded, and certification is available to persons who are not presumptively disadvantaged but can demonstrate actual social and economic disadvantage. Thus, race is made relevant in the program, but it is not a determinative factor.99

Waivers and good faith compliance are also tools that serve this purpose of reducing the burden on third parties.100 The DOT DBE regulations have also sought to reduce the program burden on non-DBEs by avoiding DBE concentration in certain specialty areas.101 These features have gained the approval of the Tenth Circuit.102

2.6 .5 OVERINCLUSION

Narrow tailoring also involves limiting the number and type of beneficiaries of the program. As noted above, there has to be evidence of discrimination to justify a group-based remedy, and over-inclusion of uninjured individuals or groups can endanger the entire program.103 Federal DBE programs have succeeded in part because regulations covering DBE certification do not provide blanket protection to minorities.104 Moreover, the Tenth Circuit noted that, “the Constitution does not erect a barrier to the government's effort to combat discrimination based on broad racial classifications that might prevent it from enumerating particular ethnic origins falling within such classifications.”105

Critically, the MBE program must be limited in its geographical scope to the boundaries of the enacting government’s marketplace. The Supreme Court indicated in Croson that a local agency has the power to address discrimination only within its own marketplace. One fault of the Richmond MBE program was that minority firms were certified from around the United States.106 In Concrete Works II, the Tenth Circuit held that the more extensive but still local designation of the entire Denver MSA constituted the marketplace to which the programs could apply.107

99 Sherbrooke Turf, 345 F.3d at 973. 100 49 CFR, Section 26, Part 53. 101 49 CFR, Section 26, Part 33. 102 Adarand v. Slater, 228 F.3d at 1182. 103 See, e.g., Builders Ass’n of Greater Chi., 256 F.3d at 647. 104 Sherbrooke Turf, 345 F.3d 964, 972-73. 105 Adarand v. Slater, 228 F.3d at 1186. 106 Croson, 488 U.S. at 508. 107 Concrete Works II, 36 F.3d at 1520.


LEGAL FRAMEWORK 2.7 SMALL BUSINESS PROCUREMENT PREFERENCES

Small business procurement preferences have existed since the 1940s. The first small business program had its origins in the Smaller War Plants Corporation (SWPC), established during World War II.108 The SWPC was created to channel war contracts to small business. In 1947, Congress passed the Armed Forces Procurement Act, declaring, “It is the policy of Congress that a fair proportion of the purchases and contracts under this chapter be placed with small business concerns.”109 Continuing this policy, the 1958 Small Business Act requires that government agencies award a “fair proportion” of procurement contracts to small business concerns.110 The regulations are designed to implement this general policy.111

Section 8(b)(11) of the Small Business Act authorizes the Small Business Administration (SBA) to set aside contracts for placement with small business concerns. The SBA has the power:

...to make studies and recommendations to the appropriate Federal agencies to insure that a fair proportion of the total purchases and contracts for property and services for the Government be placed with small-business enterprises, to insure that a fair proportion of Government contracts for research and development be placed with small-business concerns, to insure that a fair proportion of the total sales of Government property be made to small-business concerns, and to insure a fair and equitable share materials, supplies, and equipment to small-business concerns.112

Every acquisition of goods and services anticipated to be between $3,000 and $150,000 is set aside exclusively for small business unless the contracting officer has a reasonable expectation of fewer than two bids by small businesses.113

There has been only one constitutional challenge to the long-standing federal SBE programs. In J.H. Rutter Rex Manufacturing v. United States,114 a federal vendor unsuccessfully challenged the Army’s small business set-aside as in violation of the due process clause of the Fifth Amendment to the U.S. Constitution, as well as the Administrative Procedures Act and the Armed Forces Procurement Act.115 The court held that classifying businesses as small was not a “suspect classification” subject to strict scrutiny. Instead, the court ruled:

Since no fundamental rights are implicated, we need only determine whether the contested socioeconomic legislation rationally relates to a legitimate governmental purpose… Our previous discussion adequately demonstrates that the procurement statutes and the regulations promulgated there under are rationally related to the sound legislative purpose of promoting small businesses in order to contribute to the security and economic health of this Nation.116

108 See, generally, Thomas J. Hasty III, “Minority Business Enterprise Development and the Small Business Administration’s 8(a) Program: Past, Present, and (Is There a) Future?” Military Law Review 145 (Summer 1994): 1-112. 109 10 U.S.C.§ 2301 (1976). 110 15 U.S.C. § 631(a). 111 See 32 C.F.R. §§ 1-701.1 to 1-707.7. 112 15 U.S.C. § 637(b)(11). 113 Federal Acquisition Regulations 19.502-2. The dollar thresholds for small business set-asides have changed over time. 114 706 F.2d 702 (5th Cir. 1983), cert. denied, 464 U.S. 1008 (1983). 115 Administrative Procedures Act, 5 U.S.C. §§ 552(a)(1)(E) (1976) and the “fair proportion” language of the Armed Forces Procurement Act, 10 U.S.C. § 2301 et seq. (1976), and the Small Business Act, 15 U.S.C. § 631 et seq. (1976). 116 J. H. Rutter Rex Mfg., at 706 F.2d at 713 (emphasis added).


LEGAL FRAMEWORK A large number of state and local governments have maintained small business preference programs for many years.117 No district court cases were found overturning a state and local small business reference program. One reason for the low level of litigation in this area is that there is no significant organizational opposition to SBE programs. There are no reported cases of Associated General Contractors (AGC) litigation against local SBE programs. And the legal foundations that have typically sued M/WBE programs have actually promoted SBE procurement preference programs as a race-neutral substitute for M/WBE programs.

2.8 CONCLUSIONS

As summarized earlier, when governments develop and implement a contracting program that is sensitive to race and gender, they must understand the case law that has developed in the federal courts. These cases establish specific requirements that must be addressed so that such programs can withstand judicial review for constitutionality and prove to be just and fair. Under the developing trends in the application of the law, local governments must engage in specific fact-finding processes to compile a thorough, accurate, and specific evidentiary foundation to determine whether there is, in fact, discrimination sufficient to justify an affirmative action plan. Further, local governments must continue to update this information and revise their programs accordingly.

While the Supreme Court has yet to return to this exact area of law to sort out some of the conflicts, the circuit courts have settled on the core standards. Though there are differences among the circuits in the level of deference granted to the finder of fact, these differences do not appear to be profound. The differences in the individual outcomes have been, overwhelmingly, differences in the level of evidence, mostly concerning the rigor with which disparity studies have been conducted and then used as the foundation for narrowly tailored remedies. Ultimately, MBE and WBE programs can withstand challenges if local governments comply with the requirements outlined by the courts.

117 For example, Florida started a small business preference program in 1985 (FL St Sec. 287); Minnesota, in 1979 (Mn Stat 137.31); New Jersey, in 1993 (N.J.S.A 52:32-17).


3.0 RELEVANT MARKET AREA AND UTILIZATION ANALYSES

CHAPTER 3.0: MARKET AREA AND UTILIZATION ANALYSES

Chapter 3.0 presents the market area and utilization analyses results of firms used on Denver Public Schools (DPS) prime contracts (expenditures) and subcontracts (anticipated awards) for construction and construction-related professional services. At the prime contractor/consultant levels, the study reviewed construction and construction-related activity from January 1, 2009 through December 31, 2013. The study reviewed activity from 2008 and 2012 for subcontractor/subconsultant activity1.

The following section, Chapter Definitions, presents the definitions to key terms that are used in this chapter.

3.1 CHAPTER DEFINITIONS

Business Ownership Classification. To understand the analyses presented in this study, it is important to define M/WBE firms. The following section provides the study definitions for M/WBE firms.

M/WBE Firms. For the purposes of this study, M/WBE firms are firms owned by minorities or women regardless of certification status. Therefore, M/WBE firms include all identified minorities- and women-owned firms (non-certified and certified). MGT used this approach in analyzing the utilization and availability of firms and to review disparities, if any. Furthermore, courts have accepted disparity studies based on race, ethnicity, and gender of the business owner as opposed to certification status.

- M/WBE firms were defined as firms that are at least 51 percent owned or controlled by members of the following groups:

African Americans: U.S. citizens or lawfully admitted permanent residents having an origin in any of the black racial groups of Africa.

Hispanic Americans: U.S. citizens or lawfully admitted permanent residents of Mexican, Puerto Rican, Cuban, Central or South American, or other Spanish or Portuguese cultures or origins regardless of race. This includes all Hispanics (white or nonwhite).

Asian Americans: U.S. citizens or lawfully admitted permanent residents who originate from the Far East, Southeast Asia, the Indian subcontinent, or the Pacific Islands.

Native Americans: U.S. citizens or lawfully admitted permanent residents who originate from any of the original peoples of North America and who maintain cultural identification through tribal affiliation or community recognition.

Nonminority Woman (Female): U.S. citizens or lawfully admitted permanent residents who are non-Hispanic white woman.

1 The activity of subcontracting was based on 2008 and 2012 due to how data was maintained and tracked.


MARKET AREA AND UTILIZATION ANALYSES

Minority women- and male-owned firms were classified and assigned to their corresponding minority groups. For example, a Hispanic American woman- or Hispanic American male-owned firm was assigned to the Hispanic American-owned firm minority group.

Non-M/WBE Firms. Firms that were identified as nonminority male or majority-owned were classified as non-M/WBE firms. If there was no indication of business ownership, these firms were also classified as non-M/WBE firms.

The M/WBE determinations in this report were based on classifications in the DPS-provided data (such as vendor data, certified firms, and payment data). In addition, MGT conducted further research to determine the proper business owner classification. If unclear or unknown, the business owner classification was cross referenced with additional vendor lists (custom census, City and County of Denver vendor data, a national M/WBE database).

Market Area Methodology. In order to establish the appropriate geographic boundaries for the study’s analyses, an overall market area was established. The geographic units (such as counties) are based on the following considerations: 1) the courts have accepted the use of standard geographic units in conducting equal employment opportunity and disparity studies; 2) geographic units are externally determined, so there are no subjective determinations; and 3) U.S. Census and other federal agencies routinely collect data by geographic unit. The following presents the methodology used to determine the overall market area and relevant market area.

Overall Market Area Analyses. To determine the market area and establish the extent, to which DPS utilized firms on construction and construction-related professional services projects, MGT staff reviewed the geographic location by using MGT’s Zone Improvement Plan (ZIP) Code Database of each firm conducting business with DPS during the study period. Once the firms’ geographic locations were identified, where dollars were expended or awarded were analyzed and referred to the overall market area for each business category. The overall market analyses presents the results based on firms located inside the state of Colorado and outside the state of Colorado. The overall market area results by business category are presented in Section 3 of this chapter.

Relevant Market Area Analyses. Once the overall market was established, the relevant market area was determined for each business category. The firm’s geographic location that received the most dollars, all of which totaled at least 75 percent2 of the overall market area, were identified. The use of the “75 percent rule” for market area determination is generally accepted in antitrust cases. In another relevant case, the court accepted less than 100 percent of data when it was reasonable to assume that the missing data would not significantly change the results of the analysis3. Subsequently, the results were summarized by county according to the location of each firm that provided construction and construction-related professional services to DPS. Subsequently, the utilization analyses was based on firms located in the Denver-Aurora CSA. The utilization analysis also assessed the effectiveness of initiatives in promoting the inclusion of M/WBEs in DPS contracting activities. The effectiveness of the program initiatives is further examined and discussed in Chapter 7.0, Findings and Recommendations.

2 MGT uses the “75 percent rule” to determine the relevant market area. This rule is generally accepted in antitrust cases. In another relevant case, James C. Jones v. New York County Human Resources Administration, 528 F.2d 696 (.2d Cir. 1976), the court accepted less than 100 percent of the data when it was reasonable to assume that the missing data would not significantly change the results of the analysis. 3 James C. Jones v. New York County Human Resources Administration, 528 F.2d 696 (.2d Cir. 1976).



3.2 DATA COLLECTION AND MANAGEMENT

To determine the most appropriate data for our use in this study and to identify data sources, MGT of America, Inc. (MGT) conducted data assessment interviews with key DPS staff knowledgeable about DPS construction and construction-related professional services contracting. Based on the data assessment interviews and follow-up discussions with DPS staff, it was agreed that in order to collect the most comprehensive sets of data, data would be collected from various data sources. Electronic construction expenditure data (at the prime contractor level) and construction-related professional services expenditure data (at the prime consultant level) were extracted from DPS procurement/financial systems, Cayenta and Lawson. DPS staff gathered subcontractor and subconsultant (sub) level award data on construction and construction-related professional services projects. The sub award data was collected from the DPS tracking reports presented in Microsoft Excel workbooks, along with bid/proposal documents submitted by prime contractors/consultants. Once MGT received this data, that data was entered and as a result a Master Contracting Database was developed. The primary purposes of this database was to analyze the market area and utilization of firms.

3.3 MARKET AREA ANALYSIS

In order to establish the appropriate geographic boundaries for the statistical analysis, market areas were determined. As described in Section 3.1, Chapter Definitions, first, the overall market area was determined and then the relevant market area was established. The following analysis presents the results based on the relevant market area, which is the Denver-Aurora CSA. The corresponding detailed market area analyses for construction and construction-related professional services are presented in Appendix A to this report.

Exhibit 3-1 and Figure 3-A show that firms located within the relevant market area account for 90.3 percent and 60.4 percent of construction and construction-related professional services at the prime level.

EXHIBIT 3-1 MARKET AREA ANALYSIS AT THE PRIME LEVEL BY LOCATION OF FIRMS (DENVER-AURORA CSA) AND

BUSINESS CATEGORY

LOCATION OF FIRMS BY CONTRACT CATEGORY DOLLARS PAID PERCENT OF DOLLARS

Construction Inside Denver–Aurora, CO CSA $527,748,211 90.32% Outside Denver–Aurora, CO CSA $56,582,306 9.68% TOTAL CONSTRUCTION $584,330,517 100.00% Construction-Related Professional Services Inside Denver–Aurora, CO CSA $51,008,684 60.44% Outside Denver–Aurora, CO CSA $33,388,557 39.56% TOTAL CONSTRUCTION-RELATED PROFESSIONAL SERVICES $84,397,241 100.00% All Business Categories Inside Denver–Aurora, CO CSA $578,756,895 86.55% Outside Denver–Aurora, CO CSA $89,970,862 13.45% TOTAL ALL BUSINESS CATEGORIES $668,727,757 100.00%

Source: MGT developed a master database based on dollars awarded and expended by Denver Public Schools between January 1, 2009, and December 31, 2013.



FIGURE 3-A MARKET AREA ANALYSIS AT THE PRIME LEVEL BY LOCATION OF FIRMS (DENVER-AURORA CSA) AND

BUSINESS CATEGORY


Exhibit 3-2 and Figure 3-B show that firms located within the relevant market area account for 99.7 percent and 100 percent of construction and construction-related professional services at the sub level.

EXHIBIT 3-2 MARKET AREA ANALYSIS AT THE SUB LEVEL BY LOCATION OF FIRMS (DENVER-AURORA CSA) AND

BUSINESS CATEGORY

LOCATION OF FIRMS BY CONTRACT CATEGORY ANTICIPATED AWARD DOLLARS

PERCENT OF DOLLARS

Construction Inside Denver–Aurora, CO CSA $166,768,543 99.70% Outside Denver–Aurora, CO CSA $504,289 0.30% TOTAL CONSTRUCTION $167,272,832 100.00% Construction-Related Professional Services Inside Denver–Aurora, CO CSA $5,363,373 100.00% Outside Denver–Aurora, CO CSA $0 0.00% TOTAL CONSTRUCTION-RELATED PROFESSIONAL SERVICES $5,363,373 100.00% All Business Categories Inside Denver–Aurora, CO CSA $172,131,915 99.71% Outside Denver–Aurora, CO CSA $504,289 0.29% TOTAL ALL BUSINESS CATEGORIES $172,636,204 100.00%

Source: MGT developed a master database based on anticipated award dollars to subcontractors on Denver Public Schools’ projects (based on 2008 and 2012).

90.32%

60.44%

86.55%

9.68%

39.56%

13.45%

0.00% 20.00% 40.00% 60.00% 80.00% 100.00%

Construction

Construction-Related Professional Services

All Contract Categories

Inside Denver–Aurora, CO CSA Outside Denver–Aurora, CO CSA



FIGURE 3-B MARKET AREA ANALYSIS AT THE SUB LEVEL BY LOCATION OF FIRMS (DENVER-AURORA CSA) AND

BUSINESS CATEGORY


3.4 UTILIZATION ANALYSIS

As mentioned in the Section 3.1, Chapter Definitions, the utilization analyses are based on the relevant market area, which was determined to be the Denver-Aurora, CO CSA. The subsequent analyses presents MGT’s utilization analyses of firms on construction and construction-related professional services at the prime contractor and subcontractor levels.

The analysis of subcontracting is presented first, since in order to establish M/WBE subcontracting goals there must be a factual basis to establish those goals.

Exhibit 3-3 and Figure 3-C show that the utilization of MBE firms, as a whole, was 12.2 percent in construction and 15.3 percent in construction-related professional services at the subcontractor/ subconsultant level. In terms of M/WBE firms, as a whole, the utilization was 24.2 percent in construction and 23.6 percent in construction-related professional services.

99.70%

100.00%

99.71%

0.30%

0.00%

0.29%

0.00% 20.00% 40.00% 60.00% 80.00% 100.00%

Construction






EXHIBIT 3-3 UTILIZATION ANALYSIS AT THE SUB LEVEL BY BUSINESS OWNERSHIP CLASSIFICATION AND BUSINESS

CATEGORY

BUSINESS OWNERSHIP CLASSIFICATION

CONSTRUCTION CONSTRUCTION-RELATED PROFESSIONAL SERVICES TOTAL

($) ($) ($)

African American $1,256,832 $74,720 $1,331,552 Asian American $3,576,955 $279,545 $3,856,500 Hispanic American $14,769,201 $459,781 $15,228,981 Native American $757,999 $4,500 $762,499 Total MBE Firms $20,360,987 $818,546 $21,179,532 Nonminority Female $20,004,011 $446,372 $20,450,383 Total M/WBE Firms $40,364,997 $1,264,918 $41,629,915 Non-M/WBE Firms $126,403,546 $4,098,455 $130,502,001 TOTAL $166,768,543 $5,363,373 $172,131,915

(%) (%) (%) African American 0.75% 1.39% 0.77% Asian American 2.14% 5.21% 2.24% Hispanic American 8.86% 8.57% 8.85% Native American 0.45% 0.08% 0.44% Total MBE Firms 12.21% 15.26% 12.30% Nonminority Female 12.00% 8.32% 11.88% Total M/WBE Firms 24.20% 23.58% 24.18% Non-M/WBE Firms 75.80% 76.42% 75.82% TOTAL 100.00% 100.00% 100.00%


Hispanic American-owned firms accounted for 8.6 percent of utilization in construction, followed by Asian American-owned firms with 2.1 percent. Nonminority female-owned firms accounted for 12 percent of utilization in construction. Hispanic American-owned firms accounted for 8.6 of utilization in construction-related professional services, followed by Asian American-owned firms with 5.2 percent. Nonminority female-owned firms accounted for 8.3 percent of utilization in construction-related professional services.



FIGURE 3C UTILIZATION ANALYSIS AT THE SUB LEVEL BY BUSINESS OWNERSHIP CLASSIFICATION AND BUSINESS

CATEGORY


The next series of exhibits present the results of MGT’s utilization analyses on prime construction and construction-related professional projects.

Exhibit 3-4 and Figure 3-D show that the utilization of MBE firms, as a whole, was 6.1 percent in construction and 8.8 percent in construction-related professional services at the prime contractor/ consultant level. In terms of M/WBE firms, as a whole, the utilization was 8.2 percent in construction and 13.8 percent in construction-related professional services.

0.75%

2.14%

8.86%

0.45%

12.21%

12.00%

24.20%

0.00% 5.00% 10.00% 15.00% 20.00% 25.00% 30.00%

African American

Asian American

Hispanic American

Native American

Total MBE Firms

Nonminority Female

Total M/WBE Firms

CONSTRUCTION

1.39%

5.21%

8.57%

0.08%

15.26%

8.32%

23.58%

0.00% 5.00% 10.00% 15.00% 20.00% 25.00% 30.00%

African American

Asian American

Hispanic American

Native American

Total MBE Firms

Nonminority Female

Total M/WBE Firms

CONSTRUCTION-RELATED PROFESSIONAL SERVICES



EXHIBIT 3-4 UTILIZATION ANALYSIS AT THE PRIME LEVEL BY BUSINESS OWNERSHIP CLASSIFICATION AND BUSINESS

CATEGORY


CONSTRUCTION CONSTRUCTION-RELATED PROFESSIONAL SERVICES TOTAL

($) ($) ($)

African American $2,954,190 $5,808 $2,959,999 Asian American $687 $768,722 $769,409 Hispanic American $20,167,597 $1,467,682 $21,635,279 Native American $8,881,475 $2,225,513 $11,106,988 Total MBE Firms $32,003,950 $4,467,724 $36,471,674 Nonminority Female $11,288,770 $2,556,476 $13,845,246 Total M/WBE Firms $43,292,720 $7,024,201 $50,316,920 Non-M/WBE Firms $484,455,491 $43,984,483 $528,439,975 TOTAL $527,748,211 $51,008,684 $578,756,895

(%) (%) (%) African American 0.56% 0.01% 0.51% Asian American 0.00% 1.51% 0.13% Hispanic American 3.82% 2.88% 3.74% Native American 1.68% 4.36% 1.92% Total MBE Firms 6.06% 8.76% 6.30% Nonminority Female 2.14% 5.01% 2.39% Total M/WBE Firms 8.20% 13.77% 8.69% Non-M/WBE Firms 91.80% 86.23% 91.31% TOTAL 100.00% 100.00% 100.00%


Hispanic American-owned firms accounted for 3.8 percent of utilization in construction, followed by Native American-owned firms with 1.7 percent. Nonminority female-owned firms accounted for 2.1 percent of utilization in construction. Native American-owned firms accounted for 4.4 of utilization in construction-related professional services, followed by Hispanic American-owned firms with 2.9 percent. Nonminority female-owned firms accounted for 5.0 percent of utilization in construction-related professional services.



FIGURE 3-D UTILIZATION ANALYSIS AT THE PRIME LEVEL BY BUSINESS OWNERSHIP CLASSIFICATION AND BUSINESS

CATEGORY


Exhibit 3-5 shows the utilization of prime contractors on construction projects for each year of the study period by M/WBE group. In terms of percentage and year, M/WBE utilization was higher in CY2010 (11.5%) than any other year of the study period followed by CY2009 (10.4%). As far as MBE firms, utilization was higher in CY2012 (8.1%) followed by CY2010 (7.7%).

0.56%

0.00%

3.82%

1.68%

6.06%

2.14%

8.20%

0.00% 2.00% 4.00% 6.00% 8.00% 10.00% 12.00% 14.00%

African American

Asian American

Hispanic American

Native American

Total MBE Firms

Nonminority Female

Total M/WBE Firms

CONSTRUCTION

0.01%

1.51%

2.88%

4.36%

8.76%

5.01%

13.77%

0.00% 2.00% 4.00% 6.00% 8.00% 10.00% 12.00% 14.00%

African American

Asian American

Hispanic American

Native American

Total MBE Firms

Nonminority Female

Total M/WBE Firms

CONSTRUCTION-RELATED PROFESSIONAL SERVICES



EXHIBIT 3-5 UTILIZATION ANALYSIS AT THE PRIME LEVEL BY BUSINESS OWNERSHIP CLASSIFICATION AND CALENDAR

YEAR, CONSTRUCTION


2009 2010 2011 2012 2013 TOTAL

($) ($) ($) ($) ($) ($)

African American $80,554 $821,292 $661,739 $1,199,916 $190,689 $2,954,190 Asian American $0 $0 $0 $687 $0 $687 Hispanic American $574,931 $511,158 $733,349 $16,573,683 $1,774,475 $20,167,597 Native American $1,075,573 $4,441,494 $2,422,140 $515,355 $426,913 $8,881,475 Total MBE Firms $1,731,057 $5,773,945 $3,817,229 $18,289,641 $2,392,077 $32,003,950 Nonminority Female $2,145,034 $2,831,331 $1,530,849 $4,289,847 $491,708 $11,288,770 Total M/WBE Firms $3,876,092 $8,605,276 $5,348,078 $22,579,488 $2,883,786 $43,292,720 Non-M/WBE Firms $33,304,800 $66,466,765 $82,020,280 $204,529,132 $98,134,514 $484,455,491 TOTAL $37,180,892 $75,072,041 $87,368,358 $227,108,620 $101,018,299 $527,748,211

(%) (%) (%) (%) (%) (%) African American 0.22% 1.09% 0.76% 0.53% 0.19% 0.56% Asian American 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% Hispanic American 1.55% 0.68% 0.84% 7.30% 1.76% 3.82% Native American 2.89% 5.92% 2.77% 0.23% 0.42% 1.68% Total MBE Firms 4.66% 7.69% 4.37% 8.05% 2.37% 6.06% Nonminority Female 5.77% 3.77% 1.75% 1.89% 0.49% 2.14% Total M/WBE Firms 10.42% 11.46% 6.12% 9.94% 2.85% 8.20% Non-M/WBE Firms 89.58% 88.54% 93.88% 90.06% 97.15% 91.80% TOTAL 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%


Exhibit 3-6 shows the utilization of prime consultants on construction-related professional services projects for each year of the study period by M/WBE group. In terms of percentage and year, M/WBE utilization was higher in CY2009 (29.1%) than any other year of the study period followed by CY2010 (25.1%). As far as MBE firms, utilization was also higher in CY2009 (17.6%) followed by CY2010 (14.1%).

EXHIBIT 3-6 UTILIZATION ANALYSIS AT THE PRIME LEVEL BY BUSINESS OWNERSHIP CLASSIFICATION AND CALENDAR

YEAR, CONSTRUCTION-RELATED PROFESSIONAL SERVICES


2009 2010 2011 2012 2013 TOTAL

($) ($) ($) ($) ($) ($)

African American $5,808 $0 $0 $0 $0 $5,808 Asian American $299,583 $109,215 $131,501 $113,568 $114,855 $768,722 Hispanic American $227,144 $334,269 $390,133 $223,890 $292,246 $1,467,682 Native American $166,456 $157,838 $108,977 $1,127,641 $664,601 $2,225,513 Total MBE Firms $698,991 $601,321 $630,611 $1,465,099 $1,071,702 $4,467,724 Nonminority Female $458,361 $466,885 $346,270 $868,878 $416,082 $2,556,476 Total M/WBE Firms $1,157,352 $1,068,206 $976,881 $2,333,977 $1,487,784 $7,024,201 Non-M/WBE Firms $2,827,159 $3,193,809 $6,092,223 $21,217,572 $10,653,720 $43,984,483 TOTAL $3,984,512 $4,262,015 $7,069,104 $23,551,549 $12,141,504 $51,008,684




Source: MGT developed a Master Contract Database based on contracts and Professional Services Agreements awarded by the ADOT&PF between October 1, 2006, and September 30, 2011.

3.5 CONCLUSION

This chapter presented the results of MGT’s analyses on the market area and utilization of firms used on DPS prime contracts and subcontracts for construction and construction-related professional services. As a result of the market area analyses, the Denver-Aurora CSA was identified as the relevant market area.

As far as utilization, M/WBE firms on construction projects were awarded 24.2 percent of the subcontract dollars. Among M/WBE firms, all groups were utilized as construction subcontractors. In terms of construction-related professional services and subconsultant utilization, M/WBE firms were awarded 23.6 percent of the dollars. Among M/WBE firms, all groups were utilized as subconsultants on construction-related professional services projects.

The results from the prime construction utilization analyses showed that M/WBE firms received 8.2 percent of the dollars. Among M/WBE firms, all groups were utilized as prime contractors. The prime utilization on construction-related professional services showed that M/WBE firms received 13.8 percent of the dollars. Among M/WBE firms, all groups were utilized as prime consultants on construction-related professional services projects.


4.0 AVAILABILITY AND DISPARITY ANALYSES

CHAPTER 4.0: AVAILABILITY AND DISPARITY ANALYSES

Chapter 4.0 presents the results of MGT’s analyses on availability and disparity on Denver Public Schools (DPS) construction and construction-related professional services projects. In this chapter, MGT will answer, at least in part, the following research question: Are there disparities between the availability and utilization of minority- and woman-owned business enterprises (M/WBEs) in construction and construction related professional services?

To understand the analyses presented in this chapter, it is important to provide definitions to key terms discussed in this chapter. The following section presents these definitions.

4.1 CHAPTER DEFINITIONS

Availability Analysis Methodology. There is no single approach to estimating the availability of firms that has been adopted by the post-Croson case law. As a whole, the case law has emphasized firms being qualified, ready, willing, and able to pursue work with an agency. However, there is no single data source that captures all these features nor is any one source the most optimal for estimating availability. Therefore, this study presents availability estimates using the following sources: prequalified firms and custom census. The following explains how each data source was used to measure the estimates of available firms:

CUSTOM CENSUS

Availability estimates for construction and construction-related professional services at the subcontractor/subconsultant levels were based on firms represented in the study’s custom census. Some cases1 have allowed what is known as “custom census” as a source to calculate the availability of firms. Custom census involves using Dun & Bradstreet as a source of business availability. Dun & Bradstreet is a current data source contains information on individual firms, including firm revenue, number of employees, and specific areas of work. It should be noted that there are deficiencies to Dun & Bradstreet, which include:

No racial, ethnic, and gender information. No indication of whether a firm is interested to work on DPS projects. No indication of whether a firm primarily works as a prime contractor or subcontractor.

These deficiencies were addressed by first pulling a random sample of firms from Dun & Bradstreet. The sample was limited to firms located in the Denver-Aurora, Colorado Combined Statistical Area (CSA)2 and identified as providing construction and construction-related professional services based on six-digit North American Industry Classification System (NAICS) codes. Once this process was completed, the remaining deficiencies were addressed by conducting a short survey. Firms were asked:

Racial, ethnic, and gender information.

1 Northern Contracting v. Illinois DOT, 2005 U.S. Dist. LEXIS 19868 (ND IL 2005). 2 The Denver-Aurora, Colorado CSA consists of the following counties in the State of Colorado: City and County of Denver, Aurora, Jefferson, Adams, Douglas, City and County of Bloomfield, Elbert, Park, Clear Creek, Gilpin, Boulder, and Weld.


[[

AVAILABILITY AND DISPARITY ANALYSES

Had they bid or considered bidding on DPS projects (which indicates the firm’s interest).

When bidding on projects (not limited to DPS projects), if they primarily bid as a prime contractor, subcontractor, or both.

In addition to this survey, an abbreviated custom census survey process was used to supplement the custom census data referenced above. This process involved surveying construction and construction-related professional firms from the City and County of Denver’s custom census survey (City and County of Denver Minority/Women Owned/Disadvantaged Business Enterprise Disparity Study, 2013) and asking these firms if they had or considered bidding on DPS projects. Those firms that indicated that they had or considered bidding on DPS projects were also included in the custom census.

PREQUALIF IED F IRMS

In order for a contractor to respond to a DPS bid or proposal at the prime level (for construction and construction-related professional services projects), a contractor must submit a prequalification application that demonstrates the contractor has the ability, integrity and financial resources to perform the work. Therefore, DPS prequalified firms data were used to calculate availability estimates for construction and construction-related professional services at the prime levels.

Disparity Analysis Methodology. Disparity, in this context, is the analysis of the differences between the utilization of M/WBEs and the availability of those firms. As a result, disparity indices are calculated to examine whether M/WBEs received a proportional share of dollars based on the availability of M/WBEs located in the study’s relevant market area, which is the Denver-Aurora, Colorado CSA. The following explains MGT’s disparity methodology in more detail.

MGT pioneered the use of disparity indices as a means of quantifying the disparity in utilization comparative to availability. The use of disparity indices for such calculations is supported by several post-Croson cases, most notably Contractors Association of Eastern Pennsylvania v. City of Philadelphia.3 Although a variety of similar indices could be utilized, MGT’s standard for choosing a particular index methodology is that it must yield a value that is easily calculable, understandable in its interpretation, and universally comparable such that a disparity in utilization within M/WBEs can be assessed with reference to the utilization of nonminority- and women-owned firms. The following formula shows the ratio of the percentage of utilization to the percentage of availability multiplied by 100:

%Um1p1

(1) Disparity Index = X 100 %Am1p1

Where: Um1p1 = utilization of M/WBEs1 for contracting1

Am1p1 = availability of M/WBEs1 for contracting1

Due to the mathematical properties involved in the calculations, a disparity index value of zero (0.00) indicates absolutely no utilization and, therefore, absolute disparity. A disparity index of 100 indicates that utilization is perfectly proportionate to the availability, therefore indicating the absence of disparity (that is, all things being equal). Generally, firms are considered underutilized if the disparity indices are less than 100, and overutilized if the indices are above 100.

3Contractors Association of Eastern Pennsylvania, Inc. v. City of Philadelphia, 91 F 3d at 603.



Since there is no standardized measurement to evaluate the levels of underutilization or overutilization within a procurement context, MGT’s methodology to measure disparity, if disparity is found, is based on the Equal Employment Opportunity Commission’s (EEOC) Uniform Guidelines on Employee Selection Procedures “80 percent rule.” In the employment discrimination framework, an employment disparity ratio below 80 indicates a “substantial disparity.” The Supreme Court has accepted the use of the “80 percent rule” in Connecticut v. Teal (Teal), 457 U.S. 440 (1982). In Teal and other affirmative action cases, the terms “adverse impact,” “disparate impact,” and “discriminatory impact” are used interchangeably to characterize values of 80 and below. Therefore, firms are considered substantially underutilized (substantial disparity) if the disparity index is 80 or less.

4.2 AVAILABILITY ANALYSIS

Similar to Chapter 3.0, the analysis of subcontracting is presented first because there must be a factual basis in order to establish M/WBE subcontracting goals. As noted in Section 4.1, Chapter Definitions, availability estimates at the subcontracting are based on custom census. Exhibit 4-1 and Figure 4-A present the availability estimates of construction at the subcontractor level. Overall, M/WBE availability for construction subcontractors was 44.6 percent.

EXHIBIT 4-1 AVAILABILITY ESTIMATES OF CONSTRUCTION SUBCONTRACTORS

BY BUSINESS OWNERSHIP CLASSIFICATION

BUSINESS OWNERSHIP CLASSIFICATION FIRMS

(%) African American 3.04% Asian American 1.35% Hispanic American 14.53% Native American 1.01% Total MBE Firms 19.93% Nonminority Female 24.66% Total M/WBE Firms 44.59% Non-M/WBE Firms 55.41% TOTAL 100.00%

Source: MGT developed a Master Availability Database based on custom census.



FIGURE 4-A AVAILABILITY ESTIMATES OF CONSTRUCTION SUBCONTRACTORS



Exhibit 4-2 and Figure 4-B present the availability estimates of construction-related professional services at the subcontractor level. Overall, M/WBE availability for construction-related professional services subconsultants was 27.9 percent.

EXHIBIT 4-2 AVAILABILITY ESTIMATES OF CONSTRUCTION-RELATED PROFESSIONAL SERVICES SUBCONSULTANTS


BUSINESS OWNERSHIP CLASSIFICATION FIRMS (#) African American 3.83% Asian American 3.28% Hispanic American 4.10% Native American 1.09% Total MBE Firms 12.30% Nonminority Female 15.57% Total M/WBE Firms 27.87% Non-M/WBE Firms 72.13% TOTAL 100.00%


3.04% 1.35%

14.53%

1.01%

19.93%24.66%

44.59%

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

90.00%

100.00%

AfricanAmerican

AsianAmerican

HispanicAmerican

NativeAmerican

Total MBEFirms

NonminorityFemale

Total M/WBEFirms



FIGURE 4-B AVAILABILITY ESTIMATES OF CONSTRUCTION-RELATED PROFESSIONAL SERVICES SUBCONSULTANTS



Exhibit 4-3 and Figure 4-C present the availability estimates of construction at the prime level. As noted in Section 4.1, Chapter Definitions, availability estimates at the prime contracting level are based prequalified firms. Overall, M/WBE availability for construction prime contractors was 14.5 percent.

EXHIBIT 4-3 AVAILABILITY ESTIMATES OF CONSTRUCTION PRIME CONTRACTORS


BUSINESS OWNERSHIP CLASSIFICATION FIRMS (%) African American 0.80% Asian American 0.20% Hispanic American 5.62% Native American 0.80% Total MBE Firms 7.43% Nonminority Female 7.03% Total M/WBE Firms 14.46% Non-M/WBE Firms 85.54% TOTAL 100.00%

Source: MGT developed a Master Availability Database based DPS prequalified firms’ data for general contractors (construction).

3.83% 3.28% 4.10%1.09%

12.30%15.57%

27.87%

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

90.00%

100.00%

AfricanAmerican

Asian American HispanicAmerican

NativeAmerican

Total MBEFirms

NonminorityFemale

Total M/WBEFirms



FIGURE 4-C AVAILABILITY ESTIMATES OF CONSTRUCTION PRIME CONTRACTORS


Source: MGT developed a Master Availability Database based DPS prequalified firms’ data for general contractors (construction).

0.80% 0.20%5.62%

0.80%7.43% 7.03%

14.46%

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

90.00%

100.00%

AfricanAmerican

AsianAmerican

HispanicAmerican

NativeAmerican

Total MBEFirms

NonminorityFemale

Total M/WBEFirms



Exhibit 4-4 and Figure 4-D present the availability estimates of construction at the prime level. Overall, M/WBE availability for construction prime contractors was slightly more than 17 percent.

EXHIBIT 4-4 AVAILABILITY ESTIMATES OF CONSTRUCTION-RELATED PROFESSIONAL SERVICES PRIME CONSULTANTS


BUSINESS OWNERSHIP CLASSIFICATION FIRMS (%) African American 1.31% Asian American 3.93% Hispanic American 3.06% Native American 0.87% Total MBE Firms 9.17% Nonminority Female 7.86% Total M/WBE Firms 17.03% Non-M/WBE Firms 82.97% TOTAL 100.00%

Source: MGT developed a Master Availability Database based DPS prequalified firms’ data for architecture and engineering (construction-related professional services).

FIGURE 4-D AVAILABILITY ESTIMATES OF CONSTRUCTION-RELATED PROFESSIONAL SERVICES PRIME CONSULTANTS


Source: MGT developed a Master Availability Database based DPS prequalified firms’ data for architecture and engineering (construction-related professional services).

1.31% 3.93% 3.06% 0.87%

9.17% 7.86%

17.03%

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

90.00%

100.00%

AfricanAmerican

AsianAmerican

HispanicAmerican

NativeAmerican

Total MBEFirms

NonminorityFemale

Total M/WBEFirms



4.3 DISPARITY ANALYSIS

The next series of exhibits presents the disparity results for construction and construction-related professional services at the prime contractor and subcontractor levels (separately). The disparity results are based on utilization and availability of M/WBEs and non-M/WBEs, as noted in Section 4.1, Chapter Definitions.

Figure 4-E presents the disparity summary results for construction subcontracts. Overall, M/WBE firms (disparity index 54.28) were substantially underutilized as subcontractors on construction projects. Among the M/WBE groups, all M/WBE groups were substantially underutilized with the exception of Asian American-owned firms. The corresponding detail is presented in Exhibit 4-5.

FIGURE 4-E DISPARITY ANALYSES OF CONSTRUCTION SUBCONTRACTORS


Source: Source: MGT developed a master database based on anticipated award dollars to subcontractors on DPS’ projects (based on 2008 and 2012). MGT developed a Master Availability Database based on custom census. Disparity index is the ratio of the percentage of dollars to percentage of available firms multiplied by 100.00. A dotted line is drawn at 80.00. A disparity index below 80.00 indicates a substantial level of disparity. A solid red line is drawn at 100.00. A disparity index greater than 100.00 indicates overutilization.

24.79

158.72

60.96

44.85

61.25

48.64

54.28

0.00 20.00 40.00 60.00 80.00 100.00 120.00

African American

Asian American

Hispanic American

Native American

Total MBE Firms

Nonminority Female

Total M/WBE Firms



EXHIBIT 4-5 DISPARITY ANALYSES OF CONSTRUCTION SUBCONTRACTORS



% OF DOLLARS1

% OF AVAILABLE

FIRMS2

DISPARITY INDEX3

DISPARATE IMPACT OF UTILIZATION

TOTAL

African American 0.75% 3.04% 24.79 Underutilization * Asian American 2.14% 1.35% 158.72 Overutilization Hispanic American 8.86% 14.53% 60.96 Underutilization * Native American 0.45% 1.01% 44.85 Underutilization * Total MBE Firms 12.21% 19.93% 61.25 Underutilization * Nonminority Female 12.00% 24.66% 48.64 Underutilization * Total M/WBE Firms 24.20% 44.59% 54.28 Underutilization * Non-M/WBE Firms 75.80% 55.41% 136.80 Overutilization

Source: Source: MGT developed a master database based on anticipated award dollars to subcontractors on DPS’ projects (based on 2008 and 2012). MGT developed a Master Availability Database based on custom census. Percent of dollars is taken from the utilization analysis. Percent of available firms is taken from the availability estimates. Disparate index is the ratio of the percent of dollars to percent of available firms multiplied by 100. * indicate a substantial level of disparity, which is a disparity index below 80.00.

Figure 4-F presents the disparity summary results for construction-related professional services subconsultants. Overall, M/WBE firms (disparity index 84.63) were underutilized as subconsultants on construction-related professional services projects. Among the M/WBE groups, all groups were undertuilized with the exception of Hispanic American- and Asian American-owned firms. The corresponding detail is presented in Exhibit 4-6.



FIGURE 4-F DISPARITY ANALYSES OF CONSTRUCTION-RELATED PROFESSIONAL SERVICES SUBCONSULTANTS


Source: Source: MGT developed a master database based on anticipated award dollars to subcontractors on DPS’ projects (based on 2008 and 2012). MGT developed a Master Availability Database based on custom census. Disparity index is the ratio of the percentage of dollars to percentage of available firms multiplied by 100.00. A dotted line is drawn at 80.00. A disparity index below 80.00 indicates a substantial level of disparity. A solid red line is drawn at 100.00. A disparity index greater than 100.00 indicates overutilization.

EXHIBIT 4-6

DISPARITY ANALYSES OF CONSTRUCTION-RELATED PROFESSIONAL SERVICES SUBCONSULTANTS BY BUSINESS OWNERSHIP CLASSIFICATION

YEAR BUSINESS OWNERSHIP CLASSIFICATION

% OF DOLLARS1

% OF AVAILABLE

FIRMS2

DISPARITY INDEX3


TOTAL

African American 1.39% 3.83% 36.42 Underutilization * Asian American 5.21% 3.28% 158.97 Overutilization Hispanic American 8.57% 4.10% 209.17 Overutilization Native American 0.08% 1.09% 7.68 Underutilization * Total MBE Firms 15.26% 12.30% 124.13 Overutilization Nonminority Female 8.32% 15.57% 53.44 Underutilization * Total M/WBE Firms 23.58% 27.87% 84.63 Underutilization Non-M/WBE Firms 76.42% 72.13% 105.94 Overutilization

Source: Source: MGT developed a master database based on anticipated award dollars to subcontractors on DPS’ projects (based on 2008 and 2012). MGT developed a Master Availability Database based on custom census. Percent of dollars is taken from the utilization analysis. Percent of available firms is taken from the availability estimates. Disparate index is the ratio of the percent of dollars to percent of available firms multiplied by 100. * indicate a substantial level of disparity, which is a disparity index below 80.00.

36.42

158.97

209.17

7.68

124.13

53.44

84.63

0.00 20.00 40.00 60.00 80.00 100.00 120.00

African American

Asian American

Hispanic American

Native American

Total MBE Firms

Nonminority Female

Total M/WBE Firms



Figure 4-G presents the disparity summary results for construction prime contractors. Overall, M/WBE firms (disparity index 56.74) were substantially underutilized as prime contractors on construction projects. Among the M/WBE groups, all M/WBE groups were substantially underutilized with the exception of Native American-owned firms. The corresponding detail is presented in Exhibit 4-7.

FIGURE 4-G DISPARITY ANALYSES OF CONSTRUCTION PRIME CONTRACTORS


Source: MGT developed a master database based on dollars awarded and expended by DPS between January 1, 2009, and December 31, 2013. A Master Availability Database based on prequalified firms data for construction for the availability estimates. Disparity index is the ratio of the percentage of dollars to percentage of available firms multiplied by 100.00. A dotted line is drawn at 80.00. A disparity index below 80.00 indicates a substantial level of disparity. A solid red line is drawn at 100.00. A disparity index greater than 100.00 indicates overutilization.

EXHIBIT 4-7 DISPARITY ANALYSES OF CONSTRUCTION PRIME CONTRACTORS



% OF DOLLARS1

% OF AVAILABLE

FIRMS2

DISPARITY INDEX3


2009

African American 0.22% 0.80% 26.97 Underutilization * Asian American 0.00% 0.20% 0.00 Underutilization * Hispanic American 1.55% 5.62% 27.50 Underutilization * Native American 2.89% 0.80% 360.15 Overutilization Total MBE Firms 4.66% 7.43% 62.66 Underutilization * Nonminority Female 5.77% 7.03% 82.09 Underutilization Total M/WBE Firms 10.42% 14.46% 72.11 Underutilization * Non-M/WBE Firms 89.58% 85.54% 104.71 Overutilization

69.69

0.06

67.97

209.52

81.62

30.44

56.74

0.00 20.00 40.00 60.00 80.00 100.00 120.00

African American

Asian American

Hispanic American

Native American

Total MBE Firms

Nonminority Female

Total M/WBE Firms




% OF DOLLARS1

% OF AVAILABLE

FIRMS2

DISPARITY INDEX3


2010

African American 1.09% 0.80% 136.20 Overutilization Asian American 0.00% 0.20% 0.00 Underutilization * Hispanic American 0.68% 5.62% 12.11 Underutilization * Native American 5.92% 0.80% 736.58 Overutilization Total MBE Firms 7.69% 7.43% 103.52 Overutilization Nonminority Female 3.77% 7.03% 53.66 Underutilization * Total M/WBE Firms 11.46% 14.46% 79.28 Underutilization * Non-M/WBE Firms 88.54% 85.54% 103.50 Overutilization

2011

African American 0.76% 0.80% 94.30 Underutilization Asian American 0.00% 0.20% 0.00 Underutilization * Hispanic American 0.84% 5.62% 14.93 Underutilization * Native American 2.77% 0.80% 345.16 Overutilization Total MBE Firms 4.37% 7.43% 58.81 Underutilization * Nonminority Female 1.75% 7.03% 24.93 Underutilization * Total M/WBE Firms 6.12% 14.46% 42.34 Underutilization * Non-M/WBE Firms 93.88% 85.54% 109.75 Overutilization

2012

African American 0.53% 0.80% 65.78 Underutilization * Asian American 0.00% 0.20% 0.15 Underutilization * Hispanic American 7.30% 5.62% 129.79 Overutilization Native American 0.23% 0.80% 28.25 Underutilization * Total MBE Firms 8.05% 7.43% 108.39 Overutilization Nonminority Female 1.89% 7.03% 26.88 Underutilization * Total M/WBE Firms 9.94% 14.46% 68.77 Underutilization * Non-M/WBE Firms 90.06% 85.54% 105.28 Overutilization

2013

African American 0.19% 0.80% 23.50 Underutilization * Asian American 0.00% 0.20% 0.00 Underutilization * Hispanic American 1.76% 5.62% 31.24 Underutilization * Native American 0.42% 0.80% 52.61 Underutilization * Total MBE Firms 2.37% 7.43% 31.87 Underutilization * Nonminority Female 0.49% 7.03% 6.93 Underutilization * Total M/WBE Firms 2.85% 14.46% 19.75 Underutilization * Non-M/WBE Firms 97.15% 85.54% 113.56 Overutilization

TOTAL

African American 0.56% 0.80% 69.69 Underutilization * Asian American 0.00% 0.20% 0.06 Underutilization * Hispanic American 3.82% 5.62% 67.97 Underutilization * Native American 1.68% 0.80% 209.52 Overutilization Total MBE Firms 6.06% 7.43% 81.62 Underutilization Nonminority Female 2.14% 7.03% 30.44 Underutilization * Total M/WBE Firms 8.20% 14.46% 56.74 Underutilization * Non-M/WBE Firms 91.80% 85.54% 107.31 Overutilization

Source: MGT developed a master database based on dollars awarded and expended by DPS between January 1, 2009, and December 31, 2013. A Master Availability Database based on prequalified firms data for construction for the availability estimates. Percent of dollars is taken from the utilization analysis. Percent of available firms is taken from the availability estimates. Disparate index is the ratio of the percent of dollars to percent of available firms multiplied by 100. * indicate a substantial level of disparity, which is a disparity index below 80.00.



Figure 4-H presents the disparity summary results for construction-related professional services at the prime consultant level. Overall, M/WBE firms (disparity index 80.86) were underutilized as prime consultants on construction-related professional services projects. Among the M/WBE groups, all M/WBE groups were substantially underutilized with the exception of Native American-owned firms. The corresponding detail is presented in Exhibit 4-8.

FIGURE 4-H DISPARITY ANALYSES OF CONSTRUCTION-RELATED PROFESSIONAL SERVICES PRIME CONSULTANTS


Source: MGT developed a master database based on dollars awarded and expended by DPS between January 1, 2009, and December 31, 2013. A Master Availability Database based on prequalified firms data for construction-related professional services for the availability estimates. Disparity index is the ratio of the percentage of dollars to percentage of available firms multiplied by 100.00. A dotted line is drawn at 80.00. A disparity index below 80.00 indicates a substantial level of disparity. A solid red line is drawn at 100.00. A disparity index greater than 100.00 indicates overutilization.

EXHIBIT 4-8 DISPARITY ANALYSES OF CONSTRUCTION-RELATED PROFESSIONAL SERVICES PRIME CONSULTANTS



% OF DOLLARS1

% OF AVAILABLE

FIRMS2

DISPARITY INDEX3


2009

African American 0.15% 1.31% 11.13 Underutilization * Asian American 7.52% 3.93% 191.31 Overutilization Hispanic American 5.70% 3.06% 186.49 Overutilization Native American 4.18% 0.87% 478.33 Overutilization Total MBE Firms 17.54% 9.17% 191.30 Overutilization Nonminority Female 11.50% 7.86% 146.35 Overutilization Total M/WBE Firms 29.05% 17.03% 170.55 Overutilization Non-M/WBE Firms 70.95% 82.97% 85.52 Underutilization

0.87

38.35

94.13

499.56

95.51

63.76

80.86

0.00 20.00 40.00 60.00 80.00 100.00 120.00

African American

Asian American

Hispanic American

Native American

Total MBE Firms

Nonminority Female

Total M/WBE Firms




% OF DOLLARS1

% OF AVAILABLE

FIRMS2

DISPARITY INDEX3


2010

African American 0.00% 1.31% 0.00 Underutilization * Asian American 2.56% 3.93% 65.20 Underutilization * Hispanic American 7.84% 3.06% 256.58 Overutilization Native American 3.70% 0.87% 424.03 Overutilization Total MBE Firms 14.11% 9.17% 153.85 Overutilization Nonminority Female 10.95% 7.86% 139.37 Overutilization Total M/WBE Firms 25.06% 17.03% 147.17 Overutilization Non-M/WBE Firms 74.94% 82.97% 90.32 Underutilization

2011

African American 0.00% 1.31% 0.00 Underutilization * Asian American 1.86% 3.93% 47.33 Underutilization * Hispanic American 5.52% 3.06% 180.55 Overutilization Native American 1.54% 0.87% 176.51 Overutilization Total MBE Firms 8.92% 9.17% 97.28 Underutilization Nonminority Female 4.90% 7.86% 62.32 Underutilization * Total M/WBE Firms 13.82% 17.03% 81.14 Underutilization Non-M/WBE Firms 86.18% 82.97% 103.87 Overutilization

2012

African American 0.00% 1.31% 0.00 Underutilization * Asian American 0.48% 3.93% 12.27 Underutilization * Hispanic American 0.95% 3.06% 31.10 Underutilization * Native American 4.79% 0.87% 548.22 Overutilization Total MBE Firms 6.22% 9.17% 67.84 Underutilization Nonminority Female 3.69% 7.86% 46.94 Underutilization * Total M/WBE Firms 9.91% 17.03% 58.19 Underutilization Non-M/WBE Firms 90.09% 82.97% 108.58 Overutilization

2013

African American 0.00% 1.31% 0.00 Underutilization * Asian American 0.95% 3.93% 24.07 Underutilization * Hispanic American 2.41% 3.06% 78.74 Underutilization * Native American 5.47% 0.87% 626.75 Overutilization Total MBE Firms 8.83% 9.17% 96.25 Underutilization Nonminority Female 3.43% 7.86% 43.60 Underutilization * Total M/WBE Firms 12.25% 17.03% 71.95 Underutilization Non-M/WBE Firms 87.75% 82.97% 105.76 Overutilization

TOTAL

African American 0.01% 1.31% 0.87 Underutilization * Asian American 1.51% 3.93% 38.35 Underutilization * Hispanic American 2.88% 3.06% 94.13 Underutilization Native American 4.36% 0.87% 499.56 Overutilization Total MBE Firms 8.76% 9.17% 95.51 Underutilization Nonminority Female 5.01% 7.86% 63.76 Underutilization * Total M/WBE Firms 13.77% 17.03% 80.86 Underutilization Non-M/WBE Firms 86.23% 82.97% 103.93 Overutilization

Source: MGT developed a master database based on dollars awarded and expended by DPS between January 1, 2009, and December 31, 2013. A Master Availability Database based on prequalified firms data for construction-related professional services for the availability estimates. Percent of dollars is taken from the utilization analysis. Percent of available firms is taken from the availability estimates. Disparate index is the ratio of the percent of dollars to percent of available firms multiplied by 100. * indicate a substantial level of disparity, which is a disparity index below 80.00.



4.4 CONCLUSION

The disparity analyses are able to answer the following research question:

1) Are there disparities between the availability and utilization of M/WBEs in construction and construction related professional services?

FIGURE 4-I DISPARITY ANALYSES SUMMARY OF MBE AND M/WBE GROUPS BY BUSINESS CATEGORY

Source: MGT developed a master database based on utilization and availability of firms. Disparity index is the ratio of the percentage of dollars to percentage of available firms multiplied by 100.00. A dotted line is drawn at 80.00. A disparity index below 80.00 indicates a substantial level of disparity. A solid red line is drawn at 100.00. A disparity index greater than 100.00 indicates overutilization.

Figure 4-I presents the summary disparity results for overall MBE and M/WBE groups. There were disparities or substantial disparities between the utilization and availability of MBEs, as a whole, on construction projects at both the prime contractor (81.62) and subcontractor (61.25) levels, as well as for construction-related professional services (CRPS) at the prime level (95.51). In terms of M/WBEs, as a whole, there were disparities and substantial disparities for construction at both the prime contractor (56.74) and subcontractor (54.28) levels, as well as construction-related professional services (CRPS) at the prime consultant (80.86) and subconsultants (84.63) levels. The casual factors that may contribute to the disparity are discussed in Chapter 7.0, Findings and Recommendations. Further evidence to support the disparity is discussed in Chapter 5.0, Private Sector Analysis and Chapter 6.0, Anecdotal Analysis.

0.00 20.00 40.00 60.00 80.00 100.00 120.00 140.00

Total MBE Firms

Total M/WBE Firms

Total MBE Firms Total M/WBE FirmsCRPS Primes 95.51 80.86Constuction Primes 81.62 56.74CRPS Subs 124.13 84.63Construction Subs 61.25 54.28


5.0 PRIVATE SECTOR ANALYSES

CHAPTER 5.0: PRIVATE SECTOR ANALYSES

This chapter presents the results of our analyses pertaining to minority- and woman-owned business enterprises (M/WBE) in Denver Public Schools’ (DPS) private sector marketplace. The analyses examine M/WBE utilization and availability in the DPS market area private commercial construction industry to determine disparities in M/WBE utilization at the prime contractor and subcontractor levels. Once the record of private sector utilization was established, we were also able to compare the rates of M/WBE, and non-M/WBE utilization in the private sector to their utilization by DPS for public sector construction procurement.

The second analysis delves more deeply into the dynamics of the marketplace to determine their impact on M/WBE competitiveness. This analysis examines the effects of race, ethnicity, and gender on business formation and earnings to test the hypothesis that M/WBEs are treated differently than non-minority-owned firms when attempting to create and conduct business in the DPS marketplace.

The sections of Chapter 5.0 consist of the following:

5.1 Methodology

5.2 Collection and Management of Data

5.3 Private Sector Utilization Analysis, Commercial Permits

5.4 Analysis of Race, Ethnicity, and Gender Effects on Self-Employment

5.5 Analysis of Race, Ethnicity, and Gender Effects on Individual Earnings

5.6 Conclusion

5.1 METHODOLOGY

This section describes our methodology for the collection of data and the calculation of DPS market area as the basis for our analysis of private sector utilization of minority-, woman-, and nonminority-owned firms and their availability.

5.1 .1 PRIVATE SECTOR ANALYSIS - RATIONALE

In City of Richmond v J.A. Croson (Croson), the Court established that a “municipality has a compelling government interest in redressing not only discrimination committed by the municipality itself, but also discrimination committed by private parties within the municipality’s legislative jurisdiction, so long as the municipality in some way participated in the discrimination to be remedied by the program.”1 This argument was reinforced by the Court of Appeals decision in Adarand Construction, Inc. v Rodney Slater, concluding that there was a compelling interest for a government Disadvantaged Business Enterprise

1 Croson, 488 U.S. 46, 109 S.Ct. at 720-21, 744-45.


PRIVATE SECTOR ANALYSES

(DBE) program, based primarily on evidence of private sector discrimination.2 According to this argument, discriminatory practices found in the private sector marketplace may be indicative of government’s passive or, in some cases, active participation in local discrimination. To remedy such discrimination, Croson provided that government “can use its spending powers to remedy private discrimination, if it identifies that discrimination with the particularity required by the Fourteenth Amendment.”3 The purpose of a private sector analysis is to evaluate the presence or absence of discrimination in the private sector marketplace, and to determine if there is evidence to support anecdotal comments from Chapter 6.0 regarding difficulties M/WBEs have in securing work on private sector projects.

Passive discrimination was examined in a disparity analysis of the utilization of M/WBE construction subcontractors by majority prime contractors on projects funded in DPS construction market. A comparison of public sector M/WBE utilization with private sector utilization enables an assessment of the extent to which majority prime contractors have tended to hire M/WBE subcontractors only to satisfy public sector requirements. Thus, the following questions are addressed:

Are there disparities in utilization of M/WBEs as prime contractors for commercial private sector construction projects relative to their availability in DPS market area?

Are there disparities in utilization of M/WBEs as subcontractors for commercial private sector construction projects relative to their availability in DPS market area?

To what extent are contractors utilized for DPS projects also utilized in private sector construction projects? Refer to Appendix H for an examination and discussion on the nexus between commercial and DPS construction.

5.2 COLLECTION AND MANAGEMENT OF DATA

MGT staff obtained commercial construction permit data (such as building, electrical, plumbing) provided by the City and County of Denver’s Development Services Department for commercial construction projects permitted from January 1, 2009 through December 31, 2013. The value in examining permits is that it offers the most complete and up-to-date record of actual construction activity undertaken in these market areas. Based on the permit type text description, permits were categorized according to two types of work-performed categories: prime contractor work level and subcontractor work level. The data was then classified as prime and subcontractor based on the type of work performed. Upon further assessment and review of the City and County of Denver’s Development Services Department’s commercial construction permits data, permits associated with subcontractor trades and/or level of work, such as electrical or plumbing, did not have construction value dollars.

5.2 .1 M/WBE CLASSIFICATIONS AND BUSINESS CATEGORIES

In Chapter 3.0, the five M/WBE classifications described—African American, Hispanic American, Asian American, Native American, and nonminority women—were used as the basis of MGT’s private sector analysis of utilization and disparity. Since permit data does not contain contractor race, ethnic, and gender information, we were able to appropriate information contained in various vendor lists obtained from DPS, the City and County of Denver, a national M/WBE database, and trade associations to conduct a

2 Adarand v. Slater, 228 F.3d 1147 (10th Cir. 2000). 3 See Richmond v. Croson, 488 U.S. 492 (1989).



vendor match procedure. This procedure allowed us to further identify ethnic, gender, and racial classifications of firms by identifying vendors in the permit databases and assigning M/WBE classifications. In order to obtain the greatest number of potential match combinations, a conservative manual match was conducted.

For the business category analysis, findings reported in this chapter deal only with private sector construction for two reasons: (1) permit data, by its nature, pertains only to construction activities, which is also the category for which data tends to be most extensive and reliable, and (2) in the courts, historically, construction activity in a given jurisdiction has been scrutinized more than any other business category because, in both public and private sector business activity, it tends to be the most financially lucrative in terms of its impact on a local economy. The courts have asserted that jurisdictions have a “compelling interest” to advance M/WBE business interests in their local markets. Accordingly, for the analysis, the data were classified according to two categories of construction contractor—prime contractors and subcontractors—based on the permit type data field, or level of work.

5.2 .2 MARKET AREA METHODOLOGY

A United States county is the geographical unit of measure selected for determining market area. The use of counties located within the Denver-Aurora, Colorado Combined Statistical Area (CSA), DPS relevant market area, was examined. The counties within the Denver-Aurora CSA is geographical units based on the following considerations: 1) the courts have accepted counties as a standard geographical unit of analysis in conducting equal employment opportunity and disparity analysis; 2) county boundaries are externally determined and thus free from any researcher bias that might result from any arbitrary determinations of geographical units of analysis; and 3) the U.S. Census and other federal and state data are routinely collected and reported by county. Subsequently, our private sector analysis is based on the Denver-Aurora CSA (DPS market area).

5.3 PRIVATE SECTOR UTILIZATION ANALYSIS, COMMERCIAL PERMITS

The following section presents results from the analysis of the utilization of M/WBE and non-M/WBE firms in the state private sector commercial construction market.

The utilization of M/WBE and non-M/WBE firms for private commercial construction permits data at the prime contractor level within the DPS market area are presented in Exhibits 5-1 through 5-2. Exhibit 5-1 shows that from January 1, 2009 through December 31, 2013, MBEs received 1.9 percent of the private commercial construction permits at the prime contractor level and WBEs received 0.24 percent.



EXHIBIT 5-1 UTILIZATION ANALYSIS OF FIRMS

COMMERCIAL CONSTRUCTION AT THE PRIME CONTRACTOR LEVEL JANUARY 1, 2009 THROUGH DECEMBER 31, 2013

BUSINESS OWNERSHIP CLASSIFICATION TOTAL

($)

African American $2,892,242 Asian American $0 Hispanic American $0 Native American $26,261,348 Total MBE Firms $29,153,590 Nonminority Female $3,883,141 Total M/WBE Firms $33,036,731 Non-M/WBE Firms $1,603,083,092 TOTAL $1,636,119,823


Source: MGT developed a Master Commercial Private sector Database based on commercial construction permitting data awarded from January 1, 2009 through December 31, 2013.

Exhibit 5-2 shows that MBE subcontractors were issued 3.9 percent of all subcontracting permits and WBEs 1.2 percent of subcontracting permits.



EXHIBIT 5-2 COMMERCIAL CONSTRUCTION AT THE SUBCONTRACTOR LEVEL

JANUARY 1, 2009 THROUGH DECEMBER 31, 2013

BUSINESS OWNERSHIP CLASSIFICATION TOTAL

($)

African American $2,021,723 Asian American $76,500 Hispanic American $4,311,007 Native American $37,295,822 Total MBE Firms $43,705,052 Nonminority Female $13,264,903 Total M/WBE Firms $56,969,955 Non-M/WBE Firms $1,068,121,489 TOTAL $1,125,091,444


Source: MGT developed a Master Commercial Private sector Database based on commercial construction permitting data awarded from January 1, 2009 through December 31, 2013.

Figure 5-A shows M/WBE firm utilization at the prime contractor and subcontractor levels for public construction projects awarded by DPS and compares this with the private commercial construction utilization calculated from the commercial construction permits data. The figure shows that there were differences in the utilization of M/WBE firms on DPS construction projects (8.2%) and private commercial construction projects (2.02%). At the subcontractor level, M/WBE utilization was also higher on DPS projects (24.2%) compared to private commercial construction projects (5.1%)



FIGURE 5-A COMPARISON OF DENVER PUBLIC SCHOOLS AND COMMERCIAL CONSTRUCTION

M/WBE UTILIZATION PERCENTAGE OF DOLLARS AT THE PRIME CONTRACTOR AND SUBCONTRACTOR LEVELS

AfricanAmerican

AsianAmerican

HispanicAmerican

NativeAmerican

Total MBEFirms

NonminorityFemale

TotalM/WBE

FirmsCommercial Construction 0.18% 0.00% 0.00% 1.61% 1.78% 0.24% 2.02%Denver Public Schools 0.56% 0.00% 3.82% 1.68% 6.06% 2.14% 8.20%

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

30.00%

PRIME CONTRACTOR LEVEL

AfricanAmerican

AsianAmerican

HispanicAmerican

NativeAmerican

Total MBEFirms

NonminorityFemale

TotalM/WBE

FirmsCommercial Construction 0.18% 0.01% 0.38% 3.31% 3.88% 1.18% 5.06%Denver Public Schools 0.75% 2.14% 8.86% 0.45% 12.21% 12.00% 24.20%

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

30.00%

SUBCONTRACTOR LEVEL



5.4 ANALYSIS OF RACE, ETHNICITY, AND GENDER EFFECTS ON SELF-EMPLOYMENT

The purpose of this analysis is to examine the effects of race and gender, along with other individual economic and demographic characteristics, on individuals’ participation in the private sector as self-employed business operators, and on their earnings as a result of their participation in five categories of private sector business activity in the Denver-Aurora CSA. Findings for minority business enterprises are compared to the self-employment participation and earnings record of nonminority male business owners to determine if a disparity in self-employment rates and earnings exists, and if it is attributable to differences in race, gender, or ethnicity. Adopting the methodology and variables employed by a City of Denver disparity study (see Concrete Works v. City and County of Denver 4), we use Public Use Microdata Samples (PUMS) data derived from the 2010 American Community Survey, to which we apply appropriate regression statistics to draw conclusions.

To guide this investigation, three general research questions were posed. Questions and variables used to respond to each, followed by a report of findings, are reported below:

I. Are racial, ethnic, and gender minority groups less likely than nonminority males to be self-employed?

II. This analysis examined the statistical effects of the following variables on the likelihood of being self-employed in the study market area. Race, ethnicity, and gender of business owner (African American, Asian American, Hispanic American, Native American, nonminority women, nonminority men), marital status, age, self-reported health-related disabilities, availability of capital (household property value, monthly total mortgage payments, unearned income), other characteristics (number of individuals over the age of 65 living in household, number of children under the age of 18 living in household), and level of education.

III. Does race, ethnicity, and/or gender have an impact on individual’s self-employment earnings?

IV. This analysis examined the statistical effects of the following variables on income from self-employment for business owners in the market area: Race, ethnicity, and gender of business owner (African American, Asian American, Hispanic American, Native American, nonminority women, nonminority men), marital status, age, self-reported health-related disabilities, availability of capital (household property value, monthly total mortgage payments, unearned income), and level of education.

V. If Minority and Women’s Business Enterprises (M/WBEs) and nonminority males shared similar traits and marketplace “conditions” (i.e., similar “rewards” in terms of capital and asset accrual), what would be the effect on rates of self-employment by race, ethnicity, and gender?

Derived from a similar model employed by a City of Denver disparity study, we created a model that leveraged statistical findings in response to the first two questions to determine if race, gender, and ethnic effects derived from those findings would persist if nonminority male demographic and economic characteristics were combined with M/WBE self-employment data. More precisely, in contrast to Question I, which permitted a comparison of self-employment rates based on demographic and economic characteristics reported by the 2010 census for individual M/WBE categories and nonminority males, respectively, this analysis posed the question, “How would M/WBE rates change, if M/WBE’s operated in

4 Concrete Works v. City and County of Denver, 321 F.3 950 (10th Cir. 2003).



a nonminority male business world and how much of this change is attributable to race, gender, or ethnicity?”

FINDINGS5

1. Are racial, ethnic, and gender minority groups less likely than nonminority males to be self-employed in the Denver-Aurora CSA?

In all industries i, nonminority males were nearly twice as likely to be self-employed as nonminority women.

In all industries i, nonminority males were over twice as likely to be self-employed as African Americans.

Nonminority males were over ten times as likely as Hispanic Americans to be self-employed in professional services.

Nonminority males were nearly six times as likely as Hispanic Americans in professional services.

2. Does race, ethnicity, and/or gender have an impact on an individual’s self-employment earnings in the Denver-Aurora CSA?

Nonminority women reported significantly lower earnings in all business type categories.

In the goods and supplies industry, nonminority women reported significantly lower earnings than nonminority males: 52.6 percent less.

The most egregious effect on earnings elasticities was found in professional services for Hispanic Americans. In professional services, Hispanic Americans earned 50.1 percent less than nonminority males.

3. If M/WBEs and nonminority males shared similar traits and marketplace “conditions” (i.e., similar “rewards” in terms of capital and asset accrual), what would be the effect on rates of self-employment by race, ethnicity, and gender in Denver-Aurora CSA?

Overall, comparing self-employed nonminority males with self-employed Hispanic Americans, over 70 percent of the disparity in self-employment rates was attributable to race differences.

Comparing self-employed nonminority males with self-employed nonminority women in the construction industry, over 80 percent of the disparity in self-employment rates was attributable to race differences.

Comparing self-employed nonminority males with self-employed Hispanic Americans in the construction industry, over 75 percent of the disparity in self-employment rates was attributable to race differences.

Comparing self-employed nonminority males with self-employed Hispanic Americans in the other services industry, over 70 percent of the disparity in self-employment rates was attributable to race differences.

5 These ‘likelihood” characteristics were derived from Exhibit 5-4 by calculating the inverse of the reported odds ratios.



Comparing self-employed nonminority males with self-employed Asian Americans in the goods and supplies industry, over 53 percent of the disparity in self-employment rates was attributable to race differences.

5.4 .1 INTRODUCTION

The following sections analyze the availability of minority, nonminority women, and nonminority male firms in five categories of private sector business activity in the Denver-Aurora CSA. The goal of this investigation is to examine the effects of race and gender, along with other individual economic and demographic characteristics, on individuals’ participation in the private sector as self-employed business operators, and on their earnings as a result of their participation. Ultimately, we will compare these findings to the self-employment participation and earnings record of nonminority male business owners to determine if a disparity in self-employment rates and earnings exists, and if it is attributable to racial or gender discrimination in the marketplace. Data for this investigation are provided by the Public Use Microdata Samples (PUMS) data derived from the 2010 American Community Survey, to which we apply appropriate regression statistics to draw conclusions. Exhibit 5-36 presents a general picture of self-employment rates by race, median earnings, and sample sizes (n’s) in the Denver-Aurora CSA, calculated from the five percent PUMS census sample.

The next section will discuss the research basis for this examination to lay the groundwork for a description of the models and methodologies to be employed. This will be followed by a presentation of findings regarding minority status effects on self-employment rates, self-employment earnings, and attributions of these differences to discrimination, per se.

EXHIBIT 5-3 PERCENTAGE SELF-EMPLOYED

BY RACE, ETHNICITY, AND GENDER 2010 EARNINGS, DENVER-AURORA CSA

BUSINESS OWNERSHIP

CLASSIFICATION

PERCENT OF POPULATION SELF-EMPLOYED

2010 SAMPLE CENSUS

n

2010 MEDIAN EARNINGS

Nonminority Males 12.66% 375 $40,000.00

African American 4.26% 8 $22,150.00

Hispanic American 4.65% 42 $21,600.00

Asian American 7.86% 18 $28,500.00

Native American 12.68% 9 $20,000.00

Nonminority Women 6.75% 165 $39,000.00

TOTAL 9.08% 617 $36,000.00 Source: PUMS data from 2010 American Community Survey. Note: Although the sample sizes for some of the minority groups are small, the sample sizes do not represent single individuals but rather a representative percentage of the population.

6 The 2010 census ACS self-employment data for the Denver-Aurora CSA is located in Appendix I. The sample size of 2010 census ACS self-employment data for the Denver-Aurora CSA is insufficient to conduct a proper statistical analysis of self-employment by race and gender. The data does show some growth in percentage self-employment for Native Americans and Nonminority Males, but a decline for other groups.



5 .4 .2 SELF-EMPLOYMENT RATES AND EARNINGS AS AN ANALOG OF BUSINESS FORMATION AND MAINTENANCE

Research in economics consistently supports the finding of group differences by race and gender in rates of business formation (see Journal of Econometrics, Vol. 61, Issue 1, devoted entirely to the econometrics of labor market discrimination and segregation). For a disparity study, however, the fundamental question is “How much of this difference is due to factors that would appear, at least superficially, to be related to group differences other than race, ethnicity, or gender, and how much can be attributed to discrimination effects related to one’s racial, ethnic, and/or gender affiliation?” We know, for instance, that most minority groups have a lower median age than do non-Hispanic whites (ACS PUMS, 2010). We also know, in general, that the likelihood of being self-employed increases with age (ACS PUMS, 2010). When social scientists speak of nonracial group differences, they are referring to such things as general differences in religious beliefs as these might influence group attitudes toward contraception, and, in turn, both birthrates and median age. A disparity study, therefore, seeks to examine these other important demographic and economic variables in conjunction with race and ethnicity, as they influence group rates of business formation, to determine if we can assert that discrimination against minorities is sufficiently present to warrant consideration of public sector legal remedies such as affirmative action and minority set-aside contracting.

Questions about marketplace dynamics affecting self-employment—or, more specifically, the odds of being able to form one’s own business and then to excel (i.e., generate earnings growth)—are at the heart of disparity analysis research. Whereas early disparity studies tended to focus on gross racial disparities, merely documenting these is insufficient for inferring discrimination effects per se without “partialling out” effects due to nondiscriminatory factors. Moreover, to the extent that discrimination exists, it is likely to inhibit both the formation of minority business enterprises and their profits and growth. Consequently, earlier disparity study methodology and analysis have failed to account for the effects of discrimination on minority self-employment in at least two ways: (1) a failure to account adequately for the effects of discriminatory barriers minorities face “up front” in attempting to form businesses; and (2) a failure to isolate and methodologically explain discrimination effects once minority businesses are formed.

The next section addresses these shortcomings, utilizing PUMS data derived from the 2010 U.S. Census to answer research questions about the effects of discrimination on self-employment and self-employment earnings using multiple regression statistics.

5.4 .3 RESEARCH QUESTIONS, STATISTICAL MODELS, AND METHODS

Two general research questions were posed in the initial analysis:

I. Are racial, ethnic, and gender minority groups less likely than nonminority males to be self-employed?

II. Does race, ethnicity, and/or gender have an impact on individuals’ earnings?

A third question, to be addressed later—How much does race, ethnicity, and/or gender discrimination influence the probability of being self-employed?—draws conclusions based on findings from questions one and two.

To answer the first two questions, we employed two multivariate regression techniques, respectively: logistic regression and linear regression. To understand the appropriate application of these regression techniques, it is helpful to explore in greater detail the questions we are trying to answer. The dependent



variables in Questions I and II—that is, the phenomena to be explained by influences such as age, race, gender, and disability status, for example (the independent or “explanatory” variables)—are, respectively: the probability of self-employment status (a binary, categorical variable based on two possible values: 0 = not self-employed/1 = self-employed) and 2010 earnings from self-employment (a continuous variable). In our analysis, the choice of regression approach was based on the scale of the dependent variable (in Question I, a categorical scale with only two possible values, and in Question II, a continuous scale with many possible values). Because binary logistic regression is capable of performing an analysis in which the dependent variable is categorical, it was employed for the analysis of Question I.7 To analyze Question II, in which the dependent variable is continuous, we used simple linear regression.

DERIVING THE LOGISTIC REGRESSION MODEL FROM THE SIMPLE LINEAR MODEL

The logistic regression model can be derived with reference to the simple linear regression model expressed mathematically as:

Y = β0 + β I XI + β2 X2 + β3 X3 + β4 X4 + β5 X5 + … + ε

Where: Y = a continuous variable (e.g., 2010 earnings from self-employment)

β0 = the constant, representing the value of Y when XI = 0 βI = coefficient representing the magnitude of XI’s effect on Y

XI = the independent variables, such as age, human capital (e.g., level of education), availability of capital, race/ethnicity/gender, etc.

ε = the error term, representing the variance in Y unexplained by XI

This equation may be summarized as:

k

K

kk xYE ∑

=

==1

)( βµ

in which Y is the dependent variable and µ represents the expected values of Y as a result of the effects of β, the explanatory variables. When we study a random distribution of Y using the linear model, we specify its expected values as a linear combination of K unknown parameters and the covariates or explanatory variables. When this model is applied to data in the analysis, we are able to find the statistical link between the dependent variable and the explanatory or independent variables.

7 Logistical regression, or logit, models generate predicted probabilities that are almost identical to those calculated by a probit procedure, used in Concrete Works v. City and County of Denver case. Logit, however, has the added advantage of dealing more effectively with observations at the extremes of a distribution. For a complete explanation, see Interpreting Probability Models (T.F. Liao, Text 101 in the Sage University series).



Suppose we introduce a new term, η, into the linear model such that:

k

K

kk x∑

=

==1βµη

When the data are randomly distributed, the link between η and µ is linear, and a simple linear regression can be used. However, to answer the first question, the categorical dependent variable was binomially distributed. Therefore, the link between η and µ became )]1/(log[ µµη −= and logistic regression was utilized to determine the relationship between the dependent variable and the explanatory variables, calculated as a probability value (e.g., the probability of being self-employed when one is African American). The logistic regression model is expressed mathematically as:

εβαµµ ++=− ni X)]1(1/log[

Where:

(µ/1-µ) = the probability of being self-employed

α = a constant value

βi = coefficient corresponding to independent variables

nX = selected individual characteristic variables, such as age, marital status, education, race, and gender

ε = error term, representing the variance in Y unexplained by XI

This model can now be used to determine the relationship between a single categorical variable (0 = not self-employed/1 = self-employed) and a set of characteristics hypothesized to influence the probability of finding a 0 or 1 value for the categorical variable. The result of this analysis illustrates not only the extent to which a characteristic can increase or decrease the likelihood that the categorical variable will be a 0 or a 1, but also whether the effect of the influencing characteristics is positive or negative in relation to being self-employed.

5.4 .4 RESULTS OF THE SELF-EMPLOYMENT ANALYSIS

QUESTION I: ARE RACE, ETHNICITY, AND GENDER MINORITY GROUPS LESS LIKELY THAN NONMINORITY MALES TO BE SELF-EMPLOYED?

To derive a set of variables known to predict employment status (self-employed/not self-employed), we used the 5 percent PUMS data from Census 2010. Binary logistic regression was used to calculate the probability of being self-employed, the dependent variable, with respect to socioeconomic and demographic characteristics selected for their potential to influence the likelihood of self-employment. The sample for the analysis was limited to labor force participants who met to the following criteria:

Resident of the Denver-Aurora CSA



Self-employed in construction, professional services, other services, architecture and engineering,8 or goods and supplies

Employed full-time (more than 35 hours a week)

18 years of age or older

Employed in the private sector

Next, we derived the following variables hypothesized as predictors of employment status:

Race and Gender: African American, Asian American, Hispanic American, Native American, nonminority woman, nonminority male.

Availability of Capital: Homeownership, home value, mortgage rate, unearned income, residual income.

Marital Status

Ability to Speak English Well

Disability Status: From individuals’ reports of health-related disabilities.

Age and Age Squared: Squaring the age variable acknowledges the positive, curvilinear relationship between each year of age and earnings.

Owner’s Level of Education

Number of Individuals Over the Age of 65 Living in Household

Number of Children Under the Age of 18 Living in Household

FINDINGS9

Binary logistic regression analysis provided estimates of the relationship between the independent variables described above and the probability of being self-employed in the four types of business industries. In Exhibit 5-4, odds ratios are presented by minority group, reporting the effect of race, ethnicity, and gender on the odds of being self-employed in 2010, holding all other variables constant. Full regression results for all the variables are presented in Appendix I.

8 Due to inadequate sample numbers for all races in the architecture and engineering (construction-related professional services) PUMS 2010 data, construction-related professional services was merged with the Professional Services category. 9 These ‘likelihood” characteristics were derived from Exhibit 5-4 by calculating the inverse of the reported odds ratios.



EXHIBIT 5-4 SELF-EMPLOYMENT “ODDS RATIOS” OF MINORITY GROUPS RELATIVE TO NONMINORITY MALES AFTER

CONTROLLING FOR SELECTED DEMOGRAPHIC AND ECONOMIC CHARACTERISTICS

DENVER-AURORA CSA


ALL INDUSTRIES CONSTRUCTION PROFESSIONAL

SERVICES OTHER

SERVICES GOODS & SUPPLIES

African American 0.410 0.853 0.238 0.518 0.948

Hispanic American 0.511 0.644 0.167 0.622 0.979

Asian American 0.648 0.615 0.091 1.331 1.025

Native American 1.344 3.450 0.518 1.182 2.534

Nonminority Women 0.521 0.796 0.304 0.863 0.778 Source: PUMS data from 2010 American Community Survey and MGT of America, Inc., calculations using SPSS. Note: Bold indicates that the estimated “odds ratio” for the group was statistically significant. The architecture and engineering business industry was excluded from this analysis because of the insufficient data.

The results reveal the following in the Denver-Aurora CSA:

In all industries, nonminority males were nearly twice as likely to be self-employed as nonminority women.

In all industries, nonminority males were over twice as likely to be self-employed as African Americans.

Nonminority males were over ten times as likely as Hispanic Americans to be self-employed in professional services.

Nonminority males were nearly six times as likely as Hispanic Americans to be self-employed in professional services.

QUESTION I: DOES RACE, ETHNICITY, AND/OR GENDER AFFILIATION HAVE AN IMPACT ON INDIVIDUALS’ EARNINGS?

To answer this question, we compared self-employed, minority, and women entrepreneurs’ earnings to those of nonminority males in the Denver-Aurora CSA, when the effect of other demographic and economic characteristics was controlled or “neutralized.” That is, we were able to examine the earnings of self-employed individuals of similar education levels, ages, etc., to permit earnings comparisons by race, ethnicity, and gender.

To derive a set of variables known to predict earnings, the dependent variable, we used 2010 wages from employment for self-employed individuals, as reported in the 5 percent PUMS data. These included:

Race and Gender: African American, Asian American, Hispanic American, Native American, nonminority woman, nonminority males.

Availability of Capital: Homeownership, home value, mortgage rate, unearned income, residual income



Marital Status





FINDINGS

Exhibit 5-5 presents the results of the linear regression model estimating the effects of selected demographic and economic variables on self-employment earnings. Each number (i.e., coefficient) in the exhibit represents a percent change in earnings. For example, the corresponding number for a nonminority woman in all industries is -.350, meaning that nonminority woman will earn 35.0 percent less than a nonminority male when the statistical effects of the other variables in the equation are “controlled for.” Full regression results for all the variables are presented in Appendix I.

EXHIBIT 5-5 EARNINGS ELASTICITIES OF MINORITY GROUPS RELATIVE TO NONMINORITY MALES AFTER

CONTROLLING FOR SELECTED DEMOGRAPHIC AND ECONOMIC CHARACTERISTICS DENVER-AURORA CSA



SERVICES OTHER

SERVICES GOODS & SUPPLIES

African American -0.418 * -0.412 -0.173 0.107

Hispanic American -0.301 * -0.501 -0.274 0.414

Asian American -0.224 -0.214 -0.056 -0.351 1.172

Native American -0.423 -0.627 -0.433 -0.194 -0.987

Nonminority Women -0.350 -0.321 -0.406 -0.381 -0.526 Source: PUMS data from 2010 American Community Survey and MGT of America, Inc., calculations using SPSS. Note: Bold indicates that the estimated “elasticities” for the group were statistically significant. The architecture and engineering business industry was excluded from this analysis because of insufficient data. * denotes that there was insufficient U.S. Census data and therefore analysis could not be conducted..


Nonminority women reported significantly lower earnings in all business type categories.

In professional services, Hispanic Americans reported significantly lower earnings than nonminority males: 50.1 percent less.

The most egregious effect on earnings elasticities was found in the goods and supplies industry, nonminority women reported significantly lower earnings than nonminority males: 52.6 percent less.



DISPARITIES IN RATES OF SELF-EMPLOYMENT: HOW MUCH CAN BE ATTRIBUTED TO DISCRIMINATION?

Results of the analyses of self-employment rates and 2010 self-employment earnings revealed general disparities between minority and nonminority self-employed individuals whose businesses were located in the Denver-Aurora CSA.

Exhibit 5-6 presents the results of these analyses. Column A reports observed employment rates for each racial, ethnic, and gender group, calculated directly from the PUMS 2010 data. To obtain values in columns B and C, we calculated two predicted self-employment rates using the following equation:

)1/()1(Pr1

kkkk xK

k

x eeyob ββ∑=

+==

Where:

)1(Pr =yob = represents the probability of being self-employed kβ = coefficient corresponding to the independent variables used in the logistic

regression analysis of self-employment probabilities kx = the mean values of these same variables

The first of these predicted self-employment rate calculations (in column B) presents nonminority male self-employment rates as they would be if their characteristics (i.e., kx , or mean values for the independent variables) were applied to minority market structures (represented for each race by their

kβ or odds coefficient values). The second self-employment rate calculation (in column C) presents minority self-employment rates as they would be if minorities were rewarded in a similar manner as nonminority males in the nonminority male market structure: that is, by multiplying the minority means (i.e., characteristics) by the estimated nonminority coefficients for both race and the other independent variables.



EXHIBIT 5-6 OBSERVED AND PREDICTED SELF-EMPLOYMENT RATES

BUSINESS OWNERSHIP

CLASSIFICATION

OBSERVED SELF-

EMPLOYMENT RATES

WHITE CHARACTERISTICS

AND OWN MARKET STRUCTURE

OWN CHARACTERISTICS

AND WHITE MARKET

STRUCTURE

DISPARITY RATIO

(COLUMN A DIVIDED BY COLUMN C)

PORTION OF DIFFERENCE DUE

TO DISCRIMINATION

(A) (B) (C) (D) (E) Overall Nonminority Males 0.1266 0.0426 0.0426 1.000 African American 0.0426 0.0792 0.1778 0.2393 n/d Hispanic American 0.0465 0.0968 0.1097 0.4239 78.89% Asian American 0.0786 0.1198 0.1725 0.4557 n/d Native American 0.1268 0.2201 0.1490 0.8507 n/d Nonminority Women 0.0675 0.0986 0.1684 0.4008 n/d Construction Nonminority Males 0.2278 0.2278 0.2278 1.000 African American 0.0000 0.2093 0.0787 0.0000 34.56% Hispanic American 0.0000 0.1665 0.1738 0.0000 76.26% Asian American 1.0000 0.1601 0.0168 59.3667 n/d Native American 0.5000 0.5169 0.1526 3.2761 n/d Nonminority Women 0.4286 0.1980 0.2665 1.6082 80.74% Professional Services Nonminority Males 0.1421 0.0380 0.0380 1.000 African American 0.0380 0.0555 0.1423 0.2668 n/d Hispanic American 0.0456 0.0395 0.1109 0.4110 67.65% Asian American 0.0099 0.0220 0.0399 0.2483 22.68% Native American 0.1200 0.1134 0.2254 0.5324 n/d Nonminority Women 0.0449 0.0698 0.1537 0.2918 n/d Other Services Nonminority Males 0.1212 0.0471 0.0471 1.0000 African American 0.0471 0.0934 0.2298 0.2048 n/d Hispanic American 0.0512 0.1101 0.1019 0.5025 72.49% Asian American 0.1373 0.2093 0.2052 0.6688 n/d Native American 0.1212 0.1904 0.1805 0.6717 n/d Nonminority Women 0.1000 0.1465 0.1695 0.5901 n/d Goods & Supplies Nonminority Males 0.0601 0.0476 0.0476 1.000 African American 0.0476 0.0795 0.0508 0.9378 25.26% Hispanic American 0.0400 0.0819 0.1022 0.3912 n/d Asian American 0.0800 0.0854 0.0693 1.1546 53.91% Native American 0.0909 0.1875 0.1268 0.7171 n/d Nonminority Women 0.0463 0.0662 0.1111 0.4172 n/d

Source: PUMS data from 2010 American Community Survey and MGT of America, Inc., calculations using SPSS and Microsoft Excel. n/d: No discrimination was found. Note: The reason that no discrimination was found in some the representative minority populations is due in part to other factors having a role in the differences seen in the self-employment rates such as differences in education, experience, and business size between nonminority males and minorities.



Using these calculations, we were able to determine a percentage of the disparities in self-employment between minorities and nonminority males attributable to discrimination by dividing the observed self-employment rate for a particular minority group (column A) by the predicted self-employment rate as it would be if minority groups faced the same market structure as nonminority males (column C). This information is important because it highlights the true self-employment rates of M/WBEs if they participated within the same market structure as nonminority males. Therefore, the disparity ratio shows the potential availability of M/WBEs if they formed businesses within the same market structure as nonminority males. Next, we calculated the difference between the predicted self-employment rate as it would be if minority groups faced the same market structure as nonminority males and the observed self-employment rate for that minority group, and divided this value by the difference between the observed self-employment rate for nonminority males and the self-employment rate for a particular minority group. In the absence of discrimination, this number is zero, which means disparities in self-employment rates between minority groups and nonminority males can be attributed to differences in group characteristics not associated with discrimination. Conversely, as this value approaches 1.0, we are able to attribute disparities increasingly to discrimination in the marketplace.

FINDINGS

Examining the results reported in Exhibit 5-6, we found the following in the Denver-Aurora CSA:

Overall, comparing self-employed nonminority males with self-employed Hispanic Americans, over 70 percent of the disparity in self-employment rates was attributable to race differences.

Comparing self-employed nonminority males with self-employed nonminority women in the construction industry, over 80 percent of the disparity in self-employment rates was attributable to gender differences.

Comparing self-employed nonminority males with self-employed Hispanic Americans in the construction industry, over 75 percent of the disparity in self-employment rates was attributable to race differences.

Comparing self-employed nonminority males with self-employed Hispanic Americans in the other services industry, over 70 percent of the disparity in self-employment rates was attributable to race differences.

Comparing self-employed nonminority males with self-employed Asian Americans in the goods and supplies industry, over 53 percent of the disparity in self-employment rates was attributable to race differences.

5.5 ANALYSIS OF RACE, ETHNICITY, AND GENDER EFFECTS ON INDIVIDUAL EARNINGS

The purpose of this analysis is to examine the effects of race and gender, along with other individual economic and demographic characteristics, on individuals’ earnings as a result of their participation in five categories of private sector business activity in the Denver-Aurora CSA. Findings for minorities are compared to the earnings record of nonminority male to determine if a disparity in earnings exists. Adopting the methodology and variables employed by a City of Denver disparity study (see Concrete



Works v. City and County of Denver 10), we use Public Use Microdata Samples (PUMS) data derived from the 2010 American Community Survey, to which we apply appropriate regression statistics to draw conclusions.

To guide this investigation the following question was posed. Variables used to respond to this question, followed by a report of findings, are reported below:

I. Does race, ethnicity, and/or gender have an impact on individual’s earnings?

This analysis examined the statistical effects of the following variables on income for individuals in the market area: Race, ethnicity, and gender of business owner (African American, Asian American, Hispanic American, Native American, nonminority women, nonminority men), marital status, age, self-reported health-related disabilities, and availability of capital (household property value, monthly total mortgage payments, unearned income) and level of education.

FINDINGS

In the Denver-Aurora CSA, African and Hispanic Americans reported significantly lower earnings in all business type categories.

In the construction industry, African Americans reported significantly lower earnings than nonminority males in the Denver-Aurora CSA: 29.8 percent less.

In the professional services industry, Hispanic Americans reported significantly lower earnings than nonminority males in the Denver-Aurora CSA: 55 percent less.

The most egregious effect on earnings elasticities was found in professional services for Native Americans. In professional services, Native Americans earned 70.8 percent less than nonminority males.

5.5 .1 INTRODUCTION

This report analyzes the individual earnings of minority, nonminority women, and nonminority males in five categories of private sector business activity in the Denver-Aurora CSA. The goal of this investigation is to examine the effects of race and gender, along with other individual economic and demographic characteristics, on individuals’ earnings. Ultimately, we will compare these findings to the earnings record of nonminority males to determine if a disparity in exists. Data for this investigation are provided by the Public Use Microdata Samples (PUMS) data derived from the 2010 American Community Survey, to which we apply appropriate regression statistics to draw conclusions.

The next section will discuss the research basis for this examination to lay the groundwork for a description of the models and methodologies to be employed. This will be followed by a presentation of findings regarding minority status effects on self-employment rates, self-employment earnings, and attributions of these differences to discrimination, per se.

10 Concrete Works v. City and County of Denver, 321 F.3 950 (10th Cir. 2003).



5 .5 .2 RESEARCH QUESTIONS, STATISTICAL MODELS, AND METHODS

The general research question was posed in the initial analysis:

I. Does race, ethnicity, and/or gender have an impact on individuals’ earnings?

To answer this question we employed a multivariate linear regression. To understand the appropriate application of this regression technique, it is helpful to explore in greater detail the question we are trying to answer. The dependent variable—that is, the phenomena to be explained by influences such as age, race, gender, and disability status, for example (the independent or “explanatory” variables)—is 2010 (a continuous variable). In our analysis, the choice of regression approach was based on the scale of the dependent variable (a continuous scale with many possible values). To analyze the above question, in which the dependent variable is continuous, we used simple linear regression.

DERIVING THE SIMPLE LINEAR MODEL

The simple linear regression model can be expressed mathematically as:

Y = β0 + βI XI + β2 X2 + β3 X3 + β4 X4 + β5 X5 + … + ε

Where: Y = a continuous variable (e.g., 2010 individual earnings)

β0 = the constant, representing the value of Y when XI = 0 βI = coefficient representing the magnitude of XI’s effect on Y

XI = the independent variables, such as age, human capital (e.g., level of education), availability of capital, race/ethnicity/gender, etc.

ε = the error term, representing the variance in Y unexplained by XI

This equation may be summarized as:

k

K

kk xYE ∑

=

==1

)( βµ

in which Y is the dependent variable and µ represents the expected values of Y as a result of the effects of β, the explanatory variables. When we study a random distribution of Y using the linear model, we specify its expected values as a linear combination of K unknown parameters and the covariates or explanatory variables. When this model is applied to data in the analysis, we are able to find the statistical link between the dependent variable and the explanatory or independent variables, and further determine the impact of race, ethnicity, and/or gender on individual earnings.

5.5 .3 RESULTS OF THE REGRESSION ANALYSIS

DOES RACIAL, ETHNIC, AND/OR GENDER AFFILIATION HAVE AN IMPACT ON INDIVIDUALS’ EARNINGS?

To answer this question, we compared individual minority, and women earnings to those of nonminority males in the Denver-Aurora CSA, when the effect of other demographic and economic characteristics was



controlled or “neutralized.” That is, we were able to examine the earnings of individuals of similar education levels, ages, etc., to permit earnings comparisons by race, ethnicity, and gender.

To derive a set of variables known to predict earnings, the dependent variable, we used 2010 wages from employment, as reported in the 5 percent PUMS data. These included:

Race and Gender: African American, Asian American, Hispanic American, Native American, nonminority woman, nonminority males.

Availability of Capital: Homeownership, home value, mortgage rate, unearned income, residual income.

Marital Status





FINDINGS

Exhibit 5-7 presents the results of the linear regression model estimating the effects of selected demographic and economic variables on self-employment earnings. Each number (i.e., coefficient) in the exhibit represents a percent change in earnings. For example, the corresponding number for a nonminority woman in all industries is -.229, meaning that nonminority woman will earn 22.9 percent less than a nonminority male when the statistical effects of the other variables in the equation are “controlled for.” Full regression results for all the variables are presented in Appendix I.

EXHIBIT 5-7 EARNINGS ELASTICITIES OF MINORITY GROUPS RELATIVE TO NONMINORITY MALES AFTER

CONTROLLING FOR SELECTED DEMOGRAPHIC AND ECONOMIC CHARACTERISTICS

DENVER-AURORA CSA



SERVICES OTHER

SERVICES

GOODS &

SUPPLIES African American -0.411 -0.298 -0.526 -0.514 -0.175 Hispanic American -0.415 -0.443 -0.55 -0.435 -0.249 Asian American -0.245 -0.175 -0.312 -0.329 -0.149 Native American -0.288 -0.17 -0.708 -0.148 -0.14 Nonminority Women -0.229 -0.106 -0.381 -0.24 -0.266

Source: PUMS data from 2010 American Community Survey and MGT of America, Inc., calculations using SPSS. Note: Bold indicates that the estimated “elasticities” for the group were statistically significant. The architecture and engineering business industry was excluded from this analysis because of insufficient data. * There were insufficient census numbers available for analysis.




African and Hispanic Americans reported significantly lower earnings in all business type categories.

In the construction industry, African Americans reported significantly lower earnings than nonminority males: 29.8 percent less.

In the professional services industry, Hispanic Americans reported significantly lower earnings than nonminority males: 55 percent less.

The most egregious effect on earnings elasticities was found in professional services for Native Americans. In professional services, Native Americans earned 70.8 percent less than nonminority males.

5.5 .4 SUMMARY OF SELF-EMPLOYMENT ANALYSIS FINDINGS

In general, findings from the PUMS 2010 data indicate that overall minorities and nonminority women earn significantly less than nonminority males. The data further shows that the most egregious effect on earnings occurs for minorities in the professional services industry. The impact on earnings is less notable in the goods and supplies industry but minorities and nonminority women continue to earn less than their nonminority male counterparts.

5.6 CONCLUSION

Section 5.3 presents a summary of firm utilization at the prime contractor and subcontractor levels by racial, ethnic, and gender classification comparing M/WBE utilization on DPS public sector construction projects with private sector commercial construction projects. When M/WBE vendor lists were used to identify M/WBEs for public and private sector construction, M/WBE utilization was higher on DPS public construction projects, according to the private commercial construction permits analyses presented in this chapter. When compared to the private commercial construction permits analyses, M/WBE firms fared better on DPS construction projects at both the prime contractor and subcontractor levels.

Capacity alone is not a sufficient explanation for these differences, especially at the construction subcontractor level where capacity is a lesser consideration. This chapter also presented statistical evidence that disparities associated with race and gender persist after controls for capacity and business experience. Moreover, the evidence of low M/WBE utilization on commercial construction projects, supports the claims presented in the anecdotal comments (refer to Chapter 6.0) that M/WBEs face more barriers in seeking work on private sector construction projects. To the extent that M/WBE subcontractor utilization was low ion private sector commercial construction projects, credence may be given to the proposition established in Croson that government could be a passive participant in private sector discrimination if it did not require contractors who respond/bid for public sector construction projects to solicit and negotiate with M/WBE subcontractors in good faith efforts.


6.0 ANECDOTAL ANALYSIS

6.0: ANECDOTAL ANALYSIS

The following sections present MGT of America, Inc.’s (MGT) approach to collecting qualitative (anecdotal) data, the methods employed and the quantitative and qualitative results of the data collected.

To ensure the validity and integrity of anecdotal data collection, construction and construction-related professional service firms were randomly selected from the master vendor database discussed in Chapter 3.0, Relevant Market Area and Utilization Analyses. From the sample pulled, M/WBEs and non-M/WBEs were contacted to participate in surveys or personal interviews. An open invitation was distributed to area firms to participate in three scheduled public hearings. A breakdown of participants is discussed within this chapter.

Anecdotal research is a widely accepted research methodology that is based upon observations, interviews, data collected during focus groups, survey responses and other anecdotal data collection methods. The collection and analysis of anecdotal data is used in conjunction with other research tools to provide context, and to help explain findings based on quantitative data analysis. Unlike conclusions derived from other types of analysis in this report, the conclusions derived from anecdotal analysis do not rely solely on quantitative data. Anecdotal analysis also utilizes qualitative data to describe the context of the examined social, political, and economic environment in which all businesses and other relevant entities applicable to the study operate.

6.1 METHODOLOGY

The blueprint for collecting and analyzing anecdotal information for this study was provided by the U.S. Supreme Court in City of Richmond v. J.A. Croson, 488 U.S. 469, 109 S.Ct. 706 (1989) (Croson). Specifically, race-conscious programs must be supported by strong documentation of discrimination, including evidentiary findings that go beyond the demographics of a community. Anecdotal information can bolster the quantitative analyses of contract expenditures to explain whether or not minority business creation, growth, and retention are negatively affected by discrimination. In Croson, the Court held that anecdotal accounts of discrimination could help establish a compelling interest for a local government to institute a race-conscious remedy. Moreover, such information can provide a local entity with a firm basis for fashioning a program that is narrowly tailored to remedy identified forms of marketplace discrimination and other barriers to M/WBE participation in contract opportunities. Further discussion of anecdotal testimony is contained in Chapter 2.0, Legal Framework.

MGT’s experience conducting disparity studies has shown that utilizing multiple methods of anecdotal data collection provide more comprehensive information than methodologies using a single-pronged approach. For this reason, MGT used a combination of surveys, public hearings, and personal interviews to collect anecdotal information and to identify issues that were common to businesses in the market area between the calendar years of 2008 through 2013. In conjunction with the quantitative data MGT was also able to draw inferences from these data as to the prevalence of obstacles perceived as limiting the participation of M/WBEs, and other firms in the DPS’ procurement transactions.


ANECDOTAL ANALYSIS

Personal interviews, public hearings, and focus group responses were edited for grammar. Otherwise responses were unfiltered or unedited. However, it should be cautioned that comments in Sections 6.3.2, 6.4.1, 6.5.2, and 6.6.2 detail the perceptions and opinions of individuals, and the evidentiary weight of these opinions depends on how much they are corroborated by statements of others and the quantitative data in the report.

6.1 .1 SURVEY OF VENDORS

The survey of business owners gathered information on business ownership, work performed and/or bid with the DPS, work bid and/or performed in the private sector, and barriers, perceived or real, that prevented firms from doing business with the DPS during the study period. During the months of July through September 2014 businesses listed in the master vendor database were surveyed to solicit information about their firms and experiences with the DPS. MGT attempted to collect data in proportion to the distribution of M/WBEs and non-M/WBEs in the relevant market area. L.S. Gallegos, Inc., a Denver-based minority-owned business research firm administered a web-based but controlled survey and called firms using the Appendix E - Survey of Vendors Instrument which resulted in 79 completed surveys with owners and representatives. Throughout this chapter several charts detail selected survey results. See Appendix F - Survey of Vendors Results for the complete survey of vendor results and explanation of the percentage calculations.

Disparity study surveys are commonly plagued by sample size limitations, especially in the case of attempting to gather a representative sample from minority business populations where low minority numbers pose problems. For example, Native American-owned businesses in most municipalities are insufficient in number to permit a valid and representative sample. This problem is compounded when analyses are stratified further by business type. Insufficient sample sizes can pose problems for the statistical confidence of the results. Although MGT’s goal is to report data that can satisfy the 95 percent confidence level, this does not mean that data should not be reported because of slightly reduced confidence intervals, especially when extreme due diligence has been exercised in attempting to meet the 95 percent standard.

6.1 .2 PUBLIC HEARINGS

MGT facilitated three public hearings with M/WBE and non-M/WBE firms with the assistance of MGT’s subconsultant L.S. Gallegos. One hearing was held at the Manual High School, 1700 E. 28th Ave on June 18, 2014. Two additional hearings were held at the Denver Public Schools Administration, 900 Grant St. on July 16, 2014. Firms, M/WBEs and non-M/WBEs, included those who have done business with, or those interested in doing business with DPS and its primes, were invited to attend. The public hearings were advertised in The Denver Post using Appendix D - Public Hearing Notice and emailed to firms in the master vendor database. Along with using the DPS’ master vendor database, MGT notified the associations and organizations below to solicit their participation and assistance encouraging their members to attend.

Division of Small Business Opportunity (DSBO) Advisory Committee African American Construction Council (NAMC) Asian Chamber of Commerce American Business Women's Association-Denver AGC of Colorado Rocky Mountain Indian Chamber of Commerce National Association of Women Business Owners Denver Latino Chamber of Commerce of Pueblo


ANECDOTAL ANALYSIS

Hispanic Contractors of Colorado Denver Hispanic Chamber of Commerce Colorado Women's Chamber of Commerce Colorado Springs Hispanic Chamber of Commerce Colorado Springs Black Chamber of Commerce Colorado Office of Economic Development & International Trade Minority Business Office Colorado Contractors' Association Colorado Black Chamber of Commerce Business Research Services Asian Chamber of Commerce

The public hearings were transcribed by Hansen and Company, Inc., a Denver-based court reporting service.

6.1 .3 PERSONAL INTERVIEWS

The personal interviews—which are structured settings where an interviewer or facilitator uses an interview guide to solicit input from participants—provided more latitude for additional information gathering on issues that are unique to the respondents’ experiences. Interviews were conducted with M/WBEs and non-M/WBEs. The personal interviews gathered information regarding:

The firm’s primary line of business.

Ethnicity.

Education/training background of the owner.

Business history.

Firm size.

Gross revenues during selected calendar and/or fiscal years.

Information about the firms’ experiences in attempting to do, and conducting business with the DPS, both directly as a prime and/or as a subcontractor.

The Personal Interview Guide (Appendix G) included questions designed to establish a profile for each business. Additionally, we asked questions related to experiences with the DPS M/WBE program and instances of disparate treatment and/or discrimination experienced or perceived by the firm while attempting to do or conducting business with the DPS. L.S. Gallegos conducted the interviews. The interviewers made no attempt to prompt or guide responses from the participants, although follow-up questions were asked to obtain further clarification or information as necessary. At the conclusion of the interviews, each participant was asked to sign an affidavit attesting that their responses were given freely and was true and accurate reflections of their experience with the DPS.

Interviews were conducted during July through September 2014 with M/WBE and non-M/WBE firms with a cross-section of the business community in the DPS’s jurisdiction. To obtain interviewees, firms not selected for other anecdotal activities were randomly selected from the DPS’s master vendor database then e-mailed, telephoned, or faxed confirmation letters after agreeing to be interviewed. The interviews were conducted either at the firm owner’s office, or at a location designated by the firm’s owner.


ANECDOTAL ANALYSIS

6.2 DEMOGRAPHICS

The demographic characteristics of participants in the collection of anecdotal information are described in the sections below.

6.2 .1 SURVEY OF VENDORS DEMOGRAPHICS

The Survey of Vendors provided additional anecdotal data collections. Exhibit 6-1 provides the race, ethnicity, and gender of the respondents that participated in the survey.

EXHIBIT 6-1 DENVER PUBLIC SCHOOLS

SURVEY OF BUSINESS OWNERS DEMOGRAPHICS BY RACE/ETHNICITY/GENDER CLASSIFICATION

Race/Ethnicity/Gender Number of

Respondents Percentage of Total African Americans 6 7.89% Asian Americans 1 1.32%

Hispanic Americans 12 15.79% Nonminority Females 25 32.89%

Non-M/WBE Firms 29 38.16% Other1 3 3.95%

Source: Responses from survey conducted by L.S. Gallegos, Inc., 2014. Note: Native American firms did not participate in the Survey of Vendors.

Exhibit 6-2 categorizes the distribution of respondents based on their types of services. The primary type of business industries include Construction (C), Construction-Related Professional Services (CRPS), and Professional Services (PS). The “Other” industry categories means that the primary line of business is not associated with the primary line of business outlined in the survey. Definitions of business industries are discussed in Chapter 3.0, Relevant Market Area and Utilization Analyses.

1 Participants did not associate their race/ethnicity/gender with the groups selected for the survey or chose not to provide their race/ethnicity/gender.


ANECDOTAL ANALYSIS

EXHIBIT 6-2 THE CITY AND COUNTY OF DENVER

SURVEY OF BUSINESS OWNERS DEMOGRAPHICS BUSINESS INDUSTRY

Source: Responses from survey conducted by L.S. Gallegos, Inc., 2014.

Several survey questions were asked to determine the capacity of participating firms. Exhibits 6-3, 6-4, and 6-5 detail the size of the firms, the largest contracts, and largest subcontracts awarded during the study period. Exhibit 6-3 show that 42.11 percent of the firms surveyed have 0-10 employees excluding the owner which mean a majority of firms are small businesses which may have implications for the type and size of projects firms are willing to pursue.


SURVEY OF VENDORS DEMOGRAPHICS NUMBER OF EMPLOYEES


2 Participants did not associate their race/ethnicity/gender with the groups selected for the survey.

C43.42%

CRPS44.74%

Other11.84%

C

CRPS

Other

Race/Ethnicity/Gender 0-10

Employees 11-20

Employees 21-30

Employees 31-40

Employees 41+

Employees African Americans 3.95% 1.32% 0.00% 0.00% 2.63% Asian Americans 0.00% 0.00% 0.00% 1.32% 0.00%

Hispanic Americans 6.58% 2.63% 3.95% 1.32% 1.32% Nonminority Females 17.11% 7.89% 2.63% 1.32% 3.95%

Non-M/WBE Firms 13.16% 5.26% 5.26% 2.63% 11.84% Other2 1.32% 0.00% 1.32% 0.00% 1.32% Total 42.11% 17.11% 13.16% 6.58% 21.05%


ANECDOTAL ANALYSIS

Exhibit 6-4 details the responses from prime contractors/vendor on the largest contract awarded during the study period. For M/WBEs the largest contracts are in the greater than $1 million range. Largest contracts for non-M/WBEs were also in the $1 million and greater dollar range.


SURVEY OF VENDORS DEMOGRAPHICS LARGEST CONTRACT AWARDED- PRIME


Exhibit 6-5 details the responses from firms that performed work as a subcontractor on the largest contract awarded during the study period. For M/WBEs and non-M/WBEs, the largest subcontracts are in the $1 million and greater range.


SURVEY OF BUSINESS OWNERS DEMOGRAPHICS LARGEST CONTRACT AWARDED - SUBCONTRACTOR


6.2 .2 PUBLIC HEARINGS DEMOGRAPHICS

Industries represented at the public hearings were construction, special trade contractors, suppliers, business and trade organizations, engineering, and architectural firms. There were 22 firms or individuals that attended the three hearings. The ethnic and gender classification of the attendees consisted of

3 Participants did not associate their race/ethnicity/gender with the groups selected for the survey. 4 Participants did not associate their race/ethnicity/gender with the groups selected for the survey.

Race/Ethnicity/Gender Up to $50,000

$50,001 -$100,000

$100,001 -

$200,000

$200,001 -

$300,000

$300,001 -

$400,000

$400,001 -

$500,000

$500,001 - $1

million

> $1 million

African Americans 0.00% 0.00% 1.32% 0.00% 0.00% 0.00$ 0.00% 3.95% Asian Americans 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%

Hispanic Americans 0.00% 1.32% 2.63% 0.00% 1.32% 0.00% 0.00% 5.26% Nonminority Females 1.32% 2.63% 3.95% 2.63% 0.00% 5.26% 3.95% 6.58%

Non-M/WBE Firms 0.00% 2.63% 0.00% 1.32% 1.32% 0.00% 5.26% 23.68% Other3 0.00% 0.00% 0.00% 0.00% 1.32% 0.00% 0.00% 2.63% Total 1.32% 6.58% 7.89% 3.95% 3.95% 5.26% 9.21% 42.11%

Race/Ethnicity/Gender Up to $50,000

$50,001 -$100,000

$100,001 -

$200,000

$200,001 -

$300,000

$300,001 -

$400,000

$400,001 -

$500,000

$500,001 - $1

million

> $1 million

African Americans 1.32% 1.32% 0.00% 0.00% 1.32% 0.00% 1.32% 2.63% Asian Americans 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 1.32%

Hispanic Americans 1.32% 1.32% 2.63% 2.63% 1.32% 1.32% 0.00% 3.95% Nonminority Females 5.26% 2.63% 3.95% 0.00% 1.32% 7.89% 0.00% 2.63%

Non-M/WBE Firms 0.00% 2.63% 0.00% 2.63% 1.32% 1.32% 7.89% 7.89% Other4 0.00% 0.00% 0.00% 0.00% 0.00% 1.32% 0.00% 1.32% Total 7.89% 7.89% 6.58% 5.26% 5.26% 11.84% 9.21% 19.74%


ANECDOTAL ANALYSIS

African Americans, Asian Americans, Hispanic Americans, nonminority females, nonminority males, and privately-owned corporations, During the public hearings four firms or individuals provided anecdotal testimony of their experiences doing business with DPS and primes contracted with DPS.

6.2 .3 PERSONAL INTERVIEW DEMOGRAPHICS

The efforts of L.S. Gallegos resulted in 30 firms that were interviewed. Of the 30 M/WBE and non-M/WBE firms that were interviewed the ethnic and gender composition included two Asian Americans, seven Hispanic Americans, two Native Americans, five nonminority women, 12 nonminority males, and two firms that did not identify their ethnic compositions. There was a considerable effort to include African American firms, however, none wished to be interviewed.

6.3 BARRIERS TO DOING BUSINESS WITH THE DENVER PUBLIC SCHOOLS

In the normal course of business, entrepreneurs may face certain barriers when establishing and operating a business enterprise. Several factors may also prevent a business from being selected for a contract or purchase order. In this section, MGT documents participant responses concerning barriers they faced in the procurement process and factors that frequently prevented them from winning contracts or purchase orders.

6.3 .1 SURVEY OF VENDORS

Questions for the survey of business owners were designed to gather business owners’ perceptions about the procurement process and their experiences when doing business or attempting to do business with the DPS or their prime contractors/service providers. An analysis of the responses showed that the majority of firms surveyed responded to questions about barriers to doing business with the DPS.

Among the 44 M/WBEs who responded to survey questions about barriers to doing business, the biggest concern for prime contractors/service providers was DPS’ process for selection firms (61.36% of M/WBEs). M/WBE subcontractors stated there biggest barrier working with primes on DPS projects is competing with large companies (45.46% of M/WBEs).

It should be noted that 56.58 percent of the firms surveyed received direct contact from DPS of contract/procurement opportunities. Other notification methods included private bidding subscription services (40.79%); other primes/subcontractors (39-47%); and DPS’ website (28.95%). Detailed results for all respondents and statistically significant differences in MBE and WBE responses to questions are located in Appendix F – Survey of Vendors Results.

6.3 .2 ANECDOTAL RESPONSES

The following section provides anecdotal comments participants provided from the personal interviews or public hearings when asked about their experiences doing business or attempting to business with DPS or its primes.


ANECDOTAL ANALYSIS

Notification of Contract/Bid Opportunities

A nonminority woman-owned HVAC Company said her firm is notified through the general contractors.

A nonminority woman-owned Architectural firm said their firm was on the DPS list of architects, DPS sends out an email, and contacts them in a variety of other ways.

A Hispanic owned engineering firm representative stated that their firm receives notification through a service that identifies bidding opportunities and the Hispanic Contractors of Colorado.

A nonminority male owned specialty trade contractor representative stated that their firm learned about bid and proposal opportunities via a phone call from the Facility Management Grounds Department.

A Hispanic American-owned specialty trade contractor stated that his firm received a direct invitation and he has bid through the General Contractor. He stated that, “If we are bidding through a contractor, DPS tells the contractor that you don’t have to use us, but we encourage you to do so. Some of our painters worked directly for DPS for labor or time and materials too.”

A nonminority female owner of a construction-related professional services firm stated that she had a contact with DPS that is no longer employed with DPS. Since then, she has not received any notifications of opportunities. She stated that her industry is “kind of a good ole boy network”. She stated that she’s attending “Doing Business with DPS” workshops but she “still couldn’t figure it out”.

An Asian American owner of a specialty trade construction firm stated they learned about a DPS opportunity through outreach mailings.

A nonminority male-owned architectural firm received notifications through prime contractors about DPS work. He was unaware of the DPS notification process.

A representative of a nonminority male-owned specialty trade construction firm learned of DPS projects via email. She added that, for her business, using ISqFt.com would be helpful.

A nonminority male owner of a specialty trade contractor hears about projects from the general contractors, or Construction Office at DPS gives him a call, or the representative calls him. He said he stays “in touch for maintenance issues, repairs, future projects, past projects, etc.”

An Asian American-owned architectural firm owner learns about DPS opportunities from emails.

A nonminority male-owned electrical contractor said he learned about DPS work either through an email solicitation, or the DPS website.

A nonminority female-owned construction-related professional services firm learns of DPS projects through the Architects who she works with as a subconsultant.

Obstacles in the Procurement Process

A Hispanic American-owned general contractor firm said that they went through the prequalification process before and didn’t see any results, so they haven’t made the effort to be prequalified again.

An Asian American-owned engineering firm said not having experience with DPS made it impossible to get prequalified.


ANECDOTAL ANALYSIS

A nonminority male owner of an architectural firm stated that the project size requirements for previous experience “were larger than some of the projects really warranted.”

The owner of a nonminority female-owned specialty trade construction firm stated that though specifications do have a mechanical section, the mechanical information may be put into several different places. She felt this method “covered” DPS, but “screwed” the mechanical contractor because, though the information was the same, it could be interpreted differently in each section.

Treated Unfairly in the Bidding or Contract Award Process

A nonminority female owner of a construction-related professional services firm stated that her company put in a Statement of Qualifications so that they could be on the Consultants List. She said that in the three years they were on the list, they didn’t get one call. She noted there were only five companies on the list.

The owner of a Hispanic American-owned construction-related professional services firm said, “We bid a contract and we never heard back from the contracting officer. No type of debriefing.”

A representative of a Hispanic American felt they were treated unfairly only in that they have not always been solicited.

The owner of a nonminority male-owned engineering firm stated there was no explanation of why they were disqualified. He said they’ve never been able to get any feedback. “We don’t know if we submitted properly this time because we didn’t get feedback last time.”

An Asian American-owned architectural firm stated that he requested additional money for scope change became a black mark on them. “All of the Project Managers looked unfavorably on us as being too expensive”. The subsequent work for DPS, the Project Managers have been very reluctant to award us additional services. Renovation projects have scope changes; Project Managers change the scope of the project. They add things into the project and we are required to do the design work without any additional compensation.”

Limited Time Given to Prepare Bid Package or Quote

The owner of a Hispanic American owned engineering firm said that often they don’t find out about opportunities until the last minute.

A nonminority female-owned specialty trade contractor said DPS usually gives plenty of time to complete the bid or proposal.

The owner of a nonminority male engineering firm thought bids gave very short time frames.

Limited Knowledge of Purchasing Contractor Policies and Procedures

The owner of a nonminority male-owned engineering firm said that limited knowledge of purchasing contractor policies and procedures has been an issue. He said that attending the town hall meetings, which emphasize how not, to get disqualified, held by DPS over the last 6 months, has been helpful.

The nonminority male general contractor stated that DPS takes a long time to get contracts executed after they’ve awarded the job. They can’t get tax exempt certificates until they get the contracts returned. That frustrates them and the subcontractors working with them. He stated that he sometimes paid the tax just to get the job done.


ANECDOTAL ANALYSIS

A representative of a Native American-owned specialty trade construction firm said they went to a conference to learn the Lawson system, so now they don’t have problems with the purchasing contractor policies and procedures.

Selection Process

A nonminority female-owned architectural firm stated that DPS typically selects firms that already have experience working with DPS.

A nonminority female-owned architectural firm stated that her firm has been prequalified twice with DPS but has not been contacted or awarded a project she has been prequalified for. When she contacted the purchasing department to learn of upcoming opportunities, she was told that she was “too late” because contracts in her line of work was recently awarded.

The owner of a Hispanic American-owned engineering firm stated that the selection process has been an issue. It doesn’t appear that there is any transparency on how they go about selecting their consultants.

A nonminority male architectural firm owner thinks that DPS uses architects that they have already used in the past. He added that it is “challenging to get into DPS because of cumbersome processes. At the end of the day, they always go to the companies they have used before. They don’t use new firms.”

The owner of a nonminority male-owned engineering firm felt that the procedures for selection don’t appear to be “widely published, or particularly followed.”

An Asian American-owned architectural firm believes the process is very subjective. He said for the last project his company submitted on, most of the winners were large majority firms.

Unnecessary Restrictive Contract Specifications

A nonminority male construction-related professional services firm owner stated, “If you want smaller firms involved, you need to have projects that you are comfortable having smaller firms work on. The idea of bundling a lot of projects together probably does make it easier to manage. It might make things more cost effective. But the side effect is that you will make it impossible for smaller firms to compete.”

A Native American-owned specialty trade contractor stated, “Updating specifications to modern industry standards would help DPS by saving money and allowing the use of more environmentally friendly and safer methods.”

6.4 PRIME CONTRACTOR PRACTICES

Anecdotal participants were asked to discuss their experiences working with or observing primes contracted by DPS or in the private sector marketplace.


A Hispanic American specialty trade contractor said he was treated unfairly in the contract award process with DPS. The business owner stated, “They had put in a bid to do a school refinish and went in as a prime. Didn’t get that but were given opportunity to go in as a subcontractor by prime


ANECDOTAL ANALYSIS

contractor that won. That prime was based in Virginia. Prime required them to carry the insurance for the project instead of the prime doing so. He had to bid with the full insurance in mind so his bid was high. He thought he should go in under regular liability as a subcontractor.”

6.5 ACCESS TO CAPITAL

The following sections provide survey results and anecdotal comments concerning participants’ experiences accessing financial capital during the study period.

6.5 .1 SURVEY OF VENDORS RESPONSES

Survey respondents were asked if they applied for a commercial loan between 2008 and 2013 and whether they were approved or denied. If their loan was denied, a follow up question was what they believed was the basis of their denial. Of the 29 (28.16% of total) respondents that applied for a commercial loan, 47.73 percent were M/WBEs. Of the M/WBE respondents that applied for loans, 36.36 percent were denied loans.


Capital

A representative of a nonminority male-owned specialty trade construction firm said that though her company has not had difficulty accessing capital when securing DPS jobs, she could see how that would be difficult for small businesses.

A Hispanic American specialty trade contractor stated that the lack of cash flow to bid on projects was due to banking and bonding requirements and the capital problem she experienced.

A nonminority male owner of a professional services firms stated that delays in receiving payment from DPS is a barrier for his small business. He stated that it take up to 45 days to receive payment.

Bonding

A representative of a nonminority male-owned specialty trade construction firm said his firm “had to earn the ability to bond.” He said that bonding only required “the normal expectation that you have to have a proven track record.”

A Hispanic American specialty trade contractor said that bond requirements for smaller jobs can be as big as those for a full build. He said there was “no middle area” and that that “doesn’t make sense.”

6.6 DISPARATE TREATMENT AND DISCRIMINATION

Participants in the survey and other anecdotal data gathering methods were asked if they experienced discriminatory or disparate behavior by DPS, its primes, or in the private sector during the study period.


ANECDOTAL ANALYSIS

6.6 .1 SURVEY OF VENDORS RESPONSES

Exhibit 6-6 illustrates survey respondents experience of discriminatory behavior from DPS, a prime contractor/service provider contracted by DPS, or while conducting business in the private sector.

EXHIBIT 6-6 DENVER PUBLIC SCHOOLS SURVEY DEMOGRAPHICS

DISCRIMINATION

By Primes Private Sector M/WBE (Subcontractor) 2.9%

Non-M/WBE (Subcontractor) 1.5% All M/WBE Firms 10.4%

All non-M/WBE Firms 0.7% Source: Responses from telephone survey conducted by L.S. Gallegos, Inc. 2014.

With respect to disparate treatment M/WBE respondents in the DPS survey reported:

Discriminatory experiences in dealing with prime contractor – 11.4 percent.

An informal network precluded their firms from obtaining work in the private sector – 27.3 percent.

Selected to satisfy good faith efforts requirements and then dropped – 20.5 percent.

With respect to disparate treatment M/WBE respondents in the City and County of Denver 2012 survey reported:

Discriminatory experiences in dealing with prime contractors – 2.9 percent.

An informal network precluded their firms from obtaining work in the private sector – 18.1 percent.

Selected to satisfy good faith efforts requirements and then dropped – 17.5 percent.

These findings are consistent with the low minority and women business participation identified in Chapter 5.0, Private Sector Analyses.


A Hispanic American-owned specialty trade contractor stated that when she attends pre-bid meetings, “nobody says hi or talks to me directly. I do see staff and primes approaching men and white people.”

The owner of an Asian American-owned architectural firm felt that his firm was discriminated against “in that we are placed under the concrete ceiling. The types of projects that DPS award to a small minority firm are small, not large.”

A representative of a Hispanic American-owned general contractor said, “I think that on top of the discrimination and inhibiting factors of being a MBE, the bigger challenge is being a small business and trying to grow. The most inhibiting thing is the size of your business and extra funds for marketing and outreach. That is where we really need help.”


ANECDOTAL ANALYSIS

A Hispanic American-owned construction-related professional services firms stated that after 33 years of design experience and to see that our company can't get involved in the smallest of projects with Denver Public Schools, it tells me that there's a real issue and problem directly with the management and the process of managing the bond programs. He continued to state that “not a single Hispanic or African American architect has been selected to do any of those projects.”

A nonminority female-owned architectural firms stated that she has “no problem with private sector companies calling us for work and quite frankly I think that is discrimination.”

Faced Discrimination from Public Sector Agencies or in Private Sector

An Asian American-owned specialty trade contractor stated that she faced gender discrimination in the private sector. She felt it was a problem in the construction industry. When building her own home, contractors wouldn’t listen to her until they learned of her professional background. In her opinion, this was a historical attitude that hadn’t changed much in the 30 years since she graduated from engineering school.

A Hispanic American specialty trade contractor felt he has faced gender discrimination in the private sector. He said larger companies prefer to use smaller companies with WBE certification. He doesn’t think they separate WBE from MBE when looking for contractors to subcontract with them.

An Asian American owner of an architectural firm said, “We were a subconsultant on a City of Denver project to do architectural design work. After the contract was awarded, the City’s Project Manager (we were on the team to satisfy the MBE goal) wanted another design firm on the team. We were essentially removed from the team and the other majority firm did the work.”

A Hispanic American owner of a specialty trade construction firm stated that she was discriminated against on a City and County of Denver project. She stated, “One of the contractors told us we needed to get all new trucks. They seem to discriminate against you as a business person if your equipment isn’t new. We can’t get everything new as a small minority business now. We do just as good quality work as the companies that have all new equipment.”

An owner of a Native American-owned specialty trade construction firm believes that service-disabled veterans should be added to all diversity programs.

6.7 OTHER NOTEWORTHY COMMENTS

As stated in Section 6.1, it should be cautioned that the following comments are the perceptions and opinions of individuals.

“DPS tries really hard to outreach to M/WBE firms.” “DPS tries harder than any other metro area school district to get people involved.”


ANECDOTAL ANALYSIS

6.8 SUGGESTED REMEDIES

While collecting anecdotal data participants provided their ideas and recommendations for improving the procurement process and increasing M/WBE participation. A few recurring ideas and/or recommendations provided by participants are:

1. More transparency in understanding their procurement process, their score sheets or evaluation criteria.

2. Procurement of consultants should not be decided by just one person in DPS.

3. More outreach to small and minority firms.

4. Advertise opportunities in private sector outlets, i.e. iSqFt.

5. Prequalification experience requirements should match the project scope.

6. Maintain a consistent prequalification or qualification process each year.

7. Longer lead time or pre-plan repeat disciplines and contracts.

8. Breaking up contracts to more economically friendly sizing for smaller firms.

9. Begin the bidding process earlier and award the contracts sooner.

10. Work out the technical difficulties with the Lawson Portal.

11. Have a process for establishing project specific goals.

6.9 CONCLUSIONS

MGT collected anecdotal information from surveys, public hearings, and personal interviews with area firms. These activities resulted in 131 construction and construction-related professional services firm participating in the disparity study process.

Firms that provided their accounts to doing business or attempting to do business with DPS supported the outreach efforts to inform the business community of opportunities. There were anecdotal data collected from a variety of firm, minority, nonminority, general contractors, subcontractors, etc. Firms generally knew how to find out about bid or proposal opportunities but did not understand the selection process of how contracts are awarded.

Base on the anecdotal information gathered, M/WBE firms face unique barriers to fair treatment by both DPS and the private sector.


7.0 FINDINGS

CHAPTER 7.0: FINDINGS AND RECOMMENDATIONS

7.1 INTRODUCTION

In January 2014, MGT of America, Inc. (MGT), was retained to conduct a Disparity Study for the Denver Public Schools (DPS) to provide current data on DPS programs. In this chapter, MGT provides findings for DPS on minority and women business enterprise (M/WBE) utilization and availability. This study consisted of fact-finding to analyze DPS procurement trends and practices for years 2009 through 2013 for prime contracts and 2008 and 2012 for subcontracts; to evaluate the impact of race- and gender-neutral remedial efforts; and to evaluate various options for future program development.

The results of this study and conclusions drawn are presented in detail in Chapters 3.0 through 6.0 of this report. This chapter will summarize the evidence on the central research question: is there factual predicate evidence to support a race‐ and gender‐conscious M/WBE program for DPS?

7.2 FINDINGS

FINDING A: RECENT M/WBE POLICY

In 1987 DPS started a Minority Business Advisory Council and funded a purchasing study. The 1987 purchasing study found that less than 1 percent of its spending on goods and services was with M/WBEs. In 1989 DPS approved Resolution 2371 establishing a Minority and Women-Owned Business Utilization Plan. By 1991 DPS M/WBE utilization had risen to 6.3 percent. In 1992, DPS approved Resolution 2443, which established the Historically Underutilized Businesses (HUB) Department and the HUB Advisory Council (later the Business Diversity Outreach Program Advisory Council). In 1998 and 1999 DPS approved the Equal Opportunity Construction Contracting Procedures, hired a HUB consultant to assist with outreach and workshops and put together an M/WBE Directory of certified firms. Historically DPS has not certified firms, but has accepted certifications from a broad range of agencies and organizations. In June 2013 DPS approved “Resolution Supporting Equitable and Inclusive Contracting Policy,” which called for a new policy of inclusion for M/WBEs, reporting M/WBE utilization data on 2008 and 2012 bond funded projects and collecting factual predicate evidence on bond-funded projects. For bond financed projects from FY 2007 to FY 2010 DPS reported spending $4.2 million with M/WBEs (1.3 percent) and $27.1 million with SBEs (8.4 percent) (Exhibit 7-1).


FINDINGS AND RECOMMENDATIONS

EXHIBIT 7-1 SUMMARY OF M/W/SBE UTILIZATION

FOR BOND FINANCED PROJECTS BUSINESS OWNERSHIP CLASSIFICATION

DENVER PUBLIC SCHOOLS FY 2007 THROUGH FY 2010

Dollars Percent MBE $3,851,153.50 1.2% WBE $384,259.28 0.1% M/WBE $4,235,412.78 1.3% SBE $27,195,181.37 8.4% MWSBE $31,430,594.15 9.8%

Source: DPS Business Diversity Outreach Program Advisory Council, Report & Plan for Utilization of Diverse Businesses, 2010.

FINDING B : M/WBE SUBCONTRACTOR UTILIZATION AND DISPARITY

The dollar value of M/WBE subcontractor utilization on DPS projects over the study period from 2008 and 2012 within the relevant market (the Denver Consolidated Statistical Area, CSA) was as follows:

MBEs were awarded construction subcontracts totaling $20.3 million, 12.21 percent of the total construction subcontract dollars; WBEs were awarded $20.0 million in subcontracts, 12.00 percent of the total construction subcontract dollars (Exhibit 7-2). There was disparity for all M/WBE groups except Asian Americans.

MBEs were awarded construction-related professional service subcontracts totaling $818,546, 15.26 percent of the total construction-related subcontract dollars; WBEs were awarded $446,372 in contracts, 8.32 percent of the total construction-related subcontract dollars (Exhibit 7-2). There was disparity for all M/WBE groups except Hispanic Americans.

EXHIBIT 7-2 SUMMARY OF SUBCONTRACT UTILIZATION

BUSINESS OWNERSHIP CLASSIFICATION DENVER PUBLIC SCHOOLS

2008 AND 2012


Construction Construction Related Professional Services

($) ($) Minority Business $20,360,987 $818,546 Nonminority Women $20,004,011 $446,372 Total M/WBE Firms $40,364,997 $1,264,918 (%) (%) Minority Business 12.21% 15.26% Nonminority Women 12.00% 8.32% Total M/WBE Firms 24.20% 23.58%




FINDING C: M/WBE PRIME UTIL IZATION

The dollar value of M/WBE prime contractor utilization on DPS projects over the study period from 2009 through 2013 within the relevant market was as follows:

MBEs received payments on construction prime contracts totaling $32.0 million, 6.06 percent of the total construction prime contract dollars; WBEs were paid $11.2 million in contracts, 2.14 percent of the total construction prime contract dollars (Exhibit 7-3). There was disparity for all M/WBE groups except Native Americans.

MBEs were received payments on construction-related professional service prime contracts totaling $4.4 million, 8.76 percent of the total construction-related prime contract dollars; WBEs were paid $2.5 million in contracts, 5.01 percent of the total construction-related prime contract dollars (Exhibit 7-3). There was disparity for all M/WBE groups except Native Americans.

EXHIBIT 7-3 SUMMARY OF PRIME CONTRACTOR UTILIZATION

BY PROCUREMENT TYPE BUSINESS OWNERSHIP CLASSIFICATION

DENVER PUBLIC SCHOOLS 2009 THROUGH 2013


Construction Construction Related Professional Services

($) ($) Minority Business $32,003,950 $4,467,724 Nonminority Women $11,288,770 $2,556,476 Total M/WBE Firms $43,292,720 $7,024,201 (%) (%) Minority Business 6.06% 8.76% Nonminority Women 2.14% 5.01% Total M/WBE Firms 8.20% 13.77%


FINDING D: DISPARITIES IN SURVEY OF BUSINESS OWNERS DATA

In 180 disparity ratios for nine procurement categories employees, for both Colorado and the Denver CSA, only three instances of over-utilization were found for M/WBE groups.

FINDING E : PRIVATE SECTOR COMMERCIAL CONSTRUCTION

M/WBE utilization in private sector commercial construction in the Denver Metropolitan Statistical Area (MSA) was very low, as measured by data from building permits. From January 1, 2009 through December 31, 2013, MBE prime contractors won 1.78 percent of prime permits and WBEs received 0.24 percent of permits. MBE subcontractors were issued 3.88 percent of all subcontracting permits and WBEs 1.18 percent of subcontracting permits.

FINDING F : DISPARITIES IN SELF-EMPLOYMENT AND REVENUE EARNINGS

Econometric analysis using data from 2010 American Community Survey data for the Denver area found statistically significant disparities for entry into self-employment: for African Americans, Hispanic



Americans and nonminority women. There were statistically significant disparities in earnings from self-employment for Hispanic Americans, Native Americans and Nonminority Women. There were statistically significant disparities for earnings for African Americans, Hispanic Americans, Asian American, Native Americans and Nonminority Women.

FINDING G: ACCESS TO CAPITAL

An econometric analysis of data in the most recent National Survey of Small Business Finance (NSSBF) found a statistically significant positive relationship between the probability of loan denial and African American ownership. These results are consistent with data in a 2012 local survey. About 11.1 percent of non-M/WBE loan applicants reported being denied commercial bank loans, as compared to 76.1 percent of African American applicants, 42.8 percent of Hispanic American applicants, 16.6 percent of Asian American-owned firms, 50 percent of Native American applicants and 26.5 percent of Nonminority Woman applicants.

FINDING H: ANECDOTAL COMMENTS

Among the M/WBEs who responded to questions about barriers to doing business, the biggest concern for both construction and construction-related professional service prime contractors was the selection process (61.4% of M/WBE primes). Other key issues noted by M/WBE respondents included:

Primes: Competing with large firms– 47.7 percent. Limited time to prepare bid packages – 31.8 percent.

Subcontractors Competing with large firms– 45.5 percent. Selection process – 34.1 percent.

With respect to disparate treatment M/WBE respondents in the DPS survey reported:

Discriminatory experiences in dealing with prime contractor – 11.4 percent. An informal network precluded their firms from obtaining work in the private sector – 27.3

percent. Selected to satisfy good faith efforts requirements and then dropped – 20.5 percent.

With respect to disparate treatment M/WBE respondents in the City and County of Denver 2012 survey reported:

Discriminatory experiences in dealing with prime contractors – 2.9 percent. An informal network precluded their firms from obtaining work in the private sector – 18.1

percent. Selected to satisfy good faith efforts requirements and then dropped – 17.5 percent.

7.3 COMMENDATIONS AND RECOMMENDATIONS

Most of the following commendations and recommendations are based on multiple findings and do not necessarily tie to one finding. These recommendations also reflect in part that a School District is involved. School districts, while they have large construction programs, do not have the same mission of business development as cities, counties and states.



COMMENDATION AND RECOMMENDATION A: SBE PROGRAM

DPS should be commended for encouraging SBE utilization. SBE programs, including SBE project goals and SBE set-asides, have the advantage that they are generally not subject to constitutional challenge. Combining SBE programs and MBE programs has become common across agencies that maintain MBE programs. Further ideas on SBE programs are discussed in Selected Practices below.

RECOMMENDATION B: SUBCONTRACTOR PROJECT GOALS

In response to the primary research question this study provides evidence to support a DPS M/WBE program. This conclusion is based primarily on statistical disparities in current M/WBE utilization; evidence of discrimination in business formation and revenue earned from self-employment; very low M/WBE utilization in the building permit evidence; credit disparities; and anecdotal evidence of discrimination. DPS should tailor its M/WBE participation policy to remedy these disparities. The core theme should be that prime contractors should document their outreach efforts and the reasons why they may have rejected qualified M/WBEs that were the low-bidding subcontractors.

COMMENDATION C: NARROW TAILORED M/WBE PROGRAM

Developments in court cases involving federal disadvantaged business enterprise (DBE) programs provide important insight into the design of local M/WBE programs. In January 1999, the United States Department of Transportation (USDOT) published its final DBE rule in Title 49, Code of Federal Regulations, Part 26 (49 CFR 26). Federal courts found have consistently found new DBE regulations to be narrowly tailored.1 The federal DBE program has the features in Exhibit 7-4 that contribute to this characterization as a narrowly tailored remedial procurement preference program. DPS should adopt these features in any new narrowly tailored M/WBE program.

EXHIBIT 7-4 NARROWLY TAILORED M/WBE PROGRAM FEATURES

NARROWLY TAILORED GOAL-SETTING FEATURES DBE

Regulations

1. DPS should not use M/WBE quotas. 49 CFR 26(43)(a)

2. DPS should use race- or gender-conscious set-asides only in extreme cases. 49 CFR 26(43)(b)

3. DPS should meet the maximum amount of M/WBE goals through race-neutral means.

49 CFR 26(51)(a)

Source: Suggested features in a proposed narrowly tailored M/WBE program based on USDOT 49 CFR 26.

RECOMMENDATION D: ASPIRATIONAL M/WBE GOALS

Proposed aspirational M/WBE goals are proposed in Exhibit 7-5 below. These proposed aspirational goals are similar in design to the DBE goal setting process in that the goals are a weighted average of estimated M/WBE availability and utilization.

1 Adarand v. Slater, 228 F.3d 1147 (10th Cir. 2000), Gross Seed. v. State of Nebraska, 345 F.3d 968 (8th Cir. 2003); cert denied, 158 L.Ed. 2d 729 (2004).




PROPOSED ASPIRATIONAL M/WBE GOALS 2014

MBE WBE Prime Contracting

Construction 5.9% 3.4% Architecture and Engineering 6.8% 4.2%

Subcontracting Construction 13.8% 13.0% Architecture and Engineering 13.3% 10.8%

Source: MGT developed proposed aspirational M/WBE goals.

COMMENDATION AND RECOMMENDATION E: DATA MANAGEMENT

DPS should be commended for tracking M/WBE utilization; in particular, tracking awards to certified subcontractors on projects greater than $250,000, maintaining records of proposed subcontractors from prime contractors bid submittals and maintaining prequalification lists for general contractors and architects.

However, DPS could improve the collection and reporting of the data along the following lines:

Track all subcontract awards (not just certified subcontractors) on all projects.

Track the payments to all subcontractors through the collection of final payment affidavits.

Track the race, ethnicity, and gender of all subcontractors.

Consider developing or investing in a data management system to track prime contractor and subcontractor information.

Track the race, ethnicity, and gender of prequalified firms.

Maintain key information (such as contact and company information, maximum bonding capacity, current value of bonded projects, current open bonding capacity, race, ethnicity, gender) reported in the Contractor

Place prequalification form in a data management system.

Maintain the prequalification information by year.

COMMENDATION F: OUTREACH

DPS should be commended for its outreach activities with the BDOP Advisory Council, hiring a HUB coordinator, establishing a certification directory, holding workshops, relaxing the prequalification process, limiting awards that a single company can win, and working with minority contractors organizations and the Hispanic Chamber of Commerce.

COMMENDATION AND RECOMMENDATION G: DPS WEBSITE

DPS should be commended for having bid opportunities, vendor registration, how to do business information prequalification information, and direct links to on-line purchasing manuals on its website. A survey of agencies has found the following information on their M/WBE Web sites, which serves as a source of additional ideas for DPS: and, directory of certified firms, uniform certification application,



M/WBE program description, SBE program description, comprehensive contracting guides, M/WBE ordinance, bid tabulations, status of certification applications, links to management and technical assistance providers, information on the loan programs, newsletters, data on SBE and M/WBE utilization, annual M/WBE program reports, capacity, bonding, qualifications and experience data on certified firms, and 90-day forecasts of business opportunities.

RECOMMENDATION H: STAFFING

In order to increase its efforts on M/WBE inclusion DPS will probably need an increase in staffing. Other programs in the Denver area, such as Denver Regional Transportation District and the City and County of Denver have nine to twelve staff members on these programs. Some agencies approach increased staffing needs by having some existing staff become M/WBE liaisons for their departments.

CONCLUSION

This study provides factual predicate evidence for establishing a DPS M/WBE program. This evidence is based on quantitative and qualitative data from public and private sources. While DPS has made significant progress in M/WBE inclusion, any future efforts must be narrowly tailored to rectify the issues identified in this report.

SELECTED PRACTICES OF OTHER M/WBE PROGRAMS

As MGT reviews procurement policies and remedial programs of federal, state and local government M/W/SBE programs we are able to identify policies that promote local small business development. This research affords us the opportunity to create an extensive library of practices that agencies use to include minority, women, small, and disadvantaged businesses in their procurement process. Contained herein is a menu of policies that have worked in some localities, but may not have not been effective in others. Some policies have been discontinued for budgetary reasons. In many instances, it is difficult to determine whether a particular policy is directly responsible for the success of a program.

Such assistance may include direct subsidies to businesses, funds for management and technical assistance to small and new entrepreneurs, mentor-protégé programs, and bonding assistance, as well as collaboration with and support for organizations that provide management and technical assistance to businesses.

A substantial number of these agencies also have procurement preference programs for small business. Some M/W/SBE programs are nominal and some seem to have substantial resources devoted to M/W/SBE program design and implementation. In general, the demand by some courts and some legislation for race-neutral business development policies has increased the resources devoted to race-neutral M/WBE programs.

Provided below are the selected practices we have taken into consideration for Denver Schools. There is a focus on M/W/SBE programs for schools, with other types of agencies included as well.

M/WBE Program Data Management M/WBE Goals Small Business Aspirational Goals Small Business Program for Subcontracts Small Business Prime Contracting Programs M/W/SBE Inclusion in Financial and Professional Services



HUBZones DBE Programs Two Tier Certification Management and Technical Assistance Programs Financial Assistance Management and Technical Assistance

M/WBE PROGRAM DATA MANAGEMENT

It is imperative an agency to closely monitor the utilization of all businesses by race, ethnicity, and gender over time to determine program effectiveness. Many agencies issues M/WBE annual utilization reports. Some important additional elements of program data management employed by other agencies include:

Separate Reporting of M/WBE Prime Contractor and Subcontractor Utilization. Orange County, FL; City of Charlotte, NC; Port Authority and New York and New Jersey.

Tracking M/WBE and Non-M/WBE Subcontractor Utilization. City of Charlotte, NC.

Tracking M/WBE Utilization in the SBE Program. Los Angeles Unified School District, City of Charlotte, NC; Port Authority and New York and New Jersey, Phoenix, AZ.

M/WBE GOALS

M/WBE ASPIRATIONAL GOALS

Chicago Public Schools aspirational M/WBE goals are 30 percent MBE and 15 percent WBE for construction and goods and services. 2

Houston Independent School District aspirational M/WBE goals are:

Purchasing - 20% for contracts over $50,000 Professional Services - 25% for contracts over $50,000 Construction - 20% for contracts over $50,0003

Cleveland Public Schools Community Inclusion Plan has Diversity Business Enterprise (DBE, their name for are M/WBEs.) participation goals of:

15% in service contracts, 20% in contracts for goods and supplies and 30% in contracts for construction.4

Charlotte Mecklenburg Schools has the following MBE/WBE/SBE aspirational goals in Exhibit 7-6.

2 http://www.cps.edu/About_CPS/Departments/Pages/BusinessDiversity.aspx. 3 http://www.houstonisd.org/Page/33109. 4 http://net.cmsdnet.net/newschools/CommunityInclusionPlanDescription.pdf



EXHIBIT 7-6 2010 M/W/SBE ASPIRATIONAL GOALS

CHARLOTTE-MECKLENBURG SCHOOLS 5

MBE GOAL WBE GOAL SBE GOAL M/W/SBE GOAL Construction 10% 6% 5% 21% Architecture & Engineering 4% 7% 5% 16% Contracted Services (other than Construction) 5% 4% 5% 14%

Goods 3% 3% 5% 11% Source: Minority, Women, and Small Business Enterprise Program, Board of Education Management Oversight Report, August 2014, Charlotte-Mecklenburg Schools.

M/WBE PROJECT GOAL SETTING

North Carolina Department of Transportation (NCDOT). The NCDOT regulations emphasize that goals should be set on projects “determined appropriate by the Department [of Transportation].”6 Individual goals are set based on a project’s geographic location, characteristics of the project, the percentage of that type of work that is typically performed by M/WBEs, the areas in which M/WBEs are known to provide services, and the goals set by the North Carolina General Assembly.7 The NCDOT M/WBE regulations specify (although they do not limit to) particular areas for M/WBE goals: clearing and grubbing, hauling and trucking, storm drainage, concrete and masonry construction, guardrail, landscaping, erosion control, reinforcing steel, utility construction, and pavement marking.

The NCDOT goal setting process begins with an engineering estimate of the project to determine what items might reasonably be subcontracted out. Next estimates of the percentage of work that could be potentially performed by DBEs and M/WBEs are developed.8 These estimates are confidential and made available only to the Estimator (and staff), the Provisions Engineer in the Proposals and Contracts Section (and staff), and members of the DBE/M/WBE Committee at the DBE/M/WBE Committee meetings.

Next NCDOT looks at whether there are M/WBEs available based on the NCDOT DBE/M/WBE directory and the location of the project. The NCDOT Directory is a searchable database that classifies firms by location, prime contractor/subcontractor status and six-digit work type.9 The Goal Setting Committee is assisted in this process by Equal Employment Opportunity (EEO) Compliance staff in the Office of Civil Rights. Prime contractors then submit documentation of good faith efforts to achieve the individual project goal. A statement of how they will make efforts to achieve the goal satisfies the good faith effort requirements.

The NCDOT Goal Setting Committee (in collaboration with the EEO Compliance staff) seeks to set goals relative to where there is interest, availability and capacity, beyond mere looking at the certification lists. NCDOT relies on the EEO Compliance staff to provide input on whether existing businesses are fully occupied. However, if EEO Compliance says M/WBEs are not fully occupied, but prime contractors submit

5 http://www.cms.k12.nc.us/boe/Management%20Oversight%20Reports%20Schedule/Archive/MWSBE%20Pres.pdf 6 19A NCAC 02D.1108(a). 7 19A NCAC 02D.1108(a). 8 NCDOT, Division of Highways, Roadway Design and Design Services Unit, Policy and Procedure Manual, Chapter 10, at 4. 9 http://apps.dot.state.nc.us/constructionunit/directory/.



evidence that M/WBEs are fully occupied (for example, with invoices), then NCDOT accepts those explanations. As part of goal setting NCDOT regulations provide that:

A documented excessive subcontractor bid constitutes a basis for not subcontracting with an M/WBE.

A documented record of poor experience constitutes a basis for not subcontracting with an M/WBE.10

In addition, a review of NCDOT DBE and M/WBE goals has been a regular topic at the Associated General Contractors (AGC)-DOT Joint Cooperative Committee meetings.11

Oregon DOT. Oregon DOT uses both "hard goals" and "aspirational targets" for DBE project goal setting. Aspirational targets do not have numerical percentages and are not subject to good faith efforts requirements or risk of rejected bids. “Hard goals” have generally only been applied in ODOT regions with identified DBE availability and disparity. Aspirational DBE targets applied through the state. ODOT staff reported that primes took the aspirational goals seriously, even though there was often no numerical percentage associated with the aspirational goal written in the bid documents.

GOOD FAITH EFFORTS

Portsmouth Public Schools, Virginia. The Portsmouth Public Schools require bidders to meet a combination of good faith efforts that equal a numerical value established by the program. Each good faith effort option is assigned a value weighted by the greatest result of possible success of the effort.

SMALL BUSINESS ASPIRATIONAL GOALS

Commitment from the top leadership is a core element of most summaries of policies in other M/W/SBE programs.12 One starting point for such commitment is setting overall aspirational goals separate from project goals. Some agencies use fairly straightforward methods to calculate aspirational goals and other agencies use more involved methodologies.

Los Angeles Unified School District—25 percent SBE goal (since 2003)

SMALL BUSINESS PROGRAM FOR SUBCONTRACTS

SMALL BUSINESS PROJECT GOALS

City of Charlotte, North Carolina. The City of Charlotte has a comprehensive SBE program including SBE set asides and business assistance.13 In addition, the City of Charlotte sets department goals for SBE utilization, sets SBE goals on formal and informal contracts and makes SBE utilization part of department

10 The last two elements are adopted by the North Carolina DOT. 19A NCAC 02D.1110(7). 11 AGC-DOT Joint Cooperative Committee Meeting Minutes, February 2001 through August 2003. 12 See, e.g., National Women’s Business Council, 1999 NWBC Best Practices Guide: Contracting with Women (July 1999); R. Auskalnis, C. Ketchum and C. Carter, Purchasing From Minority Business Enterprise: Best Practices, Center For Strategic Supply Research 1995). 13 A description of the Charlotte SBE program can be found at ww.charmeck.org/Departments/Economic+Development/Small+Business/Home.htm.



performance review utilization numbers. The City has a waiver provisions for bidders, but has rejected bids for bidder noncompliance with the SBE program. Charlotte achieved 28.9 percent M/WBE subcontractor utilization in construction and 33.1 percent M/WBE subcontractor utilization in A&E through small business subcontracting goals.14

MANDATORY SUBCONTRACTING

As part of their SBE subcontracting program some agencies impose mandatory subcontracting clauses which would promote SBE utilization and be consistent with industry practice

Miami-Dade County Public Schools. The Miami School District policy allows for mandatory subcontracting of up to 40 percent of a contract.

SMALL BUSINESS PRIME CONTRACTING PROGRAMS

BIDDER ROTATION

Some political jurisdictions use bidder rotation schemes to limit habitual purchases from majority firms and to ensure that M/W/SBEs have an opportunity to bid along with majority firms. A number of agencies, including the City of Indianapolis, Indiana; Fairfax County, Virginia; the Port Authority of New York and New Jersey; and Miami-Dade County, Florida, use bid rotation to encourage M/W/SBE utilization, particularly in architecture and engineering.

Miami-Dade County, Florida. Miami-Dade County has used small purchase orders for the Community Business Enterprise program and rotated on that basis. In addition, Miami-Dade County has utilized an Equitable Distribution Program, whereby a pool of qualified architecture and engineering professionals are rotated awards of county miscellaneous architecture and engineering services as prime contractors and subcontractors.

SMALL BUSINESS SET-ASIDES

Miami-Dade County Public Schools. The Miami-Dade Schools SBE policy includes a sheltered market component. The policy provides that certain contracts can be placed in a sheltered market by the Office of Economic Opportunity and Miami-Dade Schools goal setting committee if: (1) there are at least three SBE/MBEs that are capable and available, (2) there is under-utilization in that business category, and (3) the extent to which the District's SBE prime contractor goals are being achieved. A contract can be removed from the sheltered market program if a responsive and responsible bid is not received or the bid received is deemed to be too high in price.

City of Denver. The Defined Selection Pool program puts contracts up to $1 million in a selection pool that can only be bid on by certified SBEs. This program applies to construction and professional service contracts. A SBE is defined as a firm that has revenue less than or equal to 50 percent of the Small Business

14 MGT, The City of Charlotte Update Disparity Study, 2011, Exhibit 7-1.



Administration’s (SBA) small business standard and the owner has a personal net worth of less than $1.3 million. In the 2010 annual report M/WBEs won 73.7 percent of selection pool contracts.15

North Carolina Department of Transportation (NCDOT). In the NCDOT program, small contractors are defined as firms with less than $1.5 million in revenue. There is a small contractor goal of $2 million for each of the 14 NCDOT divisions. The current cap on project size for small contractors is $500,000. For contracts less than $500,000, NCDOT can solicit three informal bids from small business enterprises.16 North Carolina law permits the waiving of bonds and licensing requirements for these small contracts let to SBEs.17 From FY 2004-08, M/WBEs won $29.4 million (20.3 percent) in prime contracts under the North Carolina program.18

Oregon Department of Transportation (ODOT). Under its Small Business Initiative, ODOT started a pilot program targeting small firms in Region I. The program was extended statewide in September 2007. The program initially set aside contracts of less than $75,000 for competition amongst small firms and targeted A&E firms.19 Set asides for architecture and engineering (A&E) and related services were extended to projects of up to $150,000. Set-asides for construction are limited to projects valued at $100,000 or less. The program covers Project Specific contracts and On-Call Contracts. There were approximately 400 firms registered in the program in 2011.

The selection process for A&E and related services begins with the identification of a small contracting outsourcing opportunity. If there are ten or fewer firms registered in the discipline necessary for the project, all the firms are considered in the selection process. If there are more than ten firms registered in the discipline, then at least five firms are considered. The criteria ODOT may choose for selection include, but are not limited to, qualified firms that have no current or previous prime contracts with ODOT, specific work experience deemed relevant to ODOT requirements and geographic proximity to the project site and/or familiarity with the project site. Firms chosen for further evaluation then are to respond to mini-solicitations, which may include interviews. ODOT reserves to right to use other selection methods, including emergency procurement and direct appointments. After issuing a Notice of Intent to Award ODOT negotiates the statement of work, costs and payment terms with the top ranked firm.

The selection process for construction begins with the identification of a small contracting construction need and the plans and specifications and estimate for that project. If there are three or fewer firms registered in the discipline necessary for the project, all the firms are considered in the selection process. If there are more than three firms registered in the discipline, then at least three firms are invited to bid. The criteria ODOT may choose for selection include, but are not limited to, qualified firms that have no current or previous prime contracts with ODOT, geographic proximity to the project site and firm certification status. The award is then made to the lowest responsive and responsible bid.

15 City of Denver, Office of Economic Development, Division of Small Business Opportunities, 2010 Annual Report, at 3. http://www.denvergov.org/Portals/690/documents/DSBO/DSBO%20Annual%20Report-FINAL-2010.pdf. 16 NCGS § 136-28.10(a). 17 NCGS § 136-28.10(b). 18 Equant, Measuring Business Opportunity—A Disparity Study of NCDOT’s State and Federal Programs, 2009, at 138. 19 Procurement authority for the SCPS program derives from ORS § 279A-050(3)(A),(B).



SBE BID PREFERENCES

A number of agencies have used bid preferences for SBEs [Dade County, Florida; Port Authority of New York and New Jersey SBE Program; Sacramento Municipal Utility District (SMUD); City of Sacramento; City of Oakland; East Bay Municipal Utility District]. SBE bid preferences operate along similar lines as M/WBE bid preferences.

Miami-Dade County Public Schools. The Miami Schools SBE policy allows for evaluation preferences for "best value" contracts in which factors other than price can be considered in selection. In particular, the Miami Schools policy allows for bid preferences of up to 20 percent of total points for an SBE or joint venture with an SBE. SBE prime contractors cannot subcontract more than 49 percent of a contract.

RACE-NEUTRAL JOINT VENTURES

City of Atlanta, Georgia. The City of Atlanta requires establishment of joint ventures on large projects of over $10 million.20 Primes are required to create a joint venture with a firm from a different ethnic/gender group in order to ensure prime contracting opportunities for all businesses. This rule applies to women- and minority-owned firms as well as nonminority firms. This rule has resulted in tens of millions of dollars in contract awards to women- and minority-owned firms.

Washington Suburban Sanitation Commission (WSSC). The WSSC Competitive Business Demonstration Project requires joint ventures between a local SBE and an established firm in procurement areas that do not generate enough SBE bids.

CONSTRUCTION MANAGEMENT, REQUEST FOR PROPOSALS, AND DESIGN-BUILD

One method of unbundling in construction is through the use of multi-prime construction contracts in which a construction project is divided into several prime contracts that are then managed by a construction manager at risk. For example, this approach has been used on projects where each prime contractor is responsible for installation and repair in particular areas. The construction manager is responsible for obtaining materials at volume discounts based upon total agency purchases. If one contractor defaults, a change order is issued to another prime contractor working in an adjacent area. The construction manager at risk is responsible for cost overruns that result from prime contractor default.

Construction management also facilitates the rotation of contracts within an area of work. For example, if several subcontractors have the capacity of bidding on an extended work activity (e.g., concrete flat work, traffic control, hauling), the construction manager can rotate contracting opportunities over the duration of the activity.

Using a request for proposal process can provide the flexibility for including M/WBE participation in prime contractor requirements and selection. One of the nonfinancial criteria can be the proposer's approach and past history with M/WBE subcontractor utilization as well as women and minority workforce participation.

20 City of Atlanta Ordinance Sec. 2-1450 and Sec. 2-1451.



A number of agencies around the country, the Charlotte-Mecklenburg School System, the Tri-County Metropolitan Transportation District of Oregon, and the City of Columbia, have had some success with this approach.21

The Colorado DOT has required DBE and Emerging Small Business (ESB) performance plans for bidders on design-build projects. Colorado DOT achieved $187 million in DBE utilization on the $1.2 billion T-REX project using this approach.22

STATE CONTRACTS

The use of state contracts can impede M/W/SBE utilization, even when M/W/SBEs are the low bidder. Purchase off of state contracts is particularly an issue with car purchases, a procurement where there can be a significant number of M/W/SBE vendors. Fulton County, Georgia, addressed this problem by removing car purchases from the category of purchases from state contracts.

PURCHASING CARDS

A number of agencies promote the utilization of M/W/SBEs on purchasing cards. The City of Hampton Schools, Virginia, for example, requires the purchasing card vendor to report on M/WBE utilization by agency staff. A number of universities, including the University of Wisconsin at Madison target M/WBE vendors for purchasing card transactions for travel.

OTHER SBE PRIME CONTRACTORS ASSISTANCE

North Carolina Department of Transportation (NCDOT) Fully Operated Rental Agreements. Under these arrangements, a firm may bid an hourly rate for using certain equipment and the necessary staff. In these field-let contracts, engineers select the firm with the appropriate equipment and the lowest bid rate. If that firm is not available, the engineers select the next lowest hourly rate. This rental agreement technique is used primarily to supplement NCDOT equipment in the event of NCDOT equipment failure or peak demand for NCDOT services. The rental agreement technique is attractive to small contractors because the typical small firm has much better knowledge of its own hourly costs than it does of the costs to complete an entire project.

M/W/SBE INCLUSION IN FINANCIAL AND PROFESSIONAL SERVICES

Port Authority of New York and New Jersey. The Port Authority has encouraged the use of M/W/SBEs in finance through its financial advisory call-in program which targets small firms to serve as a pool of advisors for the Port Authority Chief Financial Officer. The financial advisors address debt issuance, financial advisory services, real estate transactions, and green initiatives. There are three to four firms in each of these categories in the financial advisory call-in program.

The Port Authority of New York and New Jersey’s Specialty Insurance Program sets aside five sets of insurance policies to small brokers, and the Port’s Financial Advisors Call In program pre-qualifies small

21 Federal Transit Administration, Lessons Learned #45 (May 2002). www.fta.dot.gov/library/program/ll/man/ll45.html. 22 D. Wilson, Colorado Department of Transportation Statewide Transportation Disparity Study, 2009, at 3-20.



firms for task orders in financial advisory services, real estate transactions, debt issuance, and green initiatives.

HUBZONES

Another variant of an SBE program provides incentives for SBEs located in distressed areas. For example, under the 1997 Small Business Reauthorization Act, the federal government started the federal HUBZone program. A HUBZone firm is a small business that is: (1) owned and controlled by U.S. citizens; (2) has at least 35 percent of its employees who reside in a HUBZone; and (3) has its principal place of business located in a HUBZone.23 HUBZone programs can serve as a vehicle for encouraging M/WBE contract utilization. Nationally, there are 5,357 female and minority HUBZone firms, representing 56.2 percent of total HUBZone firms.24

City of New York, New York. The City of New York has a HUBZone type program providing subcontracting preferences to small construction firms (with less than $2 million in average revenue) that either perform 25 percent of their work in economically distressed areas or for which 25 percent of their employees are economically disadvantaged individuals.25

DBE PROGRAMS

Following the federal model, some agencies have added DBE programs.26 SBE programs focus on the disadvantage of the business, HUBZone programs focus on the disadvantage of the business location, and DBE programs focus on the disadvantage of the individual operating the business.

State of North Carolina. The State of North Carolina changed the definition of minority used in the state minority construction program to include socially and economically disadvantaged individuals, as defined in the federal rules.27 Socially disadvantaged individuals are those who have been subjected to racial or ethnic prejudice or cultural bias because of their identity as a member of a group without regard to their individual qualities.28 Economically disadvantaged individuals are those socially disadvantaged individuals whose ability to compete in the free enterprise system has been impaired due to diminished capital and credit opportunities as compared to others in the same business area that are not socially disadvantaged.29 This rule permits firms certified under the federal 8(a), DBE, and small disadvantaged business enterprise (S/DBE) programs to be certified as a minority firm in North Carolina. This rule also implies that firms owned by majority males are eligible for the program as there are firms owned by majority males that qualify for the 8(a), DBE, and S/DBE programs by making an individual showing of their social and economic disadvantage.

23 13 C.F.R. 126.200 (1999). 24 Based on the SBA pro-net database located at http://pro-net.sba.gov/pro-net/search.html. 25 New York Administrative Code § 6-108.1. For a description of the New York local business enterprise program see http://www.nyc.gov/html/sbs/html/lbe.html. 26 DBE programs and Airport Concession Disadvantaged Enterprise (ACDBE) programs are required to be developed and implemented as a part of the federal funding process. 27 NC GS § 143-128.2(g). 28 15 USC 637(a)(5). 29 15 USC 637(a)(6)(A).


http://pro-net.sba.gov/pro-net/search.html

http://www.nyc.gov/html/sbs/html/lbe.html


TWO TIER CERTIFICATION

Miami-Dade County Public Schools. Miami Schools uses the following two-tier definition of small and micro business enterprises (Exhibit 7-7):

EXHIBIT 7-7 REVENUE DEFINITIONS FOR SBE AND MBE MIAMI-DADE COUNTY PUBLIC SCHOOLS

2013

INDUSTRY MICRO BUSINESS ENTERPRISE SMALL BUSINESS ENTERPRISE

Professional Services Less than $500,000 Tier 1 - Less than $1,000,000

Tier 2 - Less than $2,000,000

Construction Less than $1,000,000 Tier 1 - Less than $3,000,000 Tier 2 - Less than $6,000,000

Specialty Trade Less than $500,000 Tier 1 - Less than $1,000,000 Tier 2 - Less than $2,000,000

Source: School Board Policy 6320.02; The School Board of Miami-Dade County, Florida Business Enterprise Program Certification Application, September 2013

FINANCIAL ASSISTANCE

LOAN GUARANTEES

Los Angeles Unified School District (LAUSD), California. LAUSD provides firms with loan guarantees of up to the lesser of 50 percent of the contract or $200,000. Loan proceeds can be used for materials, subcontracts and labor costs associated with LAUSD contracts.

PROMPT PAYMENT

M/WBE vendors still often report problems with prompt payment, particularly payments from prime contractors to subcontractors. Certain subcontractors that work on an early phase in a project, such as grading, can suffer from retainage withheld on long-lasting projects. There are several prompt payment policies that respond to this problem:

Retainage. North Carolina DOT requires that retainage be released when the tasks/activities for the subcontractors’ phase of work is accepted rather than at the end of the project.30

Two-party check program. To improve access to financing, the Port Authority of New York and New Jersey has a two-party check program in which the port authority writes checks out to the lender and the contractor. This program has not been frequently used according to staff

BONDING ASSISTANCE

Los Angeles Unified School District Contract Bondworks Program. Graduates of the LAUSD Small Business Boot Camp are eligible for bonding and finance guarantees for LAUSD construction contracts. The bond

30 49 CFR, Part 26.29(b).



guarantee maximum is $400,000 or, 40 percent of the bond whichever less, per contract, per contractor is.

MANAGEMENT AND TECHNICAL ASSISTANCE

A number of agencies hire an outside management and technical assistance provider to provide needed technical services related to business development and performance. Such a contract can be structured to include providing incentives to produce results, such as the number of M/W/SBEs being registered as qualified vendors with the city, the number of M/WBEs graduating from subcontract work to prime contracting, and rewarding firms that utilize M/WBEs in their private sector business activities.

Los Angeles Unified School District Small Business Boot Camp. The eight week program trains contractors in bonding, prequalification, bidding, scheduling, public contract law, safety plans, labor compliance and financing.

MENTOR-PROTÉGÉ PROGRAMS

New York School Construction Authority Mentor Plan. In this mentor protégé plan firms are eligible for small business loans and bonding assistance upon completion of the program. The in addition to loans and bonding the program provides project experience, technical assistance and training, help with business development and accelerated payments. The program targets construction contracts valued up to $750,000 for program participants to bid against each other.31

31 http://www.nycsca.org/Business/GettingStarted/Pages/MentorPrograms.aspx.


APPENDICES

APPENDIX A

APPENDIX A: ADDITIONAL MARKET AREA AND UTILIZATION ANALYSES

This appendix presents corresponding analyses discussed in Chapter 4.0. The corresponding analyses presents market area and utilization results.

EXHIBIT A-1 DOLLARS EXPENDED AT THE PRIME LEVEL BY CONTRACT CATEGORY, OVERALL MARKET AREA

CONTRACT CATEGORY DOLLARS PAID PERCENT OF DOLLARS

Construction $584,330,517 87.38% Construction-Related Professional Services $84,397,241 12.62% TOTAL $668,727,757 100.00%


FIGURE A-1 DOLLARS EXPENDED AT THE PRIME LEVEL BY CONTRACT CATEGORY, OVERALL MARKET AREA


Construction$584,330,517

87.38%


$84,397,24112.62%

Construction Construction-Related Professional Services

Denver Public Schools Draft Report Appendix A October 2014 A-1

ADDITIONAL MARKET AREA AND UTILIZATION ANALYSES

EXHIBIT A-2 DOLLARS EXPENDED AT THE PRIME LEVEL BY LOCATION OF FIRMS (STATE), OVERALL MARKET AREA

LOCATION OF FIRMS DOLLARS PAID PERCENT OF DOLLARS

CUMULATIVE PERCENT1

ALL CONTRACT CATEGORIES Inside Colorado $628,087,455 93.92% 93.92% Outside Colorado $40,640,302 6.08% 100.00% TOTAL $668,727,757 100.00%


FIGURE A-2 DOLLARS EXPENDED AT THE PRIME LEVEL BY LOCATION OF FIRMS (STATE), OVERALL MARKET AREA


93.92%

6.08%

0.00% 20.00% 40.00% 60.00% 80.00% 100.00%

Inside Colorado

Outside Colorado



EXHIBIT A-3 DOLLARS EXPENDED AT THE PRIME LEVEL BY LOCATION OF FIRMS (STATE) AND CONTRACT CATEGORY,

OVERALL MARKET AREA

LOCATION OF FIRMS BY BUSINESS CATEGORY DOLLARS PAID PERCENT OF DOLLARS

Construction Inside Colorado $570,821,337 97.69% Outside Colorado $13,509,180 2.31% TOTAL CONSTRUCTION $584,330,517 100.00% Construction-Related Professional Services Inside Colorado $57,266,118 67.85% Outside Colorado $27,131,123 32.15% TOTAL CONSTRUCTION-RELATED PROFESSIONAL SERVICES $84,397,241 100.00% All Business Categories Inside Colorado $628,087,455 93.92% Outside Colorado $40,640,302 6.08% TOTAL ALL BUSINESS CATEGORIES $668,727,757 100.00%


FIGURE A-3 CONSTRUCTION DOLLARS EXPENDED AT THE PRIME LEVEL BY LOCATION OF FIRMS, STATE OF

COLORADO


Inside Colorado, $570,821,337,

97.69%

Outside Colorado, $13,509,180,

2.31%

Inside Colorado Outside Colorado



FIGURE A-4 CONSTRUCTION-RELATED PROFESSIONAL SERVICES DOLLARS EXPENDED AT THE PRIME LEVEL BY

LOCATION OF FIRMS, STATE OF COLORADO


FIGURE A-5 DOLLARS EXPENDED AT THE PRIME LEVEL BY CONTRACT CATEGORY AND LOCATION OF FIRMS, STATE OF

COLORADO


Inside Colorado, $57,266,118,

67.85%

Outside Colorado, $27,131,123, 32.15%

Inside Colorado Outside Colorado

97.59%

66.92%

93.92%

2.41%

33.08%

6.08%

0.00% 20.00% 40.00% 60.00% 80.00% 100.00%

Construction



Outside Colorado Inside Colorado



EXHIBIT A-4 DOLLARS EXPENDED AT THE PRIME LEVEL BY LOCATION OF FIRMS (DENVER-AURORA CSA) AND

CONTRACT CATEGORY, OVERALL MARKET AREA

LOCATION OF FIRMS BY CONTRACT CATEGORY DOLLARS PAID

PERCENT OF DOLLARS

Construction Inside Denver–Aurora, CO CSA $527,748,211 90.32% Outside Denver–Aurora, CO CSA $56,582,306 9.68% TOTAL CONSTRUCTION $584,330,517 100.00% Construction-Related Professional Services Inside Denver–Aurora, CO CSA $51,008,684 60.44% Outside Denver–Aurora, CO CSA $33,388,557 39.56% TOTAL CONSTRUCTION-RELATED PROFESSIONAL SERVICES $84,397,241 100.00% All Business Categories Inside Denver–Aurora, CO CSA $578,756,895 86.55% Outside Denver–Aurora, CO CSA $89,970,862 13.45% TOTAL ALL BUSINESS CATEGORIES $668,727,757 100.00%


FIGURE A-6 DOLLARS EXPENDED AT THE PRIME LEVEL BY CONTRACT CATEGORY AND LOCATION OF FIRMS, DENVER-

AURORA CSA


90.32%

60.44%

86.55%

9.68%

39.56%

13.45%

0.00% 20.00% 40.00% 60.00% 80.00% 100.00%

Construction






EXHIBIT A-5 DOLLARS EXPENDED AT THE PRIME LEVEL BY LOCATION OF FIRMS (COUNTY AND STATE), MARKET AREA

ANALYSIS


CUMULATIVE PERCENT1

ALL CONTRACT CATEGORIES Inside Denver–Aurora, CO Combined Statistical Area ADAMS COUNTY, CO $82,400,218 12.32% 12.32% ARAPAHOE COUNTY, CO $97,815,377 14.63% 26.95% BOULDER COUNTY, CO $65,709,420 9.83% 36.78% BROOMFIELD COUNTY, CO $310,707 0.05% 36.82% CLEAR CREEK COUNTY, CO $0 0.00% 36.82% DENVER COUNTY, CO $215,816,412 32.27% 69.09% DOUGLAS COUNTY, CO $17,915,157 2.68% 71.77% ELBERT COUNTY, CO $0 0.00% 71.77% GILPIN COUNTY, CO $0 0.00% 71.77% JEFFERSON COUNTY, CO $88,348,505 13.21% 84.98% PARK COUNTY, CO 0.00% 84.98% WELD COUNTY, CO $10,441,098 1.56% 86.55% Inside Denver–Aurora, CO Combined Statistical Area $578,756,895 86.55% Outside Denver-Aurora, CO Combined Statistical Area Total

LARIMER COUNTY, CO $34,748,168 5.20% 91.74% WASHINGTON COUNTY, MN $25,558,112 3.82% 95.56% EL PASO COUNTY, CO $7,396,966 1.11% 96.67% PUEBLO COUNTY, CO $7,181,684 1.07% 97.74% COOK COUNTY, IL $4,536,580 0.68% 98.42% DALLAS COUNTY, TX $1,518,179 0.23% 98.65% LOS ANGELES COUNTY, CA $1,481,218 0.22% 98.87% SALT LAKE COUNTY, UT $1,423,448 0.21% 99.08% JACKSON COUNTY, MO $1,414,846 0.21% 99.30% MARICOPA COUNTY, AZ $1,346,786 0.20% 99.50% LANCASTER COUNTY, NE $770,312 0.12% 99.61% BELMONT COUNTY, OH $286,995 0.04% 99.65% JO DAVIESS COUNTY, IL $274,679 0.04% 99.70% BANNOCK COUNTY, ID $224,794 0.03% 99.73% WINONA COUNTY, MN $198,661 0.03% 99.76% MIDDLESEX COUNTY, MA $168,396 0.03% 99.78% KING COUNTY, WA $166,982 0.02% 99.81% HAMPDEN COUNTY, MA $154,139 0.02% 99.83% DAVIDSON COUNTY, TN $147,871 0.02% 99.85% HENNEPIN COUNTY, MN $134,266 0.02% 99.87% MEEKER COUNTY, MN $110,588 0.02% 99.89% ORANGE COUNTY, CA $104,148 0.02% 99.91% MAHASKA COUNTY, IA $91,545 0.01% 99.92% DUPAGE COUNTY, IL $72,082 0.01% 99.93% SAINT LOUIS COUNTY, MO $69,722 0.01% 99.94% OAKLAND COUNTY, MI $46,433 0.01% 99.95%




CUMULATIVE PERCENT1

PHILADELPHIA COUNTY, PA $44,065 0.01% 99.96% GREENVILLE COUNTY, SC $43,091 0.01% 99.96% HENDERSON COUNTY, NC $40,094 0.01% 99.97% TARRANT COUNTY, TX $35,940 0.01% 99.97% HARRIS COUNTY, TX $28,755 0.00% 99.98% WASHINGTON COUNTY, MD $24,175 0.00% 99.98% FULTON COUNTY, GA $19,434 0.00% 99.98% DENTON COUNTY, TX $14,808 0.00% 99.99% CLARK COUNTY, NV $13,450 0.00% 99.99% SAN DIEGO COUNTY, CA $12,736 0.00% 99.99% SPOKANE COUNTY, WA $12,687 0.00% 99.99% FORSYTH COUNTY, NC $12,229 0.00% 99.99% POLK COUNTY, IA $10,226 0.00% 100.00% SUMMIT COUNTY, OH $8,832 0.00% 100.00% HARTFORD COUNTY, CT $7,192 0.00% 100.00% CUYAHOGA COUNTY, OH $4,715 0.00% 100.00% MORGAN COUNTY, CO $3,742 0.00% 100.00% BROWN COUNTY, WI $2,914 0.00% 100.00% OKLAHOMA COUNTY, OK $2,868 0.00% 100.00% ROCK ISLAND COUNTY, IL $406 0.00% 100.00% WINNEBAGO COUNTY, WI $313 0.00% 100.00% RAMSEY COUNTY, MN $308 0.00% 100.00% MULTNOMAH COUNTY, OR $200 0.00% 100.00% CLACKAMAS COUNTY, OR $84 0.00% 100.00% Outside Denver-Aurora, CO Combined Statistical Area Total $89,970,862 13.45% ALL CONTRACT CATEGORIES, TOTAL $668,727,757 100.00%




EXHIBIT A-6 CONSTRUCTION DOLLARS EXPENDED AT THE PRIME LEVEL BY LOCATION OF FIRMS (COUNTY AND

STATE), MARKET AREA ANALYSIS


CUMULATIVE PERCENT1

CONSTRUCTION Inside Denver–Aurora, CO Combined Statistical Area ADAMS COUNTY, CO $86,293,496 14.77% 14.77% ARAPAHOE COUNTY, CO $92,518,916 15.83% 30.60% BOULDER COUNTY, CO $64,865,919 11.10% 41.70% BROOMFIELD COUNTY, CO $168,277 0.03% 41.73% CLEAR CREEK COUNTY, CO $0 0.00% 41.73% DENVER COUNTY, CO $174,013,957 29.78% 71.51% DOUGLAS COUNTY, CO $17,472,298 2.99% 74.50% ELBERT COUNTY, CO $0 0.00% 74.50% GILPIN COUNTY, CO $0 0.00% 74.50% JEFFERSON COUNTY, CO $82,068,264 14.04% 88.55% PARK COUNTY, CO $0 0.00% 88.55% WELD COUNTY, CO $10,347,084 1.77% 90.32% Inside Denver–Aurora, CO Combined Statistical Area Total

$527,748,211 90.32%

Outside Denver-Aurora, CO Combined Statistical Area LARIMER COUNTY, CO $31,564,786 5.40% 95.72% PUEBLO COUNTY, CO $7,178,684 1.23% 96.95% COOK COUNTY, IL $4,404,034 0.75% 97.70% EL PASO COUNTY, CO $4,325,914 0.74% 98.44% DALLAS COUNTY, TX $1,518,179 0.26% 98.70% SALT LAKE COUNTY, UT $1,423,448 0.24% 98.94% MARICOPA COUNTY, AZ $1,346,786 0.23% 99.18% JACKSON COUNTY, MO $1,322,501 0.23% 99.40% LOS ANGELES COUNTY, CA $1,296,722 0.22% 99.62% BELMONT COUNTY, OH $286,995 0.05% 99.67% JO DAVIESS COUNTY, IL $274,679 0.05% 99.72% BANNOCK COUNTY, ID $224,794 0.04% 99.76% WINONA COUNTY, MN $198,661 0.03% 99.79% MIDDLESEX COUNTY, MA $168,396 0.03% 99.82% KING COUNTY, WA $166,982 0.03% 99.85% HENNEPIN COUNTY, MN $129,666 0.02% 99.87% MEEKER COUNTY, MN $110,588 0.02% 99.89% ORANGE COUNTY, CA $104,148 0.02% 99.91% MAHASKA COUNTY, IA $91,545 0.02% 99.92% DUPAGE COUNTY, IL $72,082 0.01% 99.94% SAINT LOUIS COUNTY, MO $65,553 0.01% 99.95% PHILADELPHIA COUNTY, PA $44,065 0.01% 99.95% GREENVILLE COUNTY, SC $43,091 0.01% 99.96% HENDERSON COUNTY, NC $40,094 0.01% 99.97% TARRANT COUNTY, TX $35,940 0.01% 99.98% HARRIS COUNTY, TX $28,755 0.00% 99.98%




CUMULATIVE PERCENT1

WASHINGTON COUNTY, MD $24,175 0.00% 99.98% FULTON COUNTY, GA $19,434 0.00% 99.99% SPOKANE COUNTY, WA $12,687 0.00% 99.99% FORSYTH COUNTY, NC $12,229 0.00% 99.99% POLK COUNTY, IA $10,226 0.00% 99.99% SUMMIT COUNTY, OH $8,832 0.00% 100.00% HARTFORD COUNTY, CT $7,192 0.00% 100.00% SAN DIEGO COUNTY, CA $5,208 0.00% 100.00% CUYAHOGA COUNTY, OH $4,715 0.00% 100.00% MORGAN COUNTY, CO $3,742 0.00% 100.00% BROWN COUNTY, WI $2,914 0.00% 100.00% OKLAHOMA COUNTY, OK $2,868 0.00% 100.00% ROCK ISLAND COUNTY, IL $406 0.00% 100.00% RAMSEY COUNTY, MN $308 0.00% 100.00% MULTNOMAH COUNTY, OR $200 0.00% 100.00% CLACKAMAS COUNTY, OR $84 0.00% 100.00% Outside Denver-Aurora, CO Combined Statistical Area Total $56,582,306 9.68% TOTAL $584,330,517 100.00%




EXHIBIT A-7 CONSTRUCTION-RELATED PROFESSIONAL SERVICES DOLLARS EXPENDED AT THE PRIME LEVEL BY

LOCATION OF FIRMS (COUNTY AND STATE), MARKET AREA ANALYSIS

LOCATION OF FIRMS DOLLARS PAID

PERCENT OF

DOLLARS

CUMULATIVE PERCENT1

CONSTRUCTION-RELATED PROFESSIONAL SERVICES Inside Denver–Aurora, CO Combined Statistical Area ADAMS COUNTY, CO $331,955 0.39% 0.39% ARAPAHOE COUNTY, CO $5,296,460 6.28% 6.67% BOULDER COUNTY, CO $843,502 1.00% 7.67% BROOMFIELD COUNTY, CO $142,430 0.17% 7.84% CLEAR CREEK COUNTY, CO $0 0.00% 7.84% DENVER COUNTY, CO $41,802,455 49.53% 57.37% DOUGLAS COUNTY, CO $442,860 0.52% 57.89% ELBERT COUNTY, CO $0 0.00% 57.89% GILPIN COUNTY, CO $0 0.00% 57.89% JEFFERSON COUNTY, CO $2,055,009 2.43% 60.33% PARK COUNTY, CO $0 0.00% 60.33% WELD COUNTY, CO $94,014 0.11% 60.44% Inside Denver–Aurora, CO Combined Statistical Area Total

$51,008,684 60.44%

Outside Denver-Aurora, CO Combined Statistical Area WASHINGTON COUNTY, MN $25,558,112 30.28% 90.72% LARIMER COUNTY, CO $3,183,382 3.77% 94.49% EL PASO COUNTY, CO $3,071,052 3.64% 98.13% LANCASTER COUNTY, NE $770,312 0.91% 99.05% LOS ANGELES COUNTY, CA $184,496 0.22% 99.26% HAMPDEN COUNTY, MA $154,139 0.18% 99.45% DAVIDSON COUNTY, TN $147,871 0.18% 99.62% COOK COUNTY, IL $132,546 0.16% 99.78% JACKSON COUNTY, MO $92,345 0.11% 99.89% OAKLAND COUNTY, MI $46,433 0.06% 99.94% DENTON COUNTY, TX $14,808 0.02% 99.96% CLARK COUNTY, NV $13,450 0.02% 99.98% SAN DIEGO COUNTY, CA $7,528 0.01% 99.99% HENNEPIN COUNTY, MN $4,600 0.01% 99.99% SAINT LOUIS COUNTY, MO $4,170 0.00% 100.00% PUEBLO COUNTY, CO $3,000 0.00% 100.00% WINNEBAGO COUNTY, WI $313 0.00% 100.00% Outside Denver–Aurora, CO Combined Statistical Area Total

$33,388,557 39.56%

TOTAL $84,397,241 100.00% Source: MGT developed a master database based on dollars awarded and expended by Denver Public Schools between January 1, 2009, and December 31, 2013.



EXHIBIT A-8 ALL CONTRACT CATEGORIES DOLLARS EXPENDED AT THE PRIME LEVEL BY BUSINESS OWNERSHIP AND

CALENDAR YEAR, UTILIZATION ANALYSIS (DENVER-AURORA CSA)


2009 2010 2011 2012 2013 TOTAL

($) ($) ($) ($) ($) ($)

African American $86,362 $821,292 $661,739 $1,199,916 $190,689 $2,959,999 Asian American $299,583 $109,215 $131,501 $114,255 $114,855 $769,409 Hispanic American $802,075 $845,427 $1,123,482 $16,797,574 $2,066,721 $21,635,279 Native American $1,242,028 $4,599,332 $2,531,117 $1,642,996 $1,091,514 $11,106,988 Total MBE Firms $2,430,049 $6,375,266 $4,447,840 $19,754,740 $3,463,780 $36,471,674 Nonminority Female $2,603,396 $3,298,216 $1,877,120 $5,158,725 $907,790 $13,845,246 Total M/WBE Firms $5,033,444 $9,673,482 $6,324,959 $24,913,465 $4,371,570 $50,316,920 Non-M/WBE Firms $36,131,959 $69,660,574 $88,112,503 $225,746,704 $108,788,234 $528,439,975 TOTAL $41,165,403 $79,334,057 $94,437,462 $250,660,169 $113,159,804 $578,756,895





EXHIBIT A-9 CONSTRUCTION DOLLARS EXPENDED AT THE PRIME LEVEL BY BUSINESS OWNERSHIP AND CALENDAR

YEAR, UTILIZATION ANALYSIS (DENVER-AURORA CSA)


2009 2010 2011 2012 2013 TOTAL

($) ($) ($) ($) ($) ($)

African American $80,554 $821,292 $661,739 $1,199,916 $190,689 $2,954,190 Asian American $0 $0 $0 $687 $0 $687 Hispanic American $574,931 $511,158 $733,349 $16,573,683 $1,774,475 $20,167,597 Native American $1,075,573 $4,441,494 $2,422,140 $515,355 $426,913 $8,881,475 Total MBE Firms $1,731,057 $5,773,945 $3,817,229 $18,289,641 $2,392,077 $32,003,950 Nonminority Female $2,145,034 $2,831,331 $1,530,849 $4,289,847 $491,708 $11,288,770 Total M/WBE Firms $3,876,092 $8,605,276 $5,348,078 $22,579,488 $2,883,786 $43,292,720 Non-M/WBE Firms $33,304,800 $66,466,765 $82,020,280 $204,529,132 $98,134,514 $484,455,491 TOTAL $37,180,892 $75,072,041 $87,368,358 $227,108,620 $101,018,299 $527,748,211





EXHIBIT A-10 CONSTRUCTION-RELATED PROFESSIONAL SERVICES DOLLARS EXPENDED AT THE PRIME LEVEL BY

BUSINESS OWNERSHIP AND CALENDAR YEAR, UTILIZATION ANALYSIS (DENVER-AURORA CSA)


2009 2010 2011 2012 2013 TOTAL

($) ($) ($) ($) ($) ($)

African American $5,808 $0 $0 $0 $0 $5,808 Asian American $299,583 $109,215 $131,501 $113,568 $114,855 $768,722 Hispanic American $227,144 $334,269 $390,133 $223,890 $292,246 $1,467,682 Native American $166,456 $157,838 $108,977 $1,127,641 $664,601 $2,225,513 Total MBE Firms $698,991 $601,321 $630,611 $1,465,099 $1,071,702 $4,467,724 Nonminority Female $458,361 $466,885 $346,270 $868,878 $416,082 $2,556,476 Total M/WBE Firms $1,157,352 $1,068,206 $976,881 $2,333,977 $1,487,784 $7,024,201 Non-M/WBE Firms $2,827,159 $3,193,809 $6,092,223 $21,217,572 $10,653,720 $43,984,483 TOTAL $3,984,512 $4,262,015 $7,069,104 $23,551,549 $12,141,504 $51,008,684





FIGURE A-7 PERCENT OF DOLLARS EXPENDED AT THE PRIME LEVEL BY M/WBE CLASSIFICATION AND CONTRACT

CATEGORY, UTILIZATION ANALYSIS (DENVER-AURORA CSA)


ALL CATEGORIES CONSTRUCTION CONSTRUCTION-RELATEDPROFESSIONAL SERVICES

NONMINORITY FEMALE FIRMS (WBE) 2.39% 2.14% 5.01%

MINORTITY FIRMS (MBE) 6.30% 6.06% 8.76%

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%


APPENDIX B

APPENDIX B: DENVER PUBLIC SCHOOLS CUSTOM CENSUS SURVEY CONSTRUCTION

ENTER THE D&B D-U-N-S NUMBER

Hello. My name is _________, we are conducting a 2-minute survey for MGT of America on behalf of Denver Public Schools (Schools). The purpose of this survey is to help the Schools identify firms interested in conducting business with their agency. You recently indicated that you are interested in doing business with the City and County of Denver. We want to learn if you are also interested in doing business with the Schools.

Is this ___________________ (Company's name)? IF COMPANY NAME VERIFIED, CONTINUE.

Are you the owner or authorized decision maker in your company please? [IF NO] May I speak with that person? [IF NO, SCHEDULE CALL-BACK]?

IF OWNER IS PUT ON THE LINE: CONTINUE

IF TRANSFERRED TO ANOTHER PARTY (CFO, MANAGER, ETC): Are you able to answer questions concerning business practices of this company? IF YES, CONTINUE

Your company's information has been provided to us from Dun & Bradstreet.

Thank you for agreeing to do the survey. Your opinions are important to us, and all of your responses will be kept confidential. If you have any questions regarding the survey, I will be happy to provide you a contact at the end of the survey.

Thank you in advance for your participation.

Q1. What is your title? [REQUIRE ANSWER]

Owner/CEO/President 1 Manager/Financial Officer 2 Other (Specify) 3

Denver Public Schools Draft Report Appendix B October 2014 B-1


Q2. Please provide the following in case we have any further questions.

[REQUIRE ANSWER]

Name (First and Last Name) (1) Email Address (2)

Q3. Is your company interested in submitting a bid as a prime contractor or subcontractor, for a construction contract to the Schools over the next 12 months or near future?

[REQUIRE ANSWER]

Yes 1 No 2 Don’t Know 9

[IF THE ANSWER TO QUESTION 3 IS NO TERMINATE THE CALL AND PLEASE GO BACK TO Q2 AND TYPE “DISQUALIFIED” AFTER THE FIRST AND LAST NAME.]

Disqualification statement Thank you for your input; however, you do not qualify for this survey

Q4. Let me confirm that, based on information we have from Dun & Bradstreet, this is a for-profit construction company, as opposed to a nonprofit, foundation or government office?

[REQUIRE ANSWER]


[IF THE ANSWER TO QUESTION 4 IS NO OR DON’T KNOW, THEN TERMINATE THE CALL PLEASE GO BACK TO Q2 AND TYPE “DISQUALIFIED” AFTER THE FIRST AND LAST NAME.]

Disqualification statement Thank you for your input; however, you do not qualify for this survey.

Q5. Let us also confirm that your company provides construction services. Construction services include, but are not limited to, general contracting, concrete work, masonry, excavation work, electrical, drywall, structural steel erection, demolition, hauling, etc.

[REQUIRE ANSWER]

Yes 1 No 2 Don't Know 9

[IF THE ANSWER TO QUESTION 4 IS NO TERMINATE THE CALL AND PLEASE GO BACK TO Q2 AND TYPE “DISQUALIFIED” AFTER THE FIRST AND LAST NAME.

Disqualification statement



Thank you for your input; however, you do not qualify for this survey.

Q6. Does your company bid primarily as prime contractor? Subcontractor? or both?

[REQUIRE ANSWER]

Prime Contractor 1 Subcontractor 2 Both 3 Don’t Know 9

That completes our interview. Thank you for your input and your participation in this important survey.

If you would like more information on the Disparity Study, please contact

(DPS contact name) at (XXX) XXX-XXXX.

Interviewer Id# [REQUIRE ANSWER] _________________________________


APPENDIX B-1

APPENDIX B-1: DENVER PUBLIC SCHOOLS CUSTOM CENSUS SURVEY CONSTRUCTION












Denver Public Schools Draft Report Appendix B-1 October 2014 B-1-1



[REQUIRE ANSWER]


Q3. Is your company interested in submitting a bid as a prime contractor or subcontractor, for a construction contract to the Schools over the next 12 months or near future?

[REQUIRE ANSWER]




Q4. Let me confirm that, based on information we have from Dun & Bradstreet, this is a for-profit construction company, as opposed to a nonprofit, foundation or government office?

[REQUIRE ANSWER]




Q5. Let us also confirm that your company provides construction services. Construction services include, but are not limited to, general contracting, concrete work, masonry, excavation work, electrical, drywall, structural steel erection, demolition, hauling, etc.

[REQUIRE ANSWER]






Q6. Please provide your primary type of construction services.

Specify:

Q7. Does your company bid primarily as prime contractor? Subcontractor? or both?

[REQUIRE ANSWER]


Q8. Is more than 50 percent of your company owned and controlled by a woman or women?

[REQUIRE ANSWER]


Q9. Between October 1, 2008 and September 30, 2013 has your company submitted a bid as prime contractor or subcontractor, for a construction or construction-related professional services contract or project to a federal, state, or other local government agency in the Denver Regional Area?


Q10. Is more than 50 percent of your company owned and controlled by a person or people from one of the following racial or ethnic groups?

[REQUIRE ANSWER]

Anglo/Caucasian 1 African American 2 Asian or Pacific Islander 3 Hispanic American 4 Native American/Alaskan Native 5 Other (Specify) 6_________________________________ Don’t Know 7


If you would like more information on the Disparity Study, please contact Andrea Silva, Business Diversity and Outreach Program at (720) 423-3108.



APPENDIX B-2

APPENDIX B-2: DENVER PUBLIC SCHOOLS CUSTOM CENSUS SURVEY CONSTRUCTION RELATED PROFESSIONAL SERVICES















[REQUIRE ANSWER]


Q3. Is your company interested in submitting a bid as a prime contractor or subcontractor, for a construction-related professional services contract to the Schools over the next 12 months or near future?

[REQUIRE ANSWER]




Q4. Let me confirm that, based on information we have from Dun & Bradstreet, this is a for-profit architecture, engineering, landscape architecture or related construction-related professional services company, as opposed to a nonprofit, foundation or government office?

[REQUIRE ANSWER]




Q5. Let us also confirm that your company provide construction-related professional services. Services include, but are not limited to, architecture, engineering, land surveying, landscape architecture, etc.

[REQUIRE ANSWER]






Q6. Does your company bid primarily as prime? Subcontractor? or both?

[REQUIRE ANSWER]



If you would like more information on the Disparity Study, please contact (DPS contact name) at (XXX) XXX-XXXX.



APPENDIX B-3
















[REQUIRE ANSWER]


Q3. Is your company interested in submitting a bid as a prime contractor or subcontractor, for a construction-related professional services contract to the Schools over the next 12 months or near future?

[REQUIRE ANSWER]




Q4. Let me confirm that, based on information we have from Dun & Bradstreet, this is a for-profit architecture, engineering, landscape architecture or related construction-related professional services company, as opposed to a nonprofit, foundation or government office?

[REQUIRE ANSWER]




Q5. Let us also confirm that your company provide construction-related professional services. Services include, but are not limited to, architecture, engineering, land surveying, landscape architecture, etc.

[REQUIRE ANSWER]






Q6. Please provide your primary type of construction services.

Specify:

Q7. Does your company bid primarily as prime? Subcontractor? or both?

[REQUIRE ANSWER]


Q8. Is more than 50 percent of your company owned and controlled by a woman or women?

[REQUIRE ANSWER]


Q9. Between October 1, 2008 and September 30, 2013 has your company submitted a bid as prime contractor or subcontractor, for a construction-related professional services contract or project to a federal, state, or other local government agency in the Denver Regional Area?


Q10. Is more than 50 percent of your company owned and controlled by a person or people from one of the following racial or ethnic groups?

[REQUIRE ANSWER]

Anglo/Caucasian 1 African American 2 Asian or Pacific Islander 3 Hispanic American 4 Native American/Alaskan Native 5 Other (Specify) 6_________________________________ Don’t Know 7


If you would like more information on the Disparity Study, please contact Andrea Silva, Business Diversity and Outreach Program at (720) 423-3108.



APPENDIX C

APPENDIX C: DISPARITY STUDY ANNOUNCEMENT

APPENDIX C: DISPARITY STUDY ANNOUNCEMENT

Denver Public Schools Draft Report Appendix C October 2014 C-2

APPENDIX D

APPENDIX D: DISPARITY STUDY PUBLIC HEARING ANNOUNCEMENT

Denver Public Schools Draft Report Appendix D October 2014 D-1

APPENDIX D-1

APPENDIX D-1: DISPARITY STUDY PUBLIC HEARING ANNOUNCEMENT

Denver Public Schools Draft Report Appendix D-1 October 2014 D-1-1

APPENDIX E

APPENDIX E: DENVER PUBLIC SCHOOLS SURVEY OF VENDORS

MGT of America is conducting a survey of business owners for the Denver Public Schools (Schools) to determine the current business climate and help evaluate the procurement of construction and construction-related professional services for the Schools, the subcontracting practices of prime contractors/consultants who do business with the Schools, and the anecdotal evidence collected from a broad cross section of all interested businesses.

The following survey will gather information on business ownership, work performed and/or bid with the Schools, work bid and/or performed in the private sector, and barriers, perceived or real, that may have prevented your firm from doing business with the Schools between October 1, 2008 and September 30, 2013. The results of the study will provide the basis, if warranted, for recommendations to improve the Schools’ current procurement programs.

This is a great opportunity for you to provide feedback regarding your experience doing business with, or attempting to do business with, the Schools by agreeing to carefully complete this survey. The survey will only take a few minutes of your time to complete. Your information is aggregated for the overall analysis and used only for the purposes of conducting this study. Individual information is kept confidential.

Q1 What is your title? [REQUIRE ANSWER]

Owner 1 CEO/President 2 Manager/Financial Officer 3 Other __________________ 4

If Owner or CEO Is Selected, Then Skip To: Q2. Please verify your name and phone number

Q1a Are you able to answer questions concerning the ownership and business activities of the company? [REQUIRE ANSWER]

Yes 1 No 2 (If No, discontinue survey)

Termination Statement: Your firm’s input is very important so we request that the survey be provided to a member of management with more knowledge of the establishment and functions of the business. Thank you.

Q2 Please VERIFY your name and phone number just in case we have any further questions? [REQUIRE ANSWER]

Contact Name: ________________________________________

Contact Telephone Number:

Q3 Please specify your company’s primary line of business. [REQUIRE ANSWER]

Denver Public Schools Draft Report Appendix E October 2014 E-1


Construction (such as general contractor, electrical, site work, HVAC, drywall, roofing, etc.) (1) Specify

Construction Related Professional Services (such as architecture, engineering environmental, structural, land development, etc.) (2) Specify

Other: Specify (3)

Q4. Is more than 50% of your company owned and controlled by a woman or women? [REQUIRE ANSWER]


Q5. Is more than 50% of the company owned and controlled by one of the following racial or ethnic groups? [REQUIRE ANSWER]

Anglo/Caucasian/White 1 Black / African American 2 American Indian/Alaskan Native 3 Asian 4 Hispanic or Latino 5 Native Hawaiian/ Pacific Islander 6 Other 7 Specify: Don’t Know 99

Q6 What is the highest level of education completed by the primary owner of your company? [REQUIRE ANSWER]

Some high school 1 High school graduate 2 Trade or technical education 3 Some college 4 College degree 5 Post graduate degree 6 Don’t know 99

Q7 In what year was your company established? ____ [REQUIRE ANSWER]

Q8 How many years of experience do you or the primary owner(s) of your firm have in the line of business? ____ [REQUIRE ANSWER]

0 – 5 years 1 6 – 10 years 2 11 – 15 years 3 16 – 20 years 4 20 + years 5 Don’t know 99



Q9 In the last three years, what was the average, number of employees did your company keep on the payroll, including full-time and part-time staff? [REQUIRE ANSWER]

0 - 10 1 11 - 20 2 21 - 30 3 31 - 40 4 41+ 5 Don’t know 99

Q10 Which of the following categories best approximates your company’s gross revenues for calendar year 2013? [REQUIRE ANSWER]

Up to $50,000? 1 $50,001 to $100,000? 2 $100,001 to $300,000? 3 $300,001 to $500,000? 4 $500,001 to $1 million? 5 $1,000,001 to $3 million? 6 $3,000,001 to $5 million? 7 $5,000,001 to $10 million? 8 Over $10 million? 9 Don’t Know 99

Q11 What percentage of these gross revenues was earned from the Schools, the private sector, and other public government sector projects? (Must total 100%)

Schools ____% Private Sector _____% Public Sector _____%

Q12 Does your company hold any of the following certifications from a recognized certification agency? Check all that applies.

Minority Business Enterprise (MBE) 1 Small Business Enterprise (SBE) 2 Woman Business Enterprise (WBE) 3 Disadvantaged Business Enterprise (DBE) 4 HubZone 5 8A 6 Other 7 Specify Don’t Know 99



The following questions are related to work your company have done or attempted to with Schools.

Q13 On a scale from 1 to 6 (1 being extremely easy and 6 being extremely difficult) how would you rate your ease of obtaining notification of business opportunities with the Schools?

Extremely easy 1 Somewhat easy 2 Easy 3 Difficult 4 Somewhat Difficult 5 Extremely Difficult 6 Don’t know 99

Q14 How do you obtain notification of bid/proposal opportunities? Check all that applies

School’s website 1 Contact from the Schools 2 Private bidding subscription 3 Other Primes/Subcontractors 4 Trade or industry associations 5 Local Newspapers with general circulation 6 Other. Specify 7 Don’t know 99

Q15 In general, which of the following dollar ranges best approximates your company’s largest prime contract awarded between October 1, 2008 and September 30, 2013?

[S - IF THE ANSWER IS 1, THEN SKIP TO QUESTION 29]

Not applicable (not a prime) 1 None 2 Up to $50,000? 3 $50,001 to $100,000? 4 $100,001 to $200,000? 5 $200,001 to $300,000? 6 $300,001 to $400,000? 7 $400,001 to $500,000? 8 $500,001 to $1 million? 9 Over $1 million? 10 Don’t Know 99



Q16. In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant:

[REQUIRE ANSWER TO EACH OF THE FOLLOWING]

Yes (1) No (2) DK (98)

Prequalification requirements (1)

Bid bond requirement (2)

Performance/payment bond requirement (3)

Cost of bidding/proposing (4)

Financing (5)

Insurance (general liability, professional liability, etc.) (6)

Price of supplies/materials (7)

Proposal/Bid specifications (8)

Limited time given to prepare bid package or quote (9)

Limited knowledge of purchasing contracting policies and procedures (10)

Lack of experience (11)

Lack of personnel (12)

Contract too large (13)

Selection process (14)

Unnecessary restrictive contract specifications (15)

Slow payment or nonpayment (16)

Competing with large companies (17)



Q17. Between October 1, 2008 and September 30, 2013 how many times has your company been awarded a School project as a prime contractor/consultant? [REQUIRE ANSWER]


None 1 1-10 times 2 11-25 times 3 26-50 times 4 51-100 times 5 Over 100 times 6 Don’t know 99

Q18 When you were a prime contractor/consultant, what was the average amount of time that it typically took to receive payment for your services on Schools funded projects? [REQUIRE ANSWER]

Less than 30 days 1 31-60 days 2 61-90 days 3 91-120 days 4 Over 120 days 5 Not Applicable 6 Don’t know 99

Q19 Between October 1, 2008 and September 30, 2013, have you ever submitted a bid or proposal for a Schools contract, were informed that you were the lowest bidder, and then found out that another firm was actually doing the work. [REQUIRE ANSWER]


Q20 As a prime contractor/consultant, are you required to have bonding? [REQUIRE ANSWER]

[S - IF THE ANSWER IS 2 OR 3, THEN SKIP TO QUESTION 23]




Q21 What is your current aggregate bonding capacity? [REQUIRE ANSWER]

Below $100,000 1 $100,001 to $250,000 2 $250,001 to $500,000 3 $500,001 to $1,000,000 4 $1,000,001 to $1,500,000 5 $1,500,001 to $3,000,000 6 $3,000,001 to $5,000,000 7 Over $ 5 million 8 Don’t know 99

Q22 What is your current single limit bonding capacity? [REQUIRE ANSWER]

Below $100,000 1 $100,001 to $250,000 2 $250,001 to $500,000 3 $500,001 to $1,000,000 4 $1,000,001 to $1,500,000 5 $1,500,001 to $3,000,000 6 $3,000,001 to $5,000,000 7 Over$ 5 million 8 Don’t know 99

Q23 As a prime contractor/consultant did you experience discriminatory behavior by the Schools when bidding or working on a project between 2008 and 2013? [REQUIRE ANSWER]

[S - IF THE ANSWER IS NOT 1, THEN SKIP TO QUESTION 29]

Yes 1 No 2 Not Applicable 3 Don’t know 99

Q24 What was the most noticeable way you became aware of the discrimination against your company? [REQUIRE ANSWER]

Verbal Comment 1 Written Statement 2 Action taken against the company 3 Don’t Know 99

Q25 Which of the following do you consider the primary reason for your company being discriminated against [REQUIRE ANSWER]

Owner’s race or ethnicity 1 Owner’s gender 2 Both 3 Don’t Know 99



Q26 When did the discrimination first occur: [REQUIRE ANSWER]

During bidding process 1 After contract awarded 2 Both 3 Don’t Know 99

Q27 Did you file a complaint? [REQUIRE ANSWER]


Q28 Would you be willing to speak with the Schools about the discrimination you experienced?

Yes 1 No 2

Q29. In general, which of the following dollar ranges best approximates your company’s largest subcontract awarded between October 1, 2008 and September 30, 2013?


Not applicable (not a subcontractor) 1 None 2 Up to $50,000? 3 $50,001 to $100,000? 4 $100,001 to $200,000? 5 $200,001 to $300,000? 6 $300,001 to $400,000? 7 $400,001 to $500,000? 8 $500,001 to $1 million? 9 Over $1 million? 10 Don’t Know 99



Q30. In your experience, have any of the following been a barrier to obtaining work on projects as a subcontractor with primes on projects for the Schools:

[REQUIRE ANSWER TO EACH OF THE FOLLOWING]

Yes (1) No (2) DK (98)



Financing (3)



Limited time given to prepare bid estimate or quote (6)





Competing with large companies (11)

Q31. Between October 1, 2008 and September 30, 2013, how many times has your company been awarded a subcontract with a prime contractor for a project with the Schools? [REQUIRE ANSWER]


None 1 1-10 times 2 11-25 times 3 26-50 times 4 51-100 times 5 Over 100 times 6 Don’t know 99



Q32. Between October 1, 2008 and September 30, 2013, when you were a subcontractor what was the average amount of time that it typically took to receive payment for your services from the prime contractor/vendor? [REQUIRE ANSWER]

Less than 30 days 1 31-60 days 2 61-90 days 3 91-120 days 4 Over 120 days 5 Not Applicable 6 Don’t know 99

Q33. Between October 1, 2008 and September 30, 2013, have you ever submitted a bid with a prime contractor for a project with the Schools, were informed that you were the lowest bidder, and then found out that another subcontractor was actually doing the work. [REQUIRE ANSWER]


Q34. As a subcontractor, are you required to have bonding for company’s type of work? [REQUIRE ANSWER]

[S - IF THE ANSWER IS 2 OR 3, THEN SKIP TO QUESTION 37]


Q35 What is your current aggregate bonding limit? [REQUIRE ANSWER]




Q36. What is your current single project bonding limit? [REQUIRE ANSWER]


Q37. As a subcontractor did you experience discriminatory behavior between October 1, 2008 and September 30, 2013 from a prime contractor/consultant working or bidding/proposing on a Schools project? [REQUIRE ANSWER]


Yes 1 No 2 Not Applicable 3 Don’t know 9

Q38. What was the most noticeable way you became aware of the discrimination against your company? [REQUIRE ANSWER]

Verbal Comment 1 Written Statement 2 Action taken against the company 3 Don’t Know 99

Q39. Which of the following do you consider the primary reason for your company being discriminated against? [REQUIRE ANSWER]

Owner’s race or ethnicity 1 Owner’s gender 2 Both 3 Don’t Know 99

Q40. When did the discrimination first occur? [REQUIRE ANSWER]

During bidding process 1 After contract awarded 2 Both 3 Don’t Know 99



Q41. Did you file a complaint? [REQUIRE ANSWER]


Q42. Would you be willing to speak with the Schools about the discrimination you experienced?

Yes 1 No 2

Q43. Have you experienced or observed a situation in which a prime contractor/consultants includes minority or woman subcontractors on a bid or proposal to satisfy the “good faith effort” requirements, and then drops the company as a subcontractor after winning the award for no legitimate reason? [REQUIRE ANSWER]

Yes 1 No 2 Don’t’ Know 99

Q44. How often do prime contractors/consultants who use your firm as a subcontractor on public-sector projects with M/WBE goals solicit your firm on projects (private or public) without M/WBE goals? [REQUIRE ANSWER]

Very Often 1 Sometimes 2 Seldom 3 Never 4 Not Applicable 5 Don’t know 99



Q45. Still talking about the Schools and its prime contractors/consultants, while doing business or attempting to do business, have you experienced any of the following as a form of discrimination: [REQUIRE ANSWER]

Yes (1) No (2) DK (98)

Harassment (1)

Unequal or unfair treatment (2)

Bid shopping or bid manipulation (3)

Double standards in performance (4)

Denial of opportunity to bid (5)

Unfair denial of contract award (6)

Unfair termination (7)

Unequal price quotes from suppliers (8)

Q46. For the following statement, please indicate whether you Strongly Agree, Somewhat Agree, Neither Agree Nor Disagree, Somewhat Disagree, or Strongly Disagree. [REQUIRE ANSWER]

There is an informal network of prime contractors/consultants and subcontractors that has excluded my company from doing business in the private sector:

Strongly Agree 1 Somewhat Agree 2 Neither Agree Nor Disagree 3 Somewhat Disagree 4 Strongly Disagree 5 Don’t know 99

Q47. Has your company applied for a commercial (business) bank loan between October 1, 2008 and September 30, 2013? [REQUIRE ANSWER]


Yes 1 No 2 Don’t know 99



Q48. Were you approved or denied for a commercial (business) bank loan?

[REQUIRE ANSWER]


Approved 1 Denied 2 Don’t’ Know 99

Q49. Which of the following do you believe was the primary reason for your being denied a loan? [REQUIRE ANSWER]

Insufficient Documentation (ID) 1 Insufficient Business History (IBH) 2 Confusion about the Process (C) 3 Race or Ethnicity of Owner (RE) 4 Gender of Owner (G) 5 Other. Please Specify 6 Don’t know 99

The following questions are related to work you have done or attempted to do in the private sector marketplace. Private sector is defined as non-government businesses or companies.

Q50. Have you experienced discriminatory behavior from the private sector between October 1, 2008 and September 30, 2013? [REQUIRE ANSWER]

[S - IF THE ANSWER IS NOT 1, THEN END SURVEY]


Q51. What was the most noticeable way you became aware of the discrimination against your company? [REQUIRE ANSWER]

Verbal comment 1 Written statement 2 Action taken against company 3 Don’t’ Know 99

Q52. Which of the following do you consider the primary reason for your company being discriminated against? [REQUIRE ANSWER]

Owner’s race or ethnicity 1 Owner’s gender 2 Both 3 Don’t know 99



Q53. When did the discrimination first occur? [REQUIRE ANSWER]

During bidding process 1 After contract award 2 Both 3 Don’t know 99

Q54. Are you willing to speak directly to MGT or DPS to provide more detail of the alleged discrimination you have experienced by DPS or its prime contractors? [REQUIRE ANSWER]

Yes 1 (MGT contact Vernetta Mitchell [email protected], (704-531-4099) No 2

That completes the survey. On behalf of the Denver Public Schools, thank you very much for sharing your time and thoughts in this important project. If you would like more information on the Disparity Study, please contact Ms. Andrea Silva Business Diversity and Outreach (720) 423-3108 or [email protected].


mailto:[email protected]

mailto:[email protected]

APPENDIX F

DRAFT

APPENDIX F: SURVEY OF VENDORS RESULTS

Denver Public SchoolsDraft Report | Appendix F | October 2014 F-1

African American Firms

Asian American Firms

Hispanic American Firms

Nonminority Female Firms Non-M/WBE Firms Other Firms Total

3 0 6 15 12 1 378.11% 0.00% 16.22% 40.54% 32.43% 2.70% 100.00%

50.00% 0.00% 50.00% 60.00% 41.38% 33.33% 48.68%3.95% 0.00% 7.89% 19.74% 15.79% 1.32% 48.68%

2 0 5 4 9 0 2010.00% 0.00% 25.00% 20.00% 45.00% 0.00% 100.00%33.33% 0.00% 41.67% 16.00% 31.03% 0.00% 26.32%

2.63% 0.00% 6.58% 5.26% 11.84% 0.00% 26.32%1 0 1 4 2 1 9

11.11% 0.00% 11.11% 44.44% 22.22% 11.11% 100.00%16.67% 0.00% 8.33% 16.00% 6.90% 33.33% 11.84%

1.32% 0.00% 1.32% 5.26% 2.63% 1.32% 11.84%0 1 0 2 6 1 10

0.00% 10.00% 0.00% 20.00% 60.00% 10.00% 100.00%0.00% 100.00% 0.00% 8.00% 20.69% 33.33% 13.16%0.00% 1.32% 0.00% 2.63% 7.89% 1.32% 13.16%

6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





3 0 4 11 14 1 339.09% 0.00% 12.12% 33.33% 42.42% 3.03% 100.00%

50.00% 0.00% 33.33% 44.00% 48.28% 33.33% 43.42%3.95% 0.00% 5.26% 14.47% 18.42% 1.32% 43.42%

3 1 4 12 13 1 348.82% 2.94% 11.76% 35.29% 38.24% 2.94% 100.00%

50.00% 100.00% 33.33% 48.00% 44.83% 33.33% 44.74%3.95% 1.32% 5.26% 15.79% 17.11% 1.32% 44.74%

0 0 4 2 2 1 90.00% 0.00% 44.44% 22.22% 22.22% 11.11% 100.00%0.00% 0.00% 33.33% 8.00% 6.90% 33.33% 11.84%0.00% 0.00% 5.26% 2.63% 2.63% 1.32% 11.84%

6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





3 0 7 25 0 0 358.57% 0.00% 20.00% 71.43% 0.00% 0.00% 100.00%

50.00% 0.00% 58.33% 100.00% 0.00% 0.00% 46.05%3.95% 0.00% 9.21% 32.89% 0.00% 0.00% 46.05%

3 1 5 0 29 3 417.32% 2.44% 12.20% 0.00% 70.73% 7.32% 100.00%

50.00% 100.00% 41.67% 0.00% 100.00% 100.00% 53.95%3.95% 1.32% 6.58% 0.00% 38.16% 3.95% 53.95%

6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

Construction (such as general contractor, electrical, site work, HVAC, drywall, roofing, etc.)

Table: Q1 What is your title? * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].Race, Ethnicity, Gender Classification (Business Ownership Classification)

Owner

CEO/President

Manager/Financial Officer

Other, Please Specify

Total

Q1 What is your title?

Table: Q3 Please specify your company's primary line of business * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].Race, Ethnicity, Gender Classification (Business Ownership Classification)

Q3 Please specify your company's primary line of business

Construction-related Professional Services (such as architecture, engineering environmental, structural, land development, etc.)

Other

Total

Race, Ethnicity, Gender Classification (Business Ownership Classification)

Table: Q4 Is more than 50% of your company owned and operated by women? * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Q4 Is more than 50% of your company owned and operated by women?Yes

No

Total

DRAFT


Denver Public SchoolsDraft Report | Appendix F| October 2014 F-2





0 0 0 25 26 0 510.00% 0.00% 0.00% 49.02% 50.98% 0.00% 100.00%0.00% 0.00% 0.00% 100.00% 89.66% 0.00% 67.11%0.00% 0.00% 0.00% 32.89% 34.21% 0.00% 67.11%

6 0 0 0 0 0 6100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00%100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 7.89%

7.89% 0.00% 0.00% 0.00% 0.00% 0.00% 7.89%0 1 0 0 0 0 1

0.00% 100.00% 0.00% 0.00% 0.00% 0.00% 100.00%0.00% 100.00% 0.00% 0.00% 0.00% 0.00% 1.32%0.00% 1.32% 0.00% 0.00% 0.00% 0.00% 1.32%

0 0 12 0 0 0 120.00% 0.00% 100.00% 0.00% 0.00% 0.00% 100.00%0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 15.79%0.00% 0.00% 15.79% 0.00% 0.00% 0.00% 15.79%

0 0 0 0 3 0 30.00% 0.00% 0.00% 0.00% 100.00% 0.00% 100.00%0.00% 0.00% 0.00% 0.00% 10.34% 0.00% 3.95%0.00% 0.00% 0.00% 0.00% 3.95% 0.00% 3.95%

0 0 0 0 0 3 30.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00%0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 3.95%0.00% 0.00% 0.00% 0.00% 0.00% 3.95% 3.95%

6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





6 0 0 0 0 0 6100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00%100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 7.89%

7.89% 0.00% 0.00% 0.00% 0.00% 0.00% 7.89%0 1 0 0 0 0 1

0.00% 100.00% 0.00% 0.00% 0.00% 0.00% 100.00%0.00% 100.00% 0.00% 0.00% 0.00% 0.00% 1.32%0.00% 1.32% 0.00% 0.00% 0.00% 0.00% 1.32%

0 0 12 0 0 0 120.00% 0.00% 100.00% 0.00% 0.00% 0.00% 100.00%0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 15.79%0.00% 0.00% 15.79% 0.00% 0.00% 0.00% 15.79%

0 0 0 25 0 0 250.00% 0.00% 0.00% 100.00% 0.00% 0.00% 100.00%0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 32.89%0.00% 0.00% 0.00% 32.89% 0.00% 0.00% 32.89%

0 0 0 0 29 0 290.00% 0.00% 0.00% 0.00% 100.00% 0.00% 100.00%0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 38.16%0.00% 0.00% 0.00% 0.00% 38.16% 0.00% 38.16%

0 0 0 0 0 3 30.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00%0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 3.95%0.00% 0.00% 0.00% 0.00% 0.00% 3.95% 3.95%

6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%


Nonminority Female Firms

Non-M/WBE Firms

Other Firms

Total


Anglo/Caucasian/White

Black/African American

Asian

Hispanic or Latino

Don't Know

Prefer not to answer

Total

Table: Race, Ethnicity, Gender Classification (Business Ownership Classification) * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Race, Ethnicity, Gender Classification (Business Ownership Classification)African American Firms

Table: Q5 Is more than 50% of the company owned and controlled by one of the following racial or ethnic groups? * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q5 Is more than 50% of the company owned and controlled by one of the following racial or ethnic groups?

DRAFT







0 0 1 0 0 0 10.00% 0.00% 100.00% 0.00% 0.00% 0.00% 100.00%0.00% 0.00% 8.33% 0.00% 0.00% 0.00% 1.32%0.00% 0.00% 1.32% 0.00% 0.00% 0.00% 1.32%

1 0 1 5 3 0 1010.00% 0.00% 10.00% 50.00% 30.00% 0.00% 100.00%16.67% 0.00% 8.33% 20.00% 10.34% 0.00% 13.16%

1.32% 0.00% 1.32% 6.58% 3.95% 0.00% 13.16%2 1 6 11 16 0 36

5.56% 2.78% 16.67% 30.56% 44.44% 0.00% 100.00%33.33% 100.00% 50.00% 44.00% 55.17% 0.00% 47.37%

2.63% 1.32% 7.89% 14.47% 21.05% 0.00% 47.37%3 0 4 9 10 2 28

10.71% 0.00% 14.29% 32.14% 35.71% 7.14% 100.00%50.00% 0.00% 33.33% 36.00% 34.48% 66.67% 36.84%

3.95% 0.00% 5.26% 11.84% 13.16% 2.63% 36.84%0 0 0 0 0 1 1

0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00%0.00% 0.00% 0.00% 0.00% 0.00% 33.33% 1.32%0.00% 0.00% 0.00% 0.00% 0.00% 1.32% 1.32%

6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





0 0 0 2 4 0 60.00% 0.00% 0.00% 33.33% 66.67% 0.00% 100.00%0.00% 0.00% 0.00% 8.00% 13.79% 0.00% 7.89%0.00% 0.00% 0.00% 2.63% 5.26% 0.00% 7.89%

0 0 0 0 6 3 90.00% 0.00% 0.00% 0.00% 66.67% 33.33% 100.00%0.00% 0.00% 0.00% 0.00% 20.69% 100.00% 11.84%0.00% 0.00% 0.00% 0.00% 7.89% 3.95% 11.84%

2 0 5 10 17 0 345.88% 0.00% 14.71% 29.41% 50.00% 0.00% 100.00%

33.33% 0.00% 41.67% 40.00% 58.62% 0.00% 44.74%2.63% 0.00% 6.58% 13.16% 22.37% 0.00% 44.74%

4 1 5 8 1 0 1921.05% 5.26% 26.32% 42.11% 5.26% 0.00% 100.00%66.67% 100.00% 41.67% 32.00% 3.45% 0.00% 25.00%

5.26% 1.32% 6.58% 10.53% 1.32% 0.00% 25.00%0 0 2 5 1 0 8

0.00% 0.00% 25.00% 62.50% 12.50% 0.00% 100.00%0.00% 0.00% 16.67% 20.00% 3.45% 0.00% 10.53%0.00% 0.00% 2.63% 6.58% 1.32% 0.00% 10.53%

6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





1 0 0 4 1 0 616.67% 0.00% 0.00% 66.67% 16.67% 0.00% 100.00%16.67% 0.00% 0.00% 16.00% 3.45% 0.00% 7.89%

1.32% 0.00% 0.00% 5.26% 1.32% 0.00% 7.89%

Table: Q8 How many years of experience in your company's line of business does the primary owner(s) of your firm have in the line of business? * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q8 How many years of experience in your company's line of business does the primary owner(s) of your firm have in the line of business?6-10 years

Total

Don't Know

Total

Table: Q7 What year was your company established? * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].Race, Ethnicity, Gender Classification (Business Ownership Classification)

Q7 What year was your company established?Prior to 1960

1961 to 1980

1981 to 2000

2001 to 2007

2008 to 2014

Post Graduate Degree

Table: Q6 What is the highest level education completed by the primary owner of your company? * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q6 What is the highest level education completed by the primary owner of your company?Trade or Technical Education

Some College

College Degree

DRAFT



2 0 2 1 1 0 633.33% 0.00% 33.33% 16.67% 16.67% 0.00% 100.00%33.33% 0.00% 16.67% 4.00% 3.45% 0.00% 7.89%

2.63% 0.00% 2.63% 1.32% 1.32% 0.00% 7.89%2 0 3 4 1 0 10

20.00% 0.00% 30.00% 40.00% 10.00% 0.00% 100.00%33.33% 0.00% 25.00% 16.00% 3.45% 0.00% 13.16%

2.63% 0.00% 3.95% 5.26% 1.32% 0.00% 13.16%1 1 7 16 26 3 54

1.85% 1.85% 12.96% 29.63% 48.15% 5.56% 100.00%16.67% 100.00% 58.33% 64.00% 89.66% 100.00% 71.05%

1.32% 1.32% 9.21% 21.05% 34.21% 3.95% 71.05%6 1 12 25 29 3 76

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





3 0 5 13 10 1 329.38% 0.00% 15.63% 40.63% 31.25% 3.13% 100.00%

50.00% 0.00% 41.67% 52.00% 34.48% 33.33% 42.11%3.95% 0.00% 6.58% 17.11% 13.16% 1.32% 42.11%

1 0 2 6 4 0 137.69% 0.00% 15.38% 46.15% 30.77% 0.00% 100.00%

16.67% 0.00% 16.67% 24.00% 13.79% 0.00% 17.11%1.32% 0.00% 2.63% 7.89% 5.26% 0.00% 17.11%

0 0 3 2 4 1 100.00% 0.00% 30.00% 20.00% 40.00% 10.00% 100.00%0.00% 0.00% 25.00% 8.00% 13.79% 33.33% 13.16%0.00% 0.00% 3.95% 2.63% 5.26% 1.32% 13.16%

0 1 1 1 2 0 50.00% 20.00% 20.00% 20.00% 40.00% 0.00% 100.00%0.00% 100.00% 8.33% 4.00% 6.90% 0.00% 6.58%0.00% 1.32% 1.32% 1.32% 2.63% 0.00% 6.58%

2 0 1 3 9 1 1612.50% 0.00% 6.25% 18.75% 56.25% 6.25% 100.00%33.33% 0.00% 8.33% 12.00% 31.03% 33.33% 21.05%

2.63% 0.00% 1.32% 3.95% 11.84% 1.32% 21.05%6 1 12 25 29 3 76

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





0 0 0 3 0 0 30.00% 0.00% 0.00% 100.00% 0.00% 0.00% 100.00%0.00% 0.00% 0.00% 12.00% 0.00% 0.00% 3.95%0.00% 0.00% 0.00% 3.95% 0.00% 0.00% 3.95%

1 0 2 0 0 0 333.33% 0.00% 66.67% 0.00% 0.00% 0.00% 100.00%16.67% 0.00% 16.67% 0.00% 0.00% 0.00% 3.95%

1.32% 0.00% 2.63% 0.00% 0.00% 0.00% 3.95%1 0 1 1 3 0 6

16.67% 0.00% 16.67% 16.67% 50.00% 0.00% 100.00%16.67% 0.00% 8.33% 4.00% 10.34% 0.00% 7.89%

1.32% 0.00% 1.32% 1.32% 3.95% 0.00% 7.89%1 0 0 3 2 0 6

16.67% 0.00% 0.00% 50.00% 33.33% 0.00% 100.00%16.67% 0.00% 0.00% 12.00% 6.90% 0.00% 7.89%

1.32% 0.00% 0.00% 3.95% 2.63% 0.00% 7.89%

$300,001 to $500,000?

11-20

21-30

31-40

41+

Total

Table: Q10 Which of the following categories best approximate your company's gross revenues for calendar year 2013? * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q10 Which of the following categories best approximate your company's gross revenues for calendar year 2013?Up to $50,000?

$50,001 to $100,000?

$100,001 to $300,000?

0-10

11-15 years

16-20 years

20+ years

Total

Table: Q9 In the last three years, what was the average number of employees your company kept on payroll, including full-time and part-time staff? * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q9 In the last three years, what was the average number of employees your company kept on payroll, including full-time and part-time staff?

DRAFT



0 0 3 7 1 0 110.00% 0.00% 27.27% 63.64% 9.09% 0.00% 100.00%0.00% 0.00% 25.00% 28.00% 3.45% 0.00% 14.47%0.00% 0.00% 3.95% 9.21% 1.32% 0.00% 14.47%

1 0 2 6 6 0 156.67% 0.00% 13.33% 40.00% 40.00% 0.00% 100.00%

16.67% 0.00% 16.67% 24.00% 20.69% 0.00% 19.74%1.32% 0.00% 2.63% 7.89% 7.89% 0.00% 19.74%

0 0 2 1 5 0 80.00% 0.00% 25.00% 12.50% 62.50% 0.00% 100.00%0.00% 0.00% 16.67% 4.00% 17.24% 0.00% 10.53%0.00% 0.00% 2.63% 1.32% 6.58% 0.00% 10.53%

2 1 1 3 3 0 1020.00% 10.00% 10.00% 30.00% 30.00% 0.00% 100.00%33.33% 100.00% 8.33% 12.00% 10.34% 0.00% 13.16%

2.63% 1.32% 1.32% 3.95% 3.95% 0.00% 13.16%0 0 1 0 9 2 12

0.00% 0.00% 8.33% 0.00% 75.00% 16.67% 100.00%0.00% 0.00% 8.33% 0.00% 31.03% 66.67% 15.79%0.00% 0.00% 1.32% 0.00% 11.84% 2.63% 15.79%

0 0 0 1 0 1 20.00% 0.00% 0.00% 50.00% 0.00% 50.00% 100.00%0.00% 0.00% 0.00% 4.00% 0.00% 33.33% 2.63%0.00% 0.00% 0.00% 1.32% 0.00% 1.32% 2.63%

6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





6 1 12 24 29 3 758.00% 1.33% 16.00% 32.00% 38.67% 4.00% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%8.00% 1.33% 16.00% 32.00% 38.67% 4.00% 100.00%

6 1 12 24 29 3 758.00% 1.33% 16.00% 32.00% 38.67% 4.00% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%8.00% 1.33% 16.00% 32.00% 38.67% 4.00% 100.00%





1 0 1 4 5 0 119.09% 0.00% 9.09% 36.36% 45.45% 0.00% 100.00%

16.67% 0.00% 8.33% 17.39% 18.52% 0.00% 15.28%1.39% 0.00% 1.39% 5.56% 6.94% 0.00% 15.28%

2 0 1 2 3 0 825.00% 0.00% 12.50% 25.00% 37.50% 0.00% 100.00%33.33% 0.00% 8.33% 8.70% 11.11% 0.00% 11.11%

2.78% 0.00% 1.39% 2.78% 4.17% 0.00% 11.11%0 0 2 2 3 1 8

0.00% 0.00% 25.00% 25.00% 37.50% 12.50% 100.00%0.00% 0.00% 16.67% 8.70% 11.11% 33.33% 11.11%0.00% 0.00% 2.78% 2.78% 4.17% 1.39% 11.11%

1 0 3 5 3 1 137.69% 0.00% 23.08% 38.46% 23.08% 7.69% 100.00%

16.67% 0.00% 25.00% 21.74% 11.11% 33.33% 18.06%1.39% 0.00% 4.17% 6.94% 4.17% 1.39% 18.06%

0 0 0 3 5 0 80.00% 0.00% 0.00% 37.50% 62.50% 0.00% 100.00%0.00% 0.00% 0.00% 13.04% 18.52% 0.00% 11.11%0.00% 0.00% 0.00% 4.17% 6.94% 0.00% 11.11%

Total

$500,001 to $1 million?

$1,000,001 to $3 million?

$3,000,001 to $5 million?

$5,000,001 to $10 million?

Over $10 million?

Don't Know

Table: Q11 What percentage of these gross revenues was earned from the Schools, the private sector, and other public government projects? Schools * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q11 What percentage of these gross revenues was earned from the Schools, the private sector, and other public government projects? SchoolsUp to 10%

Total

21% to 30%

31% to 40%

41% to 50%

Table: Q11 What percentage of these gross revenues was earned from the Schools, the private sector, and other public government projects? Private Sector * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].


Q11 What percentage of these gross revenues was earned from the Schools, the private sector, and other public government projects? Private SectorUp to 10%

11% to 20%

DRAFT



0 0 1 1 3 1 60.00% 0.00% 16.67% 16.67% 50.00% 16.67% 100.00%0.00% 0.00% 8.33% 4.35% 11.11% 33.33% 8.33%0.00% 0.00% 1.39% 1.39% 4.17% 1.39% 8.33%

0 0 0 1 1 0 20.00% 0.00% 0.00% 50.00% 50.00% 0.00% 100.00%0.00% 0.00% 0.00% 4.35% 3.70% 0.00% 2.78%0.00% 0.00% 0.00% 1.39% 1.39% 0.00% 2.78%

1 1 1 2 0 0 520.00% 20.00% 20.00% 40.00% 0.00% 0.00% 100.00%16.67% 100.00% 8.33% 8.70% 0.00% 0.00% 6.94%

1.39% 1.39% 1.39% 2.78% 0.00% 0.00% 6.94%1 0 3 3 4 0 11

9.09% 0.00% 27.27% 27.27% 36.36% 0.00% 100.00%16.67% 0.00% 25.00% 13.04% 14.81% 0.00% 15.28%

1.39% 0.00% 4.17% 4.17% 5.56% 0.00% 15.28%6 1 12 23 27 3 72

8.33% 1.39% 16.67% 31.94% 37.50% 4.17% 100.00%100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

8.33% 1.39% 16.67% 31.94% 37.50% 4.17% 100.00%





1 0 2 4 7 0 147.14% 0.00% 14.29% 28.57% 50.00% 0.00% 100.00%

16.67% 0.00% 18.18% 17.39% 25.93% 0.00% 20.00%1.43% 0.00% 2.86% 5.71% 10.00% 0.00% 20.00%

2 0 1 2 3 0 825.00% 0.00% 12.50% 25.00% 37.50% 0.00% 100.00%33.33% 0.00% 9.09% 8.70% 11.11% 0.00% 11.43%

2.86% 0.00% 1.43% 2.86% 4.29% 0.00% 11.43%0 1 3 3 6 1 14

0.00% 7.14% 21.43% 21.43% 42.86% 7.14% 100.00%0.00% 100.00% 27.27% 13.04% 22.22% 50.00% 20.00%0.00% 1.43% 4.29% 4.29% 8.57% 1.43% 20.00%

0 0 0 2 3 1 60.00% 0.00% 0.00% 33.33% 50.00% 16.67% 100.00%0.00% 0.00% 0.00% 8.70% 11.11% 50.00% 8.57%0.00% 0.00% 0.00% 2.86% 4.29% 1.43% 8.57%

0 0 0 2 3 0 50.00% 0.00% 0.00% 40.00% 60.00% 0.00% 100.00%0.00% 0.00% 0.00% 8.70% 11.11% 0.00% 7.14%0.00% 0.00% 0.00% 2.86% 4.29% 0.00% 7.14%

0 0 0 3 0 0 30.00% 0.00% 0.00% 100.00% 0.00% 0.00% 100.00%0.00% 0.00% 0.00% 13.04% 0.00% 0.00% 4.29%0.00% 0.00% 0.00% 4.29% 0.00% 0.00% 4.29%

0 0 2 1 2 0 50.00% 0.00% 40.00% 20.00% 40.00% 0.00% 100.00%0.00% 0.00% 18.18% 4.35% 7.41% 0.00% 7.14%0.00% 0.00% 2.86% 1.43% 2.86% 0.00% 7.14%

2 0 2 1 0 0 540.00% 0.00% 40.00% 20.00% 0.00% 0.00% 100.00%33.33% 0.00% 18.18% 4.35% 0.00% 0.00% 7.14%

2.86% 0.00% 2.86% 1.43% 0.00% 0.00% 7.14%1 0 1 5 3 0 10

10.00% 0.00% 10.00% 50.00% 30.00% 0.00% 100.00%16.67% 0.00% 9.09% 21.74% 11.11% 0.00% 14.29%

1.43% 0.00% 1.43% 7.14% 4.29% 0.00% 14.29%6 1 11 23 27 2 70

8.57% 1.43% 15.71% 32.86% 38.57% 2.86% 100.00%100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

8.57% 1.43% 15.71% 32.86% 38.57% 2.86% 100.00%

Total

Up to 10%

11% to 20%

21% to 30%

31% to 40%

41% to 50%

51% to 60%

61% to 70%

71% to 80%

81% to 100%

Table: Q11 What percentage of these gross revenues was earned from the Schools, the private sector, and other public government projects? Public Sector * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].


Q11 What percentage of these gross revenues was earned from the Schools, the private sector, and other public government projects? Public Sector

Total

71% to 80%

81% to 100%

51% to 60%

61% to 70%

DRAFT







6 1 9 3 0 0 1931.58% 5.26% 47.37% 15.79% 0.00% 0.00% 100.00%

100.00% 100.00% 75.00% 12.00% 0.00% 0.00% 25.00%7.89% 1.32% 11.84% 3.95% 0.00% 0.00% 25.00%

0 0 3 22 29 3 570.00% 0.00% 5.26% 38.60% 50.88% 5.26% 100.00%0.00% 0.00% 25.00% 88.00% 100.00% 100.00% 75.00%0.00% 0.00% 3.95% 28.95% 38.16% 3.95% 75.00%

6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





6 1 10 19 6 0 4214.29% 2.38% 23.81% 45.24% 14.29% 0.00% 100.00%

100.00% 100.00% 83.33% 76.00% 20.69% 0.00% 55.26%7.89% 1.32% 13.16% 25.00% 7.89% 0.00% 55.26%

0 0 2 6 23 3 340.00% 0.00% 5.88% 17.65% 67.65% 8.82% 100.00%0.00% 0.00% 16.67% 24.00% 79.31% 100.00% 44.74%0.00% 0.00% 2.63% 7.89% 30.26% 3.95% 44.74%

6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





3 0 3 17 0 0 2313.04% 0.00% 13.04% 73.91% 0.00% 0.00% 100.00%50.00% 0.00% 25.00% 68.00% 0.00% 0.00% 30.26%

3.95% 0.00% 3.95% 22.37% 0.00% 0.00% 30.26%3 1 9 8 29 3 53

5.66% 1.89% 16.98% 15.09% 54.72% 5.66% 100.00%50.00% 100.00% 75.00% 32.00% 100.00% 100.00% 69.74%

3.95% 1.32% 11.84% 10.53% 38.16% 3.95% 69.74%6 1 12 25 29 3 76

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





6 1 7 12 0 0 2623.08% 3.85% 26.92% 46.15% 0.00% 0.00% 100.00%

100.00% 100.00% 58.33% 48.00% 0.00% 0.00% 34.21%7.89% 1.32% 9.21% 15.79% 0.00% 0.00% 34.21%

0 0 5 13 29 3 500.00% 0.00% 10.00% 26.00% 58.00% 6.00% 100.00%0.00% 0.00% 41.67% 52.00% 100.00% 100.00% 65.79%0.00% 0.00% 6.58% 17.11% 38.16% 3.95% 65.79%

Yes

No Response

Total

Table: Q12 Does your company hold any of the following certifications from a recognized certification agency? Minority Business Enterprise (MBE) * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q12 Does your company hold any of the following certifications from a recognized certification agency? Minority Business Enterprise (MBE)Yes

No Response

Total

Table: Q12 Does your company hold any of the following certifications from a recognized certification agency? Small Business Enterprise (SBE) * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q12 Does your company hold any of the following certifications from a recognized certification agency? Small Business Enterprise (SBE)

Table: Q12 Does your company hold any of the following certifications from a recognized certification agency? Woman Business Enterprise (WBE) * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q12 Does your company hold any of the following certifications from a recognized certification agency? Woman Business Enterprise (WBE)Yes

No Response

Total

Table: Q12 Does your company hold any of the following certifications from a recognized certification agency? Disadvantaged Business Enterprise (DBE) * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q12 Does your company hold any of the following certifications from a recognized certification agency? Disadvantaged Business Enterprise (DBE)Yes

No Response

DRAFT



6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





2 0 0 1 0 1 450.00% 0.00% 0.00% 25.00% 0.00% 25.00% 100.00%33.33% 0.00% 0.00% 4.00% 0.00% 33.33% 5.26%

2.63% 0.00% 0.00% 1.32% 0.00% 1.32% 5.26%4 1 12 24 29 2 72

5.56% 1.39% 16.67% 33.33% 40.28% 2.78% 100.00%66.67% 100.00% 100.00% 96.00% 100.00% 66.67% 94.74%

5.26% 1.32% 15.79% 31.58% 38.16% 2.63% 94.74%6 1 12 25 29 3 76

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





0 0 3 0 0 0 30.00% 0.00% 100.00% 0.00% 0.00% 0.00% 100.00%0.00% 0.00% 25.00% 0.00% 0.00% 0.00% 3.95%0.00% 0.00% 3.95% 0.00% 0.00% 0.00% 3.95%

6 1 9 25 29 3 738.22% 1.37% 12.33% 34.25% 39.73% 4.11% 100.00%

100.00% 100.00% 75.00% 100.00% 100.00% 100.00% 96.05%7.89% 1.32% 11.84% 32.89% 38.16% 3.95% 96.05%

6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





0 0 0 0 7 0 70.00% 0.00% 0.00% 0.00% 100.00% 0.00% 100.00%0.00% 0.00% 0.00% 0.00% 24.14% 0.00% 9.21%0.00% 0.00% 0.00% 0.00% 9.21% 0.00% 9.21%

6 1 12 25 22 3 698.70% 1.45% 17.39% 36.23% 31.88% 4.35% 100.00%

100.00% 100.00% 100.00% 100.00% 75.86% 100.00% 90.79%7.89% 1.32% 15.79% 32.89% 28.95% 3.95% 90.79%

6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





6 1 8 19 13 1 4812.50% 2.08% 16.67% 39.58% 27.08% 2.08% 100.00%

100.00% 100.00% 66.67% 76.00% 44.83% 33.33% 63.16%7.89% 1.32% 10.53% 25.00% 17.11% 1.32% 63.16%

Total

Table: Q12 Does your company hold any of the following certifications from a recognized certification agency? HUBZone * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].


Q12 Does your company hold any of the following certifications from a recognized certification agency? HUBZoneYes

No Response

Total

Table: Q12 Does your company hold any of the following certifications from a recognized certification agency? 8(A) * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q12 Does your company hold any of the following certifications from a recognized certification agency? 8(A)Yes

No Response

Total

Table: Q12 Does your company hold any of the following certifications from a recognized certification agency? Don't Know * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].


Q12 Does your company hold any of the following certifications from a recognized certification agency? Don't KnowYes

No Response

Total

Table: Q12 Does your company hold any of the following certifications from a recognized certification agency? Other? * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q12 Does your company hold any of the following certifications from a recognized certification agency? Other?

DRAFT



0 0 0 1 0 0 10.00% 0.00% 0.00% 100.00% 0.00% 0.00% 100.00%0.00% 0.00% 0.00% 4.00% 0.00% 0.00% 1.32%0.00% 0.00% 0.00% 1.32% 0.00% 0.00% 1.32%

0 0 0 0 0 1 10.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00%0.00% 0.00% 0.00% 0.00% 0.00% 33.33% 1.32%0.00% 0.00% 0.00% 0.00% 0.00% 1.32% 1.32%

0 0 1 0 0 0 10.00% 0.00% 100.00% 0.00% 0.00% 0.00% 100.00%0.00% 0.00% 8.33% 0.00% 0.00% 0.00% 1.32%0.00% 0.00% 1.32% 0.00% 0.00% 0.00% 1.32%

0 0 0 1 0 0 10.00% 0.00% 0.00% 100.00% 0.00% 0.00% 100.00%0.00% 0.00% 0.00% 4.00% 0.00% 0.00% 1.32%0.00% 0.00% 0.00% 1.32% 0.00% 0.00% 1.32%

0 0 0 1 12 1 140.00% 0.00% 0.00% 7.14% 85.71% 7.14% 100.00%0.00% 0.00% 0.00% 4.00% 41.38% 33.33% 18.42%0.00% 0.00% 0.00% 1.32% 15.79% 1.32% 18.42%

0 0 0 1 3 0 40.00% 0.00% 0.00% 25.00% 75.00% 0.00% 100.00%0.00% 0.00% 0.00% 4.00% 10.34% 0.00% 5.26%0.00% 0.00% 0.00% 1.32% 3.95% 0.00% 5.26%

0 0 0 1 0 0 10.00% 0.00% 0.00% 100.00% 0.00% 0.00% 100.00%0.00% 0.00% 0.00% 4.00% 0.00% 0.00% 1.32%0.00% 0.00% 0.00% 1.32% 0.00% 0.00% 1.32%

0 0 1 0 0 0 10.00% 0.00% 100.00% 0.00% 0.00% 0.00% 100.00%0.00% 0.00% 8.33% 0.00% 0.00% 0.00% 1.32%0.00% 0.00% 1.32% 0.00% 0.00% 0.00% 1.32%

0 0 1 0 0 0 10.00% 0.00% 100.00% 0.00% 0.00% 0.00% 100.00%0.00% 0.00% 8.33% 0.00% 0.00% 0.00% 1.32%0.00% 0.00% 1.32% 0.00% 0.00% 0.00% 1.32%

0 0 1 0 1 0 20.00% 0.00% 50.00% 0.00% 50.00% 0.00% 100.00%0.00% 0.00% 8.33% 0.00% 3.45% 0.00% 2.63%0.00% 0.00% 1.32% 0.00% 1.32% 0.00% 2.63%

0 0 0 1 0 0 10.00% 0.00% 0.00% 100.00% 0.00% 0.00% 100.00%0.00% 0.00% 0.00% 4.00% 0.00% 0.00% 1.32%0.00% 0.00% 0.00% 1.32% 0.00% 0.00% 1.32%

6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





2 0 1 2 6 1 1216.67% 0.00% 8.33% 16.67% 50.00% 8.33% 100.00%33.33% 0.00% 8.33% 8.00% 20.69% 33.33% 15.79%

2.63% 0.00% 1.32% 2.63% 7.89% 1.32% 15.79%0 0 2 8 8 0 18

0.00% 0.00% 11.11% 44.44% 44.44% 0.00% 100.00%0.00% 0.00% 16.67% 32.00% 27.59% 0.00% 23.68%0.00% 0.00% 2.63% 10.53% 10.53% 0.00% 23.68%

2 0 1 4 14 0 219.52% 0.00% 4.76% 19.05% 66.67% 0.00% 100.00%

33.33% 0.00% 8.33% 16.00% 48.28% 0.00% 27.63%2.63% 0.00% 1.32% 5.26% 18.42% 0.00% 27.63%

City and County of Denver, SBA, RTD

Do not wish to respond

EBE

ESB (CDOT); EDWOSB (SBA)

None

None of the above

None. We do business based on our own merit.

RTD SBE

SBA WBE/DBE self-certification

Veteran owned

WOSB, WBE pending

Total

Table: Q13 On a scale from 1 to 6 (1 being extremely easy and 6 being extremely difficult) how would you rate your ease of obtaining notification of business opportunities with the Schools? * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].


Q13 On a scale from 1 to 6 (1 being extremely easy and 6 being extremely difficult) how would you rate your ease of obtaining notification of business opportunities with the Schools?Extremely Easy

Somewhat Easy

Easy

DRAFT



2 1 3 4 0 1 1118.18% 9.09% 27.27% 36.36% 0.00% 9.09% 100.00%33.33% 100.00% 25.00% 16.00% 0.00% 33.33% 14.47%

2.63% 1.32% 3.95% 5.26% 0.00% 1.32% 14.47%0 0 2 2 1 1 6

0.00% 0.00% 33.33% 33.33% 16.67% 16.67% 100.00%0.00% 0.00% 16.67% 8.00% 3.45% 33.33% 7.89%0.00% 0.00% 2.63% 2.63% 1.32% 1.32% 7.89%

0 0 3 3 0 0 60.00% 0.00% 50.00% 50.00% 0.00% 0.00% 100.00%0.00% 0.00% 25.00% 12.00% 0.00% 0.00% 7.89%0.00% 0.00% 3.95% 3.95% 0.00% 0.00% 7.89%

0 0 0 2 0 0 20.00% 0.00% 0.00% 100.00% 0.00% 0.00% 100.00%0.00% 0.00% 0.00% 8.00% 0.00% 0.00% 2.63%0.00% 0.00% 0.00% 2.63% 0.00% 0.00% 2.63%

6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





1 0 3 2 15 1 224.55% 0.00% 13.64% 9.09% 68.18% 4.55% 100.00%

16.67% 0.00% 25.00% 8.00% 51.72% 33.33% 28.95%1.32% 0.00% 3.95% 2.63% 19.74% 1.32% 28.95%

5 1 9 23 14 2 549.26% 1.85% 16.67% 42.59% 25.93% 3.70% 100.00%

83.33% 100.00% 75.00% 92.00% 48.28% 66.67% 71.05%6.58% 1.32% 11.84% 30.26% 18.42% 2.63% 71.05%

6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





2 1 7 12 20 1 434.65% 2.33% 16.28% 27.91% 46.51% 2.33% 100.00%

33.33% 100.00% 58.33% 48.00% 68.97% 33.33% 56.58%2.63% 1.32% 9.21% 15.79% 26.32% 1.32% 56.58%

4 0 5 13 9 2 3312.12% 0.00% 15.15% 39.39% 27.27% 6.06% 100.00%66.67% 0.00% 41.67% 52.00% 31.03% 66.67% 43.42%

5.26% 0.00% 6.58% 17.11% 11.84% 2.63% 43.42%6 1 12 25 29 3 76

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





0 0 6 11 12 2 310.00% 0.00% 19.35% 35.48% 38.71% 6.45% 100.00%0.00% 0.00% 50.00% 44.00% 41.38% 66.67% 40.79%0.00% 0.00% 7.89% 14.47% 15.79% 2.63% 40.79%

6 1 6 14 17 1 4513.33% 2.22% 13.33% 31.11% 37.78% 2.22% 100.00%

100.00% 100.00% 50.00% 56.00% 58.62% 33.33% 59.21%7.89% 1.32% 7.89% 18.42% 22.37% 1.32% 59.21%

Difficult

Somewhat Difficult

Extremely Difficult

Don't Know

Total

Table: Q14 How do you obtain notification of bid/proposal opportunities? Schools' website * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q14 How do you obtain notification of bid/proposal opportunities? Schools' websiteYes

No Response

Total

Table: Q14 How do you obtain notification of bid/proposal opportunities? Contact from the Schools * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q14 How do you obtain notification of bid/proposal opportunities? Contact from the SchoolsYes

No Response

Total

Table: Q14 How do you obtain notification of bid/proposal opportunities? Private bidding subscription * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q14 How do you obtain notification of bid/proposal opportunities? Private bidding subscriptionYes

No Response

DRAFT



6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





2 1 7 12 8 0 306.67% 3.33% 23.33% 40.00% 26.67% 0.00% 100.00%

33.33% 100.00% 58.33% 48.00% 27.59% 0.00% 39.47%2.63% 1.32% 9.21% 15.79% 10.53% 0.00% 39.47%

4 0 5 13 21 3 468.70% 0.00% 10.87% 28.26% 45.65% 6.52% 100.00%

66.67% 0.00% 41.67% 52.00% 72.41% 100.00% 60.53%5.26% 0.00% 6.58% 17.11% 27.63% 3.95% 60.53%

6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





1 1 2 5 3 0 128.33% 8.33% 16.67% 41.67% 25.00% 0.00% 100.00%

16.67% 100.00% 16.67% 20.00% 10.34% 0.00% 15.79%1.32% 1.32% 2.63% 6.58% 3.95% 0.00% 15.79%

5 0 10 20 26 3 647.81% 0.00% 15.63% 31.25% 40.63% 4.69% 100.00%

83.33% 0.00% 83.33% 80.00% 89.66% 100.00% 84.21%6.58% 0.00% 13.16% 26.32% 34.21% 3.95% 84.21%

6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





0 0 0 1 5 0 60.00% 0.00% 0.00% 16.67% 83.33% 0.00% 100.00%0.00% 0.00% 0.00% 4.00% 17.24% 0.00% 7.89%0.00% 0.00% 0.00% 1.32% 6.58% 0.00% 7.89%

6 1 12 24 24 3 708.57% 1.43% 17.14% 34.29% 34.29% 4.29% 100.00%

100.00% 100.00% 100.00% 96.00% 82.76% 100.00% 92.11%7.89% 1.32% 15.79% 31.58% 31.58% 3.95% 92.11%

6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





0 0 0 0 1 0 10.00% 0.00% 0.00% 0.00% 100.00% 0.00% 100.00%0.00% 0.00% 0.00% 0.00% 3.45% 0.00% 1.32%0.00% 0.00% 0.00% 0.00% 1.32% 0.00% 1.32%

6 1 12 25 28 3 758.00% 1.33% 16.00% 33.33% 37.33% 4.00% 100.00%

100.00% 100.00% 100.00% 100.00% 96.55% 100.00% 98.68%7.89% 1.32% 15.79% 32.89% 36.84% 3.95% 98.68%

Total

Table: Q14 How do you obtain notification of bid/proposal opportunities? Other Primes/Subcontractors * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q14 How do you obtain notification of bid/proposal opportunities? Other Primes/SubcontractorsYes

No Response

Total

Table: Q14 How do you obtain notification of bid/proposal opportunities? Trade or industry associations * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q14 How do you obtain notification of bid/proposal opportunities? Trade or industry associationsYes

No Response

Total

Table: Q14 How do you obtain notification of bid/proposal opportunities? Local newspapers with general circulation * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q14 How do you obtain notification of bid/proposal opportunities? Local newspapers with general circulationYes

No Response

Total

Table: Q14 How do you obtain notification of bid/proposal opportunities? Don't Know * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q14 How do you obtain notification of bid/proposal opportunities? Don't Know7

No Response

DRAFT



6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





3 1 9 21 26 2 624.84% 1.61% 14.52% 33.87% 41.94% 3.23% 100.00%

50.00% 100.00% 75.00% 84.00% 89.66% 66.67% 81.58%3.95% 1.32% 11.84% 27.63% 34.21% 2.63% 81.58%

1 0 0 0 0 0 1100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00%

16.67% 0.00% 0.00% 0.00% 0.00% 0.00% 1.32%1.32% 0.00% 0.00% 0.00% 0.00% 0.00% 1.32%

0 0 0 0 0 1 10.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00%0.00% 0.00% 0.00% 0.00% 0.00% 33.33% 1.32%0.00% 0.00% 0.00% 0.00% 0.00% 1.32% 1.32%

0 0 1 0 0 0 10.00% 0.00% 100.00% 0.00% 0.00% 0.00% 100.00%0.00% 0.00% 8.33% 0.00% 0.00% 0.00% 1.32%0.00% 0.00% 1.32% 0.00% 0.00% 0.00% 1.32%

0 0 0 1 0 0 10.00% 0.00% 0.00% 100.00% 0.00% 0.00% 100.00%0.00% 0.00% 0.00% 4.00% 0.00% 0.00% 1.32%0.00% 0.00% 0.00% 1.32% 0.00% 0.00% 1.32%

0 0 1 0 0 0 10.00% 0.00% 100.00% 0.00% 0.00% 0.00% 100.00%0.00% 0.00% 8.33% 0.00% 0.00% 0.00% 1.32%0.00% 0.00% 1.32% 0.00% 0.00% 0.00% 1.32%

0 0 0 1 0 0 10.00% 0.00% 0.00% 100.00% 0.00% 0.00% 100.00%0.00% 0.00% 0.00% 4.00% 0.00% 0.00% 1.32%0.00% 0.00% 0.00% 1.32% 0.00% 0.00% 1.32%

0 0 0 1 0 0 10.00% 0.00% 0.00% 100.00% 0.00% 0.00% 100.00%0.00% 0.00% 0.00% 4.00% 0.00% 0.00% 1.32%0.00% 0.00% 0.00% 1.32% 0.00% 0.00% 1.32%

0 0 0 0 1 0 10.00% 0.00% 0.00% 0.00% 100.00% 0.00% 100.00%0.00% 0.00% 0.00% 0.00% 3.45% 0.00% 1.32%0.00% 0.00% 0.00% 0.00% 1.32% 0.00% 1.32%

0 0 0 0 1 0 10.00% 0.00% 0.00% 0.00% 100.00% 0.00% 100.00%0.00% 0.00% 0.00% 0.00% 3.45% 0.00% 1.32%0.00% 0.00% 0.00% 0.00% 1.32% 0.00% 1.32%

0 0 0 1 0 0 10.00% 0.00% 0.00% 100.00% 0.00% 0.00% 100.00%0.00% 0.00% 0.00% 4.00% 0.00% 0.00% 1.32%0.00% 0.00% 0.00% 1.32% 0.00% 0.00% 1.32%

0 0 0 0 1 0 10.00% 0.00% 0.00% 0.00% 100.00% 0.00% 100.00%0.00% 0.00% 0.00% 0.00% 3.45% 0.00% 1.32%0.00% 0.00% 0.00% 0.00% 1.32% 0.00% 1.32%

1 0 0 0 0 0 1100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00%

16.67% 0.00% 0.00% 0.00% 0.00% 0.00% 1.32%1.32% 0.00% 0.00% 0.00% 0.00% 0.00% 1.32%

1 0 0 0 0 0 1100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00%

16.67% 0.00% 0.00% 0.00% 0.00% 0.00% 1.32%1.32% 0.00% 0.00% 0.00% 0.00% 0.00% 1.32%

Total

Table: Q14 How do you obtain notification of bid/proposal opportunities? Other. Specify * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q14 How do you obtain notification of bid/proposal opportunities? Other. Specify

Architects

Cold calling, some colorado bids system, previously on bidders list, etc.

Construction companies

E mail blasts

None

Notification from General Contractors

School Bond: Proposed Improvement Lists

Schools Bid emails by DPS Purchasing

Via emails

cluttered emails from school dist.

email

email notifications

emails bids

DRAFT



0 0 1 0 0 0 10.00% 0.00% 100.00% 0.00% 0.00% 0.00% 100.00%0.00% 0.00% 8.33% 0.00% 0.00% 0.00% 1.32%0.00% 0.00% 1.32% 0.00% 0.00% 0.00% 1.32%

6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





1 1 1 3 2 0 812.50% 12.50% 12.50% 37.50% 25.00% 0.00% 100.00%16.67% 100.00% 8.33% 12.00% 6.90% 0.00% 10.53%

1.32% 1.32% 1.32% 3.95% 2.63% 0.00% 10.53%1 0 3 0 0 0 4

25.00% 0.00% 75.00% 0.00% 0.00% 0.00% 100.00%16.67% 0.00% 25.00% 0.00% 0.00% 0.00% 5.26%

1.32% 0.00% 3.95% 0.00% 0.00% 0.00% 5.26%0 0 0 1 0 0 1

0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 100.00%0.00% 0.00% 0.00% 4.00% 0.00% 0.00% 1.32%0.00% 0.00% 0.00% 1.32% 0.00% 0.00% 1.32%

0 0 1 2 2 0 50.00% 0.00% 20.00% 40.00% 40.00% 0.00% 100.00%0.00% 0.00% 8.33% 8.00% 6.90% 0.00% 6.58%0.00% 0.00% 1.32% 2.63% 2.63% 0.00% 6.58%

1 0 2 3 0 0 616.67% 0.00% 33.33% 50.00% 0.00% 0.00% 100.00%16.67% 0.00% 16.67% 12.00% 0.00% 0.00% 7.89%

1.32% 0.00% 2.63% 3.95% 0.00% 0.00% 7.89%0 0 0 2 1 0 3

0.00% 0.00% 0.00% 66.67% 33.33% 0.00% 100.00%0.00% 0.00% 0.00% 8.00% 3.45% 0.00% 3.95%0.00% 0.00% 0.00% 2.63% 1.32% 0.00% 3.95%

0 0 1 0 1 1 30.00% 0.00% 33.33% 0.00% 33.33% 33.33% 100.00%0.00% 0.00% 8.33% 0.00% 3.45% 33.33% 3.95%0.00% 0.00% 1.32% 0.00% 1.32% 1.32% 3.95%

0 0 0 4 0 0 40.00% 0.00% 0.00% 100.00% 0.00% 0.00% 100.00%0.00% 0.00% 0.00% 16.00% 0.00% 0.00% 5.26%0.00% 0.00% 0.00% 5.26% 0.00% 0.00% 5.26%

0 0 0 3 4 0 70.00% 0.00% 0.00% 42.86% 57.14% 0.00% 100.00%0.00% 0.00% 0.00% 12.00% 13.79% 0.00% 9.21%0.00% 0.00% 0.00% 3.95% 5.26% 0.00% 9.21%

3 0 4 5 18 2 329.38% 0.00% 12.50% 15.63% 56.25% 6.25% 100.00%

50.00% 0.00% 33.33% 20.00% 62.07% 66.67% 42.11%3.95% 0.00% 5.26% 6.58% 23.68% 2.63% 42.11%

0 0 0 2 1 0 30.00% 0.00% 0.00% 66.67% 33.33% 0.00% 100.00%0.00% 0.00% 0.00% 8.00% 3.45% 0.00% 3.95%0.00% 0.00% 0.00% 2.63% 1.32% 0.00% 3.95%

6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

emails forwarded from different resources

Total

Table: Q15 In general, which of the following dollar ranges best approximate your company's largest prime contract awarded between 2008 and 2013? * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q15 In general, which of the following dollar ranges best approximate your company's largest prime contract awarded between 2008 and 2013?Not applicable (not a prime)

None

Up to $50,000?

$50,001 to $100,000?

$100,001 to $200,000?

$200,001 to $300,000?

$300,001 to $400,000?

$400,001 to $500,000?

$500,001 to $1 million?

Over $1 million?

Don't Know

Total

DRAFT







3 0 5 3 9 1 2114.29% 0.00% 23.81% 14.29% 42.86% 4.76% 100.00%50.00% 0.00% 41.67% 12.00% 31.03% 33.33% 27.63%

3.95% 0.00% 6.58% 3.95% 11.84% 1.32% 27.63%1 0 5 17 17 2 42

2.38% 0.00% 11.90% 40.48% 40.48% 4.76% 100.00%16.67% 0.00% 41.67% 68.00% 58.62% 66.67% 55.26%

1.32% 0.00% 6.58% 22.37% 22.37% 2.63% 55.26%1 0 1 2 1 0 5

20.00% 0.00% 20.00% 40.00% 20.00% 0.00% 100.00%16.67% 0.00% 8.33% 8.00% 3.45% 0.00% 6.58%

1.32% 0.00% 1.32% 2.63% 1.32% 0.00% 6.58%1 1 1 3 2 0 8

12.50% 12.50% 12.50% 37.50% 25.00% 0.00% 100.00%16.67% 100.00% 8.33% 12.00% 6.90% 0.00% 10.53%

1.32% 1.32% 1.32% 3.95% 2.63% 0.00% 10.53%6 1 12 25 29 3 76

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





2 0 0 3 4 1 1020.00% 0.00% 0.00% 30.00% 40.00% 10.00% 100.00%33.33% 0.00% 0.00% 12.00% 13.79% 33.33% 13.16%

2.63% 0.00% 0.00% 3.95% 5.26% 1.32% 13.16%2 0 11 16 22 2 53

3.77% 0.00% 20.75% 30.19% 41.51% 3.77% 100.00%33.33% 0.00% 91.67% 64.00% 75.86% 66.67% 69.74%

2.63% 0.00% 14.47% 21.05% 28.95% 2.63% 69.74%1 0 0 3 1 0 5

20.00% 0.00% 0.00% 60.00% 20.00% 0.00% 100.00%16.67% 0.00% 0.00% 12.00% 3.45% 0.00% 6.58%

1.32% 0.00% 0.00% 3.95% 1.32% 0.00% 6.58%1 1 1 3 2 0 8

12.50% 12.50% 12.50% 37.50% 25.00% 0.00% 100.00%16.67% 100.00% 8.33% 12.00% 6.90% 0.00% 10.53%

1.32% 1.32% 1.32% 3.95% 2.63% 0.00% 10.53%6 1 12 25 29 3 76

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





2 0 0 3 4 0 922.22% 0.00% 0.00% 33.33% 44.44% 0.00% 100.00%33.33% 0.00% 0.00% 12.00% 13.79% 0.00% 11.84%

2.63% 0.00% 0.00% 3.95% 5.26% 0.00% 11.84%2 0 10 16 22 2 52

3.85% 0.00% 19.23% 30.77% 42.31% 3.85% 100.00%33.33% 0.00% 83.33% 64.00% 75.86% 66.67% 68.42%

2.63% 0.00% 13.16% 21.05% 28.95% 2.63% 68.42%

Table: Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Prequalification Requirements? * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Prequalification Requirements?Yes

No

Don't Know

No Response

Total

Table: Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Bid bond requirements? * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Bid bond requirements?Yes

No

Don't Know

No Response

Total

Table: Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Performance/payment bond requirement? * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Performance/payment bond requirement?Yes

No

DRAFT



1 0 1 3 1 1 714.29% 0.00% 14.29% 42.86% 14.29% 14.29% 100.00%16.67% 0.00% 8.33% 12.00% 3.45% 33.33% 9.21%

1.32% 0.00% 1.32% 3.95% 1.32% 1.32% 9.21%1 1 1 3 2 0 8

12.50% 12.50% 12.50% 37.50% 25.00% 0.00% 100.00%16.67% 100.00% 8.33% 12.00% 6.90% 0.00% 10.53%

1.32% 1.32% 1.32% 3.95% 2.63% 0.00% 10.53%6 1 12 25 29 3 76

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





1 0 3 8 4 1 175.88% 0.00% 17.65% 47.06% 23.53% 5.88% 100.00%

16.67% 0.00% 25.00% 32.00% 13.79% 33.33% 22.37%1.32% 0.00% 3.95% 10.53% 5.26% 1.32% 22.37%

3 0 7 12 23 2 476.38% 0.00% 14.89% 25.53% 48.94% 4.26% 100.00%

50.00% 0.00% 58.33% 48.00% 79.31% 66.67% 61.84%3.95% 0.00% 9.21% 15.79% 30.26% 2.63% 61.84%

1 0 1 2 0 0 425.00% 0.00% 25.00% 50.00% 0.00% 0.00% 100.00%16.67% 0.00% 8.33% 8.00% 0.00% 0.00% 5.26%

1.32% 0.00% 1.32% 2.63% 0.00% 0.00% 5.26%1 1 1 3 2 0 8

12.50% 12.50% 12.50% 37.50% 25.00% 0.00% 100.00%16.67% 100.00% 8.33% 12.00% 6.90% 0.00% 10.53%

1.32% 1.32% 1.32% 3.95% 2.63% 0.00% 10.53%6 1 12 25 29 3 76

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





1 0 1 3 4 0 911.11% 0.00% 11.11% 33.33% 44.44% 0.00% 100.00%16.67% 0.00% 8.33% 12.00% 13.79% 0.00% 11.84%

1.32% 0.00% 1.32% 3.95% 5.26% 0.00% 11.84%3 0 10 16 22 3 54

5.56% 0.00% 18.52% 29.63% 40.74% 5.56% 100.00%50.00% 0.00% 83.33% 64.00% 75.86% 100.00% 71.05%

3.95% 0.00% 13.16% 21.05% 28.95% 3.95% 71.05%1 0 0 3 1 0 5

20.00% 0.00% 0.00% 60.00% 20.00% 0.00% 100.00%16.67% 0.00% 0.00% 12.00% 3.45% 0.00% 6.58%

1.32% 0.00% 0.00% 3.95% 1.32% 0.00% 6.58%1 1 1 3 2 0 8

12.50% 12.50% 12.50% 37.50% 25.00% 0.00% 100.00%16.67% 100.00% 8.33% 12.00% 6.90% 0.00% 10.53%

1.32% 1.32% 1.32% 3.95% 2.63% 0.00% 10.53%6 1 12 25 29 3 76

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

Don't Know

No Response

Total

Table: Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Cost of bidding/proposing? * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Cost of bidding/proposing?Yes

No

Don't Know

No Response

Total

Table: Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Financing? * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Financing?Yes

No

Don't Know

No Response

Total

DRAFT







1 0 1 2 2 0 616.67% 0.00% 16.67% 33.33% 33.33% 0.00% 100.00%16.67% 0.00% 8.33% 8.00% 6.90% 0.00% 7.89%

1.32% 0.00% 1.32% 2.63% 2.63% 0.00% 7.89%3 0 10 18 25 3 59

5.08% 0.00% 16.95% 30.51% 42.37% 5.08% 100.00%50.00% 0.00% 83.33% 72.00% 86.21% 100.00% 77.63%

3.95% 0.00% 13.16% 23.68% 32.89% 3.95% 77.63%1 0 0 2 0 0 3

33.33% 0.00% 0.00% 66.67% 0.00% 0.00% 100.00%16.67% 0.00% 0.00% 8.00% 0.00% 0.00% 3.95%

1.32% 0.00% 0.00% 2.63% 0.00% 0.00% 3.95%1 1 1 3 2 0 8

12.50% 12.50% 12.50% 37.50% 25.00% 0.00% 100.00%16.67% 100.00% 8.33% 12.00% 6.90% 0.00% 10.53%

1.32% 1.32% 1.32% 3.95% 2.63% 0.00% 10.53%6 1 12 25 29 3 76

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





2 0 2 2 2 0 825.00% 0.00% 25.00% 25.00% 25.00% 0.00% 100.00%33.33% 0.00% 16.67% 8.00% 6.90% 0.00% 10.53%

2.63% 0.00% 2.63% 2.63% 2.63% 0.00% 10.53%2 0 8 17 24 3 54

3.70% 0.00% 14.81% 31.48% 44.44% 5.56% 100.00%33.33% 0.00% 66.67% 68.00% 82.76% 100.00% 71.05%

2.63% 0.00% 10.53% 22.37% 31.58% 3.95% 71.05%1 0 1 3 1 0 6

16.67% 0.00% 16.67% 50.00% 16.67% 0.00% 100.00%16.67% 0.00% 8.33% 12.00% 3.45% 0.00% 7.89%

1.32% 0.00% 1.32% 3.95% 1.32% 0.00% 7.89%1 1 1 3 2 0 8

12.50% 12.50% 12.50% 37.50% 25.00% 0.00% 100.00%16.67% 100.00% 8.33% 12.00% 6.90% 0.00% 10.53%

1.32% 1.32% 1.32% 3.95% 2.63% 0.00% 10.53%6 1 12 25 29 3 76

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





2 0 4 5 7 2 2010.00% 0.00% 20.00% 25.00% 35.00% 10.00% 100.00%33.33% 0.00% 33.33% 20.00% 24.14% 66.67% 26.32%

2.63% 0.00% 5.26% 6.58% 9.21% 2.63% 26.32%2 0 6 15 20 1 44

4.55% 0.00% 13.64% 34.09% 45.45% 2.27% 100.00%33.33% 0.00% 50.00% 60.00% 68.97% 33.33% 57.89%

2.63% 0.00% 7.89% 19.74% 26.32% 1.32% 57.89%

Table: Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Insurance (general liability, professional liability, etc.)? * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Insurance (general liability, professional liability, etc.)?Yes

No

Don't Know

No Response

Total

Table: Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Price of supplies/materials * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Price of supplies/materialsYes

No

Don't Know

No Response

Total

Table: Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Proposal/Bid specifications * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Proposal/Bid specificationsYes

No

DRAFT



1 0 1 2 0 0 425.00% 0.00% 25.00% 50.00% 0.00% 0.00% 100.00%16.67% 0.00% 8.33% 8.00% 0.00% 0.00% 5.26%

1.32% 0.00% 1.32% 2.63% 0.00% 0.00% 5.26%1 1 1 3 2 0 8

12.50% 12.50% 12.50% 37.50% 25.00% 0.00% 100.00%16.67% 100.00% 8.33% 12.00% 6.90% 0.00% 10.53%

1.32% 1.32% 1.32% 3.95% 2.63% 0.00% 10.53%6 1 12 25 29 3 76

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





3 0 6 5 11 2 2711.11% 0.00% 22.22% 18.52% 40.74% 7.41% 100.00%50.00% 0.00% 50.00% 20.00% 37.93% 66.67% 35.53%

3.95% 0.00% 7.89% 6.58% 14.47% 2.63% 35.53%0 0 5 14 16 1 36

0.00% 0.00% 13.89% 38.89% 44.44% 2.78% 100.00%0.00% 0.00% 41.67% 56.00% 55.17% 33.33% 47.37%0.00% 0.00% 6.58% 18.42% 21.05% 1.32% 47.37%

2 0 0 3 0 0 540.00% 0.00% 0.00% 60.00% 0.00% 0.00% 100.00%33.33% 0.00% 0.00% 12.00% 0.00% 0.00% 6.58%

2.63% 0.00% 0.00% 3.95% 0.00% 0.00% 6.58%1 1 1 3 2 0 8

12.50% 12.50% 12.50% 37.50% 25.00% 0.00% 100.00%16.67% 100.00% 8.33% 12.00% 6.90% 0.00% 10.53%

1.32% 1.32% 1.32% 3.95% 2.63% 0.00% 10.53%6 1 12 25 29 3 76

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





0 0 4 3 5 0 120.00% 0.00% 33.33% 25.00% 41.67% 0.00% 100.00%0.00% 0.00% 33.33% 12.00% 17.24% 0.00% 15.79%0.00% 0.00% 5.26% 3.95% 6.58% 0.00% 15.79%

3 0 7 17 22 3 525.77% 0.00% 13.46% 32.69% 42.31% 5.77% 100.00%

50.00% 0.00% 58.33% 68.00% 75.86% 100.00% 68.42%3.95% 0.00% 9.21% 22.37% 28.95% 3.95% 68.42%

2 0 0 2 0 0 450.00% 0.00% 0.00% 50.00% 0.00% 0.00% 100.00%33.33% 0.00% 0.00% 8.00% 0.00% 0.00% 5.26%

2.63% 0.00% 0.00% 2.63% 0.00% 0.00% 5.26%1 1 1 3 2 0 8

12.50% 12.50% 12.50% 37.50% 25.00% 0.00% 100.00%16.67% 100.00% 8.33% 12.00% 6.90% 0.00% 10.53%

1.32% 1.32% 1.32% 3.95% 2.63% 0.00% 10.53%6 1 12 25 29 3 76

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

Don't Know

No Response

Total

Table: Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Limited time given to prepare bid package or quote * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].


Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Limited time given to prepare bid package or quoteYes

No

Don't Know

No Response

Total

Table: Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Limited knowledge of purchasing contracting policies and procedures * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Limited knowledge of purchasing contracting policies and proceduresYes

No

Don't Know

No Response

Total

DRAFT







2 0 3 3 5 0 1315.38% 0.00% 23.08% 23.08% 38.46% 0.00% 100.00%33.33% 0.00% 25.00% 12.00% 17.24% 0.00% 17.11%

2.63% 0.00% 3.95% 3.95% 6.58% 0.00% 17.11%3 0 6 17 22 3 51

5.88% 0.00% 11.76% 33.33% 43.14% 5.88% 100.00%50.00% 0.00% 50.00% 68.00% 75.86% 100.00% 67.11%

3.95% 0.00% 7.89% 22.37% 28.95% 3.95% 67.11%0 0 2 2 0 0 4

0.00% 0.00% 50.00% 50.00% 0.00% 0.00% 100.00%0.00% 0.00% 16.67% 8.00% 0.00% 0.00% 5.26%0.00% 0.00% 2.63% 2.63% 0.00% 0.00% 5.26%

1 1 1 3 2 0 812.50% 12.50% 12.50% 37.50% 25.00% 0.00% 100.00%16.67% 100.00% 8.33% 12.00% 6.90% 0.00% 10.53%

1.32% 1.32% 1.32% 3.95% 2.63% 0.00% 10.53%6 1 12 25 29 3 76

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





1 0 1 2 7 0 119.09% 0.00% 9.09% 18.18% 63.64% 0.00% 100.00%

16.67% 0.00% 8.33% 8.00% 24.14% 0.00% 14.47%1.32% 0.00% 1.32% 2.63% 9.21% 0.00% 14.47%

4 0 10 18 20 3 557.27% 0.00% 18.18% 32.73% 36.36% 5.45% 100.00%

66.67% 0.00% 83.33% 72.00% 68.97% 100.00% 72.37%5.26% 0.00% 13.16% 23.68% 26.32% 3.95% 72.37%

0 0 0 2 0 0 20.00% 0.00% 0.00% 100.00% 0.00% 0.00% 100.00%0.00% 0.00% 0.00% 8.00% 0.00% 0.00% 2.63%0.00% 0.00% 0.00% 2.63% 0.00% 0.00% 2.63%

1 1 1 3 2 0 812.50% 12.50% 12.50% 37.50% 25.00% 0.00% 100.00%16.67% 100.00% 8.33% 12.00% 6.90% 0.00% 10.53%

1.32% 1.32% 1.32% 3.95% 2.63% 0.00% 10.53%6 1 12 25 29 3 76

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





1 0 1 3 6 1 128.33% 0.00% 8.33% 25.00% 50.00% 8.33% 100.00%

16.67% 0.00% 8.33% 12.00% 20.69% 33.33% 15.79%1.32% 0.00% 1.32% 3.95% 7.89% 1.32% 15.79%

4 0 9 16 21 2 527.69% 0.00% 17.31% 30.77% 40.38% 3.85% 100.00%

66.67% 0.00% 75.00% 64.00% 72.41% 66.67% 68.42%5.26% 0.00% 11.84% 21.05% 27.63% 2.63% 68.42%

Table: Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Lack of experience * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Lack of experienceYes

No

Don't Know

No Response

Total

Table: Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Lack of personnel * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].


Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Lack of personnelYes

No

Don't Know

No Response

Total

Table: Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Contract too large * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Contract too largeYes

No

DRAFT



0 0 1 3 0 0 40.00% 0.00% 25.00% 75.00% 0.00% 0.00% 100.00%0.00% 0.00% 8.33% 12.00% 0.00% 0.00% 5.26%0.00% 0.00% 1.32% 3.95% 0.00% 0.00% 5.26%

1 1 1 3 2 0 812.50% 12.50% 12.50% 37.50% 25.00% 0.00% 100.00%16.67% 100.00% 8.33% 12.00% 6.90% 0.00% 10.53%

1.32% 1.32% 1.32% 3.95% 2.63% 0.00% 10.53%6 1 12 25 29 3 76

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





5 0 10 12 15 3 4511.11% 0.00% 22.22% 26.67% 33.33% 6.67% 100.00%83.33% 0.00% 83.33% 48.00% 51.72% 100.00% 59.21%

6.58% 0.00% 13.16% 15.79% 19.74% 3.95% 59.21%0 0 1 7 12 0 20

0.00% 0.00% 5.00% 35.00% 60.00% 0.00% 100.00%0.00% 0.00% 8.33% 28.00% 41.38% 0.00% 26.32%0.00% 0.00% 1.32% 9.21% 15.79% 0.00% 26.32%

0 0 0 3 0 0 30.00% 0.00% 0.00% 100.00% 0.00% 0.00% 100.00%0.00% 0.00% 0.00% 12.00% 0.00% 0.00% 3.95%0.00% 0.00% 0.00% 3.95% 0.00% 0.00% 3.95%

1 1 1 3 2 0 812.50% 12.50% 12.50% 37.50% 25.00% 0.00% 100.00%16.67% 100.00% 8.33% 12.00% 6.90% 0.00% 10.53%

1.32% 1.32% 1.32% 3.95% 2.63% 0.00% 10.53%6 1 12 25 29 3 76

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





3 0 4 8 10 2 2711.11% 0.00% 14.81% 29.63% 37.04% 7.41% 100.00%50.00% 0.00% 33.33% 32.00% 34.48% 66.67% 35.53%

3.95% 0.00% 5.26% 10.53% 13.16% 2.63% 35.53%1 0 5 9 17 1 33

3.03% 0.00% 15.15% 27.27% 51.52% 3.03% 100.00%16.67% 0.00% 41.67% 36.00% 58.62% 33.33% 43.42%

1.32% 0.00% 6.58% 11.84% 22.37% 1.32% 43.42%1 0 2 5 0 0 8

12.50% 0.00% 25.00% 62.50% 0.00% 0.00% 100.00%16.67% 0.00% 16.67% 20.00% 0.00% 0.00% 10.53%

1.32% 0.00% 2.63% 6.58% 0.00% 0.00% 10.53%1 1 1 3 2 0 8

12.50% 12.50% 12.50% 37.50% 25.00% 0.00% 100.00%16.67% 100.00% 8.33% 12.00% 6.90% 0.00% 10.53%

1.32% 1.32% 1.32% 3.95% 2.63% 0.00% 10.53%6 1 12 25 29 3 76

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

Don't Know

No Response

Total

Table: Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Selection process * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].


Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Selection processYes

No

Don't Know

No Response

Total

Table: Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Unnecessary restrictive contract specifications * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].


Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Unnecessary restrictive contract specificationsYes

No

Don't Know

No Response

Total

DRAFT







0 0 3 6 5 0 140.00% 0.00% 21.43% 42.86% 35.71% 0.00% 100.00%0.00% 0.00% 25.00% 24.00% 17.24% 0.00% 18.42%0.00% 0.00% 3.95% 7.89% 6.58% 0.00% 18.42%

2 0 6 12 22 2 444.55% 0.00% 13.64% 27.27% 50.00% 4.55% 100.00%

33.33% 0.00% 50.00% 48.00% 75.86% 66.67% 57.89%2.63% 0.00% 7.89% 15.79% 28.95% 2.63% 57.89%

3 0 2 4 0 1 1030.00% 0.00% 20.00% 40.00% 0.00% 10.00% 100.00%50.00% 0.00% 16.67% 16.00% 0.00% 33.33% 13.16%

3.95% 0.00% 2.63% 5.26% 0.00% 1.32% 13.16%1 1 1 3 2 0 8

12.50% 12.50% 12.50% 37.50% 25.00% 0.00% 100.00%16.67% 100.00% 8.33% 12.00% 6.90% 0.00% 10.53%

1.32% 1.32% 1.32% 3.95% 2.63% 0.00% 10.53%6 1 12 25 29 3 76

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





4 0 6 11 10 2 3312.12% 0.00% 18.18% 33.33% 30.30% 6.06% 100.00%66.67% 0.00% 50.00% 44.00% 34.48% 66.67% 43.42%

5.26% 0.00% 7.89% 14.47% 13.16% 2.63% 43.42%1 0 4 7 16 1 29

3.45% 0.00% 13.79% 24.14% 55.17% 3.45% 100.00%16.67% 0.00% 33.33% 28.00% 55.17% 33.33% 38.16%

1.32% 0.00% 5.26% 9.21% 21.05% 1.32% 38.16%0 0 1 4 1 0 6

0.00% 0.00% 16.67% 66.67% 16.67% 0.00% 100.00%0.00% 0.00% 8.33% 16.00% 3.45% 0.00% 7.89%0.00% 0.00% 1.32% 5.26% 1.32% 0.00% 7.89%

1 1 1 3 2 0 812.50% 12.50% 12.50% 37.50% 25.00% 0.00% 100.00%16.67% 100.00% 8.33% 12.00% 6.90% 0.00% 10.53%

1.32% 1.32% 1.32% 3.95% 2.63% 0.00% 10.53%6 1 12 25 29 3 76

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





5 0 7 10 8 0 3016.67% 0.00% 23.33% 33.33% 26.67% 0.00% 100.00%83.33% 0.00% 58.33% 40.00% 27.59% 0.00% 39.47%

6.58% 0.00% 9.21% 13.16% 10.53% 0.00% 39.47%0 0 3 7 9 1 20

0.00% 0.00% 15.00% 35.00% 45.00% 5.00% 100.00%0.00% 0.00% 25.00% 28.00% 31.03% 33.33% 26.32%0.00% 0.00% 3.95% 9.21% 11.84% 1.32% 26.32%

Table: Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Slow payment or nonpayment * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Slow payment or nonpaymentYes

No

Don't Know

No Response

Total

Table: Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Competing with large companies * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Competing with large companiesYes

No

Don't Know

No Response

Total

Table: Q17 Between 2008 and 2013, how many times has your company been awarded a School project as a prime contractor/consultant? * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q17 Between 2008 and 2013, how many times has your company been awarded a School project as a prime contractor/consultant?None

1-10 Times

DRAFT



0 0 0 3 6 1 100.00% 0.00% 0.00% 30.00% 60.00% 10.00% 100.00%0.00% 0.00% 0.00% 12.00% 20.69% 33.33% 13.16%0.00% 0.00% 0.00% 3.95% 7.89% 1.32% 13.16%

0 0 1 1 2 0 40.00% 0.00% 25.00% 25.00% 50.00% 0.00% 100.00%0.00% 0.00% 8.33% 4.00% 6.90% 0.00% 5.26%0.00% 0.00% 1.32% 1.32% 2.63% 0.00% 5.26%

0 0 0 0 0 1 10.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00%0.00% 0.00% 0.00% 0.00% 0.00% 33.33% 1.32%0.00% 0.00% 0.00% 0.00% 0.00% 1.32% 1.32%

0 0 0 0 1 0 10.00% 0.00% 0.00% 0.00% 100.00% 0.00% 100.00%0.00% 0.00% 0.00% 0.00% 3.45% 0.00% 1.32%0.00% 0.00% 0.00% 0.00% 1.32% 0.00% 1.32%

0 0 0 1 1 0 20.00% 0.00% 0.00% 50.00% 50.00% 0.00% 100.00%0.00% 0.00% 0.00% 4.00% 3.45% 0.00% 2.63%0.00% 0.00% 0.00% 1.32% 1.32% 0.00% 2.63%

1 1 1 3 2 0 812.50% 12.50% 12.50% 37.50% 25.00% 0.00% 100.00%16.67% 100.00% 8.33% 12.00% 6.90% 0.00% 10.53%

1.32% 1.32% 1.32% 3.95% 2.63% 0.00% 10.53%6 1 12 25 29 3 76

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





0 0 0 1 6 0 70.00% 0.00% 0.00% 14.29% 85.71% 0.00% 100.00%0.00% 0.00% 0.00% 4.00% 20.69% 0.00% 9.21%0.00% 0.00% 0.00% 1.32% 7.89% 0.00% 9.21%

0 0 3 5 10 2 200.00% 0.00% 15.00% 25.00% 50.00% 10.00% 100.00%0.00% 0.00% 25.00% 20.00% 34.48% 66.67% 26.32%0.00% 0.00% 3.95% 6.58% 13.16% 2.63% 26.32%

0 0 0 5 2 1 80.00% 0.00% 0.00% 62.50% 25.00% 12.50% 100.00%0.00% 0.00% 0.00% 20.00% 6.90% 33.33% 10.53%0.00% 0.00% 0.00% 6.58% 2.63% 1.32% 10.53%

0 0 1 1 0 0 20.00% 0.00% 50.00% 50.00% 0.00% 0.00% 100.00%0.00% 0.00% 8.33% 4.00% 0.00% 0.00% 2.63%0.00% 0.00% 1.32% 1.32% 0.00% 0.00% 2.63%

0 0 0 0 1 0 10.00% 0.00% 0.00% 0.00% 100.00% 0.00% 100.00%0.00% 0.00% 0.00% 0.00% 3.45% 0.00% 1.32%0.00% 0.00% 0.00% 0.00% 1.32% 0.00% 1.32%

0 0 0 1 0 0 10.00% 0.00% 0.00% 100.00% 0.00% 0.00% 100.00%0.00% 0.00% 0.00% 4.00% 0.00% 0.00% 1.32%0.00% 0.00% 0.00% 1.32% 0.00% 0.00% 1.32%

6 1 8 12 10 0 3716.22% 2.70% 21.62% 32.43% 27.03% 0.00% 100.00%

100.00% 100.00% 66.67% 48.00% 34.48% 0.00% 48.68%7.89% 1.32% 10.53% 15.79% 13.16% 0.00% 48.68%

6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

11-25 Times

26-50 Times

51-100 Times

Over 100 Times

Don't Know

No Response

Total

Table: Q18 When you were a prime contractor/consultant, what was the average amount of time that it typically took to receive payment for your services on Schools funded projects * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].


Q18 When you were a prime contractor/consultant, what was the average amount of time that it typically took to receive payment for your services on Schools funded projectsLess Than 30 Days

31-60 Days

61-90 Days

91-120 Days

Over 120 Days

Not Applicable

No Response

Total

DRAFT







2 0 1 2 4 2 1118.18% 0.00% 9.09% 18.18% 36.36% 18.18% 100.00%33.33% 0.00% 8.33% 8.00% 13.79% 66.67% 14.47%

2.63% 0.00% 1.32% 2.63% 5.26% 2.63% 14.47%3 0 9 17 22 0 51

5.88% 0.00% 17.65% 33.33% 43.14% 0.00% 100.00%50.00% 0.00% 75.00% 68.00% 75.86% 0.00% 67.11%

3.95% 0.00% 11.84% 22.37% 28.95% 0.00% 67.11%0 0 1 3 1 1 6

0.00% 0.00% 16.67% 50.00% 16.67% 16.67% 100.00%0.00% 0.00% 8.33% 12.00% 3.45% 33.33% 7.89%0.00% 0.00% 1.32% 3.95% 1.32% 1.32% 7.89%

1 1 1 3 2 0 812.50% 12.50% 12.50% 37.50% 25.00% 0.00% 100.00%16.67% 100.00% 8.33% 12.00% 6.90% 0.00% 10.53%

1.32% 1.32% 1.32% 3.95% 2.63% 0.00% 10.53%6 1 12 25 29 3 76

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





3 0 4 8 14 2 319.68% 0.00% 12.90% 25.81% 45.16% 6.45% 100.00%

50.00% 0.00% 33.33% 32.00% 48.28% 66.67% 40.79%3.95% 0.00% 5.26% 10.53% 18.42% 2.63% 40.79%

2 0 6 10 13 1 326.25% 0.00% 18.75% 31.25% 40.63% 3.13% 100.00%

33.33% 0.00% 50.00% 40.00% 44.83% 33.33% 42.11%2.63% 0.00% 7.89% 13.16% 17.11% 1.32% 42.11%

0 0 1 4 0 0 50.00% 0.00% 20.00% 80.00% 0.00% 0.00% 100.00%0.00% 0.00% 8.33% 16.00% 0.00% 0.00% 6.58%0.00% 0.00% 1.32% 5.26% 0.00% 0.00% 6.58%

1 1 1 3 2 0 812.50% 12.50% 12.50% 37.50% 25.00% 0.00% 100.00%16.67% 100.00% 8.33% 12.00% 6.90% 0.00% 10.53%

1.32% 1.32% 1.32% 3.95% 2.63% 0.00% 10.53%6 1 12 25 29 3 76

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





1 0 0 1 0 0 250.00% 0.00% 0.00% 50.00% 0.00% 0.00% 100.00%16.67% 0.00% 0.00% 4.00% 0.00% 0.00% 2.63%

1.32% 0.00% 0.00% 1.32% 0.00% 0.00% 2.63%0 0 1 0 1 0 2

0.00% 0.00% 50.00% 0.00% 50.00% 0.00% 100.00%0.00% 0.00% 8.33% 0.00% 3.45% 0.00% 2.63%0.00% 0.00% 1.32% 0.00% 1.32% 0.00% 2.63%

0 0 0 3 1 0 40.00% 0.00% 0.00% 75.00% 25.00% 0.00% 100.00%0.00% 0.00% 0.00% 12.00% 3.45% 0.00% 5.26%0.00% 0.00% 0.00% 3.95% 1.32% 0.00% 5.26%

Table: Q19 Between 2008 and 2013, have you ever submitted a bid or quote for a Schools contract, were informed that you were the lowest bidder, and then found out that another firm was actually doing the work? * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q19 Between 2008 and 2013, have you ever submitted a bid or quote for a Schools contract, were nformed that you were the lowest bidder, and then found out that another firm was actually doing the work?Yes

No

Don't Know

No Response

Total

Table: Q20 As a prime contractor/consultant, are you required to have bonding? * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q20 As a prime contractor/consultant, are you required to have bonding?Yes

No

Don't Know

No Response

Total

Table: Q21 What is your current aggregate bonding limit? * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].Race, Ethnicity, Gender Classification (Business Ownership Classification)

Q21 What is your current aggregate bonding limit?Below $100,000

$100,001 to $250,000

$500,001 to $1 million

DRAFT



1 0 0 1 0 0 250.00% 0.00% 0.00% 50.00% 0.00% 0.00% 100.00%16.67% 0.00% 0.00% 4.00% 0.00% 0.00% 2.63%

1.32% 0.00% 0.00% 1.32% 0.00% 0.00% 2.63%0 0 0 1 2 0 3

0.00% 0.00% 0.00% 33.33% 66.67% 0.00% 100.00%0.00% 0.00% 0.00% 4.00% 6.90% 0.00% 3.95%0.00% 0.00% 0.00% 1.32% 2.63% 0.00% 3.95%

0 0 0 1 0 0 10.00% 0.00% 0.00% 100.00% 0.00% 0.00% 100.00%0.00% 0.00% 0.00% 4.00% 0.00% 0.00% 1.32%0.00% 0.00% 0.00% 1.32% 0.00% 0.00% 1.32%

0 0 3 1 8 2 140.00% 0.00% 21.43% 7.14% 57.14% 14.29% 100.00%0.00% 0.00% 25.00% 4.00% 27.59% 66.67% 18.42%0.00% 0.00% 3.95% 1.32% 10.53% 2.63% 18.42%

1 0 0 0 2 0 333.33% 0.00% 0.00% 0.00% 66.67% 0.00% 100.00%16.67% 0.00% 0.00% 0.00% 6.90% 0.00% 3.95%

1.32% 0.00% 0.00% 0.00% 2.63% 0.00% 3.95%3 1 8 17 15 1 45

6.67% 2.22% 17.78% 37.78% 33.33% 2.22% 100.00%50.00% 100.00% 66.67% 68.00% 51.72% 33.33% 59.21%

3.95% 1.32% 10.53% 22.37% 19.74% 1.32% 59.21%6 1 12 25 29 3 76

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





1 0 0 1 0 0 250.00% 0.00% 0.00% 50.00% 0.00% 0.00% 100.00%16.67% 0.00% 0.00% 4.00% 0.00% 0.00% 2.63%

1.32% 0.00% 0.00% 1.32% 0.00% 0.00% 2.63%0 0 1 0 1 0 2

0.00% 0.00% 50.00% 0.00% 50.00% 0.00% 100.00%0.00% 0.00% 8.33% 0.00% 3.45% 0.00% 2.63%0.00% 0.00% 1.32% 0.00% 1.32% 0.00% 2.63%

0 0 0 1 1 0 20.00% 0.00% 0.00% 50.00% 50.00% 0.00% 100.00%0.00% 0.00% 0.00% 4.00% 3.45% 0.00% 2.63%0.00% 0.00% 0.00% 1.32% 1.32% 0.00% 2.63%

0 0 0 2 1 0 30.00% 0.00% 0.00% 66.67% 33.33% 0.00% 100.00%0.00% 0.00% 0.00% 8.00% 3.45% 0.00% 3.95%0.00% 0.00% 0.00% 2.63% 1.32% 0.00% 3.95%

0 0 0 1 1 0 20.00% 0.00% 0.00% 50.00% 50.00% 0.00% 100.00%0.00% 0.00% 0.00% 4.00% 3.45% 0.00% 2.63%0.00% 0.00% 0.00% 1.32% 1.32% 0.00% 2.63%

0 0 0 0 2 0 20.00% 0.00% 0.00% 0.00% 100.00% 0.00% 100.00%0.00% 0.00% 0.00% 0.00% 6.90% 0.00% 2.63%0.00% 0.00% 0.00% 0.00% 2.63% 0.00% 2.63%

1 0 1 2 0 0 425.00% 0.00% 25.00% 50.00% 0.00% 0.00% 100.00%16.67% 0.00% 8.33% 8.00% 0.00% 0.00% 5.26%

1.32% 0.00% 1.32% 2.63% 0.00% 0.00% 5.26%0 0 2 0 7 2 11

0.00% 0.00% 18.18% 0.00% 63.64% 18.18% 100.00%0.00% 0.00% 16.67% 0.00% 24.14% 66.67% 14.47%0.00% 0.00% 2.63% 0.00% 9.21% 2.63% 14.47%

1 0 0 1 1 0 333.33% 0.00% 0.00% 33.33% 33.33% 0.00% 100.00%16.67% 0.00% 0.00% 4.00% 3.45% 0.00% 3.95%

1.32% 0.00% 0.00% 1.32% 1.32% 0.00% 3.95%

$1,000,001 to $1.5 million

$1,500,001 to $3 million

$3,00,001 to $5 million

Over $5 million

Don't Know

No Response

Total

Table: Q22 What is your current single project bonding limit? * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].Race, Ethnicity, Gender Classification (Business Ownership Classification)

Q22 What is your current single project bonding limit?Below $100,000

$100,001 to $250,000

$250,001 to $500,000


$1,000,001 to $1.5 million

$1,500,001 to $3 million

$3,00,001 to $5 million

Over $5 million

Don't Know

DRAFT



3 1 8 17 15 1 456.67% 2.22% 17.78% 37.78% 33.33% 2.22% 100.00%

50.00% 100.00% 66.67% 68.00% 51.72% 33.33% 59.21%3.95% 1.32% 10.53% 22.37% 19.74% 1.32% 59.21%

6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





3 0 1 2 2 0 837.50% 0.00% 12.50% 25.00% 25.00% 0.00% 100.00%50.00% 0.00% 8.33% 8.00% 6.90% 0.00% 10.53%

3.95% 0.00% 1.32% 2.63% 2.63% 0.00% 10.53%1 0 5 11 17 1 35

2.86% 0.00% 14.29% 31.43% 48.57% 2.86% 100.00%16.67% 0.00% 41.67% 44.00% 58.62% 33.33% 46.05%

1.32% 0.00% 6.58% 14.47% 22.37% 1.32% 46.05%1 0 1 4 6 1 13

7.69% 0.00% 7.69% 30.77% 46.15% 7.69% 100.00%16.67% 0.00% 8.33% 16.00% 20.69% 33.33% 17.11%

1.32% 0.00% 1.32% 5.26% 7.89% 1.32% 17.11%0 0 4 5 2 1 12

0.00% 0.00% 33.33% 41.67% 16.67% 8.33% 100.00%0.00% 0.00% 33.33% 20.00% 6.90% 33.33% 15.79%0.00% 0.00% 5.26% 6.58% 2.63% 1.32% 15.79%

1 1 1 3 2 0 812.50% 12.50% 12.50% 37.50% 25.00% 0.00% 100.00%16.67% 100.00% 8.33% 12.00% 6.90% 0.00% 10.53%

1.32% 1.32% 1.32% 3.95% 2.63% 0.00% 10.53%6 1 12 25 29 3 76

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





1 0 0 2 2 0 520.00% 0.00% 0.00% 40.00% 40.00% 0.00% 100.00%16.67% 0.00% 0.00% 8.00% 6.90% 0.00% 6.58%

1.32% 0.00% 0.00% 2.63% 2.63% 0.00% 6.58%2 0 1 0 0 0 3

66.67% 0.00% 33.33% 0.00% 0.00% 0.00% 100.00%33.33% 0.00% 8.33% 0.00% 0.00% 0.00% 3.95%

2.63% 0.00% 1.32% 0.00% 0.00% 0.00% 3.95%3 1 11 23 27 3 68

4.41% 1.47% 16.18% 33.82% 39.71% 4.41% 100.00%50.00% 100.00% 91.67% 92.00% 93.10% 100.00% 89.47%

3.95% 1.32% 14.47% 30.26% 35.53% 3.95% 89.47%6 1 12 25 29 3 76

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





2 0 1 0 0 0 366.67% 0.00% 33.33% 0.00% 0.00% 0.00% 100.00%33.33% 0.00% 8.33% 0.00% 0.00% 0.00% 3.95%

2.63% 0.00% 1.32% 0.00% 0.00% 0.00% 3.95%

No Response

Total

Table: Q23 As a prime contractor/consultant did you experience discriminatory behavior by the Schools when bidding or working on a project between 2008 and 2013? * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q23 As a prime contractor/consultant did you experience discriminatory behavior by the Schools when bidding or working on a project between 2008 and 2013?Yes

No

Not Applicable

Don't Know

No Response

Total


Table: Q24 What was the most noticeable way you became aware of the discrimination against your company? * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Q24 What was the most noticeable way you became aware of the discrimination against your company?Verbal Comment

Action Taken Against the Company

No Response

Total

Table: Q25 Which of the following do you consider the primary reason for your company being discriminated against? * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q25 Which of the following do you consider the primary reason for your company being discriminated against?Owner's Race or Ethnicity

DRAFT



0 0 0 1 0 0 10.00% 0.00% 0.00% 100.00% 0.00% 0.00% 100.00%0.00% 0.00% 0.00% 4.00% 0.00% 0.00% 1.32%0.00% 0.00% 0.00% 1.32% 0.00% 0.00% 1.32%

1 0 0 0 1 0 250.00% 0.00% 0.00% 0.00% 50.00% 0.00% 100.00%16.67% 0.00% 0.00% 0.00% 3.45% 0.00% 2.63%

1.32% 0.00% 0.00% 0.00% 1.32% 0.00% 2.63%0 0 0 1 1 0 2

0.00% 0.00% 0.00% 50.00% 50.00% 0.00% 100.00%0.00% 0.00% 0.00% 4.00% 3.45% 0.00% 2.63%0.00% 0.00% 0.00% 1.32% 1.32% 0.00% 2.63%

3 1 11 23 27 3 684.41% 1.47% 16.18% 33.82% 39.71% 4.41% 100.00%

50.00% 100.00% 91.67% 92.00% 93.10% 100.00% 89.47%3.95% 1.32% 14.47% 30.26% 35.53% 3.95% 89.47%

6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





2 0 0 0 2 0 450.00% 0.00% 0.00% 0.00% 50.00% 0.00% 100.00%33.33% 0.00% 0.00% 0.00% 6.90% 0.00% 5.26%

2.63% 0.00% 0.00% 0.00% 2.63% 0.00% 5.26%0 0 0 1 0 0 1

0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 100.00%0.00% 0.00% 0.00% 4.00% 0.00% 0.00% 1.32%0.00% 0.00% 0.00% 1.32% 0.00% 0.00% 1.32%

1 0 1 1 0 0 333.33% 0.00% 33.33% 33.33% 0.00% 0.00% 100.00%16.67% 0.00% 8.33% 4.00% 0.00% 0.00% 3.95%

1.32% 0.00% 1.32% 1.32% 0.00% 0.00% 3.95%3 1 11 23 27 3 68

4.41% 1.47% 16.18% 33.82% 39.71% 4.41% 100.00%50.00% 100.00% 91.67% 92.00% 93.10% 100.00% 89.47%

3.95% 1.32% 14.47% 30.26% 35.53% 3.95% 89.47%6 1 12 25 29 3 76

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





1 0 1 0 0 0 250.00% 0.00% 50.00% 0.00% 0.00% 0.00% 100.00%16.67% 0.00% 8.33% 0.00% 0.00% 0.00% 2.63%

1.32% 0.00% 1.32% 0.00% 0.00% 0.00% 2.63%2 0 0 2 2 0 6

33.33% 0.00% 0.00% 33.33% 33.33% 0.00% 100.00%33.33% 0.00% 0.00% 8.00% 6.90% 0.00% 7.89%

2.63% 0.00% 0.00% 2.63% 2.63% 0.00% 7.89%3 1 11 23 27 3 68

4.41% 1.47% 16.18% 33.82% 39.71% 4.41% 100.00%50.00% 100.00% 91.67% 92.00% 93.10% 100.00% 89.47%

3.95% 1.32% 14.47% 30.26% 35.53% 3.95% 89.47%6 1 12 25 29 3 76

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

Owner's Gender

Both

Don't Know

No Response

Total

Table: Q26 When did the discrimination first occur? * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].Race, Ethnicity, Gender Classification (Business Ownership Classification)

Q26 When did the discrimination first occur?During Bidding Process

After Contract Award

Both

No Response

Total

Table: Q27 Did you file a complaint? * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].Race, Ethnicity, Gender Classification (Business Ownership Classification)

Q27 Did you file a complaint?Yes

No

No Response

Total

DRAFT







3 0 1 2 1 0 742.86% 0.00% 14.29% 28.57% 14.29% 0.00% 100.00%50.00% 0.00% 8.33% 8.00% 3.45% 0.00% 9.21%

3.95% 0.00% 1.32% 2.63% 1.32% 0.00% 9.21%0 0 0 0 1 0 1

0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 100.00%0.00% 0.00% 0.00% 0.00% 3.45% 0.00% 1.32%0.00% 0.00% 0.00% 0.00% 1.32% 0.00% 1.32%

3 1 11 23 27 3 684.41% 1.47% 16.18% 33.82% 39.71% 4.41% 100.00%

50.00% 100.00% 91.67% 92.00% 93.10% 100.00% 89.47%3.95% 1.32% 14.47% 30.26% 35.53% 3.95% 89.47%

6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





0 0 0 3 10 0 130.00% 0.00% 0.00% 23.08% 76.92% 0.00% 100.00%0.00% 0.00% 0.00% 12.00% 34.48% 0.00% 17.11%0.00% 0.00% 0.00% 3.95% 13.16% 0.00% 17.11%

0 0 1 2 1 0 40.00% 0.00% 25.00% 50.00% 25.00% 0.00% 100.00%0.00% 0.00% 8.33% 8.00% 3.45% 0.00% 5.26%0.00% 0.00% 1.32% 2.63% 1.32% 0.00% 5.26%

1 0 1 4 0 0 616.67% 0.00% 16.67% 66.67% 0.00% 0.00% 100.00%16.67% 0.00% 8.33% 16.00% 0.00% 0.00% 7.89%

1.32% 0.00% 1.32% 5.26% 0.00% 0.00% 7.89%1 0 1 2 2 0 6

16.67% 0.00% 16.67% 33.33% 33.33% 0.00% 100.00%16.67% 0.00% 8.33% 8.00% 6.90% 0.00% 7.89%

1.32% 0.00% 1.32% 2.63% 2.63% 0.00% 7.89%0 0 2 3 0 0 5

0.00% 0.00% 40.00% 60.00% 0.00% 0.00% 100.00%0.00% 0.00% 16.67% 12.00% 0.00% 0.00% 6.58%0.00% 0.00% 2.63% 3.95% 0.00% 0.00% 6.58%

0 0 2 0 2 0 40.00% 0.00% 50.00% 0.00% 50.00% 0.00% 100.00%0.00% 0.00% 16.67% 0.00% 6.90% 0.00% 5.26%0.00% 0.00% 2.63% 0.00% 2.63% 0.00% 5.26%

1 0 1 1 1 0 425.00% 0.00% 25.00% 25.00% 25.00% 0.00% 100.00%16.67% 0.00% 8.33% 4.00% 3.45% 0.00% 5.26%

1.32% 0.00% 1.32% 1.32% 1.32% 0.00% 5.26%0 0 1 6 1 1 9

0.00% 0.00% 11.11% 66.67% 11.11% 11.11% 100.00%0.00% 0.00% 8.33% 24.00% 3.45% 33.33% 11.84%0.00% 0.00% 1.32% 7.89% 1.32% 1.32% 11.84%

1 0 0 0 6 0 714.29% 0.00% 0.00% 0.00% 85.71% 0.00% 100.00%16.67% 0.00% 0.00% 0.00% 20.69% 0.00% 9.21%

1.32% 0.00% 0.00% 0.00% 7.89% 0.00% 9.21%2 1 3 2 6 1 15

13.33% 6.67% 20.00% 13.33% 40.00% 6.67% 100.00%33.33% 100.00% 25.00% 8.00% 20.69% 33.33% 19.74%

2.63% 1.32% 3.95% 2.63% 7.89% 1.32% 19.74%0 0 0 2 0 1 3

0.00% 0.00% 0.00% 66.67% 0.00% 33.33% 100.00%0.00% 0.00% 0.00% 8.00% 0.00% 33.33% 3.95%0.00% 0.00% 0.00% 2.63% 0.00% 1.32% 3.95%

Table: Q28 Would you be willing so speak with the Schools about the discrimination you experienced? * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q28 Would you be willing so speak with the Schools about the discrimination you experienced?Yes

No

No Response

Total

Table: Q29 In general, which of the following dollar ranges best approximates your company's largest subcontract awarded between 2008 and 2013? * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q29 In general, which of the following dollar ranges best approximates your company's largest subcontract awarded between 2008 and 2013?Not applicable (not a subcontractor)

None

Up to $50,000?

$50,001 to $100,000?

$100,001 to $200,000?

$200,001 to $300,000?

$300,001 to $400,00?

$400,001 to $500,000?

$500,001 to $1 million?

Over $1 million?

Don't Know

DRAFT



6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





2 0 1 2 0 1 633.33% 0.00% 16.67% 33.33% 0.00% 16.67% 100.00%33.33% 0.00% 8.33% 8.00% 0.00% 33.33% 7.89%

2.63% 0.00% 1.32% 2.63% 0.00% 1.32% 7.89%3 1 10 17 18 2 51

5.88% 1.96% 19.61% 33.33% 35.29% 3.92% 100.00%50.00% 100.00% 83.33% 68.00% 62.07% 66.67% 67.11%

3.95% 1.32% 13.16% 22.37% 23.68% 2.63% 67.11%1 0 1 3 1 0 6

16.67% 0.00% 16.67% 50.00% 16.67% 0.00% 100.00%16.67% 0.00% 8.33% 12.00% 3.45% 0.00% 7.89%

1.32% 0.00% 1.32% 3.95% 1.32% 0.00% 7.89%0 0 0 3 10 0 13

0.00% 0.00% 0.00% 23.08% 76.92% 0.00% 100.00%0.00% 0.00% 0.00% 12.00% 34.48% 0.00% 17.11%0.00% 0.00% 0.00% 3.95% 13.16% 0.00% 17.11%

6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





1 0 1 5 1 1 911.11% 0.00% 11.11% 55.56% 11.11% 11.11% 100.00%16.67% 0.00% 8.33% 20.00% 3.45% 33.33% 11.84%

1.32% 0.00% 1.32% 6.58% 1.32% 1.32% 11.84%4 1 10 14 17 2 48

8.33% 2.08% 20.83% 29.17% 35.42% 4.17% 100.00%66.67% 100.00% 83.33% 56.00% 58.62% 66.67% 63.16%

5.26% 1.32% 13.16% 18.42% 22.37% 2.63% 63.16%1 0 1 3 1 0 6

16.67% 0.00% 16.67% 50.00% 16.67% 0.00% 100.00%16.67% 0.00% 8.33% 12.00% 3.45% 0.00% 7.89%

1.32% 0.00% 1.32% 3.95% 1.32% 0.00% 7.89%0 0 0 3 10 0 13

0.00% 0.00% 0.00% 23.08% 76.92% 0.00% 100.00%0.00% 0.00% 0.00% 12.00% 34.48% 0.00% 17.11%0.00% 0.00% 0.00% 3.95% 13.16% 0.00% 17.11%

6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





1 0 3 6 1 0 119.09% 0.00% 27.27% 54.55% 9.09% 0.00% 100.00%

16.67% 0.00% 25.00% 24.00% 3.45% 0.00% 14.47%1.32% 0.00% 3.95% 7.89% 1.32% 0.00% 14.47%

Total

Table: Q30 In your experience, have any of the following been a barrier to obtaining work on School projects as a subcontractor with primes? Performance/payment bond requirement? * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q30 In your experience, have any of the following been a barrier to obtaining work on School projects as a subcontractor with primes? Performance/payment bond requirement?Yes

No

Don't Know

No Response

Total

Table: Q30 In your experience, have any of the following been a barrier to obtaining work on School projects as a subcontractor with primes? Cost of bidding/proposing? * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q30 In your experience, have any of the following been a barrier to obtaining work on School projects as a subcontractor with primes? Cost of bidding/proposing?Yes

No

Don't Know

No Response

Total

Table: Q30 In your experience, have any of the following been a barrier to obtaining work on School projects as a subcontractor with primes? Financing? * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].


Q30 In your experience, have any of the following been a barrier to obtaining work on School projects as a subcontractor with primes? Financing?Yes

DRAFT



4 1 9 14 17 3 488.33% 2.08% 18.75% 29.17% 35.42% 6.25% 100.00%

66.67% 100.00% 75.00% 56.00% 58.62% 100.00% 63.16%5.26% 1.32% 11.84% 18.42% 22.37% 3.95% 63.16%

1 0 0 2 1 0 425.00% 0.00% 0.00% 50.00% 25.00% 0.00% 100.00%16.67% 0.00% 0.00% 8.00% 3.45% 0.00% 5.26%

1.32% 0.00% 0.00% 2.63% 1.32% 0.00% 5.26%0 0 0 3 10 0 13

0.00% 0.00% 0.00% 23.08% 76.92% 0.00% 100.00%0.00% 0.00% 0.00% 12.00% 34.48% 0.00% 17.11%0.00% 0.00% 0.00% 3.95% 13.16% 0.00% 17.11%

6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





0 0 2 2 0 0 40.00% 0.00% 50.00% 50.00% 0.00% 0.00% 100.00%0.00% 0.00% 16.67% 8.00% 0.00% 0.00% 5.26%0.00% 0.00% 2.63% 2.63% 0.00% 0.00% 5.26%

5 1 10 19 18 3 568.93% 1.79% 17.86% 33.93% 32.14% 5.36% 100.00%

83.33% 100.00% 83.33% 76.00% 62.07% 100.00% 73.68%6.58% 1.32% 13.16% 25.00% 23.68% 3.95% 73.68%

1 0 0 1 1 0 333.33% 0.00% 0.00% 33.33% 33.33% 0.00% 100.00%16.67% 0.00% 0.00% 4.00% 3.45% 0.00% 3.95%

1.32% 0.00% 0.00% 1.32% 1.32% 0.00% 3.95%0 0 0 3 10 0 13

0.00% 0.00% 0.00% 23.08% 76.92% 0.00% 100.00%0.00% 0.00% 0.00% 12.00% 34.48% 0.00% 17.11%0.00% 0.00% 0.00% 3.95% 13.16% 0.00% 17.11%

6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





2 0 3 3 0 0 825.00% 0.00% 37.50% 37.50% 0.00% 0.00% 100.00%33.33% 0.00% 25.00% 12.00% 0.00% 0.00% 10.53%

2.63% 0.00% 3.95% 3.95% 0.00% 0.00% 10.53%3 1 9 16 17 3 49

6.12% 2.04% 18.37% 32.65% 34.69% 6.12% 100.00%50.00% 100.00% 75.00% 64.00% 58.62% 100.00% 64.47%

3.95% 1.32% 11.84% 21.05% 22.37% 3.95% 64.47%1 0 0 3 2 0 6

16.67% 0.00% 0.00% 50.00% 33.33% 0.00% 100.00%16.67% 0.00% 0.00% 12.00% 6.90% 0.00% 7.89%

1.32% 0.00% 0.00% 3.95% 2.63% 0.00% 7.89%0 0 0 3 10 0 13

0.00% 0.00% 0.00% 23.08% 76.92% 0.00% 100.00%0.00% 0.00% 0.00% 12.00% 34.48% 0.00% 17.11%0.00% 0.00% 0.00% 3.95% 13.16% 0.00% 17.11%

No

Don't Know

No Response

Total

Table: Q30 In your experience, have any of the following been a barrier to obtaining work on School projects as a subcontractor with primes? Insurance (general liability, professional liability, etc.)? * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q30 In your experience, have any of the following been a barrier to obtaining work on School projects as a subcontractor with primes? Insurance (general liability, professional liability, etc.)?Yes

No

Don't Know

No Response

Total

Table: Q30 In your experience, have any of the following been a barrier to obtaining work on School projects as a subcontractor with primes? Price of supplies/materials * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q30 In your experience, have any of the following been a barrier to obtaining work on School projects as a subcontractor with primes? Price of supplies/materialsYes

No

Don't Know

No Response

DRAFT



6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





2 0 7 7 4 1 219.52% 0.00% 33.33% 33.33% 19.05% 4.76% 100.00%

33.33% 0.00% 58.33% 28.00% 13.79% 33.33% 27.63%2.63% 0.00% 9.21% 9.21% 5.26% 1.32% 27.63%

3 1 5 13 14 2 387.89% 2.63% 13.16% 34.21% 36.84% 5.26% 100.00%

50.00% 100.00% 41.67% 52.00% 48.28% 66.67% 50.00%3.95% 1.32% 6.58% 17.11% 18.42% 2.63% 50.00%

1 0 0 2 1 0 425.00% 0.00% 0.00% 50.00% 25.00% 0.00% 100.00%16.67% 0.00% 0.00% 8.00% 3.45% 0.00% 5.26%

1.32% 0.00% 0.00% 2.63% 1.32% 0.00% 5.26%0 0 0 3 10 0 13

0.00% 0.00% 0.00% 23.08% 76.92% 0.00% 100.00%0.00% 0.00% 0.00% 12.00% 34.48% 0.00% 17.11%0.00% 0.00% 0.00% 3.95% 13.16% 0.00% 17.11%

6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





2 0 2 2 0 0 633.33% 0.00% 33.33% 33.33% 0.00% 0.00% 100.00%33.33% 0.00% 16.67% 8.00% 0.00% 0.00% 7.89%

2.63% 0.00% 2.63% 2.63% 0.00% 0.00% 7.89%4 1 9 19 18 3 54

7.41% 1.85% 16.67% 35.19% 33.33% 5.56% 100.00%66.67% 100.00% 75.00% 76.00% 62.07% 100.00% 71.05%

5.26% 1.32% 11.84% 25.00% 23.68% 3.95% 71.05%0 0 1 1 1 0 3

0.00% 0.00% 33.33% 33.33% 33.33% 0.00% 100.00%0.00% 0.00% 8.33% 4.00% 3.45% 0.00% 3.95%0.00% 0.00% 1.32% 1.32% 1.32% 0.00% 3.95%

0 0 0 3 10 0 130.00% 0.00% 0.00% 23.08% 76.92% 0.00% 100.00%0.00% 0.00% 0.00% 12.00% 34.48% 0.00% 17.11%0.00% 0.00% 0.00% 3.95% 13.16% 0.00% 17.11%

6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

Table: Q30 In your experience, have any of the following been a barrier to obtaining work on School projects as a subcontractor with primes? Limited time given to prepare bid package or quote * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].


Q30 In your experience, have any of the following been a barrier to obtaining work on School projects as a subcontractor with primes? Limited time given to prepare bid package or quoteYes

No

Don't Know

No Response

Total

Total

Table: Q30 In your experience, have any of the following been a barrier to obtaining work on School projects as a subcontractor with primes? Lack of experience * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q30 In your experience, have any of the following been a barrier to obtaining work on School projects as a subcontractor with primes? Lack of experienceYes

No

Don't Know

No Response

Total

DRAFT







1 0 1 3 3 0 812.50% 0.00% 12.50% 37.50% 37.50% 0.00% 100.00%16.67% 0.00% 8.33% 12.00% 10.34% 0.00% 10.53%

1.32% 0.00% 1.32% 3.95% 3.95% 0.00% 10.53%5 1 11 18 15 3 53

9.43% 1.89% 20.75% 33.96% 28.30% 5.66% 100.00%83.33% 100.00% 91.67% 72.00% 51.72% 100.00% 69.74%

6.58% 1.32% 14.47% 23.68% 19.74% 3.95% 69.74%0 0 0 1 1 0 2

0.00% 0.00% 0.00% 50.00% 50.00% 0.00% 100.00%0.00% 0.00% 0.00% 4.00% 3.45% 0.00% 2.63%0.00% 0.00% 0.00% 1.32% 1.32% 0.00% 2.63%

0 0 0 3 10 0 130.00% 0.00% 0.00% 23.08% 76.92% 0.00% 100.00%0.00% 0.00% 0.00% 12.00% 34.48% 0.00% 17.11%0.00% 0.00% 0.00% 3.95% 13.16% 0.00% 17.11%

6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





1 0 2 2 2 0 714.29% 0.00% 28.57% 28.57% 28.57% 0.00% 100.00%16.67% 0.00% 16.67% 8.00% 6.90% 0.00% 9.21%

1.32% 0.00% 2.63% 2.63% 2.63% 0.00% 9.21%5 1 8 18 16 3 51

9.80% 1.96% 15.69% 35.29% 31.37% 5.88% 100.00%83.33% 100.00% 66.67% 72.00% 55.17% 100.00% 67.11%

6.58% 1.32% 10.53% 23.68% 21.05% 3.95% 67.11%0 0 2 2 1 0 5

0.00% 0.00% 40.00% 40.00% 20.00% 0.00% 100.00%0.00% 0.00% 16.67% 8.00% 3.45% 0.00% 6.58%0.00% 0.00% 2.63% 2.63% 1.32% 0.00% 6.58%

0 0 0 3 10 0 130.00% 0.00% 0.00% 23.08% 76.92% 0.00% 100.00%0.00% 0.00% 0.00% 12.00% 34.48% 0.00% 17.11%0.00% 0.00% 0.00% 3.95% 13.16% 0.00% 17.11%

6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





0 0 5 10 3 0 180.00% 0.00% 27.78% 55.56% 16.67% 0.00% 100.00%0.00% 0.00% 41.67% 40.00% 10.34% 0.00% 23.68%0.00% 0.00% 6.58% 13.16% 3.95% 0.00% 23.68%

3 1 6 8 15 3 368.33% 2.78% 16.67% 22.22% 41.67% 8.33% 100.00%

50.00% 100.00% 50.00% 32.00% 51.72% 100.00% 47.37%3.95% 1.32% 7.89% 10.53% 19.74% 3.95% 47.37%

Table: Q30 In your experience, have any of the following been a barrier to obtaining work on School projects as a subcontractor with primes? Lack of personnel * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q30 In your experience, have any of the following been a barrier to obtaining work on School projects as a subcontractor with primes? Lack of personnelYes

No

Don't Know

No Response

Total

Table: Q30 In your experience, have any of the following been a barrier to obtaining work on School projects as a subcontractor with primes? Contract too large * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q30 In your experience, have any of the following been a barrier to obtaining work on School projects as a subcontractor with primes? Contract too largeYes

No

Don't Know

No Response

Total

Table: Q30 In your experience, have any of the following been a barrier to obtaining work on School projects as a subcontractor with primes? Selection process * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q30 In your experience, have any of the following been a barrier to obtaining work on School projects as a subcontractor with primes? Selection processYes

No

DRAFT



3 0 1 4 1 0 933.33% 0.00% 11.11% 44.44% 11.11% 0.00% 100.00%50.00% 0.00% 8.33% 16.00% 3.45% 0.00% 11.84%

3.95% 0.00% 1.32% 5.26% 1.32% 0.00% 11.84%0 0 0 3 10 0 13

0.00% 0.00% 0.00% 23.08% 76.92% 0.00% 100.00%0.00% 0.00% 0.00% 12.00% 34.48% 0.00% 17.11%0.00% 0.00% 0.00% 3.95% 13.16% 0.00% 17.11%

6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





3 1 7 9 3 2 2512.00% 4.00% 28.00% 36.00% 12.00% 8.00% 100.00%50.00% 100.00% 58.33% 36.00% 10.34% 66.67% 32.89%

3.95% 1.32% 9.21% 11.84% 3.95% 2.63% 32.89%2 0 4 11 15 1 33

6.06% 0.00% 12.12% 33.33% 45.45% 3.03% 100.00%33.33% 0.00% 33.33% 44.00% 51.72% 33.33% 43.42%

2.63% 0.00% 5.26% 14.47% 19.74% 1.32% 43.42%1 0 1 2 1 0 5

20.00% 0.00% 20.00% 40.00% 20.00% 0.00% 100.00%16.67% 0.00% 8.33% 8.00% 3.45% 0.00% 6.58%

1.32% 0.00% 1.32% 2.63% 1.32% 0.00% 6.58%0 0 0 3 10 0 13

0.00% 0.00% 0.00% 23.08% 76.92% 0.00% 100.00%0.00% 0.00% 0.00% 12.00% 34.48% 0.00% 17.11%0.00% 0.00% 0.00% 3.95% 13.16% 0.00% 17.11%

6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





5 0 8 9 6 1 2917.24% 0.00% 27.59% 31.03% 20.69% 3.45% 100.00%83.33% 0.00% 66.67% 36.00% 20.69% 33.33% 38.16%

6.58% 0.00% 10.53% 11.84% 7.89% 1.32% 38.16%1 1 3 9 7 1 22

4.55% 4.55% 13.64% 40.91% 31.82% 4.55% 100.00%16.67% 100.00% 25.00% 36.00% 24.14% 33.33% 28.95%

1.32% 1.32% 3.95% 11.84% 9.21% 1.32% 28.95%0 0 0 0 4 1 5

0.00% 0.00% 0.00% 0.00% 80.00% 20.00% 100.00%0.00% 0.00% 0.00% 0.00% 13.79% 33.33% 6.58%0.00% 0.00% 0.00% 0.00% 5.26% 1.32% 6.58%

0 0 1 3 1 0 50.00% 0.00% 20.00% 60.00% 20.00% 0.00% 100.00%0.00% 0.00% 8.33% 12.00% 3.45% 0.00% 6.58%0.00% 0.00% 1.32% 3.95% 1.32% 0.00% 6.58%

0 0 0 1 1 0 20.00% 0.00% 0.00% 50.00% 50.00% 0.00% 100.00%0.00% 0.00% 0.00% 4.00% 3.45% 0.00% 2.63%0.00% 0.00% 0.00% 1.32% 1.32% 0.00% 2.63%

0 0 0 3 10 0 130.00% 0.00% 0.00% 23.08% 76.92% 0.00% 100.00%0.00% 0.00% 0.00% 12.00% 34.48% 0.00% 17.11%0.00% 0.00% 0.00% 3.95% 13.16% 0.00% 17.11%

Don't Know

No Response

Total

Table: Q30 In your experience, have any of the following been a barrier to obtaining work on School projects as a subcontractor with primes? Competing with large companies * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q30 In your experience, have any of the following been a barrier to obtaining work on School projects as a subcontractor with primes? Competing with large companiesYes

No

Don't Know

No Response

Total

Table: Q31 Between 2008 and 2013, how many times has your company been awarded a subcontract with a prime contractor for a project with the Schools? * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q31 Between 2008 and 2013, how many times has your company been awarded a subcontract with a prime contractor for a project with the Schools?None

1-10 Times

11-25 Times

26-50 Times

Don't Know

No Response

DRAFT



6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





1 0 1 4 8 1 156.67% 0.00% 6.67% 26.67% 53.33% 6.67% 100.00%

16.67% 0.00% 8.33% 16.00% 27.59% 33.33% 19.74%1.32% 0.00% 1.32% 5.26% 10.53% 1.32% 19.74%

0 1 0 4 4 0 90.00% 11.11% 0.00% 44.44% 44.44% 0.00% 100.00%0.00% 100.00% 0.00% 16.00% 13.79% 0.00% 11.84%0.00% 1.32% 0.00% 5.26% 5.26% 0.00% 11.84%

0 0 2 2 0 1 50.00% 0.00% 40.00% 40.00% 0.00% 20.00% 100.00%0.00% 0.00% 16.67% 8.00% 0.00% 33.33% 6.58%0.00% 0.00% 2.63% 2.63% 0.00% 1.32% 6.58%

0 0 1 2 0 0 30.00% 0.00% 33.33% 66.67% 0.00% 0.00% 100.00%0.00% 0.00% 8.33% 8.00% 0.00% 0.00% 3.95%0.00% 0.00% 1.32% 2.63% 0.00% 0.00% 3.95%

0 0 0 0 1 0 10.00% 0.00% 0.00% 0.00% 100.00% 0.00% 100.00%0.00% 0.00% 0.00% 0.00% 3.45% 0.00% 1.32%0.00% 0.00% 0.00% 0.00% 1.32% 0.00% 1.32%

0 0 0 1 0 0 10.00% 0.00% 0.00% 100.00% 0.00% 0.00% 100.00%0.00% 0.00% 0.00% 4.00% 0.00% 0.00% 1.32%0.00% 0.00% 0.00% 1.32% 0.00% 0.00% 1.32%

5 0 8 12 16 1 4211.90% 0.00% 19.05% 28.57% 38.10% 2.38% 100.00%83.33% 0.00% 66.67% 48.00% 55.17% 33.33% 55.26%

6.58% 0.00% 10.53% 15.79% 21.05% 1.32% 55.26%6 1 12 25 29 3 76

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





2 0 0 2 4 0 825.00% 0.00% 0.00% 25.00% 50.00% 0.00% 100.00%33.33% 0.00% 0.00% 8.00% 13.79% 0.00% 10.53%

2.63% 0.00% 0.00% 2.63% 5.26% 0.00% 10.53%3 1 9 16 12 1 42

7.14% 2.38% 21.43% 38.10% 28.57% 2.38% 100.00%50.00% 100.00% 75.00% 64.00% 41.38% 33.33% 55.26%

3.95% 1.32% 11.84% 21.05% 15.79% 1.32% 55.26%1 0 3 4 3 2 13

7.69% 0.00% 23.08% 30.77% 23.08% 15.38% 100.00%16.67% 0.00% 25.00% 16.00% 10.34% 66.67% 17.11%

1.32% 0.00% 3.95% 5.26% 3.95% 2.63% 17.11%0 0 0 3 10 0 13

0.00% 0.00% 0.00% 23.08% 76.92% 0.00% 100.00%0.00% 0.00% 0.00% 12.00% 34.48% 0.00% 17.11%0.00% 0.00% 0.00% 3.95% 13.16% 0.00% 17.11%

Total

Table: Q32 Between 2008 and 2013, when you were a subcontractor what was the average amount of time that it typically took to receive payment for your services from the prime contractor/vendor? * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q32 Between 2008 and 2013, when you were a subcontractor what was the average amount of time that it typically took to receive payment for your services from the prime contractor/vendor?31-60 Days

61-90 Days

91-120 Days

Over 120 Days

Not Applicable

Don't Know

No Response

Total

Table: Q33 Between 2008 and 2013, have you ever submitted a bid with a prime contractor for a project with the Schools, were informed that you were the lowest bidder, and then found out that another subcontractor was actually doing the work? * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q33 Between 2008 and 2013, have you ever submitted a bid with a prime contractor for a project with the Schools, were informed that you were the lowest bidder, and then found out that another subcontractor was actually doing the work?Yes

No

Don't Know

No Response

DRAFT



6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





2 0 5 4 6 0 1711.76% 0.00% 29.41% 23.53% 35.29% 0.00% 100.00%33.33% 0.00% 41.67% 16.00% 20.69% 0.00% 22.37%

2.63% 0.00% 6.58% 5.26% 7.89% 0.00% 22.37%4 1 6 15 10 3 39

10.26% 2.56% 15.38% 38.46% 25.64% 7.69% 100.00%66.67% 100.00% 50.00% 60.00% 34.48% 100.00% 51.32%

5.26% 1.32% 7.89% 19.74% 13.16% 3.95% 51.32%0 0 1 3 3 0 7

0.00% 0.00% 14.29% 42.86% 42.86% 0.00% 100.00%0.00% 0.00% 8.33% 12.00% 10.34% 0.00% 9.21%0.00% 0.00% 1.32% 3.95% 3.95% 0.00% 9.21%

0 0 0 3 10 0 130.00% 0.00% 0.00% 23.08% 76.92% 0.00% 100.00%0.00% 0.00% 0.00% 12.00% 34.48% 0.00% 17.11%0.00% 0.00% 0.00% 3.95% 13.16% 0.00% 17.11%

6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





1 0 0 1 0 0 250.00% 0.00% 0.00% 50.00% 0.00% 0.00% 100.00%16.67% 0.00% 0.00% 4.00% 0.00% 0.00% 2.63%

1.32% 0.00% 0.00% 1.32% 0.00% 0.00% 2.63%0 0 1 0 0 0 1

0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 100.00%0.00% 0.00% 8.33% 0.00% 0.00% 0.00% 1.32%0.00% 0.00% 1.32% 0.00% 0.00% 0.00% 1.32%

0 0 0 0 1 0 10.00% 0.00% 0.00% 0.00% 100.00% 0.00% 100.00%0.00% 0.00% 0.00% 0.00% 3.45% 0.00% 1.32%0.00% 0.00% 0.00% 0.00% 1.32% 0.00% 1.32%

0 0 0 2 0 0 20.00% 0.00% 0.00% 100.00% 0.00% 0.00% 100.00%0.00% 0.00% 0.00% 8.00% 0.00% 0.00% 2.63%0.00% 0.00% 0.00% 2.63% 0.00% 0.00% 2.63%

0 0 0 0 1 0 10.00% 0.00% 0.00% 0.00% 100.00% 0.00% 100.00%0.00% 0.00% 0.00% 0.00% 3.45% 0.00% 1.32%0.00% 0.00% 0.00% 0.00% 1.32% 0.00% 1.32%

1 0 0 1 0 0 250.00% 0.00% 0.00% 50.00% 0.00% 0.00% 100.00%16.67% 0.00% 0.00% 4.00% 0.00% 0.00% 2.63%

1.32% 0.00% 0.00% 1.32% 0.00% 0.00% 2.63%0 0 3 0 3 0 6

0.00% 0.00% 50.00% 0.00% 50.00% 0.00% 100.00%0.00% 0.00% 25.00% 0.00% 10.34% 0.00% 7.89%0.00% 0.00% 3.95% 0.00% 3.95% 0.00% 7.89%

0 0 1 0 1 0 20.00% 0.00% 50.00% 0.00% 50.00% 0.00% 100.00%0.00% 0.00% 8.33% 0.00% 3.45% 0.00% 2.63%0.00% 0.00% 1.32% 0.00% 1.32% 0.00% 2.63%

4 1 7 21 23 3 596.78% 1.69% 11.86% 35.59% 38.98% 5.08% 100.00%

66.67% 100.00% 58.33% 84.00% 79.31% 100.00% 77.63%5.26% 1.32% 9.21% 27.63% 30.26% 3.95% 77.63%

Total

Table: Q34 As a subcontractor, are you required to have bonding for company's type of work? * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q34 As a subcontractor, are you required to have bonding for company's type of work?Yes

No

Don't Know

No Response

Total

Table: Q35 What is your current aggregate bonding limit? * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].Race, Ethnicity, Gender Classification (Business Ownership Classification)

Q35 What is your current aggregate bonding limit?Below $100,000

$100,001 to $250,000


$1,000,001 to $1.5 million

$1,500,001 to $3 million

$3,00,001 to $5 million

Over $5 million

Don't Know

No Response

DRAFT



6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





1 0 0 1 0 0 250.00% 0.00% 0.00% 50.00% 0.00% 0.00% 100.00%16.67% 0.00% 0.00% 4.00% 0.00% 0.00% 2.63%

1.32% 0.00% 0.00% 1.32% 0.00% 0.00% 2.63%0 0 1 0 0 0 1

0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 100.00%0.00% 0.00% 8.33% 0.00% 0.00% 0.00% 1.32%0.00% 0.00% 1.32% 0.00% 0.00% 0.00% 1.32%

0 0 0 0 1 0 10.00% 0.00% 0.00% 0.00% 100.00% 0.00% 100.00%0.00% 0.00% 0.00% 0.00% 3.45% 0.00% 1.32%0.00% 0.00% 0.00% 0.00% 1.32% 0.00% 1.32%

0 0 0 1 1 0 20.00% 0.00% 0.00% 50.00% 50.00% 0.00% 100.00%0.00% 0.00% 0.00% 4.00% 3.45% 0.00% 2.63%0.00% 0.00% 0.00% 1.32% 1.32% 0.00% 2.63%

1 0 0 1 0 0 250.00% 0.00% 0.00% 50.00% 0.00% 0.00% 100.00%16.67% 0.00% 0.00% 4.00% 0.00% 0.00% 2.63%

1.32% 0.00% 0.00% 1.32% 0.00% 0.00% 2.63%0 0 1 1 0 0 2

0.00% 0.00% 50.00% 50.00% 0.00% 0.00% 100.00%0.00% 0.00% 8.33% 4.00% 0.00% 0.00% 2.63%0.00% 0.00% 1.32% 1.32% 0.00% 0.00% 2.63%

0 0 2 0 3 0 50.00% 0.00% 40.00% 0.00% 60.00% 0.00% 100.00%0.00% 0.00% 16.67% 0.00% 10.34% 0.00% 6.58%0.00% 0.00% 2.63% 0.00% 3.95% 0.00% 6.58%

0 0 1 0 1 0 20.00% 0.00% 50.00% 0.00% 50.00% 0.00% 100.00%0.00% 0.00% 8.33% 0.00% 3.45% 0.00% 2.63%0.00% 0.00% 1.32% 0.00% 1.32% 0.00% 2.63%

4 1 7 21 23 3 596.78% 1.69% 11.86% 35.59% 38.98% 5.08% 100.00%

66.67% 100.00% 58.33% 84.00% 79.31% 100.00% 77.63%5.26% 1.32% 9.21% 27.63% 30.26% 3.95% 77.63%

6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





2 0 1 2 1 0 633.33% 0.00% 16.67% 33.33% 16.67% 0.00% 100.00%33.33% 0.00% 8.33% 8.00% 3.45% 0.00% 7.89%

2.63% 0.00% 1.32% 2.63% 1.32% 0.00% 7.89%3 1 6 12 11 2 35

8.57% 2.86% 17.14% 34.29% 31.43% 5.71% 100.00%50.00% 100.00% 50.00% 48.00% 37.93% 66.67% 46.05%

3.95% 1.32% 7.89% 15.79% 14.47% 2.63% 46.05%0 0 0 2 4 1 7

0.00% 0.00% 0.00% 28.57% 57.14% 14.29% 100.00%0.00% 0.00% 0.00% 8.00% 13.79% 33.33% 9.21%0.00% 0.00% 0.00% 2.63% 5.26% 1.32% 9.21%

Total

Table: Q36 What is your current single project bonding limit? * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].Race, Ethnicity, Gender Classification (Business Ownership Classification)

Q36 What is your current single project bonding limit?Below $100,000

$100,001 to $250,000

$250,001 to $500,000


$1,000,001 to $1.5 million

$3,00,001 to $5 million

Over $5 million

Don't Know

No Response

Total

Table: Q37 As a subcontractor did you experience discriminatory behavior between 2008 and 2013 from a prime contractor/consultant working or bidding/proposing on a Schools project? * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].


Q37 As a subcontractor did you experience discriminatory behavior between 2008 and 2013 from a prime contractor/consultant working or bidding/proposing on a Schools project?Yes

No

Not Applicable

DRAFT



1 0 5 6 3 0 156.67% 0.00% 33.33% 40.00% 20.00% 0.00% 100.00%

16.67% 0.00% 41.67% 24.00% 10.34% 0.00% 19.74%1.32% 0.00% 6.58% 7.89% 3.95% 0.00% 19.74%

0 0 0 3 10 0 130.00% 0.00% 0.00% 23.08% 76.92% 0.00% 100.00%0.00% 0.00% 0.00% 12.00% 34.48% 0.00% 17.11%0.00% 0.00% 0.00% 3.95% 13.16% 0.00% 17.11%

6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





1 0 0 1 2 0 425.00% 0.00% 0.00% 25.00% 50.00% 0.00% 100.00%16.67% 0.00% 0.00% 4.00% 6.90% 0.00% 5.26%

1.32% 0.00% 0.00% 1.32% 2.63% 0.00% 5.26%1 0 1 1 0 0 3

33.33% 0.00% 33.33% 33.33% 0.00% 0.00% 100.00%16.67% 0.00% 8.33% 4.00% 0.00% 0.00% 3.95%

1.32% 0.00% 1.32% 1.32% 0.00% 0.00% 3.95%4 1 11 23 27 3 69

5.80% 1.45% 15.94% 33.33% 39.13% 4.35% 100.00%66.67% 100.00% 91.67% 92.00% 93.10% 100.00% 90.79%

5.26% 1.32% 14.47% 30.26% 35.53% 3.95% 90.79%6 1 12 25 29 3 76

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





1 0 1 0 0 0 250.00% 0.00% 50.00% 0.00% 0.00% 0.00% 100.00%16.67% 0.00% 8.33% 0.00% 0.00% 0.00% 2.63%

1.32% 0.00% 1.32% 0.00% 0.00% 0.00% 2.63%0 0 0 1 0 0 1

0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 100.00%0.00% 0.00% 0.00% 4.00% 0.00% 0.00% 1.32%0.00% 0.00% 0.00% 1.32% 0.00% 0.00% 1.32%

1 0 0 0 2 0 333.33% 0.00% 0.00% 0.00% 66.67% 0.00% 100.00%16.67% 0.00% 0.00% 0.00% 6.90% 0.00% 3.95%

1.32% 0.00% 0.00% 0.00% 2.63% 0.00% 3.95%0 0 0 1 0 0 1

0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 100.00%0.00% 0.00% 0.00% 4.00% 0.00% 0.00% 1.32%0.00% 0.00% 0.00% 1.32% 0.00% 0.00% 1.32%

4 1 11 23 27 3 695.80% 1.45% 15.94% 33.33% 39.13% 4.35% 100.00%

66.67% 100.00% 91.67% 92.00% 93.10% 100.00% 90.79%5.26% 1.32% 14.47% 30.26% 35.53% 3.95% 90.79%

6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

Don't Know

No Response

Total


Race, Ethnicity, Gender Classification (Business Ownership Classification)Q38 What was the most noticeable way you became aware of the discrimination against your company?Verbal Comment


No Response

Total


Race, Ethnicity, Gender Classification (Business Ownership Classification)Q39 Which of the following do you consider the primary reason for your company being discriminated against?Owner's Race or Ethnicity

Owner's Gender

Both

Don't Know

No Response

Total

DRAFT







2 0 0 1 2 0 540.00% 0.00% 0.00% 20.00% 40.00% 0.00% 100.00%33.33% 0.00% 0.00% 4.00% 6.90% 0.00% 6.58%

2.63% 0.00% 0.00% 1.32% 2.63% 0.00% 6.58%0 0 0 1 0 0 1

0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 100.00%0.00% 0.00% 0.00% 4.00% 0.00% 0.00% 1.32%0.00% 0.00% 0.00% 1.32% 0.00% 0.00% 1.32%

0 0 1 0 0 0 10.00% 0.00% 100.00% 0.00% 0.00% 0.00% 100.00%0.00% 0.00% 8.33% 0.00% 0.00% 0.00% 1.32%0.00% 0.00% 1.32% 0.00% 0.00% 0.00% 1.32%

4 1 11 23 27 3 695.80% 1.45% 15.94% 33.33% 39.13% 4.35% 100.00%

66.67% 100.00% 91.67% 92.00% 93.10% 100.00% 90.79%5.26% 1.32% 14.47% 30.26% 35.53% 3.95% 90.79%

6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





0 0 1 1 0 0 20.00% 0.00% 50.00% 50.00% 0.00% 0.00% 100.00%0.00% 0.00% 8.33% 4.00% 0.00% 0.00% 2.63%0.00% 0.00% 1.32% 1.32% 0.00% 0.00% 2.63%

2 0 0 1 2 0 540.00% 0.00% 0.00% 20.00% 40.00% 0.00% 100.00%33.33% 0.00% 0.00% 4.00% 6.90% 0.00% 6.58%

2.63% 0.00% 0.00% 1.32% 2.63% 0.00% 6.58%4 1 11 23 27 3 69

5.80% 1.45% 15.94% 33.33% 39.13% 4.35% 100.00%66.67% 100.00% 91.67% 92.00% 93.10% 100.00% 90.79%

5.26% 1.32% 14.47% 30.26% 35.53% 3.95% 90.79%6 1 12 25 29 3 76

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





2 0 1 2 1 0 633.33% 0.00% 16.67% 33.33% 16.67% 0.00% 100.00%33.33% 0.00% 8.33% 8.00% 3.45% 0.00% 7.89%

2.63% 0.00% 1.32% 2.63% 1.32% 0.00% 7.89%0 0 0 0 1 0 1

0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 100.00%0.00% 0.00% 0.00% 0.00% 3.45% 0.00% 1.32%0.00% 0.00% 0.00% 0.00% 1.32% 0.00% 1.32%

4 1 11 23 27 3 695.80% 1.45% 15.94% 33.33% 39.13% 4.35% 100.00%

66.67% 100.00% 91.67% 92.00% 93.10% 100.00% 90.79%5.26% 1.32% 14.47% 30.26% 35.53% 3.95% 90.79%

6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%


Q40 When did the discrimination first occur?During the bidding process

After contract awarded

Both

No Response

Total

Table: Q41 Did you file a complaint? * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].Race, Ethnicity, Gender Classification (Business Ownership Classification)

Q41 Did you file a complaint?Yes

No

No Response

Total

Table: Q42 Would you be willing to speak with the Schools about the discrimination you experienced? * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q42 Would you be willing to speak with the Schools about the discrimination you experienced?Yes

No

No Response

Total

DRAFT







3 0 3 3 4 0 1323.08% 0.00% 23.08% 23.08% 30.77% 0.00% 100.00%50.00% 0.00% 25.00% 12.00% 13.79% 0.00% 17.11%

3.95% 0.00% 3.95% 3.95% 5.26% 0.00% 17.11%0 1 6 16 19 2 44

0.00% 2.27% 13.64% 36.36% 43.18% 4.55% 100.00%0.00% 100.00% 50.00% 64.00% 65.52% 66.67% 57.89%0.00% 1.32% 7.89% 21.05% 25.00% 2.63% 57.89%

3 0 3 6 6 1 1915.79% 0.00% 15.79% 31.58% 31.58% 5.26% 100.00%50.00% 0.00% 25.00% 24.00% 20.69% 33.33% 25.00%

3.95% 0.00% 3.95% 7.89% 7.89% 1.32% 25.00%6 1 12 25 29 3 76

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





0 0 1 3 6 1 110.00% 0.00% 9.09% 27.27% 54.55% 9.09% 100.00%0.00% 0.00% 8.33% 12.00% 20.69% 33.33% 14.47%0.00% 0.00% 1.32% 3.95% 7.89% 1.32% 14.47%

0 1 2 9 4 0 160.00% 6.25% 12.50% 56.25% 25.00% 0.00% 100.00%0.00% 100.00% 16.67% 36.00% 13.79% 0.00% 21.05%0.00% 1.32% 2.63% 11.84% 5.26% 0.00% 21.05%

1 0 4 5 0 0 1010.00% 0.00% 40.00% 50.00% 0.00% 0.00% 100.00%16.67% 0.00% 33.33% 20.00% 0.00% 0.00% 13.16%

1.32% 0.00% 5.26% 6.58% 0.00% 0.00% 13.16%4 0 4 3 4 0 15

26.67% 0.00% 26.67% 20.00% 26.67% 0.00% 100.00%66.67% 0.00% 33.33% 12.00% 13.79% 0.00% 19.74%

5.26% 0.00% 5.26% 3.95% 5.26% 0.00% 19.74%0 0 0 2 12 1 15

0.00% 0.00% 0.00% 13.33% 80.00% 6.67% 100.00%0.00% 0.00% 0.00% 8.00% 41.38% 33.33% 19.74%0.00% 0.00% 0.00% 2.63% 15.79% 1.32% 19.74%

1 0 1 3 3 0 812.50% 0.00% 12.50% 37.50% 37.50% 0.00% 100.00%16.67% 0.00% 8.33% 12.00% 10.34% 0.00% 10.53%

1.32% 0.00% 1.32% 3.95% 3.95% 0.00% 10.53%0 0 0 0 0 1 1

0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00%0.00% 0.00% 0.00% 0.00% 0.00% 33.33% 1.32%0.00% 0.00% 0.00% 0.00% 0.00% 1.32% 1.32%

6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

Table: Q43 Have you experienced or observed a situation in which a prime contractors/consultants includes minority or woman subcontractors on a bid or proposal to satisfy the good faith effort requirements, and then drops the company as a subcontractor after win * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %,

column %, total %].Race, Ethnicity, Gender Classification (Business Ownership Classification)

Q43 Have you experienced or observed a situation in which a prime contractors/consultants includes minority or woman subcontractors on a bid or proposal to satisfy the good faith effort requirements, and then drops the company as a subcontractor after winYes

No

Don't Know

Total

Table: Q44 How often do prime contractors/consultants who use your firm as a subcontractor on public-sector projects with M/WBE goals solicit your firm on projects (private or public) without M/WBE goals? * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q44 How often do prime contractors/consultants who use your firm as a subcontractor on public-sector projects with M/WBE goals solicit your firm on projects (private or public) without M/WBE goals?Very Often

Sometimes

Seldom

Never

Not Applicable

Don't Know

No Response

Total

DRAFT







1 0 1 1 0 0 333.33% 0.00% 33.33% 33.33% 0.00% 0.00% 100.00%16.67% 0.00% 8.33% 4.00% 0.00% 0.00% 3.95%

1.32% 0.00% 1.32% 1.32% 0.00% 0.00% 3.95%4 1 9 22 29 2 67

5.97% 1.49% 13.43% 32.84% 43.28% 2.99% 100.00%66.67% 100.00% 75.00% 88.00% 100.00% 66.67% 88.16%

5.26% 1.32% 11.84% 28.95% 38.16% 2.63% 88.16%1 0 2 2 0 0 5

20.00% 0.00% 40.00% 40.00% 0.00% 0.00% 100.00%16.67% 0.00% 16.67% 8.00% 0.00% 0.00% 6.58%

1.32% 0.00% 2.63% 2.63% 0.00% 0.00% 6.58%0 0 0 0 0 1 1

0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00%0.00% 0.00% 0.00% 0.00% 0.00% 33.33% 1.32%0.00% 0.00% 0.00% 0.00% 0.00% 1.32% 1.32%

6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





3 0 1 6 4 0 1421.43% 0.00% 7.14% 42.86% 28.57% 0.00% 100.00%50.00% 0.00% 8.33% 24.00% 13.79% 0.00% 18.42%

3.95% 0.00% 1.32% 7.89% 5.26% 0.00% 18.42%2 1 8 16 25 2 54

3.70% 1.85% 14.81% 29.63% 46.30% 3.70% 100.00%33.33% 100.00% 66.67% 64.00% 86.21% 66.67% 71.05%

2.63% 1.32% 10.53% 21.05% 32.89% 2.63% 71.05%1 0 3 3 0 0 7

14.29% 0.00% 42.86% 42.86% 0.00% 0.00% 100.00%16.67% 0.00% 25.00% 12.00% 0.00% 0.00% 9.21%

1.32% 0.00% 3.95% 3.95% 0.00% 0.00% 9.21%0 0 0 0 0 1 1

0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00%0.00% 0.00% 0.00% 0.00% 0.00% 33.33% 1.32%0.00% 0.00% 0.00% 0.00% 0.00% 1.32% 1.32%

6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

Table: Q45 Still talking about the Schools and its prime contractors/consultants, while doing business or attempting to do business, have you experienced any of the following as a form in discrimination: Harassment. * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q45 Still talking about the Schools and its prime contractors/consultants, while doing business or attempting to do business, have you experienced any of the following as a form in discrimination: Harassment.Yes

No

Don't Know

No Response

Total

Table: Q45 Still talking about the Schools and its prime contractors/consultants, while doing business or attempting to do business, have you experienced any of the following as a form in discrimination: Unequal or unfair treatment. * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].


Q45 Still talking about the Schools and its prime contractors/consultants, while doing business or attempting to do business, have you experienced any of the following as a form in discrimination: Unequal or unfair treatment.Yes

No

Don't Know

No Response

Total

DRAFT







3 0 3 8 3 0 1717.65% 0.00% 17.65% 47.06% 17.65% 0.00% 100.00%50.00% 0.00% 25.00% 32.00% 10.34% 0.00% 22.37%

3.95% 0.00% 3.95% 10.53% 3.95% 0.00% 22.37%2 1 6 13 23 2 47

4.26% 2.13% 12.77% 27.66% 48.94% 4.26% 100.00%33.33% 100.00% 50.00% 52.00% 79.31% 66.67% 61.84%

2.63% 1.32% 7.89% 17.11% 30.26% 2.63% 61.84%1 0 3 4 3 0 11

9.09% 0.00% 27.27% 36.36% 27.27% 0.00% 100.00%16.67% 0.00% 25.00% 16.00% 10.34% 0.00% 14.47%

1.32% 0.00% 3.95% 5.26% 3.95% 0.00% 14.47%0 0 0 0 0 1 1

0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00%0.00% 0.00% 0.00% 0.00% 0.00% 33.33% 1.32%0.00% 0.00% 0.00% 0.00% 0.00% 1.32% 1.32%

6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





2 0 3 6 4 0 1513.33% 0.00% 20.00% 40.00% 26.67% 0.00% 100.00%33.33% 0.00% 25.00% 24.00% 13.79% 0.00% 19.74%

2.63% 0.00% 3.95% 7.89% 5.26% 0.00% 19.74%3 1 7 16 25 2 54

5.56% 1.85% 12.96% 29.63% 46.30% 3.70% 100.00%50.00% 100.00% 58.33% 64.00% 86.21% 66.67% 71.05%

3.95% 1.32% 9.21% 21.05% 32.89% 2.63% 71.05%1 0 2 3 0 0 6

16.67% 0.00% 33.33% 50.00% 0.00% 0.00% 100.00%16.67% 0.00% 16.67% 12.00% 0.00% 0.00% 7.89%

1.32% 0.00% 2.63% 3.95% 0.00% 0.00% 7.89%0 0 0 0 0 1 1

0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00%0.00% 0.00% 0.00% 0.00% 0.00% 33.33% 1.32%0.00% 0.00% 0.00% 0.00% 0.00% 1.32% 1.32%

6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

Table: Q45 Still talking about the Schools and its prime contractors/consultants, while doing business or attempting to do business, have you experienced any of the following as a form in discrimination: Bid shopping or bid manipulation. * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].


Q45 Still talking about the Schools and its prime contractors/consultants, while doing business or attempting to do business, have you experienced any of the following as a form in discrimination: Bid shopping or bid manipulation.Yes

No

Don't Know

No Response

Total

Table: Q45 Still talking about the Schools and its prime contractors/consultants, while doing business or attempting to do business, have you experienced any of the following as a form in discrimination: Double standards in performance. * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].


Q45 Still talking about the Schools and its prime contractors/consultants, while doing business or attempting to do business, have you experienced any of the following as a form in discrimination: Double standards in performance.Yes

No

Don't Know

No Response

Total

DRAFT







3 0 1 3 4 0 1127.27% 0.00% 9.09% 27.27% 36.36% 0.00% 100.00%50.00% 0.00% 8.33% 12.00% 13.79% 0.00% 14.47%

3.95% 0.00% 1.32% 3.95% 5.26% 0.00% 14.47%2 1 9 17 24 2 55

3.64% 1.82% 16.36% 30.91% 43.64% 3.64% 100.00%33.33% 100.00% 75.00% 68.00% 82.76% 66.67% 72.37%

2.63% 1.32% 11.84% 22.37% 31.58% 2.63% 72.37%1 0 2 5 1 0 9

11.11% 0.00% 22.22% 55.56% 11.11% 0.00% 100.00%16.67% 0.00% 16.67% 20.00% 3.45% 0.00% 11.84%

1.32% 0.00% 2.63% 6.58% 1.32% 0.00% 11.84%0 0 0 0 0 1 1

0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00%0.00% 0.00% 0.00% 0.00% 0.00% 33.33% 1.32%0.00% 0.00% 0.00% 0.00% 0.00% 1.32% 1.32%

6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





3 0 1 2 0 0 650.00% 0.00% 16.67% 33.33% 0.00% 0.00% 100.00%50.00% 0.00% 8.33% 8.00% 0.00% 0.00% 7.89%

3.95% 0.00% 1.32% 2.63% 0.00% 0.00% 7.89%2 1 8 18 29 2 60

3.33% 1.67% 13.33% 30.00% 48.33% 3.33% 100.00%33.33% 100.00% 66.67% 72.00% 100.00% 66.67% 78.95%

2.63% 1.32% 10.53% 23.68% 38.16% 2.63% 78.95%1 0 3 5 0 0 9

11.11% 0.00% 33.33% 55.56% 0.00% 0.00% 100.00%16.67% 0.00% 25.00% 20.00% 0.00% 0.00% 11.84%

1.32% 0.00% 3.95% 6.58% 0.00% 0.00% 11.84%0 0 0 0 0 1 1

0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00%0.00% 0.00% 0.00% 0.00% 0.00% 33.33% 1.32%0.00% 0.00% 0.00% 0.00% 0.00% 1.32% 1.32%

6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

Table: Q45 Still talking about the Schools and its prime contractors/consultants, while doing business or attempting to do business, have you experienced any of the following as a form in discrimination: Denial of opportunity to bid. * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].


Q45 Still talking about the Schools and its prime contractors/consultants, while doing business or attempting to do business, have you experienced any of the following as a form in discrimination: Denial of opportunity to bid.Yes

No

Don't Know

No Response

Total

Table: Q45 Still talking about the Schools and its prime contractors/consultants, while doing business or attempting to do business, have you experienced any of the following as a form in discrimination: Unfair denial of contract award. * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].


Q45 Still talking about the Schools and its prime contractors/consultants, while doing business or attempting to do business, have you experienced any of the following as a form in discrimination: Unfair denial of contract award.Yes

No

Don't Know

No Response

Total

DRAFT







1 0 0 1 0 0 250.00% 0.00% 0.00% 50.00% 0.00% 0.00% 100.00%16.67% 0.00% 0.00% 4.00% 0.00% 0.00% 2.63%

1.32% 0.00% 0.00% 1.32% 0.00% 0.00% 2.63%4 1 11 21 29 2 68

5.88% 1.47% 16.18% 30.88% 42.65% 2.94% 100.00%66.67% 100.00% 91.67% 84.00% 100.00% 66.67% 89.47%

5.26% 1.32% 14.47% 27.63% 38.16% 2.63% 89.47%1 0 1 3 0 0 5

20.00% 0.00% 20.00% 60.00% 0.00% 0.00% 100.00%16.67% 0.00% 8.33% 12.00% 0.00% 0.00% 6.58%

1.32% 0.00% 1.32% 3.95% 0.00% 0.00% 6.58%0 0 0 0 0 1 1

0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00%0.00% 0.00% 0.00% 0.00% 0.00% 33.33% 1.32%0.00% 0.00% 0.00% 0.00% 0.00% 1.32% 1.32%

6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





3 0 2 3 2 0 1030.00% 0.00% 20.00% 30.00% 20.00% 0.00% 100.00%50.00% 0.00% 16.67% 12.00% 6.90% 0.00% 13.16%

3.95% 0.00% 2.63% 3.95% 2.63% 0.00% 13.16%2 1 7 18 26 2 56

3.57% 1.79% 12.50% 32.14% 46.43% 3.57% 100.00%33.33% 100.00% 58.33% 72.00% 89.66% 66.67% 73.68%

2.63% 1.32% 9.21% 23.68% 34.21% 2.63% 73.68%1 0 3 4 1 0 9

11.11% 0.00% 33.33% 44.44% 11.11% 0.00% 100.00%16.67% 0.00% 25.00% 16.00% 3.45% 0.00% 11.84%

1.32% 0.00% 3.95% 5.26% 1.32% 0.00% 11.84%0 0 0 0 0 1 1

0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00%0.00% 0.00% 0.00% 0.00% 0.00% 33.33% 1.32%0.00% 0.00% 0.00% 0.00% 0.00% 1.32% 1.32%

6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





3 0 2 1 1 0 742.86% 0.00% 28.57% 14.29% 14.29% 0.00% 100.00%50.00% 0.00% 16.67% 4.00% 3.45% 0.00% 9.21%

3.95% 0.00% 2.63% 1.32% 1.32% 0.00% 9.21%

Table: Q45 Still talking about the Schools and its prime contractors/consultants, while doing business or attempting to do business, have you experienced any of the following as a form in discrimination: Unfair termination. * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q45 Still talking about the Schools and its prime contractors/consultants, while doing business or attempting to do business, have you experienced any of the following as a form in discrimination: Unfair termination.Yes

No

Don't Know

No Response

Total

Table: Q45 Still talking about the Schools and its prime contractors/consultants, while doing business or attempting to do business, have you experienced any of the following as a form in discrimination: Unequal price quotes from suppliers. * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].


Q45 Still talking about the Schools and its prime contractors/consultants, while doing business or attempting to do business, have you experienced any of the following as a form in discrimination: Unequal price quotes from suppliers.Yes

No

Don't Know

No Response

Total

Table: Q46 There is an informal network of prime contractors/subcontractors that has excluded my company from doing business in the private sector: * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q46 There is an informal network of prime contractors/subcontractors that has excluded my company from doing business in the private sector:Strongly Agree

DRAFT



0 0 1 5 1 0 70.00% 0.00% 14.29% 71.43% 14.29% 0.00% 100.00%0.00% 0.00% 8.33% 20.00% 3.45% 0.00% 9.21%0.00% 0.00% 1.32% 6.58% 1.32% 0.00% 9.21%

2 1 2 3 3 0 1118.18% 9.09% 18.18% 27.27% 27.27% 0.00% 100.00%33.33% 100.00% 16.67% 12.00% 10.34% 0.00% 14.47%

2.63% 1.32% 2.63% 3.95% 3.95% 0.00% 14.47%0 0 1 1 1 0 3

0.00% 0.00% 33.33% 33.33% 33.33% 0.00% 100.00%0.00% 0.00% 8.33% 4.00% 3.45% 0.00% 3.95%0.00% 0.00% 1.32% 1.32% 1.32% 0.00% 3.95%

0 0 4 5 14 1 240.00% 0.00% 16.67% 20.83% 58.33% 4.17% 100.00%0.00% 0.00% 33.33% 20.00% 48.28% 33.33% 31.58%0.00% 0.00% 5.26% 6.58% 18.42% 1.32% 31.58%

1 0 2 10 9 1 234.35% 0.00% 8.70% 43.48% 39.13% 4.35% 100.00%

16.67% 0.00% 16.67% 40.00% 31.03% 33.33% 30.26%1.32% 0.00% 2.63% 13.16% 11.84% 1.32% 30.26%

0 0 0 0 0 1 10.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00%0.00% 0.00% 0.00% 0.00% 0.00% 33.33% 1.32%0.00% 0.00% 0.00% 0.00% 0.00% 1.32% 1.32%

6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





3 0 7 11 8 0 2910.34% 0.00% 24.14% 37.93% 27.59% 0.00% 100.00%50.00% 0.00% 58.33% 44.00% 27.59% 0.00% 38.16%

3.95% 0.00% 9.21% 14.47% 10.53% 0.00% 38.16%3 1 5 13 16 0 38

7.89% 2.63% 13.16% 34.21% 42.11% 0.00% 100.00%50.00% 100.00% 41.67% 52.00% 55.17% 0.00% 50.00%

3.95% 1.32% 6.58% 17.11% 21.05% 0.00% 50.00%0 0 0 1 5 2 8

0.00% 0.00% 0.00% 12.50% 62.50% 25.00% 100.00%0.00% 0.00% 0.00% 4.00% 17.24% 66.67% 10.53%0.00% 0.00% 0.00% 1.32% 6.58% 2.63% 10.53%

0 0 0 0 0 1 10.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00%0.00% 0.00% 0.00% 0.00% 0.00% 33.33% 1.32%0.00% 0.00% 0.00% 0.00% 0.00% 1.32% 1.32%

6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





1 0 6 9 5 0 214.76% 0.00% 28.57% 42.86% 23.81% 0.00% 100.00%

16.67% 0.00% 50.00% 36.00% 17.24% 0.00% 27.63%1.32% 0.00% 7.89% 11.84% 6.58% 0.00% 27.63%

2 0 1 3 3 0 922.22% 0.00% 11.11% 33.33% 33.33% 0.00% 100.00%33.33% 0.00% 8.33% 12.00% 10.34% 0.00% 11.84%

2.63% 0.00% 1.32% 3.95% 3.95% 0.00% 11.84%3 1 5 13 21 3 46

6.52% 2.17% 10.87% 28.26% 45.65% 6.52% 100.00%50.00% 100.00% 41.67% 52.00% 72.41% 100.00% 60.53%

3.95% 1.32% 6.58% 17.11% 27.63% 3.95% 60.53%

Somewhat Agree

Neither Agree Nor Disagree

Somewhat Disagree

Strongly Disagree

Don't Know

No Response

Total

Table: Q47 Has your company applied for a commercial (business) bank loan between 2008 and 2013? * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q47 Has your company applied for a commercial (business) bank loan between 2008 and 2013?Yes

No

Don't Know

No Response

Total

Table: Q48 Were you approved or denied for a commercial (business) bank loan? * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q48 Were you approved or denied for a commercial (business) bank loan?Approved

Denied

No Response

DRAFT



6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





0 0 1 0 0 0 10.00% 0.00% 100.00% 0.00% 0.00% 0.00% 100.00%0.00% 0.00% 8.33% 0.00% 0.00% 0.00% 1.32%0.00% 0.00% 1.32% 0.00% 0.00% 0.00% 1.32%

2 0 0 0 0 0 2100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00%

33.33% 0.00% 0.00% 0.00% 0.00% 0.00% 2.63%2.63% 0.00% 0.00% 0.00% 0.00% 0.00% 2.63%

0 0 0 2 3 0 50.00% 0.00% 0.00% 40.00% 60.00% 0.00% 100.00%0.00% 0.00% 0.00% 8.00% 10.34% 0.00% 6.58%0.00% 0.00% 0.00% 2.63% 3.95% 0.00% 6.58%

4 1 11 23 26 3 685.88% 1.47% 16.18% 33.82% 38.24% 4.41% 100.00%

66.67% 100.00% 91.67% 92.00% 89.66% 100.00% 89.47%5.26% 1.32% 14.47% 30.26% 34.21% 3.95% 89.47%

6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





3 0 3 1 1 0 837.50% 0.00% 37.50% 12.50% 12.50% 0.00% 100.00%50.00% 0.00% 25.00% 4.00% 3.45% 0.00% 10.53%

3.95% 0.00% 3.95% 1.32% 1.32% 0.00% 10.53%2 1 7 17 22 1 50

4.00% 2.00% 14.00% 34.00% 44.00% 2.00% 100.00%33.33% 100.00% 58.33% 68.00% 75.86% 33.33% 65.79%

2.63% 1.32% 9.21% 22.37% 28.95% 1.32% 65.79%1 0 2 7 6 1 17

5.88% 0.00% 11.76% 41.18% 35.29% 5.88% 100.00%16.67% 0.00% 16.67% 28.00% 20.69% 33.33% 22.37%

1.32% 0.00% 2.63% 9.21% 7.89% 1.32% 22.37%0 0 0 0 0 1 1

0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00%0.00% 0.00% 0.00% 0.00% 0.00% 33.33% 1.32%0.00% 0.00% 0.00% 0.00% 0.00% 1.32% 1.32%

6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





3 0 2 2 1 0 837.50% 0.00% 25.00% 25.00% 12.50% 0.00% 100.00%50.00% 0.00% 16.67% 8.00% 3.45% 0.00% 10.53%

3.95% 0.00% 2.63% 2.63% 1.32% 0.00% 10.53%0 0 1 0 0 0 1

0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 100.00%0.00% 0.00% 8.33% 0.00% 0.00% 0.00% 1.32%0.00% 0.00% 1.32% 0.00% 0.00% 0.00% 1.32%

Total

Table: Q49 Which of the following do you believe was the primary reason for your being denied a loan? * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q49 Which of the following do you believe was the primary reason for your being denied a loan?Insufficient Business History

Race or Ethnicity of Owner

Other, please specify

No Response

Total

Table: Q50 Have you experienced discriminatory behavior from the private sector between 2008 and 2013? * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q50 Have you experienced discriminatory behavior from the private sector between 2008 and 2013?Yes

No

Don't Know

No Response

Total


Race, Ethnicity, Gender Classification (Business Ownership Classification)Q51 What was the most noticeable way you became aware of the discrimination against your company?Verbal comment

Action taken against the company

DRAFT



3 1 9 23 28 3 674.48% 1.49% 13.43% 34.33% 41.79% 4.48% 100.00%

50.00% 100.00% 75.00% 92.00% 96.55% 100.00% 88.16%3.95% 1.32% 11.84% 30.26% 36.84% 3.95% 88.16%

6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





0 0 1 0 0 0 10.00% 0.00% 100.00% 0.00% 0.00% 0.00% 100.00%0.00% 0.00% 8.33% 0.00% 0.00% 0.00% 1.32%0.00% 0.00% 1.32% 0.00% 0.00% 0.00% 1.32%

0 0 1 1 0 0 20.00% 0.00% 50.00% 50.00% 0.00% 0.00% 100.00%0.00% 0.00% 8.33% 4.00% 0.00% 0.00% 2.63%0.00% 0.00% 1.32% 1.32% 0.00% 0.00% 2.63%

3 0 1 0 1 0 560.00% 0.00% 20.00% 0.00% 20.00% 0.00% 100.00%50.00% 0.00% 8.33% 0.00% 3.45% 0.00% 6.58%

3.95% 0.00% 1.32% 0.00% 1.32% 0.00% 6.58%3 1 9 24 28 3 68

4.41% 1.47% 13.24% 35.29% 41.18% 4.41% 100.00%50.00% 100.00% 75.00% 96.00% 96.55% 100.00% 89.47%

3.95% 1.32% 11.84% 31.58% 36.84% 3.95% 89.47%6 1 12 25 29 3 76

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%





2 0 1 1 1 0 540.00% 0.00% 20.00% 20.00% 20.00% 0.00% 100.00%33.33% 0.00% 8.33% 4.00% 3.45% 0.00% 6.58%

2.63% 0.00% 1.32% 1.32% 1.32% 0.00% 6.58%0 0 2 0 0 0 2

0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 100.00%0.00% 0.00% 16.67% 0.00% 0.00% 0.00% 2.63%0.00% 0.00% 2.63% 0.00% 0.00% 0.00% 2.63%

1 0 0 0 0 0 1100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00%

16.67% 0.00% 0.00% 0.00% 0.00% 0.00% 1.32%1.32% 0.00% 0.00% 0.00% 0.00% 0.00% 1.32%

3 1 9 24 28 3 684.41% 1.47% 13.24% 35.29% 41.18% 4.41% 100.00%

50.00% 100.00% 75.00% 96.00% 96.55% 100.00% 89.47%3.95% 1.32% 11.84% 31.58% 36.84% 3.95% 89.47%

6 1 12 25 29 3 767.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

No Response

Total


Race, Ethnicity, Gender Classification (Business Ownership Classification)Q52 Which of the following do you consider the primary reason for your company being discriminated against?Owner's race or ethnicity

Owner's gender

Both

No Response

Total


Q53 When did the discrimination first occur?During Bidding Process

Both

Don't Know

No Response

Total

DRAFT







2 0 1 1 1 0 540.00% 0.00% 20.00% 20.00% 20.00% 0.00% 100.00%33.33% 0.00% 8.33% 4.00% 3.45% 0.00% 6.58%

2.63% 0.00% 1.32% 1.32% 1.32% 0.00% 6.58%1 0 2 0 0 0 3

33.33% 0.00% 66.67% 0.00% 0.00% 0.00% 100.00%16.67% 0.00% 16.67% 0.00% 0.00% 0.00% 3.95%

1.32% 0.00% 2.63% 0.00% 0.00% 0.00% 3.95%3 1 9 24 28 3 68

4.41% 1.47% 13.24% 35.29% 41.18% 4.41% 100.00%50.00% 100.00% 75.00% 96.00% 96.55% 100.00% 89.47%

3.95% 1.32% 11.84% 31.58% 36.84% 3.95% 89.47%6 1 12 25 29 3 76

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

7.89% 1.32% 15.79% 32.89% 38.16% 3.95% 100.00%

No

No Response

Total

Table: Q54 Are you willing so speak with MGT or DPS to provide more detail of the alleged discrimination you have experienced by DPS or its prime contractors? * Race, Ethnicity, Gender Classification (Business Ownership Classification) [count, row %, column %, total %].

Race, Ethnicity, Gender Classification (Business Ownership Classification)Q54 Are you willing so speak with MGT or DPS to provide more detail of the alleged discrimination you have experienced by DPS or its prime contractors?Yes

DRAFT



M/WBE Firms Non-M/WBE Firms Other Total24 12 1 37

64.86% 32.43% 2.70% 100.00%54.55% 41.38% 33.33% 48.68%31.58% 15.79% 1.32% 48.68%

11 9 0 2055.00% 45.00% 0.00% 100.00%25.00% 31.03% 0.00% 26.32%14.47% 11.84% 0.00% 26.32%

6 2 1 966.67% 22.22% 11.11% 100.00%13.64% 6.90% 33.33% 11.84%

7.89% 2.63% 1.32% 11.84%3 6 1 10

30.00% 60.00% 10.00% 100.00%6.82% 20.69% 33.33% 13.16%3.95% 7.89% 1.32% 13.16%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


54.55% 42.42% 3.03% 100.00%40.91% 48.28% 33.33% 43.42%23.68% 18.42% 1.32% 43.42%

20 13 1 3458.82% 38.24% 2.94% 100.00%45.45% 44.83% 33.33% 44.74%26.32% 17.11% 1.32% 44.74%

6 2 1 966.67% 22.22% 11.11% 100.00%13.64% 6.90% 33.33% 11.84%

7.89% 2.63% 1.32% 11.84%44 29 3 76

57.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00%

57.89% 38.16% 3.95% 100.00%

Table: Q1 What is your title? * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].M/WBE or Non-M/WBE Classification (not based on certification)

Q1 What is your title?Owner

CEO/President

Manager/Financial Officer

Construction-related Professional Services (such as architecture, engineering environmental, structural, land development, etc.)

Other

Total

Other, Please Specify

Total

Table: Q3 Please specify your company's primary line of business * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].

M/WBE or Non-M/WBE Classification (not based on certification)Q3 Please specify your company's primary line of businessConstruction (such as general contractor, electrical, site work, HVAC, drywall, roofing, etc.)

DRAFT




100.00% 0.00% 0.00% 100.00%79.55% 0.00% 0.00% 46.05%46.05% 0.00% 0.00% 46.05%

9 29 3 4121.95% 70.73% 7.32% 100.00%20.45% 100.00% 100.00% 53.95%11.84% 38.16% 3.95% 53.95%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


49.02% 50.98% 0.00% 100.00%56.82% 89.66% 0.00% 67.11%32.89% 34.21% 0.00% 67.11%

6 0 0 6100.00% 0.00% 0.00% 100.00%

13.64% 0.00% 0.00% 7.89%7.89% 0.00% 0.00% 7.89%

1 0 0 1100.00% 0.00% 0.00% 100.00%

2.27% 0.00% 0.00% 1.32%1.32% 0.00% 0.00% 1.32%

12 0 0 12100.00% 0.00% 0.00% 100.00%

27.27% 0.00% 0.00% 15.79%15.79% 0.00% 0.00% 15.79%

0 3 0 30.00% 100.00% 0.00% 100.00%0.00% 10.34% 0.00% 3.95%0.00% 3.95% 0.00% 3.95%

0 0 3 30.00% 0.00% 100.00% 100.00%0.00% 0.00% 100.00% 3.95%0.00% 0.00% 3.95% 3.95%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%

Table: Q4 Is more than 50% of your company owned and operated by women? * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].

M/WBE or Non-M/WBE Classification (not based on certification)Q4 Is more than 50% of your company owned and operated by women?

Anglo/Caucasian/White

Black/African American

Asian

Hispanic or Latino

Don't Know

Prefer not to answer

Yes

No

Total

Table: Q5 Is more than 50% of the company owned and controlled by one of the following racial or ethnic groups? * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].

M/WBE or Non-M/WBE Classification (not based on certification)Q5 Is more than 50% of the company owned and controlled by one of the following racial or ethnic groups?

Total

DRAFT




100.00% 0.00% 0.00% 100.00%13.64% 0.00% 0.00% 7.89%

7.89% 0.00% 0.00% 7.89%1 0 0 1

100.00% 0.00% 0.00% 100.00%2.27% 0.00% 0.00% 1.32%1.32% 0.00% 0.00% 1.32%

12 0 0 12100.00% 0.00% 0.00% 100.00%

27.27% 0.00% 0.00% 15.79%15.79% 0.00% 0.00% 15.79%

25 0 0 25100.00% 0.00% 0.00% 100.00%

56.82% 0.00% 0.00% 32.89%32.89% 0.00% 0.00% 32.89%

0 29 0 290.00% 100.00% 0.00% 100.00%0.00% 100.00% 0.00% 38.16%0.00% 38.16% 0.00% 38.16%

0 0 3 30.00% 0.00% 100.00% 100.00%0.00% 0.00% 100.00% 3.95%0.00% 0.00% 3.95% 3.95%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


100.00% 0.00% 0.00% 100.00%2.27% 0.00% 0.00% 1.32%1.32% 0.00% 0.00% 1.32%

7 3 0 1070.00% 30.00% 0.00% 100.00%15.91% 10.34% 0.00% 13.16%

9.21% 3.95% 0.00% 13.16%20 16 0 36

55.56% 44.44% 0.00% 100.00%45.45% 55.17% 0.00% 47.37%26.32% 21.05% 0.00% 47.37%


Nonminority Female Firms

Non-M/WBE Firms

Other Firms

Total

Table: Q6 What is the highest level education completed by the primary owner of your company? * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].

Table: Race, Ethnicity, Gender Classification (Business Ownership Classification) * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].

M/WBE or Non-M/WBE Classification (not based on certification)Race, Ethnicity, Gender Classification (Business Ownership Classification)African American Firms


M/WBE or Non-M/WBE Classification (not based on certification)Q6 What is the highest level education completed by the primary owner of your company?Trade or Technical Education

Some College

College Degree

DRAFT



16 10 2 2857.14% 35.71% 7.14% 100.00%36.36% 34.48% 66.67% 36.84%21.05% 13.16% 2.63% 36.84%

0 0 1 10.00% 0.00% 100.00% 100.00%0.00% 0.00% 33.33% 1.32%0.00% 0.00% 1.32% 1.32%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


33.33% 66.67% 0.00% 100.00%4.55% 13.79% 0.00% 7.89%2.63% 5.26% 0.00% 7.89%

0 6 3 90.00% 66.67% 33.33% 100.00%0.00% 20.69% 100.00% 11.84%0.00% 7.89% 3.95% 11.84%

17 17 0 3450.00% 50.00% 0.00% 100.00%38.64% 58.62% 0.00% 44.74%22.37% 22.37% 0.00% 44.74%

18 1 0 1994.74% 5.26% 0.00% 100.00%40.91% 3.45% 0.00% 25.00%23.68% 1.32% 0.00% 25.00%

7 1 0 887.50% 12.50% 0.00% 100.00%15.91% 3.45% 0.00% 10.53%

9.21% 1.32% 0.00% 10.53%44 29 3 76

57.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00%

57.89% 38.16% 3.95% 100.00%


83.33% 16.67% 0.00% 100.00%11.36% 3.45% 0.00% 7.89%

6.58% 1.32% 0.00% 7.89%

Don't Know

Total

Table: Q7 What year was your company established? * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].

M/WBE or Non-M/WBE Classification (not based on certification)Q7 What year was your company established?Prior to 1960

Post Graduate Degree

M/WBE or Non-M/WBE Classification (not based on certification)Q8 How many years of experience in your company's line of business does the primary owner(s) of your firm have in the line of business?6-10 years

1961 to 1980

1981 to 2000

2001 to 2007

2008 to 2014

Total

Table: Q8 How many years of experience in your company's line of business does the primary owner(s) of your firm have in the line of business? * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].

DRAFT



5 1 0 683.33% 16.67% 0.00% 100.00%11.36% 3.45% 0.00% 7.89%

6.58% 1.32% 0.00% 7.89%9 1 0 10

90.00% 10.00% 0.00% 100.00%20.45% 3.45% 0.00% 13.16%11.84% 1.32% 0.00% 13.16%

25 26 3 5446.30% 48.15% 5.56% 100.00%56.82% 89.66% 100.00% 71.05%32.89% 34.21% 3.95% 71.05%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


65.63% 31.25% 3.13% 100.00%47.73% 34.48% 33.33% 42.11%27.63% 13.16% 1.32% 42.11%

9 4 0 1369.23% 30.77% 0.00% 100.00%20.45% 13.79% 0.00% 17.11%11.84% 5.26% 0.00% 17.11%

5 4 1 1050.00% 40.00% 10.00% 100.00%11.36% 13.79% 33.33% 13.16%

6.58% 5.26% 1.32% 13.16%3 2 0 5

60.00% 40.00% 0.00% 100.00%6.82% 6.90% 0.00% 6.58%3.95% 2.63% 0.00% 6.58%

6 9 1 1637.50% 56.25% 6.25% 100.00%13.64% 31.03% 33.33% 21.05%

7.89% 11.84% 1.32% 21.05%44 29 3 76

57.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00%

57.89% 38.16% 3.95% 100.00%

11-15 years

16-20 years

20+ years

21-30

31-40

41+

Total

Total

Table: Q9 In the last three years, what was the average number of employees your company kept on payroll, including full-time and part-time staff? * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].

M/WBE or Non-M/WBE Classification (not based on certification)Q9 In the last three years, what was the average number of employees your company kept on payroll, including full-time and part-time staff?0-10

11-20

DRAFT




100.00% 0.00% 0.00% 100.00%6.82% 0.00% 0.00% 3.95%3.95% 0.00% 0.00% 3.95%

3 0 0 3100.00% 0.00% 0.00% 100.00%

6.82% 0.00% 0.00% 3.95%3.95% 0.00% 0.00% 3.95%

3 3 0 650.00% 50.00% 0.00% 100.00%

6.82% 10.34% 0.00% 7.89%3.95% 3.95% 0.00% 7.89%

4 2 0 666.67% 33.33% 0.00% 100.00%

9.09% 6.90% 0.00% 7.89%5.26% 2.63% 0.00% 7.89%

10 1 0 1190.91% 9.09% 0.00% 100.00%22.73% 3.45% 0.00% 14.47%13.16% 1.32% 0.00% 14.47%

9 6 0 1560.00% 40.00% 0.00% 100.00%20.45% 20.69% 0.00% 19.74%11.84% 7.89% 0.00% 19.74%

3 5 0 837.50% 62.50% 0.00% 100.00%

6.82% 17.24% 0.00% 10.53%3.95% 6.58% 0.00% 10.53%

7 3 0 1070.00% 30.00% 0.00% 100.00%15.91% 10.34% 0.00% 13.16%

9.21% 3.95% 0.00% 13.16%1 9 2 12

8.33% 75.00% 16.67% 100.00%2.27% 31.03% 66.67% 15.79%1.32% 11.84% 2.63% 15.79%

1 0 1 250.00% 0.00% 50.00% 100.00%

2.27% 0.00% 33.33% 2.63%1.32% 0.00% 1.32% 2.63%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%

Table: Q10 Which of the following categories best approximate your company's gross revenues for calendar year 2013? * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].

M/WBE or Non-M/WBE Classification (not based on certification)Q10 Which of the following categories best approximate your company's gross revenues for calendar year 2013?

$3,000,001 to $5 million?

$5,000,001 to $10 million?

Over $10 million?

Don't Know

Total

Up to $50,000?

$50,001 to $100,000?

$100,001 to $300,000?

$300,001 to $500,000?

$500,001 to $1 million?

$1,000,001 to $3 million?

DRAFT




57.33% 38.67% 4.00% 100.00%100.00% 100.00% 100.00% 100.00%

57.33% 38.67% 4.00% 100.00%43 29 3 75

57.33% 38.67% 4.00% 100.00%100.00% 100.00% 100.00% 100.00%

57.33% 38.67% 4.00% 100.00%


54.55% 45.45% 0.00% 100.00%14.29% 18.52% 0.00% 15.28%

8.33% 6.94% 0.00% 15.28%5 3 0 8

62.50% 37.50% 0.00% 100.00%11.90% 11.11% 0.00% 11.11%

6.94% 4.17% 0.00% 11.11%4 3 1 8

50.00% 37.50% 12.50% 100.00%9.52% 11.11% 33.33% 11.11%5.56% 4.17% 1.39% 11.11%

9 3 1 1369.23% 23.08% 7.69% 100.00%21.43% 11.11% 33.33% 18.06%12.50% 4.17% 1.39% 18.06%

3 5 0 837.50% 62.50% 0.00% 100.00%

7.14% 18.52% 0.00% 11.11%4.17% 6.94% 0.00% 11.11%

2 3 1 633.33% 50.00% 16.67% 100.00%

4.76% 11.11% 33.33% 8.33%2.78% 4.17% 1.39% 8.33%

1 1 0 250.00% 50.00% 0.00% 100.00%

2.38% 3.70% 0.00% 2.78%1.39% 1.39% 0.00% 2.78%

Table: Q11 What percentage of these gross revenues was earned from the Schools, the private sector, and other public government projects? Schools * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].

Up to 10%

11% to 20%

21% to 30%

31% to 40%

41% to 50%

51% to 60%

M/WBE or Non-M/WBE Classification (not based on certification)

Q11 What percentage of these gross revenues was earned from the Schools, the private sector, and other public government projects? SchoolsUp to 10%

Total

Table: Q11 What percentage of these gross revenues was earned from the Schools, the private sector, and other public government projects? Private Sector * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].

M/WBE or Non-M/WBE Classification (not based on certification)Q11 What percentage of these gross revenues was earned from the Schools, the private sector, and other public government projects? Private Sector

61% to 70%

DRAFT



5 0 0 5100.00% 0.00% 0.00% 100.00%

11.90% 0.00% 0.00% 6.94%6.94% 0.00% 0.00% 6.94%

7 4 0 1163.64% 36.36% 0.00% 100.00%16.67% 14.81% 0.00% 15.28%

9.72% 5.56% 0.00% 15.28%42 27 3 72

58.33% 37.50% 4.17% 100.00%100.00% 100.00% 100.00% 100.00%

58.33% 37.50% 4.17% 100.00%


50.00% 50.00% 0.00% 100.00%17.07% 25.93% 0.00% 20.00%10.00% 10.00% 0.00% 20.00%

5 3 0 862.50% 37.50% 0.00% 100.00%12.20% 11.11% 0.00% 11.43%

7.14% 4.29% 0.00% 11.43%7 6 1 14

50.00% 42.86% 7.14% 100.00%17.07% 22.22% 50.00% 20.00%10.00% 8.57% 1.43% 20.00%

2 3 1 633.33% 50.00% 16.67% 100.00%

4.88% 11.11% 50.00% 8.57%2.86% 4.29% 1.43% 8.57%

2 3 0 540.00% 60.00% 0.00% 100.00%

4.88% 11.11% 0.00% 7.14%2.86% 4.29% 0.00% 7.14%

3 0 0 3100.00% 0.00% 0.00% 100.00%

7.32% 0.00% 0.00% 4.29%4.29% 0.00% 0.00% 4.29%

3 2 0 560.00% 40.00% 0.00% 100.00%

7.32% 7.41% 0.00% 7.14%4.29% 2.86% 0.00% 7.14%

5 0 0 5100.00% 0.00% 0.00% 100.00%

12.20% 0.00% 0.00% 7.14%7.14% 0.00% 0.00% 7.14%

Up to 10%

11% to 20%

21% to 30%

31% to 40%

41% to 50%

51% to 60%

71% to 80%

81% to 100%

Total

Table: Q11 What percentage of these gross revenues was earned from the Schools, the private sector, and other public government projects? Public Sector * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].

M/WBE or Non-M/WBE Classification (not based on certification)Q11 What percentage of these gross revenues was earned from the Schools, the private sector, and other public government projects? Public Sector

61% to 70%

71% to 80%

DRAFT



7 3 0 1070.00% 30.00% 0.00% 100.00%17.07% 11.11% 0.00% 14.29%10.00% 4.29% 0.00% 14.29%

41 27 2 7058.57% 38.57% 2.86% 100.00%

100.00% 100.00% 100.00% 100.00%58.57% 38.57% 2.86% 100.00%


100.00% 0.00% 0.00% 100.00%43.18% 0.00% 0.00% 25.00%25.00% 0.00% 0.00% 25.00%

25 29 3 5743.86% 50.88% 5.26% 100.00%56.82% 100.00% 100.00% 75.00%32.89% 38.16% 3.95% 75.00%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


85.71% 14.29% 0.00% 100.00%81.82% 20.69% 0.00% 55.26%47.37% 7.89% 0.00% 55.26%

8 23 3 3423.53% 67.65% 8.82% 100.00%18.18% 79.31% 100.00% 44.74%10.53% 30.26% 3.95% 44.74%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%

Yes

No Response

Total

Table: Q12 Does your company hold any of the following certifications from a recognized certification agency? Small Business Enterprise (SBE) * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].

M/WBE or Non-M/WBE Classification (not based on certification)Q12 Does your company hold any of the following certifications from a recognized certification agency? Small Business Enterprise (SBE)

81% to 100%

Total

Table: Q12 Does your company hold any of the following certifications from a recognized certification agency? Minority Business Enterprise (MBE) * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].

M/WBE or Non-M/WBE Classification (not based on certification)Q12 Does your company hold any of the following certifications from a recognized certification agency? Minority Business Enterprise (MBE)

Yes

No Response

Total

DRAFT




100.00% 0.00% 0.00% 100.00%52.27% 0.00% 0.00% 30.26%30.26% 0.00% 0.00% 30.26%

21 29 3 5339.62% 54.72% 5.66% 100.00%47.73% 100.00% 100.00% 69.74%27.63% 38.16% 3.95% 69.74%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


100.00% 0.00% 0.00% 100.00%59.09% 0.00% 0.00% 34.21%34.21% 0.00% 0.00% 34.21%

18 29 3 5036.00% 58.00% 6.00% 100.00%40.91% 100.00% 100.00% 65.79%23.68% 38.16% 3.95% 65.79%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


75.00% 0.00% 25.00% 100.00%6.82% 0.00% 33.33% 5.26%3.95% 0.00% 1.32% 5.26%

41 29 2 7256.94% 40.28% 2.78% 100.00%93.18% 100.00% 66.67% 94.74%53.95% 38.16% 2.63% 94.74%

Yes

No Response

Total

Table: Q12 Does your company hold any of the following certifications from a recognized certification agency? Disadvantaged Business Enterprise (DBE) * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].

M/WBE or Non-M/WBE Classification (not based on certification)Q12 Does your company hold any of the following certifications from a recognized certification agency? Disadvantaged Business Enterprise (DBE)

Table: Q12 Does your company hold any of the following certifications from a recognized certification agency? Woman Business Enterprise (WBE) * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].

M/WBE or Non-M/WBE Classification (not based on certification)Q12 Does your company hold any of the following certifications from a recognized certification agency? Woman Business Enterprise (WBE)

Yes

No Response

Yes

No Response

Total

Table: Q12 Does your company hold any of the following certifications from a recognized certification agency? HUBZone * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].

M/WBE or Non-M/WBE Classification (not based on certification)Q12 Does your company hold any of the following certifications from a recognized certification agency? HUBZone

DRAFT



44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


100.00% 0.00% 0.00% 100.00%6.82% 0.00% 0.00% 3.95%3.95% 0.00% 0.00% 3.95%

41 29 3 7356.16% 39.73% 4.11% 100.00%93.18% 100.00% 100.00% 96.05%53.95% 38.16% 3.95% 96.05%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


0.00% 100.00% 0.00% 100.00%0.00% 24.14% 0.00% 9.21%0.00% 9.21% 0.00% 9.21%

44 22 3 6963.77% 31.88% 4.35% 100.00%

100.00% 75.86% 100.00% 90.79%57.89% 28.95% 3.95% 90.79%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


70.83% 27.08% 2.08% 100.00%77.27% 44.83% 33.33% 63.16%44.74% 17.11% 1.32% 63.16%

Total

Table: Q12 Does your company hold any of the following certifications from a recognized certification agency? 8(A) * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].

M/WBE or Non-M/WBE Classification (not based on certification)Q12 Does your company hold any of the following certifications from a recognized certification agency? 8(A)

Yes

No Response

Total

Table: Q12 Does your company hold any of the following certifications from a recognized certification agency? Other? * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].

M/WBE or Non-M/WBE Classification (not based on certification)Q12 Does your company hold any of the following certifications from a recognized certification agency? Other?

Yes

No Response

Total

Table: Q12 Does your company hold any of the following certifications from a recognized certification agency? Don't Know * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].

M/WBE or Non-M/WBE Classification (not based on certification)Q12 Does your company hold any of the following certifications from a recognized certification agency? Don't Know

DRAFT



1 0 0 1100.00% 0.00% 0.00% 100.00%

2.27% 0.00% 0.00% 1.32%1.32% 0.00% 0.00% 1.32%

0 0 1 10.00% 0.00% 100.00% 100.00%0.00% 0.00% 33.33% 1.32%0.00% 0.00% 1.32% 1.32%

1 0 0 1100.00% 0.00% 0.00% 100.00%

2.27% 0.00% 0.00% 1.32%1.32% 0.00% 0.00% 1.32%

1 0 0 1100.00% 0.00% 0.00% 100.00%

2.27% 0.00% 0.00% 1.32%1.32% 0.00% 0.00% 1.32%

1 12 1 147.14% 85.71% 7.14% 100.00%2.27% 41.38% 33.33% 18.42%1.32% 15.79% 1.32% 18.42%

1 3 0 425.00% 75.00% 0.00% 100.00%

2.27% 10.34% 0.00% 5.26%1.32% 3.95% 0.00% 5.26%

1 0 0 1100.00% 0.00% 0.00% 100.00%

2.27% 0.00% 0.00% 1.32%1.32% 0.00% 0.00% 1.32%

1 0 0 1100.00% 0.00% 0.00% 100.00%

2.27% 0.00% 0.00% 1.32%1.32% 0.00% 0.00% 1.32%

1 0 0 1100.00% 0.00% 0.00% 100.00%

2.27% 0.00% 0.00% 1.32%1.32% 0.00% 0.00% 1.32%

1 1 0 250.00% 50.00% 0.00% 100.00%

2.27% 3.45% 0.00% 2.63%1.32% 1.32% 0.00% 2.63%

1 0 0 1100.00% 0.00% 0.00% 100.00%

2.27% 0.00% 0.00% 1.32%1.32% 0.00% 0.00% 1.32%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%

None of the above

None. We do business based on our own merit.

RTD SBE

SBA WBE/DBE self-certification

Veteran owned

WOSB, WBE pending

City and County of Denver, SBA, RTD

Do not wish to respond

EBE

ESB (CDOT); EDWOSB (SBA)

None

Total

DRAFT




41.67% 50.00% 8.33% 100.00%11.36% 20.69% 33.33% 15.79%

6.58% 7.89% 1.32% 15.79%10 8 0 18

55.56% 44.44% 0.00% 100.00%22.73% 27.59% 0.00% 23.68%13.16% 10.53% 0.00% 23.68%

7 14 0 2133.33% 66.67% 0.00% 100.00%15.91% 48.28% 0.00% 27.63%

9.21% 18.42% 0.00% 27.63%10 0 1 11

90.91% 0.00% 9.09% 100.00%22.73% 0.00% 33.33% 14.47%13.16% 0.00% 1.32% 14.47%

4 1 1 666.67% 16.67% 16.67% 100.00%

9.09% 3.45% 33.33% 7.89%5.26% 1.32% 1.32% 7.89%

6 0 0 6100.00% 0.00% 0.00% 100.00%

13.64% 0.00% 0.00% 7.89%7.89% 0.00% 0.00% 7.89%

2 0 0 2100.00% 0.00% 0.00% 100.00%

4.55% 0.00% 0.00% 2.63%2.63% 0.00% 0.00% 2.63%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


27.27% 68.18% 4.55% 100.00%13.64% 51.72% 33.33% 28.95%

7.89% 19.74% 1.32% 28.95%

Easy

Difficult

Somewhat Difficult

Extremely Difficult

Don't Know

Total

Table: Q13 On a scale from 1 to 6 (1 being extremely easy and 6 being extremely difficult) how would you rate your ease of obtaining notification of business opportunities with the Schools? * M/WBE or Non-M/WBE Classification (not based on certification) [count,

row %, column %, total %].M/WBE or Non-M/WBE Classification (not based on certification)

Q13 On a scale from 1 to 6 (1 being extremely easy and 6 being extremely difficult) how would you rate your ease of obtaining notification of business opportunities with the Schools?Extremely Easy

Somewhat Easy

Table: Q14 How do you obtain notification of bid/proposal opportunities? Schools' website * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].

M/WBE or Non-M/WBE Classification (not based on certification)Q14 How do you obtain notification of bid/proposal opportunities? Schools' websiteYes

DRAFT



38 14 2 5470.37% 25.93% 3.70% 100.00%86.36% 48.28% 66.67% 71.05%50.00% 18.42% 2.63% 71.05%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


51.16% 46.51% 2.33% 100.00%50.00% 68.97% 33.33% 56.58%28.95% 26.32% 1.32% 56.58%

22 9 2 3366.67% 27.27% 6.06% 100.00%50.00% 31.03% 66.67% 43.42%28.95% 11.84% 2.63% 43.42%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


54.84% 38.71% 6.45% 100.00%38.64% 41.38% 66.67% 40.79%22.37% 15.79% 2.63% 40.79%

27 17 1 4560.00% 37.78% 2.22% 100.00%61.36% 58.62% 33.33% 59.21%35.53% 22.37% 1.32% 59.21%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%

Table: Q14 How do you obtain notification of bid/proposal opportunities? Contact from the Schools * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].

M/WBE or Non-M/WBE Classification (not based on certification)Q14 How do you obtain notification of bid/proposal opportunities? Contact from the SchoolsYes

No Response

Total

No Response

Total

Table: Q14 How do you obtain notification of bid/proposal opportunities? Private bidding subscription * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].

M/WBE or Non-M/WBE Classification (not based on certification)Q14 How do you obtain notification of bid/proposal opportunities? Private bidding subscriptionYes

No Response

Total

DRAFT




73.33% 26.67% 0.00% 100.00%50.00% 27.59% 0.00% 39.47%28.95% 10.53% 0.00% 39.47%

22 21 3 4647.83% 45.65% 6.52% 100.00%50.00% 72.41% 100.00% 60.53%28.95% 27.63% 3.95% 60.53%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


75.00% 25.00% 0.00% 100.00%20.45% 10.34% 0.00% 15.79%11.84% 3.95% 0.00% 15.79%

35 26 3 6454.69% 40.63% 4.69% 100.00%79.55% 89.66% 100.00% 84.21%46.05% 34.21% 3.95% 84.21%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


16.67% 83.33% 0.00% 100.00%2.27% 17.24% 0.00% 7.89%1.32% 6.58% 0.00% 7.89%

43 24 3 7061.43% 34.29% 4.29% 100.00%97.73% 82.76% 100.00% 92.11%56.58% 31.58% 3.95% 92.11%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%

Table: Q14 How do you obtain notification of bid/proposal opportunities? Other Primes/Subcontractors * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].

M/WBE or Non-M/WBE Classification (not based on certification)Q14 How do you obtain notification of bid/proposal opportunities? Other Primes/SubcontractorsYes

No Response

Total

Table: Q14 How do you obtain notification of bid/proposal opportunities? Local newspapers with general circulation * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].

M/WBE or Non-M/WBE Classification (not based on certification)Q14 How do you obtain notification of bid/proposal opportunities? Local newspapers with general circulationYes

No Response

Total

Table: Q14 How do you obtain notification of bid/proposal opportunities? Trade or industry associations * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].

M/WBE or Non-M/WBE Classification (not based on certification)Q14 How do you obtain notification of bid/proposal opportunities? Trade or industry associationsYes

No Response

Total

DRAFT




0.00% 100.00% 0.00% 100.00%0.00% 3.45% 0.00% 1.32%0.00% 1.32% 0.00% 1.32%

44 28 3 7558.67% 37.33% 4.00% 100.00%

100.00% 96.55% 100.00% 98.68%57.89% 36.84% 3.95% 98.68%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


54.84% 41.94% 3.23% 100.00%77.27% 89.66% 66.67% 81.58%44.74% 34.21% 2.63% 81.58%

1 0 0 1100.00% 0.00% 0.00% 100.00%

2.27% 0.00% 0.00% 1.32%1.32% 0.00% 0.00% 1.32%

0 0 1 10.00% 0.00% 100.00% 100.00%0.00% 0.00% 33.33% 1.32%0.00% 0.00% 1.32% 1.32%

1 0 0 1100.00% 0.00% 0.00% 100.00%

2.27% 0.00% 0.00% 1.32%1.32% 0.00% 0.00% 1.32%

1 0 0 1100.00% 0.00% 0.00% 100.00%

2.27% 0.00% 0.00% 1.32%1.32% 0.00% 0.00% 1.32%

1 0 0 1100.00% 0.00% 0.00% 100.00%

2.27% 0.00% 0.00% 1.32%1.32% 0.00% 0.00% 1.32%

1 0 0 1100.00% 0.00% 0.00% 100.00%

2.27% 0.00% 0.00% 1.32%1.32% 0.00% 0.00% 1.32%

Table: Q14 How do you obtain notification of bid/proposal opportunities? Other. Specify * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].

M/WBE or Non-M/WBE Classification (not based on certification)Q14 How do you obtain notification of bid/proposal opportunities? Other. Specify

Architects

Cold calling, some Colorado bids system, previously on bidders list, etc.

Table: Q14 How do you obtain notification of bid/proposal opportunities? Don't Know * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].

M/WBE or Non-M/WBE Classification (not based on certification)Q14 How do you obtain notification of bid/proposal opportunities? Don't Know7

No Response

Total

Construction companies

E mail blasts

None

Notification from General Contractors

DRAFT



1 0 0 1100.00% 0.00% 0.00% 100.00%

2.27% 0.00% 0.00% 1.32%1.32% 0.00% 0.00% 1.32%

0 1 0 10.00% 100.00% 0.00% 100.00%0.00% 3.45% 0.00% 1.32%0.00% 1.32% 0.00% 1.32%

0 1 0 10.00% 100.00% 0.00% 100.00%0.00% 3.45% 0.00% 1.32%0.00% 1.32% 0.00% 1.32%

1 0 0 1100.00% 0.00% 0.00% 100.00%

2.27% 0.00% 0.00% 1.32%1.32% 0.00% 0.00% 1.32%

0 1 0 10.00% 100.00% 0.00% 100.00%0.00% 3.45% 0.00% 1.32%0.00% 1.32% 0.00% 1.32%

1 0 0 1100.00% 0.00% 0.00% 100.00%

2.27% 0.00% 0.00% 1.32%1.32% 0.00% 0.00% 1.32%

1 0 0 1100.00% 0.00% 0.00% 100.00%

2.27% 0.00% 0.00% 1.32%1.32% 0.00% 0.00% 1.32%

1 0 0 1100.00% 0.00% 0.00% 100.00%

2.27% 0.00% 0.00% 1.32%1.32% 0.00% 0.00% 1.32%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


75.00% 25.00% 0.00% 100.00%13.64% 6.90% 0.00% 10.53%

7.89% 2.63% 0.00% 10.53%4 0 0 4

100.00% 0.00% 0.00% 100.00%9.09% 0.00% 0.00% 5.26%5.26% 0.00% 0.00% 5.26%

Via emails

cluttered emails from school dist.

email

email notifications

emails bids

emails forwarded from different resources

School Bond: Proposed Improvement Lists

Schools Bid emails by DPS Purchasing

Total

Table: Q15 In general, which of the following dollar ranges best approximate your company's largest prime contract awarded between 2008 and 2013 * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].

M/WBE or Non-M/WBE Classification (not based on certification)Q15 In general, which of the following dollar ranges best approximate your company's largest prime contract awarded between 2008 and 2013Not applicable (not a prime)

None

DRAFT



1 0 0 1100.00% 0.00% 0.00% 100.00%

2.27% 0.00% 0.00% 1.32%1.32% 0.00% 0.00% 1.32%

3 2 0 560.00% 40.00% 0.00% 100.00%

6.82% 6.90% 0.00% 6.58%3.95% 2.63% 0.00% 6.58%

6 0 0 6100.00% 0.00% 0.00% 100.00%

13.64% 0.00% 0.00% 7.89%7.89% 0.00% 0.00% 7.89%

2 1 0 366.67% 33.33% 0.00% 100.00%

4.55% 3.45% 0.00% 3.95%2.63% 1.32% 0.00% 3.95%

1 1 1 333.33% 33.33% 33.33% 100.00%

2.27% 3.45% 33.33% 3.95%1.32% 1.32% 1.32% 3.95%

4 0 0 4100.00% 0.00% 0.00% 100.00%

9.09% 0.00% 0.00% 5.26%5.26% 0.00% 0.00% 5.26%

3 4 0 742.86% 57.14% 0.00% 100.00%

6.82% 13.79% 0.00% 9.21%3.95% 5.26% 0.00% 9.21%

12 18 2 3237.50% 56.25% 6.25% 100.00%27.27% 62.07% 66.67% 42.11%15.79% 23.68% 2.63% 42.11%

2 1 0 366.67% 33.33% 0.00% 100.00%

4.55% 3.45% 0.00% 3.95%2.63% 1.32% 0.00% 3.95%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


52.38% 42.86% 4.76% 100.00%25.00% 31.03% 33.33% 27.63%14.47% 11.84% 1.32% 27.63%

Up to $50,000?

$50,001 to $100,000?

$100,001 to $200,000?

$200,001 to $300,000?

$300,001 to $400,000?

$400,001 to $500,000?

Yes

$500,001 to $1 million?

Over $1 million?

Don't Know

Total

Table: Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Prequalification Requirements? * M/WBE or Non-M/WBE Classification (not based on certification) [count,

row %, column %, total %].M/WBE or Non-M/WBE Classification (not based on certification)

Q y p , y following been a barrier to obtaining work on School projects as a prime contractor/consultant? Prequalification

DRAFT



23 17 2 4254.76% 40.48% 4.76% 100.00%52.27% 58.62% 66.67% 55.26%30.26% 22.37% 2.63% 55.26%

4 1 0 580.00% 20.00% 0.00% 100.00%

9.09% 3.45% 0.00% 6.58%5.26% 1.32% 0.00% 6.58%

6 2 0 875.00% 25.00% 0.00% 100.00%13.64% 6.90% 0.00% 10.53%

7.89% 2.63% 0.00% 10.53%44 29 3 76

57.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00%

57.89% 38.16% 3.95% 100.00%


50.00% 40.00% 10.00% 100.00%11.36% 13.79% 33.33% 13.16%

6.58% 5.26% 1.32% 13.16%29 22 2 53

54.72% 41.51% 3.77% 100.00%65.91% 75.86% 66.67% 69.74%38.16% 28.95% 2.63% 69.74%

4 1 0 580.00% 20.00% 0.00% 100.00%

9.09% 3.45% 0.00% 6.58%5.26% 1.32% 0.00% 6.58%

6 2 0 875.00% 25.00% 0.00% 100.00%13.64% 6.90% 0.00% 10.53%

7.89% 2.63% 0.00% 10.53%44 29 3 76

57.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00%

57.89% 38.16% 3.95% 100.00%

No

Don't Know

No Response

Total

Table: Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Bid bond requirements? * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %,

column %, total %].

Total


Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Bid bond requirements?Yes

No

Don't Know

No Response

DRAFT




55.56% 44.44% 0.00% 100.00%11.36% 13.79% 0.00% 11.84%

6.58% 5.26% 0.00% 11.84%28 22 2 52

53.85% 42.31% 3.85% 100.00%63.64% 75.86% 66.67% 68.42%36.84% 28.95% 2.63% 68.42%

5 1 1 771.43% 14.29% 14.29% 100.00%11.36% 3.45% 33.33% 9.21%

6.58% 1.32% 1.32% 9.21%6 2 0 8

75.00% 25.00% 0.00% 100.00%13.64% 6.90% 0.00% 10.53%

7.89% 2.63% 0.00% 10.53%44 29 3 76

57.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00%

57.89% 38.16% 3.95% 100.00%


70.59% 23.53% 5.88% 100.00%27.27% 13.79% 33.33% 22.37%15.79% 5.26% 1.32% 22.37%

22 23 2 4746.81% 48.94% 4.26% 100.00%50.00% 79.31% 66.67% 61.84%28.95% 30.26% 2.63% 61.84%

4 0 0 4100.00% 0.00% 0.00% 100.00%

9.09% 0.00% 0.00% 5.26%5.26% 0.00% 0.00% 5.26%

6 2 0 875.00% 25.00% 0.00% 100.00%13.64% 6.90% 0.00% 10.53%

7.89% 2.63% 0.00% 10.53%

Table: Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Performance/payment bond requirement? * M/WBE or Non-M/WBE Classification (not based on

certification) [count, row %, column %, total %].M/WBE or Non-M/WBE Classification (not based on certification)

Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Performance/payment bond requirement?Yes

No

Yes

No

Don't Know

No Response

Don't Know

No Response

Total

Table: Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Cost of bidding/proposing? * M/WBE or Non-M/WBE Classification (not based on certification) [count, row

%, column %, total %].M/WBE or Non-M/WBE Classification (not based on certification)Q16 In your experience, have any of the

following been a barrier to obtaining work on School projects as a prime contractor/consultant? Cost of bidding/proposing?

DRAFT



44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


55.56% 44.44% 0.00% 100.00%11.36% 13.79% 0.00% 11.84%

6.58% 5.26% 0.00% 11.84%29 22 3 54

53.70% 40.74% 5.56% 100.00%65.91% 75.86% 100.00% 71.05%38.16% 28.95% 3.95% 71.05%

4 1 0 580.00% 20.00% 0.00% 100.00%

9.09% 3.45% 0.00% 6.58%5.26% 1.32% 0.00% 6.58%

6 2 0 875.00% 25.00% 0.00% 100.00%13.64% 6.90% 0.00% 10.53%

7.89% 2.63% 0.00% 10.53%44 29 3 76

57.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00%

57.89% 38.16% 3.95% 100.00%


66.67% 33.33% 0.00% 100.00%9.09% 6.90% 0.00% 7.89%5.26% 2.63% 0.00% 7.89%

31 25 3 5952.54% 42.37% 5.08% 100.00%70.45% 86.21% 100.00% 77.63%40.79% 32.89% 3.95% 77.63%

3 0 0 3100.00% 0.00% 0.00% 100.00%

6.82% 0.00% 0.00% 3.95%3.95% 0.00% 0.00% 3.95%

Total

Table: Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Financing? * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %,

total %].

Total

Table: Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Insurance (general liability, professional liability, etc.)? * M/WBE or Non-M/WBE Classification (not based on

certification) [count, row %, column %, total %].M/WBE or Non-M/WBE Classification (not based on certification)Q16 In your experience, have any of the

following been a barrier to obtaining work on School projects as a prime contractor/consultant? Insurance (general liability, professional liability, etc.)?Yes

No

M/WBE or Non-M/WBE Classification (not based on certification)Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Financing?Yes

No

Don't Know

No Response

Don't Know

DRAFT



6 2 0 875.00% 25.00% 0.00% 100.00%13.64% 6.90% 0.00% 10.53%

7.89% 2.63% 0.00% 10.53%44 29 3 76

57.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00%

57.89% 38.16% 3.95% 100.00%


75.00% 25.00% 0.00% 100.00%13.64% 6.90% 0.00% 10.53%

7.89% 2.63% 0.00% 10.53%27 24 3 54

50.00% 44.44% 5.56% 100.00%61.36% 82.76% 100.00% 71.05%35.53% 31.58% 3.95% 71.05%

5 1 0 683.33% 16.67% 0.00% 100.00%11.36% 3.45% 0.00% 7.89%

6.58% 1.32% 0.00% 7.89%6 2 0 8

75.00% 25.00% 0.00% 100.00%13.64% 6.90% 0.00% 10.53%

7.89% 2.63% 0.00% 10.53%44 29 3 76

57.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00%

57.89% 38.16% 3.95% 100.00%


55.00% 35.00% 10.00% 100.00%25.00% 24.14% 66.67% 26.32%14.47% 9.21% 2.63% 26.32%

23 20 1 4452.27% 45.45% 2.27% 100.00%52.27% 68.97% 33.33% 57.89%30.26% 26.32% 1.32% 57.89%

Yes

No

Don't Know

No Response

Total

Table: Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Proposal/Bid specifications * M/WBE or Non-M/WBE Classification (not based on certification) [count, row

%, column %, total %].

No Response

Total

Table: Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Price of supplies/materials * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %,

column %, total %].M/WBE or Non-M/WBE Classification (not based on certification)

Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Price of supplies/materials

M/WBE or Non-M/WBE Classification (not based on certification)Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Proposal/Bid specificationsYes

No

DRAFT



4 0 0 4100.00% 0.00% 0.00% 100.00%

9.09% 0.00% 0.00% 5.26%5.26% 0.00% 0.00% 5.26%

6 2 0 875.00% 25.00% 0.00% 100.00%13.64% 6.90% 0.00% 10.53%

7.89% 2.63% 0.00% 10.53%44 29 3 76

57.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00%

57.89% 38.16% 3.95% 100.00%


51.85% 40.74% 7.41% 100.00%31.82% 37.93% 66.67% 35.53%18.42% 14.47% 2.63% 35.53%

19 16 1 3652.78% 44.44% 2.78% 100.00%43.18% 55.17% 33.33% 47.37%25.00% 21.05% 1.32% 47.37%

5 0 0 5100.00% 0.00% 0.00% 100.00%

11.36% 0.00% 0.00% 6.58%6.58% 0.00% 0.00% 6.58%

6 2 0 875.00% 25.00% 0.00% 100.00%13.64% 6.90% 0.00% 10.53%

7.89% 2.63% 0.00% 10.53%44 29 3 76

57.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00%

57.89% 38.16% 3.95% 100.00%

Total

Table: Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Limited time given to prepare bid package or quote * M/WBE or Non-M/WBE Classification (not based on

certification) [count, row %, column %, total %].M/WBE or Non-M/WBE Classification (not based on certification)Q16 In your experience, have any of the

following been a barrier to obtaining work on School projects as a prime contractor/consultant? Limited time given to prepare bid package or quoteYes

No

Don't Know

No Response

Don't Know

No Response

Total

DRAFT




58.33% 41.67% 0.00% 100.00%15.91% 17.24% 0.00% 15.79%

9.21% 6.58% 0.00% 15.79%27 22 3 52

51.92% 42.31% 5.77% 100.00%61.36% 75.86% 100.00% 68.42%35.53% 28.95% 3.95% 68.42%

4 0 0 4100.00% 0.00% 0.00% 100.00%

9.09% 0.00% 0.00% 5.26%5.26% 0.00% 0.00% 5.26%

6 2 0 875.00% 25.00% 0.00% 100.00%13.64% 6.90% 0.00% 10.53%

7.89% 2.63% 0.00% 10.53%44 29 3 76

57.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00%

57.89% 38.16% 3.95% 100.00%


61.54% 38.46% 0.00% 100.00%18.18% 17.24% 0.00% 17.11%10.53% 6.58% 0.00% 17.11%

26 22 3 5150.98% 43.14% 5.88% 100.00%59.09% 75.86% 100.00% 67.11%34.21% 28.95% 3.95% 67.11%

4 0 0 4100.00% 0.00% 0.00% 100.00%

9.09% 0.00% 0.00% 5.26%5.26% 0.00% 0.00% 5.26%

6 2 0 875.00% 25.00% 0.00% 100.00%13.64% 6.90% 0.00% 10.53%

7.89% 2.63% 0.00% 10.53%

Yes

No

Don't Know

No Response

Total

Table: Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Lack of experience * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column

%, total %].

Table: Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Limited knowledge of purchasing contracting policies and procedures * M/WBE or Non-M/WBE Classification

(not based on certification) [count, row %, column %, total %].M/WBE or Non-M/WBE Classification (not based on certification)

Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Limited knowledge of purchasing contracting policies and procedures

M/WBE or Non-M/WBE Classification (not based on certification)Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Lack of experienceYes

No

Don't Know

No Response

DRAFT



44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


36.36% 63.64% 0.00% 100.00%9.09% 24.14% 0.00% 14.47%5.26% 9.21% 0.00% 14.47%

32 20 3 5558.18% 36.36% 5.45% 100.00%72.73% 68.97% 100.00% 72.37%42.11% 26.32% 3.95% 72.37%

2 0 0 2100.00% 0.00% 0.00% 100.00%

4.55% 0.00% 0.00% 2.63%2.63% 0.00% 0.00% 2.63%

6 2 0 875.00% 25.00% 0.00% 100.00%13.64% 6.90% 0.00% 10.53%

7.89% 2.63% 0.00% 10.53%44 29 3 76

57.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00%

57.89% 38.16% 3.95% 100.00%


41.67% 50.00% 8.33% 100.00%11.36% 20.69% 33.33% 15.79%

6.58% 7.89% 1.32% 15.79%29 21 2 52

55.77% 40.38% 3.85% 100.00%65.91% 72.41% 66.67% 68.42%38.16% 27.63% 2.63% 68.42%

4 0 0 4100.00% 0.00% 0.00% 100.00%

9.09% 0.00% 0.00% 5.26%5.26% 0.00% 0.00% 5.26%

Total

Table: Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Lack of personnel * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column

%, total %].M/WBE or Non-M/WBE Classification (not based on certification)Q16 In your experience, have any of the

following been a barrier to obtaining work on School projects as a prime contractor/consultant? Lack of personnelYes

No

Yes

No

Don't Know

Don't Know

No Response

Total

Table: Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Contract too large * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column

%, total %].M/WBE or Non-M/WBE Classification (not based on certification)Q16 In your experience, have any of the

following been a barrier to obtaining work on School projects as a prime contractor/consultant? Contract too large

DRAFT



6 2 0 875.00% 25.00% 0.00% 100.00%13.64% 6.90% 0.00% 10.53%

7.89% 2.63% 0.00% 10.53%44 29 3 76

57.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00%

57.89% 38.16% 3.95% 100.00%


60.00% 33.33% 6.67% 100.00%61.36% 51.72% 100.00% 59.21%35.53% 19.74% 3.95% 59.21%

8 12 0 2040.00% 60.00% 0.00% 100.00%18.18% 41.38% 0.00% 26.32%10.53% 15.79% 0.00% 26.32%

3 0 0 3100.00% 0.00% 0.00% 100.00%

6.82% 0.00% 0.00% 3.95%3.95% 0.00% 0.00% 3.95%

6 2 0 875.00% 25.00% 0.00% 100.00%13.64% 6.90% 0.00% 10.53%

7.89% 2.63% 0.00% 10.53%44 29 3 76

57.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00%

57.89% 38.16% 3.95% 100.00%


55.56% 37.04% 7.41% 100.00%34.09% 34.48% 66.67% 35.53%19.74% 13.16% 2.63% 35.53%

15 17 1 3345.45% 51.52% 3.03% 100.00%34.09% 58.62% 33.33% 43.42%19.74% 22.37% 1.32% 43.42%

No Response

Total

Table: Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Selection process * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column

%, total %].

Total

Table: Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Unnecessary restrictive contract specifications * M/WBE or Non-M/WBE Classification (not based on


Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Unnecessary restrictive contract specificationsYes

No

M/WBE or Non-M/WBE Classification (not based on certification)Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Selection processYes

No

Don't Know

No Response

DRAFT



8 0 0 8100.00% 0.00% 0.00% 100.00%

18.18% 0.00% 0.00% 10.53%10.53% 0.00% 0.00% 10.53%

6 2 0 875.00% 25.00% 0.00% 100.00%13.64% 6.90% 0.00% 10.53%

7.89% 2.63% 0.00% 10.53%44 29 3 76

57.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00%

57.89% 38.16% 3.95% 100.00%


64.29% 35.71% 0.00% 100.00%20.45% 17.24% 0.00% 18.42%11.84% 6.58% 0.00% 18.42%

20 22 2 4445.45% 50.00% 4.55% 100.00%45.45% 75.86% 66.67% 57.89%26.32% 28.95% 2.63% 57.89%

9 0 1 1090.00% 0.00% 10.00% 100.00%20.45% 0.00% 33.33% 13.16%11.84% 0.00% 1.32% 13.16%

6 2 0 875.00% 25.00% 0.00% 100.00%13.64% 6.90% 0.00% 10.53%

7.89% 2.63% 0.00% 10.53%44 29 3 76

57.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00%

57.89% 38.16% 3.95% 100.00%

Yes

No

Don't Know

No Response

Total

Don't Know

No Response

Total

Table: Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Slow payment or nonpayment * M/WBE or Non-M/WBE Classification (not based on certification) [count,

row %, column %, total %].M/WBE or Non-M/WBE Classification (not based on certification)Q16 In your experience, have any of the

following been a barrier to obtaining work on School projects as a prime contractor/consultant? Slow payment or nonpayment

DRAFT




63.64% 30.30% 6.06% 100.00%47.73% 34.48% 66.67% 43.42%27.63% 13.16% 2.63% 43.42%

12 16 1 2941.38% 55.17% 3.45% 100.00%27.27% 55.17% 33.33% 38.16%15.79% 21.05% 1.32% 38.16%

5 1 0 683.33% 16.67% 0.00% 100.00%11.36% 3.45% 0.00% 7.89%

6.58% 1.32% 0.00% 7.89%6 2 0 8

75.00% 25.00% 0.00% 100.00%13.64% 6.90% 0.00% 10.53%

7.89% 2.63% 0.00% 10.53%44 29 3 76

57.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00%

57.89% 38.16% 3.95% 100.00%


73.33% 26.67% 0.00% 100.00%50.00% 27.59% 0.00% 39.47%28.95% 10.53% 0.00% 39.47%

10 9 1 2050.00% 45.00% 5.00% 100.00%22.73% 31.03% 33.33% 26.32%13.16% 11.84% 1.32% 26.32%

3 6 1 1030.00% 60.00% 10.00% 100.00%

6.82% 20.69% 33.33% 13.16%3.95% 7.89% 1.32% 13.16%

2 2 0 450.00% 50.00% 0.00% 100.00%

4.55% 6.90% 0.00% 5.26%2.63% 2.63% 0.00% 5.26%

Table: Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Competing with large companies * M/WBE or Non-M/WBE Classification (not based on certification) [count,

row %, column %, total %].

Total

Table: Q17 Between 2008 and 2013, how many times has your company been awarded a School project as a prime contractor/consultant? * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].


Q17 Between 2008 and 2013, how many times has your company been awarded a School project as a prime contractor/consultant?None

1-10 Times

M/WBE or Non-M/WBE Classification (not based on certification)Q16 In your experience, have any of the following been a barrier to obtaining work on School projects as a prime contractor/consultant? Competing with large companiesYes

No

Don't Know

No Response

11-25 Times

26-50 Times

DRAFT



0 0 1 10.00% 0.00% 100.00% 100.00%0.00% 0.00% 33.33% 1.32%0.00% 0.00% 1.32% 1.32%

0 1 0 10.00% 100.00% 0.00% 100.00%0.00% 3.45% 0.00% 1.32%0.00% 1.32% 0.00% 1.32%

1 1 0 250.00% 50.00% 0.00% 100.00%

2.27% 3.45% 0.00% 2.63%1.32% 1.32% 0.00% 2.63%

6 2 0 875.00% 25.00% 0.00% 100.00%13.64% 6.90% 0.00% 10.53%

7.89% 2.63% 0.00% 10.53%44 29 3 76

57.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00%

57.89% 38.16% 3.95% 100.00%


14.29% 85.71% 0.00% 100.00%2.27% 20.69% 0.00% 9.21%1.32% 7.89% 0.00% 9.21%

8 10 2 2040.00% 50.00% 10.00% 100.00%18.18% 34.48% 66.67% 26.32%10.53% 13.16% 2.63% 26.32%

5 2 1 862.50% 25.00% 12.50% 100.00%11.36% 6.90% 33.33% 10.53%

6.58% 2.63% 1.32% 10.53%2 0 0 2

100.00% 0.00% 0.00% 100.00%4.55% 0.00% 0.00% 2.63%2.63% 0.00% 0.00% 2.63%

0 1 0 10.00% 100.00% 0.00% 100.00%0.00% 3.45% 0.00% 1.32%0.00% 1.32% 0.00% 1.32%

1 0 0 1100.00% 0.00% 0.00% 100.00%

2.27% 0.00% 0.00% 1.32%1.32% 0.00% 0.00% 1.32%

Total

Table: Q18 When you were a prime contractor/consultant, what was the average amount of time that it typically took to receive payment for your services on Schools funded projects * M/WBE or Non-M/WBE Classification (not based on certification) [count,

row %, column %, total %].M/WBE or Non-M/WBE Classification (not based on certification)Q18 When you were a prime

contractor/consultant, what was the average amount of time that it typically took to receive payment for your services on Schools funded projectsLess Than 30 Days

51-100 Times

Over 100 Times

Don't Know

No Response

31-60 Days

61-90 Days

91-120 Days

Over 120 Days

Not Applicable

DRAFT



27 10 0 3772.97% 27.03% 0.00% 100.00%61.36% 34.48% 0.00% 48.68%35.53% 13.16% 0.00% 48.68%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


45.45% 36.36% 18.18% 100.00%11.36% 13.79% 66.67% 14.47%

6.58% 5.26% 2.63% 14.47%29 22 0 51

56.86% 43.14% 0.00% 100.00%65.91% 75.86% 0.00% 67.11%38.16% 28.95% 0.00% 67.11%

4 1 1 666.67% 16.67% 16.67% 100.00%

9.09% 3.45% 33.33% 7.89%5.26% 1.32% 1.32% 7.89%

6 2 0 875.00% 25.00% 0.00% 100.00%13.64% 6.90% 0.00% 10.53%

7.89% 2.63% 0.00% 10.53%44 29 3 76

57.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00%

57.89% 38.16% 3.95% 100.00%


48.39% 45.16% 6.45% 100.00%34.09% 48.28% 66.67% 40.79%19.74% 18.42% 2.63% 40.79%

18 13 1 3256.25% 40.63% 3.13% 100.00%40.91% 44.83% 33.33% 42.11%23.68% 17.11% 1.32% 42.11%

Total

Table: Q19 Between 2008 and 2013, have you ever submitted a bid or quote for a Schools contract, were informed that you were the lowest bidder, and then found out that another firm was actually doing the work? * M/WBE or Non-M/WBE Classification (not

based on certification) [count, row %, column %, total %].M/WBE or Non-M/WBE Classification (not based on certification)Q19 Between 2008 and 2013, have you ever

submitted a bid or quote for a Schools contract, were informed that you were the lowest bidder, and then found out that another firm was actually doing the work?Yes

No

No Response

Yes

No

Don't Know

No Response

Total

Table: Q20 As a prime contractor/consultant, are you required to have bonding? * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].

M/WBE or Non-M/WBE Classification (not based on certification)Q20 As a prime contractor/consultant, are you required to have bonding?

DRAFT



5 0 0 5100.00% 0.00% 0.00% 100.00%

11.36% 0.00% 0.00% 6.58%6.58% 0.00% 0.00% 6.58%

6 2 0 875.00% 25.00% 0.00% 100.00%13.64% 6.90% 0.00% 10.53%

7.89% 2.63% 0.00% 10.53%44 29 3 76

57.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00%

57.89% 38.16% 3.95% 100.00%


100.00% 0.00% 0.00% 100.00%4.55% 0.00% 0.00% 2.63%2.63% 0.00% 0.00% 2.63%

1 1 0 250.00% 50.00% 0.00% 100.00%

2.27% 3.45% 0.00% 2.63%1.32% 1.32% 0.00% 2.63%

3 1 0 475.00% 25.00% 0.00% 100.00%

6.82% 3.45% 0.00% 5.26%3.95% 1.32% 0.00% 5.26%

2 0 0 2100.00% 0.00% 0.00% 100.00%

4.55% 0.00% 0.00% 2.63%2.63% 0.00% 0.00% 2.63%

1 2 0 333.33% 66.67% 0.00% 100.00%

2.27% 6.90% 0.00% 3.95%1.32% 2.63% 0.00% 3.95%

1 0 0 1100.00% 0.00% 0.00% 100.00%

2.27% 0.00% 0.00% 1.32%1.32% 0.00% 0.00% 1.32%

4 8 2 1428.57% 57.14% 14.29% 100.00%

9.09% 27.59% 66.67% 18.42%5.26% 10.53% 2.63% 18.42%

1 2 0 333.33% 66.67% 0.00% 100.00%

2.27% 6.90% 0.00% 3.95%1.32% 2.63% 0.00% 3.95%

29 15 1 4564.44% 33.33% 2.22% 100.00%65.91% 51.72% 33.33% 59.21%38.16% 19.74% 1.32% 59.21%

Don't Know

No Response

Total

Table: Q21 What is your current aggregate bonding limit? * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].

$1,500,001 to $3 million

$3,00,001 to $5 million

Over $5 million

Don't Know

No Response

M/WBE or Non-M/WBE Classification (not based on certification)Q21 What is your current aggregate bonding limit?Below $100,000

$100,001 to $250,000


$1,000,001 to $1.5 million

DRAFT



44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


100.00% 0.00% 0.00% 100.00%4.55% 0.00% 0.00% 2.63%2.63% 0.00% 0.00% 2.63%

1 1 0 250.00% 50.00% 0.00% 100.00%

2.27% 3.45% 0.00% 2.63%1.32% 1.32% 0.00% 2.63%

1 1 0 250.00% 50.00% 0.00% 100.00%

2.27% 3.45% 0.00% 2.63%1.32% 1.32% 0.00% 2.63%

2 1 0 366.67% 33.33% 0.00% 100.00%

4.55% 3.45% 0.00% 3.95%2.63% 1.32% 0.00% 3.95%

1 1 0 250.00% 50.00% 0.00% 100.00%

2.27% 3.45% 0.00% 2.63%1.32% 1.32% 0.00% 2.63%

0 2 0 20.00% 100.00% 0.00% 100.00%0.00% 6.90% 0.00% 2.63%0.00% 2.63% 0.00% 2.63%

4 0 0 4100.00% 0.00% 0.00% 100.00%

9.09% 0.00% 0.00% 5.26%5.26% 0.00% 0.00% 5.26%

2 7 2 1118.18% 63.64% 18.18% 100.00%

4.55% 24.14% 66.67% 14.47%2.63% 9.21% 2.63% 14.47%

2 1 0 366.67% 33.33% 0.00% 100.00%

4.55% 3.45% 0.00% 3.95%2.63% 1.32% 0.00% 3.95%

29 15 1 4564.44% 33.33% 2.22% 100.00%65.91% 51.72% 33.33% 59.21%38.16% 19.74% 1.32% 59.21%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%

Total


$1,000,001 to $1.5 million

$1,500,001 to $3 million

$3,00,001 to $5 million

Over $5 million

Don't Know

Table: Q22 What is your current single project bonding limit? * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].

M/WBE or Non-M/WBE Classification (not based on certification)Q22 What is your current single project bonding limit?Below $100,000

$100,001 to $250,000

$250,001 to $500,000

No Response

Total

DRAFT




75.00% 25.00% 0.00% 100.00%13.64% 6.90% 0.00% 10.53%

7.89% 2.63% 0.00% 10.53%17 17 1 35

48.57% 48.57% 2.86% 100.00%38.64% 58.62% 33.33% 46.05%22.37% 22.37% 1.32% 46.05%

6 6 1 1346.15% 46.15% 7.69% 100.00%13.64% 20.69% 33.33% 17.11%

7.89% 7.89% 1.32% 17.11%9 2 1 12

75.00% 16.67% 8.33% 100.00%20.45% 6.90% 33.33% 15.79%11.84% 2.63% 1.32% 15.79%

6 2 0 875.00% 25.00% 0.00% 100.00%13.64% 6.90% 0.00% 10.53%

7.89% 2.63% 0.00% 10.53%44 29 3 76

57.89% 38.16% 3.95% 100.00%100.00% 100.00% 100.00% 100.00%

57.89% 38.16% 3.95% 100.00%


60.00% 40.00% 0.00% 100.00%6.82% 6.90% 0.00% 6.58%3.95% 2.63% 0.00% 6.58%

3 0 0 3100.00% 0.00% 0.00% 100.00%

6.82% 0.00% 0.00% 3.95%3.95% 0.00% 0.00% 3.95%

38 27 3 6855.88% 39.71% 4.41% 100.00%86.36% 93.10% 100.00% 89.47%50.00% 35.53% 3.95% 89.47%

No

Not Applicable

Don't Know

No Response

Total

Table: Q24 What was the most noticeable way you became aware of the discrimination against your company? * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].

Table: Q23 As a prime contractor/consultant did you experience discriminatory behavior by the Schools when bidding or working on a project between 2008 and 2013? * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %,

total %].M/WBE or Non-M/WBE Classification (not based on certification)Q23 As a prime contractor/consultant did you

experience discriminatory behavior by the Schools when bidding or working on a project between 2008 and 2013?Yes

M/WBE or Non-M/WBE Classification (not based on certification)Q24 What was the most noticeable way you became aware of the discrimination against your company?Verbal Comment


No Response

DRAFT



44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


100.00% 0.00% 0.00% 100.00%6.82% 0.00% 0.00% 3.95%3.95% 0.00% 0.00% 3.95%

1 0 0 1100.00% 0.00% 0.00% 100.00%

2.27% 0.00% 0.00% 1.32%1.32% 0.00% 0.00% 1.32%

1 1 0 250.00% 50.00% 0.00% 100.00%

2.27% 3.45% 0.00% 2.63%1.32% 1.32% 0.00% 2.63%

1 1 0 250.00% 50.00% 0.00% 100.00%

2.27% 3.45% 0.00% 2.63%1.32% 1.32% 0.00% 2.63%

38 27 3 6855.88% 39.71% 4.41% 100.00%86.36% 93.10% 100.00% 89.47%50.00% 35.53% 3.95% 89.47%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


50.00% 50.00% 0.00% 100.00%4.55% 6.90% 0.00% 5.26%2.63% 2.63% 0.00% 5.26%

1 0 0 1100.00% 0.00% 0.00% 100.00%

2.27% 0.00% 0.00% 1.32%1.32% 0.00% 0.00% 1.32%

3 0 0 3100.00% 0.00% 0.00% 100.00%

6.82% 0.00% 0.00% 3.95%3.95% 0.00% 0.00% 3.95%

Table: Q25 Which of the following do you consider the primary reason for your company being discriminated against? * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].

M/WBE or Non-M/WBE Classification (not based on certification)Q25 Which of the following do you consider the primary reason for your company being discriminated against?Owner's Race or Ethnicity

Owner's Gender

Both

Total

During Bidding Process

After Contract Award

Both

Don't Know

No Response

Total

Table: Q26 When did the discrimination first occur? * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].

M/WBE or Non-M/WBE Classification (not based on certification)Q26 When did the discrimination first occur?

DRAFT



38 27 3 6855.88% 39.71% 4.41% 100.00%86.36% 93.10% 100.00% 89.47%50.00% 35.53% 3.95% 89.47%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


100.00% 0.00% 0.00% 100.00%4.55% 0.00% 0.00% 2.63%2.63% 0.00% 0.00% 2.63%

4 2 0 666.67% 33.33% 0.00% 100.00%

9.09% 6.90% 0.00% 7.89%5.26% 2.63% 0.00% 7.89%

38 27 3 6855.88% 39.71% 4.41% 100.00%86.36% 93.10% 100.00% 89.47%50.00% 35.53% 3.95% 89.47%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


85.71% 14.29% 0.00% 100.00%13.64% 3.45% 0.00% 9.21%

7.89% 1.32% 0.00% 9.21%0 1 0 1

0.00% 100.00% 0.00% 100.00%0.00% 3.45% 0.00% 1.32%0.00% 1.32% 0.00% 1.32%

38 27 3 6855.88% 39.71% 4.41% 100.00%86.36% 93.10% 100.00% 89.47%50.00% 35.53% 3.95% 89.47%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%

No Response

Total

Table: Q27 Did you file a complaint? * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].

Table: Q28 Would you be willing so speak with the Schools about the discrimination you experienced? * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].

M/WBE or Non-M/WBE Classification (not based on certification)Q28 Would you be willing so speak with the Schools about the discrimination you experienced?Yes

No

No Response

M/WBE or Non-M/WBE Classification (not based on certification)Q27 Did you file a complaint?Yes

No

No Response

Total

Total

DRAFT




23.08% 76.92% 0.00% 100.00%6.82% 34.48% 0.00% 17.11%3.95% 13.16% 0.00% 17.11%

3 1 0 475.00% 25.00% 0.00% 100.00%

6.82% 3.45% 0.00% 5.26%3.95% 1.32% 0.00% 5.26%

6 0 0 6100.00% 0.00% 0.00% 100.00%

13.64% 0.00% 0.00% 7.89%7.89% 0.00% 0.00% 7.89%

4 2 0 666.67% 33.33% 0.00% 100.00%

9.09% 6.90% 0.00% 7.89%5.26% 2.63% 0.00% 7.89%

5 0 0 5100.00% 0.00% 0.00% 100.00%

11.36% 0.00% 0.00% 6.58%6.58% 0.00% 0.00% 6.58%

2 2 0 450.00% 50.00% 0.00% 100.00%

4.55% 6.90% 0.00% 5.26%2.63% 2.63% 0.00% 5.26%

3 1 0 475.00% 25.00% 0.00% 100.00%

6.82% 3.45% 0.00% 5.26%3.95% 1.32% 0.00% 5.26%

7 1 1 977.78% 11.11% 11.11% 100.00%15.91% 3.45% 33.33% 11.84%

9.21% 1.32% 1.32% 11.84%1 6 0 7

14.29% 85.71% 0.00% 100.00%2.27% 20.69% 0.00% 9.21%1.32% 7.89% 0.00% 9.21%

8 6 1 1553.33% 40.00% 6.67% 100.00%18.18% 20.69% 33.33% 19.74%10.53% 7.89% 1.32% 19.74%

2 0 1 366.67% 0.00% 33.33% 100.00%

4.55% 0.00% 33.33% 3.95%2.63% 0.00% 1.32% 3.95%

Up to $50,000?

$50,001 to $100,000?

$100,001 to $200,000?

$200,001 to $300,000?

$300,001 to $400,00?

$400,001 to $500,000?

Table: Q29 In general, which of the following dollar ranges best approximates your company's largest subcontract awarded between 2008 and 2013? * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].

M/WBE or Non-M/WBE Classification (not based on certification)Q29 In general, which of the following dollar ranges best approximates your company's largest subcontract awarded between 2008 and 2013?Not applicable (not a subcontractor

None

$500,001 to $1 million?

Over $1 million?

Don't Know

DRAFT



44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


83.33% 0.00% 16.67% 100.00%11.36% 0.00% 33.33% 7.89%

6.58% 0.00% 1.32% 7.89%31 18 2 51

60.78% 35.29% 3.92% 100.00%70.45% 62.07% 66.67% 67.11%40.79% 23.68% 2.63% 67.11%

5 1 0 683.33% 16.67% 0.00% 100.00%11.36% 3.45% 0.00% 7.89%

6.58% 1.32% 0.00% 7.89%3 10 0 13

23.08% 76.92% 0.00% 100.00%6.82% 34.48% 0.00% 17.11%3.95% 13.16% 0.00% 17.11%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


77.78% 11.11% 11.11% 100.00%15.91% 3.45% 33.33% 11.84%

9.21% 1.32% 1.32% 11.84%29 17 2 48

60.42% 35.42% 4.17% 100.00%65.91% 58.62% 66.67% 63.16%38.16% 22.37% 2.63% 63.16%

5 1 0 683.33% 16.67% 0.00% 100.00%11.36% 3.45% 0.00% 7.89%

6.58% 1.32% 0.00% 7.89%

Yes

No

Don't Know

No Response

Total

Table: Q30 In your experience, have any of the following been a barrier to obtaining work on School projects as a subcontractor with primes? Cost of bidding/proposing? * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %,

total %].

Total

Table: Q30 In your experience, have any of the following been a barrier to obtaining work on School projects as a subcontractor with primes? Performance/payment bond requirement? * M/WBE or Non-M/WBE Classification (not based on certification) [count, row

%, column %, total %].M/WBE or Non-M/WBE Classification (not based on certification)

Q30 In your experience, have any of the following been a barrier to obtaining work on School projects as a subcontractor with primes? Performance/payment bond requirement?

M/WBE or Non-M/WBE Classification (not based on certification)Q30 In your experience, have any of the following been a barrier to obtaining work on School projects as a subcontractor with primes? Cost of bidding/proposing?Yes

No

Don't Know

DRAFT



3 10 0 1323.08% 76.92% 0.00% 100.00%

6.82% 34.48% 0.00% 17.11%3.95% 13.16% 0.00% 17.11%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


90.91% 9.09% 0.00% 100.00%22.73% 3.45% 0.00% 14.47%13.16% 1.32% 0.00% 14.47%

28 17 3 4858.33% 35.42% 6.25% 100.00%63.64% 58.62% 100.00% 63.16%36.84% 22.37% 3.95% 63.16%

3 1 0 475.00% 25.00% 0.00% 100.00%

6.82% 3.45% 0.00% 5.26%3.95% 1.32% 0.00% 5.26%

3 10 0 1323.08% 76.92% 0.00% 100.00%

6.82% 34.48% 0.00% 17.11%3.95% 13.16% 0.00% 17.11%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


100.00% 0.00% 0.00% 100.00%9.09% 0.00% 0.00% 5.26%5.26% 0.00% 0.00% 5.26%

35 18 3 5662.50% 32.14% 5.36% 100.00%79.55% 62.07% 100.00% 73.68%46.05% 23.68% 3.95% 73.68%

Total

Table: Q30 In your experience, have any of the following been a barrier to obtaining work on School projects as a subcontractor with primes? Financing? * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].

M/WBE or Non-M/WBE Classification (not based on certification)Q30 In your experience, have any of the following been a barrier to obtaining work on School projects as a subcontractor with primes? Financing?Yes

No

No Response

Yes

No

Don't Know

No Response

Total

Table: Q30 In your experience, have any of the following been a barrier to obtaining work on School projects as a subcontractor with primes? Insurance (general liability, professional liability, etc.)? * M/WBE or Non-M/WBE Classification (not based on certification)

[count, row %, column %, total %].M/WBE or Non-M/WBE Classification (not based on certification)Q30 In your experience, have any of the

following been a barrier to obtaining work on School projects as a subcontractor with primes? Insurance (general liability, professional liability, etc.)?

DRAFT



2 1 0 366.67% 33.33% 0.00% 100.00%

4.55% 3.45% 0.00% 3.95%2.63% 1.32% 0.00% 3.95%

3 10 0 1323.08% 76.92% 0.00% 100.00%

6.82% 34.48% 0.00% 17.11%3.95% 13.16% 0.00% 17.11%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


100.00% 0.00% 0.00% 100.00%18.18% 0.00% 0.00% 10.53%10.53% 0.00% 0.00% 10.53%

29 17 3 4959.18% 34.69% 6.12% 100.00%65.91% 58.62% 100.00% 64.47%38.16% 22.37% 3.95% 64.47%

4 2 0 666.67% 33.33% 0.00% 100.00%

9.09% 6.90% 0.00% 7.89%5.26% 2.63% 0.00% 7.89%

3 10 0 1323.08% 76.92% 0.00% 100.00%

6.82% 34.48% 0.00% 17.11%3.95% 13.16% 0.00% 17.11%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


76.19% 19.05% 4.76% 100.00%36.36% 13.79% 33.33% 27.63%21.05% 5.26% 1.32% 27.63%

Don't Know

No Response

Total

Table: Q30 In your experience, have any of the following been a barrier to obtaining work on School projects as a subcontractor with primes? Price of supplies/materials * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %,

total %].

Total

Table: Q30 In your experience, have any of the following been a barrier to obtaining work on School projects as a subcontractor with primes? Limited time given to prepare bid package or quote * M/WBE or Non-M/WBE Classification (not based on certification)

[count, row %, column %, total %].M/WBE or Non-M/WBE Classification (not based on certification)Q30 In your experience, have any of the

following been a barrier to obtaining work on School projects as a subcontractor with primes? Limited time given to prepare bid package or quoteYes

M/WBE or Non-M/WBE Classification (not based on certification)Q30 In your experience, have any of the following been a barrier to obtaining work on School projects as a subcontractor with primes? Price of supplies/materialsYes

No

Don't Know

No Response

DRAFT



22 14 2 3857.89% 36.84% 5.26% 100.00%50.00% 48.28% 66.67% 50.00%28.95% 18.42% 2.63% 50.00%

3 1 0 475.00% 25.00% 0.00% 100.00%

6.82% 3.45% 0.00% 5.26%3.95% 1.32% 0.00% 5.26%

3 10 0 1323.08% 76.92% 0.00% 100.00%

6.82% 34.48% 0.00% 17.11%3.95% 13.16% 0.00% 17.11%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


100.00% 0.00% 0.00% 100.00%13.64% 0.00% 0.00% 7.89%

7.89% 0.00% 0.00% 7.89%33 18 3 54

61.11% 33.33% 5.56% 100.00%75.00% 62.07% 100.00% 71.05%43.42% 23.68% 3.95% 71.05%

2 1 0 366.67% 33.33% 0.00% 100.00%

4.55% 3.45% 0.00% 3.95%2.63% 1.32% 0.00% 3.95%

3 10 0 1323.08% 76.92% 0.00% 100.00%

6.82% 34.48% 0.00% 17.11%3.95% 13.16% 0.00% 17.11%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%

No

Yes

No

Don't Know

No Response

Total

Don't Know

No Response

Total

Table: Q30 In your experience, have any of the following been a barrier to obtaining work on School projects as a subcontractor with primes? Lack of experience * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].

M/WBE or Non-M/WBE Classification (not based on certification)Q30 In your experience, have any of the following been a barrier to obtaining work on School projects as a subcontractor with primes? Lack of experience

DRAFT




62.50% 37.50% 0.00% 100.00%11.36% 10.34% 0.00% 10.53%

6.58% 3.95% 0.00% 10.53%35 15 3 53

66.04% 28.30% 5.66% 100.00%79.55% 51.72% 100.00% 69.74%46.05% 19.74% 3.95% 69.74%

1 1 0 250.00% 50.00% 0.00% 100.00%

2.27% 3.45% 0.00% 2.63%1.32% 1.32% 0.00% 2.63%

3 10 0 1323.08% 76.92% 0.00% 100.00%

6.82% 34.48% 0.00% 17.11%3.95% 13.16% 0.00% 17.11%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


71.43% 28.57% 0.00% 100.00%11.36% 6.90% 0.00% 9.21%

6.58% 2.63% 0.00% 9.21%32 16 3 51

62.75% 31.37% 5.88% 100.00%72.73% 55.17% 100.00% 67.11%42.11% 21.05% 3.95% 67.11%

4 1 0 580.00% 20.00% 0.00% 100.00%

9.09% 3.45% 0.00% 6.58%5.26% 1.32% 0.00% 6.58%

3 10 0 1323.08% 76.92% 0.00% 100.00%

6.82% 34.48% 0.00% 17.11%3.95% 13.16% 0.00% 17.11%

Table: Q30 In your experience, have any of the following been a barrier to obtaining work on School projects as a subcontractor with primes? Lack of personnel * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].

Total

Table: Q30 In your experience, have any of the following been a barrier to obtaining work on School projects as a subcontractor with primes? Contract too large * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].

M/WBE or Non-M/WBE Classification (not based on certification)Q30 In your experience, have any of the following been a barrier to obtaining work on School projects as a subcontractor with primes? Contract too largeYes

No

M/WBE or Non-M/WBE Classification (not based on certification)Q30 In your experience, have any of the following been a barrier to obtaining work on School projects as a subcontractor with primes? Lack of personnelYes

No

Don't Know

No Response

Don't Know

No Response

DRAFT



44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


83.33% 16.67% 0.00% 100.00%34.09% 10.34% 0.00% 23.68%19.74% 3.95% 0.00% 23.68%

18 15 3 3650.00% 41.67% 8.33% 100.00%40.91% 51.72% 100.00% 47.37%23.68% 19.74% 3.95% 47.37%

8 1 0 988.89% 11.11% 0.00% 100.00%18.18% 3.45% 0.00% 11.84%10.53% 1.32% 0.00% 11.84%

3 10 0 1323.08% 76.92% 0.00% 100.00%

6.82% 34.48% 0.00% 17.11%3.95% 13.16% 0.00% 17.11%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


80.00% 12.00% 8.00% 100.00%45.45% 10.34% 66.67% 32.89%26.32% 3.95% 2.63% 32.89%

17 15 1 3351.52% 45.45% 3.03% 100.00%38.64% 51.72% 33.33% 43.42%22.37% 19.74% 1.32% 43.42%

4 1 0 580.00% 20.00% 0.00% 100.00%

9.09% 3.45% 0.00% 6.58%5.26% 1.32% 0.00% 6.58%

Yes

No

Don't Know

No Response

Total

Table: Q30 In your experience, have any of the following been a barrier to obtaining work on School projects as a subcontractor with primes? Competing with large companies * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column

%, total %].

Total

Table: Q30 In your experience, have any of the following been a barrier to obtaining work on School projects as a subcontractor with primes? Selection process * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].

M/WBE or Non-M/WBE Classification (not based on certification)Q30 In your experience, have any of the following been a barrier to obtaining work on School projects as a subcontractor with primes? Selection process

M/WBE or Non-M/WBE Classification (not based on certification)Q30 In your experience, have any of the following been a barrier to obtaining work on School projects as a subcontractor with primes? Competing with large companiesYes

No

Don't Know

DRAFT



3 10 0 1323.08% 76.92% 0.00% 100.00%

6.82% 34.48% 0.00% 17.11%3.95% 13.16% 0.00% 17.11%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


75.86% 20.69% 3.45% 100.00%50.00% 20.69% 33.33% 38.16%28.95% 7.89% 1.32% 38.16%

14 7 1 2263.64% 31.82% 4.55% 100.00%31.82% 24.14% 33.33% 28.95%18.42% 9.21% 1.32% 28.95%

0 4 1 50.00% 80.00% 20.00% 100.00%0.00% 13.79% 33.33% 6.58%0.00% 5.26% 1.32% 6.58%

4 1 0 580.00% 20.00% 0.00% 100.00%

9.09% 3.45% 0.00% 6.58%5.26% 1.32% 0.00% 6.58%

1 1 0 250.00% 50.00% 0.00% 100.00%

2.27% 3.45% 0.00% 2.63%1.32% 1.32% 0.00% 2.63%

3 10 0 1323.08% 76.92% 0.00% 100.00%

6.82% 34.48% 0.00% 17.11%3.95% 13.16% 0.00% 17.11%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%

Total

Table: Q31 Between 2008 and 2013, how many times has your company been awarded a subcontract with a prime contractor for a project with the Schools? * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].

M/WBE or Non-M/WBE Classification (not based on certification)Q31 Between 2008 and 2013, how many times has your company been awarded a subcontract with a prime contractor for a project with the Schools?None

1-10 Times

No Response

11-25 Times

26-50 Times

Don't Know

No Response

Total

DRAFT




40.00% 53.33% 6.67% 100.00%13.64% 27.59% 33.33% 19.74%

7.89% 10.53% 1.32% 19.74%5 4 0 9

55.56% 44.44% 0.00% 100.00%11.36% 13.79% 0.00% 11.84%

6.58% 5.26% 0.00% 11.84%4 0 1 5

80.00% 0.00% 20.00% 100.00%9.09% 0.00% 33.33% 6.58%5.26% 0.00% 1.32% 6.58%

3 0 0 3100.00% 0.00% 0.00% 100.00%

6.82% 0.00% 0.00% 3.95%3.95% 0.00% 0.00% 3.95%

0 1 0 10.00% 100.00% 0.00% 100.00%0.00% 3.45% 0.00% 1.32%0.00% 1.32% 0.00% 1.32%

1 0 0 1100.00% 0.00% 0.00% 100.00%

2.27% 0.00% 0.00% 1.32%1.32% 0.00% 0.00% 1.32%

25 16 1 4259.52% 38.10% 2.38% 100.00%56.82% 55.17% 33.33% 55.26%32.89% 21.05% 1.32% 55.26%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%

M/WBE or Non-M/WBE Classification (not based on certification)Q32 Between 2008 and 2013, when you were a subcontractor what was the average amount of time that it typically took to receive payment for your services from the prime contractor/vendor?31-60 Days

61-90 Days

91-120 Days

Over 120 Days

Table: Q32 Between 2008 and 2013, when you were a subcontractor what was the average amount of time that it typically took to receive payment for your services from the prime contractor/vendor? * M/WBE or Non-M/WBE Classification (not based on

certification) [count, row %, column %, total %].

Not Applicable

Don't Know

No Response

Total

DRAFT




50.00% 50.00% 0.00% 100.00%9.09% 13.79% 0.00% 10.53%5.26% 5.26% 0.00% 10.53%

29 12 1 4269.05% 28.57% 2.38% 100.00%65.91% 41.38% 33.33% 55.26%38.16% 15.79% 1.32% 55.26%

8 3 2 1361.54% 23.08% 15.38% 100.00%18.18% 10.34% 66.67% 17.11%10.53% 3.95% 2.63% 17.11%

3 10 0 1323.08% 76.92% 0.00% 100.00%

6.82% 34.48% 0.00% 17.11%3.95% 13.16% 0.00% 17.11%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


64.71% 35.29% 0.00% 100.00%25.00% 20.69% 0.00% 22.37%14.47% 7.89% 0.00% 22.37%

26 10 3 3966.67% 25.64% 7.69% 100.00%59.09% 34.48% 100.00% 51.32%34.21% 13.16% 3.95% 51.32%

4 3 0 757.14% 42.86% 0.00% 100.00%

9.09% 10.34% 0.00% 9.21%5.26% 3.95% 0.00% 9.21%

3 10 0 1323.08% 76.92% 0.00% 100.00%

6.82% 34.48% 0.00% 17.11%3.95% 13.16% 0.00% 17.11%

Yes

No

Don't Know

No Response

Total

Table: Q34 As a subcontractor, are you required to have bonding for company's type of work? * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].

Table: Q33 Between 2008 and 2013, have you ever submitted a bid with a prime contractor for a project with the Schools, were informed that you were the lowest bidder, and then found out that another subcontractor was actually doing the work? * M/WBE or

Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].M/WBE or Non-M/WBE Classification (not based on certification)Q33 Between 2008 and 2013, have you ever

submitted a bid with a prime contractor for a project with the Schools, were informed that you were the lowest bidder, and then found out that another subcontractor was actually doing the work?

M/WBE or Non-M/WBE Classification (not based on certification)Q34 As a subcontractor, are you required to have bonding for company's type of work?Yes

No

Don't Know

No Response

DRAFT



44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


100.00% 0.00% 0.00% 100.00%4.55% 0.00% 0.00% 2.63%2.63% 0.00% 0.00% 2.63%

1 0 0 1100.00% 0.00% 0.00% 100.00%

2.27% 0.00% 0.00% 1.32%1.32% 0.00% 0.00% 1.32%

0 1 0 10.00% 100.00% 0.00% 100.00%0.00% 3.45% 0.00% 1.32%0.00% 1.32% 0.00% 1.32%

2 0 0 2100.00% 0.00% 0.00% 100.00%

4.55% 0.00% 0.00% 2.63%2.63% 0.00% 0.00% 2.63%

0 1 0 10.00% 100.00% 0.00% 100.00%0.00% 3.45% 0.00% 1.32%0.00% 1.32% 0.00% 1.32%

2 0 0 2100.00% 0.00% 0.00% 100.00%

4.55% 0.00% 0.00% 2.63%2.63% 0.00% 0.00% 2.63%

3 3 0 650.00% 50.00% 0.00% 100.00%

6.82% 10.34% 0.00% 7.89%3.95% 3.95% 0.00% 7.89%

1 1 0 250.00% 50.00% 0.00% 100.00%

2.27% 3.45% 0.00% 2.63%1.32% 1.32% 0.00% 2.63%

33 23 3 5955.93% 38.98% 5.08% 100.00%75.00% 79.31% 100.00% 77.63%43.42% 30.26% 3.95% 77.63%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%

Total

Table: Q35 What is your current aggregate bonding limit? * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].

M/WBE or Non-M/WBE Classification (not based on certification)Q35 What is your current aggregate bonding limit?Below $100,000

$100,001 to $250,000

No Response

Total


$1,000,001 to $1.5 million

$1,500,001 to $3 million

$3,00,001 to $5 million

Over $5 million

Don't Know

DRAFT




100.00% 0.00% 0.00% 100.00%4.55% 0.00% 0.00% 2.63%2.63% 0.00% 0.00% 2.63%

1 0 0 1100.00% 0.00% 0.00% 100.00%

2.27% 0.00% 0.00% 1.32%1.32% 0.00% 0.00% 1.32%

0 1 0 10.00% 100.00% 0.00% 100.00%0.00% 3.45% 0.00% 1.32%0.00% 1.32% 0.00% 1.32%

1 1 0 250.00% 50.00% 0.00% 100.00%

2.27% 3.45% 0.00% 2.63%1.32% 1.32% 0.00% 2.63%

2 0 0 2100.00% 0.00% 0.00% 100.00%

4.55% 0.00% 0.00% 2.63%2.63% 0.00% 0.00% 2.63%

2 0 0 2100.00% 0.00% 0.00% 100.00%

4.55% 0.00% 0.00% 2.63%2.63% 0.00% 0.00% 2.63%

2 3 0 540.00% 60.00% 0.00% 100.00%

4.55% 10.34% 0.00% 6.58%2.63% 3.95% 0.00% 6.58%

1 1 0 250.00% 50.00% 0.00% 100.00%

2.27% 3.45% 0.00% 2.63%1.32% 1.32% 0.00% 2.63%

33 23 3 5955.93% 38.98% 5.08% 100.00%75.00% 79.31% 100.00% 77.63%43.42% 30.26% 3.95% 77.63%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%

Table: Q36 What is your current single project bonding limit? * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].

M/WBE or Non-M/WBE Classification (not based on certification)Q36 What is your current single project bonding limit?Below $100,000

Don't Know

No Response

Total

$100,001 to $250,000

$250,001 to $500,000


$1,000,001 to $1.5 million

$3,00,001 to $5 million

Over $5 million

DRAFT




83.33% 16.67% 0.00% 100.00%11.36% 3.45% 0.00% 7.89%

6.58% 1.32% 0.00% 7.89%22 11 2 35

62.86% 31.43% 5.71% 100.00%50.00% 37.93% 66.67% 46.05%28.95% 14.47% 2.63% 46.05%

2 4 1 728.57% 57.14% 14.29% 100.00%

4.55% 13.79% 33.33% 9.21%2.63% 5.26% 1.32% 9.21%

12 3 0 1580.00% 20.00% 0.00% 100.00%27.27% 10.34% 0.00% 19.74%15.79% 3.95% 0.00% 19.74%

3 10 0 1323.08% 76.92% 0.00% 100.00%

6.82% 34.48% 0.00% 17.11%3.95% 13.16% 0.00% 17.11%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


50.00% 50.00% 0.00% 100.00%4.55% 6.90% 0.00% 5.26%2.63% 2.63% 0.00% 5.26%

3 0 0 3100.00% 0.00% 0.00% 100.00%

6.82% 0.00% 0.00% 3.95%3.95% 0.00% 0.00% 3.95%

39 27 3 6956.52% 39.13% 4.35% 100.00%88.64% 93.10% 100.00% 90.79%51.32% 35.53% 3.95% 90.79%

Table: Q37 As a subcontractor did you experience discriminatory behavior between 2008 and 2013 from a prime contractor/consultant working or bidding/proposing on a Schools project? * M/WBE or Non-M/WBE Classification (not based on


Q37 As a subcontractor did you experience discriminatory behavior between 2008 and 2013 from a prime contractor/consultant working or bidding/proposing on a Schools project?


M/WBE or Non-M/WBE Classification (not based on certification)Q38 What was the most noticeable way you became aware of the discrimination against your company?Verbal Comment


No Response

Yes

No

Not Applicable

Don't Know

No Response

Total

DRAFT



44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


100.00% 0.00% 0.00% 100.00%4.55% 0.00% 0.00% 2.63%2.63% 0.00% 0.00% 2.63%

1 0 0 1100.00% 0.00% 0.00% 100.00%

2.27% 0.00% 0.00% 1.32%1.32% 0.00% 0.00% 1.32%

1 2 0 333.33% 66.67% 0.00% 100.00%

2.27% 6.90% 0.00% 3.95%1.32% 2.63% 0.00% 3.95%

1 0 0 1100.00% 0.00% 0.00% 100.00%

2.27% 0.00% 0.00% 1.32%1.32% 0.00% 0.00% 1.32%

39 27 3 6956.52% 39.13% 4.35% 100.00%88.64% 93.10% 100.00% 90.79%51.32% 35.53% 3.95% 90.79%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


60.00% 40.00% 0.00% 100.00%6.82% 6.90% 0.00% 6.58%3.95% 2.63% 0.00% 6.58%

1 0 0 1100.00% 0.00% 0.00% 100.00%

2.27% 0.00% 0.00% 1.32%1.32% 0.00% 0.00% 1.32%

1 0 0 1100.00% 0.00% 0.00% 100.00%

2.27% 0.00% 0.00% 1.32%1.32% 0.00% 0.00% 1.32%

Both

Don't Know

No Response

Total


M/WBE or Non-M/WBE Classification (not based on certification)Q40 When did the discrimination first occur?

Total


M/WBE or Non-M/WBE Classification (not based on certification)Q39 Which of the following do you consider the primary reason for your company being discriminated against?Owner's Race or Ethnicity

Owner's Gender

During the bidding process

After contract awarded

Both

DRAFT



39 27 3 6956.52% 39.13% 4.35% 100.00%88.64% 93.10% 100.00% 90.79%51.32% 35.53% 3.95% 90.79%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


100.00% 0.00% 0.00% 100.00%4.55% 0.00% 0.00% 2.63%2.63% 0.00% 0.00% 2.63%

3 2 0 560.00% 40.00% 0.00% 100.00%

6.82% 6.90% 0.00% 6.58%3.95% 2.63% 0.00% 6.58%

39 27 3 6956.52% 39.13% 4.35% 100.00%88.64% 93.10% 100.00% 90.79%51.32% 35.53% 3.95% 90.79%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


83.33% 16.67% 0.00% 100.00%11.36% 3.45% 0.00% 7.89%

6.58% 1.32% 0.00% 7.89%0 1 0 1

0.00% 100.00% 0.00% 100.00%0.00% 3.45% 0.00% 1.32%0.00% 1.32% 0.00% 1.32%

39 27 3 6956.52% 39.13% 4.35% 100.00%88.64% 93.10% 100.00% 90.79%51.32% 35.53% 3.95% 90.79%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%

M/WBE or Non-M/WBE Classification (not based on certification)Q41 Did you file a complaint?Yes

No

No Response

Total

No Response

Total

Table: Q41 Did you file a complaint? * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].

Total

Table: Q42 Would you be willing to speak with the Schools about the discrimination you experienced? * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].

M/WBE or Non-M/WBE Classification (not based on certification)Q42 Would you be willing to speak with the Schools about the discrimination you experienced?Yes

No

No Response

DRAFT




69.23% 30.77% 0.00% 100.00%20.45% 13.79% 0.00% 17.11%11.84% 5.26% 0.00% 17.11%

23 19 2 4452.27% 43.18% 4.55% 100.00%52.27% 65.52% 66.67% 57.89%30.26% 25.00% 2.63% 57.89%

12 6 1 1963.16% 31.58% 5.26% 100.00%27.27% 20.69% 33.33% 25.00%15.79% 7.89% 1.32% 25.00%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


36.36% 54.55% 9.09% 100.00%9.09% 20.69% 33.33% 14.47%5.26% 7.89% 1.32% 14.47%

12 4 0 1675.00% 25.00% 0.00% 100.00%27.27% 13.79% 0.00% 21.05%15.79% 5.26% 0.00% 21.05%

10 0 0 10100.00% 0.00% 0.00% 100.00%

22.73% 0.00% 0.00% 13.16%13.16% 0.00% 0.00% 13.16%

11 4 0 1573.33% 26.67% 0.00% 100.00%25.00% 13.79% 0.00% 19.74%14.47% 5.26% 0.00% 19.74%

Table: Q43 Have you experienced or observed a situation in which a prime contractors/consultants includes minority or woman subcontractors on a bid or proposal to satisfy the good faith effort requirements, and then drops the company as a subcontractor after win

* M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].M/WBE or Non-M/WBE Classification (not based on certification)Q43 Have you experienced or observed a

situation in which a prime contractors/consultants includes minority or woman subcontractors on a bid or proposal to satisfy the good faith effort requirements, and then drops the company as a subcontractor after winYes

No

Sometimes

Seldom

Never

Don't Know

Total

Table: Q44 How often do prime contractors/consultants who use your firm as a subcontractor on public-sector projects with M/WBE goals solicit your firm on projects (private or public) without M/WBE goals? * M/WBE or Non-M/WBE Classification (not based on


Q44 How often do prime contractors/consultants who use your firm as a subcontractor on public-sector projects with M/WBE goals solicit your firm on projects (private or public) without M/WBE goals?Very Often

DRAFT



2 12 1 1513.33% 80.00% 6.67% 100.00%

4.55% 41.38% 33.33% 19.74%2.63% 15.79% 1.32% 19.74%

5 3 0 862.50% 37.50% 0.00% 100.00%11.36% 10.34% 0.00% 10.53%

6.58% 3.95% 0.00% 10.53%0 0 1 1

0.00% 0.00% 100.00% 100.00%0.00% 0.00% 33.33% 1.32%0.00% 0.00% 1.32% 1.32%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


100.00% 0.00% 0.00% 100.00%6.82% 0.00% 0.00% 3.95%3.95% 0.00% 0.00% 3.95%

36 29 2 6753.73% 43.28% 2.99% 100.00%81.82% 100.00% 66.67% 88.16%47.37% 38.16% 2.63% 88.16%

5 0 0 5100.00% 0.00% 0.00% 100.00%

11.36% 0.00% 0.00% 6.58%6.58% 0.00% 0.00% 6.58%

0 0 1 10.00% 0.00% 100.00% 100.00%0.00% 0.00% 33.33% 1.32%0.00% 0.00% 1.32% 1.32%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%

Not Applicable

Don't Know

No Response

Don't Know

No Response

Total

Total

Table: Q45 Still talking about the Schools and its prime contractors/consultants, while doing business or attempting to do business, have you experienced any of the following as a form in discrimination: Harassment. * M/WBE or Non-M/WBE Classification (not

based on certification) [count, row %, column %, total %].M/WBE or Non-M/WBE Classification (not based on certification)Q45 Still talking about the Schools and its prime

contractors/consultants, while doing business or attempting to do business, have you experienced any of the following as a form in discrimination: Harassment.Yes

No

DRAFT




71.43% 28.57% 0.00% 100.00%22.73% 13.79% 0.00% 18.42%13.16% 5.26% 0.00% 18.42%

27 25 2 5450.00% 46.30% 3.70% 100.00%61.36% 86.21% 66.67% 71.05%35.53% 32.89% 2.63% 71.05%

7 0 0 7100.00% 0.00% 0.00% 100.00%

15.91% 0.00% 0.00% 9.21%9.21% 0.00% 0.00% 9.21%

0 0 1 10.00% 0.00% 100.00% 100.00%0.00% 0.00% 33.33% 1.32%0.00% 0.00% 1.32% 1.32%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


82.35% 17.65% 0.00% 100.00%31.82% 10.34% 0.00% 22.37%18.42% 3.95% 0.00% 22.37%

22 23 2 4746.81% 48.94% 4.26% 100.00%50.00% 79.31% 66.67% 61.84%28.95% 30.26% 2.63% 61.84%

8 3 0 1172.73% 27.27% 0.00% 100.00%18.18% 10.34% 0.00% 14.47%10.53% 3.95% 0.00% 14.47%

Table: Q45 Still talking about the Schools and its prime contractors/consultants, while doing business or attempting to do business, have you experienced any of the following as a form in discrimination: Unequal or unfair treatment. * M/WBE or Non-M/WBE

Classification (not based on certification) [count, row %, column %, total %].M/WBE or Non-M/WBE Classification (not based on certification)

Q45 Still talking about the Schools and its prime contractors/consultants, while doing business or attempting to do business, have you experienced any of the following as a form in discrimination: Unequal or unfair treatment.

M/WBE or Non-M/WBE Classification (not based on certification)Q45 Still talking about the Schools and its prime contractors/consultants, while doing business or attempting to do business, have you experienced any of the following as a form in discrimination: Bid shopping or bid manipulation.Yes

No

Don't Know

Yes

No

Don't Know

No Response

Total

Table: Q45 Still talking about the Schools and its prime contractors/consultants, while doing business or attempting to do business, have you experienced any of the following as a form in discrimination: Bid shopping or bid manipulation. * M/WBE or Non-M/WBE

Classification (not based on certification) [count, row %, column %, total %].

DRAFT



0 0 1 10.00% 0.00% 100.00% 100.00%0.00% 0.00% 33.33% 1.32%0.00% 0.00% 1.32% 1.32%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


73.33% 26.67% 0.00% 100.00%25.00% 13.79% 0.00% 19.74%14.47% 5.26% 0.00% 19.74%

27 25 2 5450.00% 46.30% 3.70% 100.00%61.36% 86.21% 66.67% 71.05%35.53% 32.89% 2.63% 71.05%

6 0 0 6100.00% 0.00% 0.00% 100.00%

13.64% 0.00% 0.00% 7.89%7.89% 0.00% 0.00% 7.89%

0 0 1 10.00% 0.00% 100.00% 100.00%0.00% 0.00% 33.33% 1.32%0.00% 0.00% 1.32% 1.32%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


63.64% 36.36% 0.00% 100.00%15.91% 13.79% 0.00% 14.47%

9.21% 5.26% 0.00% 14.47%

No Response

Don't Know

No Response

Total

Table: Q45 Still talking about the Schools and its prime contractors/consultants, while doing business or attempting to do business, have you experienced any of the following as a form in discrimination: Denial of opportunity to bid. * M/WBE or Non-M/WBE

Classification (not based on certification) [count, row %, column %, total %].M/WBE or Non-M/WBE Classification (not based on certification)

Q45 Still talking about the Schools and its prime contractors/consultants, while doing business or attempting to do business, have you experienced any of the following as a form in discrimination: Denial of opportunity to bid.

Total

Table: Q45 Still talking about the Schools and its prime contractors/consultants, while doing business or attempting to do business, have you experienced any of the following as a form in discrimination: Double standards in performance. * M/WBE or Non-M/WBE

Classification (not based on certification) [count, row %, column %, total %].M/WBE or Non-M/WBE Classification (not based on certification)Q45 Still talking about the Schools and its prime

contractors/consultants, while doing business or attempting to do business, have you experienced any of the following as a form in discrimination: Double standards in performance.Yes

No

Yes

DRAFT



29 24 2 5552.73% 43.64% 3.64% 100.00%65.91% 82.76% 66.67% 72.37%38.16% 31.58% 2.63% 72.37%

8 1 0 988.89% 11.11% 0.00% 100.00%18.18% 3.45% 0.00% 11.84%10.53% 1.32% 0.00% 11.84%

0 0 1 10.00% 0.00% 100.00% 100.00%0.00% 0.00% 33.33% 1.32%0.00% 0.00% 1.32% 1.32%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


100.00% 0.00% 0.00% 100.00%13.64% 0.00% 0.00% 7.89%

7.89% 0.00% 0.00% 7.89%29 29 2 60

48.33% 48.33% 3.33% 100.00%65.91% 100.00% 66.67% 78.95%38.16% 38.16% 2.63% 78.95%

9 0 0 9100.00% 0.00% 0.00% 100.00%

20.45% 0.00% 0.00% 11.84%11.84% 0.00% 0.00% 11.84%

0 0 1 10.00% 0.00% 100.00% 100.00%0.00% 0.00% 33.33% 1.32%0.00% 0.00% 1.32% 1.32%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


Q45 Still talking about the Schools and its prime contractors/consultants, while doing business or attempting to do business, have you experienced any of the following as a form in discrimination: Unfair denial of contract award.Yes

No

Don't Know

No Response

No

Don't Know

No Response

Total

Table: Q45 Still talking about the Schools and its prime contractors/consultants, while doing business or attempting to do business, have you experienced any of the following as a form in discrimination: Unfair denial of contract award. * M/WBE or Non-M/WBE

Classification (not based on certification) [count, row %, column %, total %].

Total

DRAFT




100.00% 0.00% 0.00% 100.00%4.55% 0.00% 0.00% 2.63%2.63% 0.00% 0.00% 2.63%

37 29 2 6854.41% 42.65% 2.94% 100.00%84.09% 100.00% 66.67% 89.47%48.68% 38.16% 2.63% 89.47%

5 0 0 5100.00% 0.00% 0.00% 100.00%

11.36% 0.00% 0.00% 6.58%6.58% 0.00% 0.00% 6.58%

0 0 1 10.00% 0.00% 100.00% 100.00%0.00% 0.00% 33.33% 1.32%0.00% 0.00% 1.32% 1.32%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


80.00% 20.00% 0.00% 100.00%18.18% 6.90% 0.00% 13.16%10.53% 2.63% 0.00% 13.16%

28 26 2 5650.00% 46.43% 3.57% 100.00%63.64% 89.66% 66.67% 73.68%36.84% 34.21% 2.63% 73.68%

8 1 0 988.89% 11.11% 0.00% 100.00%18.18% 3.45% 0.00% 11.84%10.53% 1.32% 0.00% 11.84%

Don't Know

No Response

Total

Table: Q45 Still talking about the Schools and its prime contractors/consultants, while doing business or attempting to do business, have you experienced any of the following as a form in discrimination: Unequal price quotes from suppliers. * M/WBE or Non-

M/WBE Classification (not based on certification) [count, row %, column %, total %].M/WBE or Non-M/WBE Classification (not based on certification)Q45 Still talking about the Schools and its prime

contractors/consultants, while doing business or attempting to do business, have you experienced any of the following as a form in discrimination: Unequal price quotes from suppliers.

Table: Q45 Still talking about the Schools and its prime contractors/consultants, while doing business or attempting to do business, have you experienced any of the following as a form in discrimination: Unfair termination. * M/WBE or Non-M/WBE Classification

(not based on certification) [count, row %, column %, total %].M/WBE or Non-M/WBE Classification (not based on certification)Q45 Still talking about the Schools and its prime

contractors/consultants, while doing business or attempting to do business, have you experienced any of the following as a form in discrimination: Unfair termination.Yes

No

Yes

No

Don't Know

DRAFT



0 0 1 10.00% 0.00% 100.00% 100.00%0.00% 0.00% 33.33% 1.32%0.00% 0.00% 1.32% 1.32%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


85.71% 14.29% 0.00% 100.00%13.64% 3.45% 0.00% 9.21%

7.89% 1.32% 0.00% 9.21%6 1 0 7

85.71% 14.29% 0.00% 100.00%13.64% 3.45% 0.00% 9.21%

7.89% 1.32% 0.00% 9.21%8 3 0 11

72.73% 27.27% 0.00% 100.00%18.18% 10.34% 0.00% 14.47%10.53% 3.95% 0.00% 14.47%

2 1 0 366.67% 33.33% 0.00% 100.00%

4.55% 3.45% 0.00% 3.95%2.63% 1.32% 0.00% 3.95%

9 14 1 2437.50% 58.33% 4.17% 100.00%20.45% 48.28% 33.33% 31.58%11.84% 18.42% 1.32% 31.58%

13 9 1 2356.52% 39.13% 4.35% 100.00%29.55% 31.03% 33.33% 30.26%17.11% 11.84% 1.32% 30.26%

0 0 1 10.00% 0.00% 100.00% 100.00%0.00% 0.00% 33.33% 1.32%0.00% 0.00% 1.32% 1.32%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%

M/WBE or Non-M/WBE Classification (not based on certification)Q46 There is an informal network of prime contractors/subcontractors that has excluded my company from doing business in the private sector:Strongly Agree

Somewhat Agree

Neither Agree Nor Disagree

Somewhat Disagree

No Response

Total

Table: Q46 There is an informal network of prime contractors/subcontractors that has excluded my company from doing business in the private sector: * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].

Strongly Disagree

Don't Know

No Response

Total

DRAFT




72.41% 27.59% 0.00% 100.00%47.73% 27.59% 0.00% 38.16%27.63% 10.53% 0.00% 38.16%

22 16 0 3857.89% 42.11% 0.00% 100.00%50.00% 55.17% 0.00% 50.00%28.95% 21.05% 0.00% 50.00%

1 5 2 812.50% 62.50% 25.00% 100.00%

2.27% 17.24% 66.67% 10.53%1.32% 6.58% 2.63% 10.53%

0 0 1 10.00% 0.00% 100.00% 100.00%0.00% 0.00% 33.33% 1.32%0.00% 0.00% 1.32% 1.32%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


76.19% 23.81% 0.00% 100.00%36.36% 17.24% 0.00% 27.63%21.05% 6.58% 0.00% 27.63%

6 3 0 966.67% 33.33% 0.00% 100.00%13.64% 10.34% 0.00% 11.84%

7.89% 3.95% 0.00% 11.84%22 21 3 46

47.83% 45.65% 6.52% 100.00%50.00% 72.41% 100.00% 60.53%28.95% 27.63% 3.95% 60.53%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%

Yes

No

Don't Know

No Response

Total

Table: Q48 Were you approved or denied for a commercial (business) bank loan? * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].

Table: Q47 Has your company applied for a commercial (business) bank loan between 2008 and 2013? * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].

M/WBE or Non-M/WBE Classification (not based on certification)Q47 Has your company applied for a commercial (business) bank loan between 2008 and 2013?

M/WBE or Non-M/WBE Classification (not based on certification)Q48 Were you approved or denied for a commercial (business) bank loan?Approved

Denied

No Response

Total

DRAFT




100.00% 0.00% 0.00% 100.00%2.27% 0.00% 0.00% 1.32%1.32% 0.00% 0.00% 1.32%

2 0 0 2100.00% 0.00% 0.00% 100.00%

4.55% 0.00% 0.00% 2.63%2.63% 0.00% 0.00% 2.63%

2 3 0 540.00% 60.00% 0.00% 100.00%

4.55% 10.34% 0.00% 6.58%2.63% 3.95% 0.00% 6.58%

39 26 3 6857.35% 38.24% 4.41% 100.00%88.64% 89.66% 100.00% 89.47%51.32% 34.21% 3.95% 89.47%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


87.50% 12.50% 0.00% 100.00%15.91% 3.45% 0.00% 10.53%

9.21% 1.32% 0.00% 10.53%27 22 1 50

54.00% 44.00% 2.00% 100.00%61.36% 75.86% 33.33% 65.79%35.53% 28.95% 1.32% 65.79%

10 6 1 1758.82% 35.29% 5.88% 100.00%22.73% 20.69% 33.33% 22.37%13.16% 7.89% 1.32% 22.37%

0 0 1 10.00% 0.00% 100.00% 100.00%0.00% 0.00% 33.33% 1.32%0.00% 0.00% 1.32% 1.32%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%

Table: Q49 Which of the following do you believe was the primary reason for your being denied a loan? * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].

M/WBE or Non-M/WBE Classification (not based on certification)Q49 Which of the following do you believe was the primary reason for your being denied a loan?Insufficient Business History

Race or Ethnicity of Owner

Other, please specify

No

Don't Know

No Response

Total

No Response

Total

Table: Q50 Have you experienced discriminatory behavior from the private sector between 2008 and 2013? * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].

M/WBE or Non-M/WBE Classification (not based on certification)Q50 Have you experienced discriminatory behavior from the private sector between 2008 and 2013?Yes

DRAFT




87.50% 12.50% 0.00% 100.00%15.91% 3.45% 0.00% 10.53%

9.21% 1.32% 0.00% 10.53%1 0 0 1

100.00% 0.00% 0.00% 100.00%2.27% 0.00% 0.00% 1.32%1.32% 0.00% 0.00% 1.32%

36 28 3 6753.73% 41.79% 4.48% 100.00%81.82% 96.55% 100.00% 88.16%47.37% 36.84% 3.95% 88.16%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


100.00% 0.00% 0.00% 100.00%2.27% 0.00% 0.00% 1.32%1.32% 0.00% 0.00% 1.32%

2 0 0 2100.00% 0.00% 0.00% 100.00%

4.55% 0.00% 0.00% 2.63%2.63% 0.00% 0.00% 2.63%

4 1 0 580.00% 20.00% 0.00% 100.00%

9.09% 3.45% 0.00% 6.58%5.26% 1.32% 0.00% 6.58%

37 28 3 6854.41% 41.18% 4.41% 100.00%84.09% 96.55% 100.00% 89.47%48.68% 36.84% 3.95% 89.47%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


M/WBE or Non-M/WBE Classification (not based on certification)Q51 What was the most noticeable way you became aware of the discrimination against your company?

Owner's race or ethnicity

Owner's gender

Both

No Response

Total

Verbal comment

Action taken against the company

No Response

Total


M/WBE or Non-M/WBE Classification (not based on certification)Q52 Which of the following do you consider the primary reason for your company being discriminated against?

DRAFT




80.00% 20.00% 0.00% 100.00%9.09% 3.45% 0.00% 6.58%5.26% 1.32% 0.00% 6.58%

2 0 0 2100.00% 0.00% 0.00% 100.00%

4.55% 0.00% 0.00% 2.63%2.63% 0.00% 0.00% 2.63%

1 0 0 1100.00% 0.00% 0.00% 100.00%

2.27% 0.00% 0.00% 1.32%1.32% 0.00% 0.00% 1.32%

37 28 3 6854.41% 41.18% 4.41% 100.00%84.09% 96.55% 100.00% 89.47%48.68% 36.84% 3.95% 89.47%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


80.00% 20.00% 0.00% 100.00%9.09% 3.45% 0.00% 6.58%5.26% 1.32% 0.00% 6.58%

3 0 0 3100.00% 0.00% 0.00% 100.00%

6.82% 0.00% 0.00% 3.95%3.95% 0.00% 0.00% 3.95%

37 28 3 6854.41% 41.18% 4.41% 100.00%84.09% 96.55% 100.00% 89.47%48.68% 36.84% 3.95% 89.47%

44 29 3 7657.89% 38.16% 3.95% 100.00%

100.00% 100.00% 100.00% 100.00%57.89% 38.16% 3.95% 100.00%


No Response

Total

Total

Table: Q54 Are you willing so speak with MGT or DPS to provide more detail of the alleged discrimination you have experienced by DPS or its prime contractors? * M/WBE or Non-M/WBE Classification (not based on certification) [count, row %, column %, total %].

M/WBE or Non-M/WBE Classification (not based on certification)Q54 Are you willing so speak with MGT or DPS to provide more detail of the alleged discrimination you have experienced by DPS or its prime contractors?Yes

No

M/WBE or Non-M/WBE Classification (not based on certification)Q53 When did the discrimination first occur?During Bidding Process

Both

Don't Know

No Response

APPENDIX G

APPENDIX G: DENVER PUBLIC SCHOOLS PERSONAL INTERVIEW GUIDE

READ: The purpose of this interview is to gather information on your experiences, perceptions, points of view on doing business with or attempting to do business with the Denver Public Schools (DPS) and in the private sector. Your responses and comments should be focused on the time period between October 1, 2008 and September 30, 2013.

1. Please specify your company’s primary line of business? (Try to get a good feel for what they do.)

1. Construction (ex: heavy construction, general contracting, carpentry, electrical, site work, HVAC, drywall, etc.):

2. Construction-Related Professional Services (ex: architecture, engineering, structural engineering, construction management, surveying, etc.):

2. How many years of experience do you or the primary owner(s) of your firm have in the line of business?

0 – 5 years 1 6 – 10 years 2 11 – 15 years 3 16 – 20 years 4 20 + years 5

3. Think back to the last time you submitted a bid or proposal with DPS – how did you learn about that opportunity?

3a. Was that notification method helpful?

Yes 1 No 2

3b. What other bid/proposal notification methods would you like to see DPS incorporate?

4. Think back to the years between 2008 and 2013, has any DPS staff made attempts to encourage you to respond to bid or proposal solicitations?

Yes 1 No 2

4a. If yes, please describe their outreach efforts.

4b. What outreach efforts would you like to see implemented.

Denver Public Schools Draft Report Appendix G October 2014 G-1


5. Have any of the following been a barrier to obtaining or successfully completing contracts for DPS?

Yes (1) No (2)

Prequalification requirements (1)

Bid bond requirement (2)



Financing (5)



Proposal/Bid specifications (8)

Limited time given to prepare bid package or quote (9)

Limited knowledge of purchasing contracting policies and procedures (10)




Selection process (14)

Unnecessary restrictive contract specifications (15)


Competing with large companies (17) 6. Does your company hold any of the following certifications from a recognized certification agency?

Check all that applies.

Minority Business Enterprise (MBE) 1 Small Business Enterprise (SBE) 2 Woman Business Enterprise (WBE) 3 Disadvantaged Business Enterprise (DBE) 4 HubZone 5 8A 6 Other 7 Specify None/Don’t Know 8

7. Has your status as an M/WBE facilitated your ability to win DPS contracts or subcontracts?

Yes 1 No 2

7a. If yes, how has your M/WBE status helped you get work?

8. In your opinion, what are the biggest obstacles faced by M/WBE businesses in securing contracts with DPS?



9. Have you been awarded a contract with DPS as a prime? (Note: prime is defined as a firm that had a direct contract with or been awarded a purchase order by DPS)

Yes 1 No 2

SKIP – if respondent answers NO, then skip to Question 11.

10. How many times have you been awarded a prime contract with DPS?

1-10 times 1 11-25 times 2 26-50 times 3 51-100 times 4 Over 100 times 5

11. Do you bid/propose as a subcontractor/subconsultant on DPS projects?

Yes 1 No 2

[SKIP – if respondent answers NO, then skip to Question 13]

12. Do primes who use your firm as a subcontractor on projects with M/WBE goals solicit your firm on projects without M/WBE goals?

Yes 1 No 2

13. Have you ever protested a bid or contract awarded by DPS?

Yes 1 No 2

13a. If yes, what bid or contract did you protest and what was the outcome?

14. Have you witnessed or are you aware of nonminority (male) businesses hiring affiliate companies to circumvent “good faith effort” (GFE) outreach and contracting with legitimate minority-, and women-owned businesses as subcontractors?

15. Do you believe there are certified nonminority female-owned firms that are “fronts” or don’t truly qualify to be certified?

15a. If yes, does that impact your ability to win contracts or subcontracts?

16. Do you believe there are double standards in the qualification and performance of M/WBE firms that makes it more difficult for minority-, and women-owned businesses to win contracts or subcontracts?

17. Do you feel DPS has ever treated your company unfairly in the bidding or contract award process?



Yes 1 No 2

17a. If yes, please provide as much detail as possible.

18. Do you have any suggestions on how DPS can improve the procurement and/or selection process, if applicable?

19. What was your company’s largest contract awarded between October 1, 2008 and September 30, 2013? (Note: approximate and any agency)

$________________________

[If respondent does not provide an answer, read following ranges for respondent to select one.]

Not applicable 1 Up to $50,000? 2 $50,001 to $100,000? 3 $100,001 to $200,000? 4 $200,001 to $300,000? 5 $300,001 to $400,000? 6 $400,001 to $500,000? 7 $500,001 to $1 million? 8 Over $1 million? 9 Don’t Know 99

20. Do you believe there is an informal network of primes that has excluded your company from doing business in the private sector?

Yes 1 No 2

20a. If yes, how does this informal network effect you winning contracts?

Yes 1 No 2

21. Is more than 50 percent of your company owned and controlled by a woman or women?




22. Is more than 50 percent of the company owned and controlled by one of the following racial or ethnic groups? [Get as much detail as possible.]

Anglo/Caucasian/White 1 Black or African American 2 Asian 3 Hispanic or Latino 4 American Indian/Alaskan Native 5 Alaskan Native Corporation 6 Tribal Corporation 7 Native Hawaiian /Other 8 Pacific Islander Groups 9 Other 10 Specify: Don’t Know 99

23. Excluding yourself, on average, how many employees does your company keep on the payroll, including full-time and part-time staff?

0 - 10 1 11 - 20 2 21 - 30 3 31 - 40 4 41+ 5

24. Do you have suggestions on how DPS can improve the Business Diversity Outreach program?

25. Do you believe your company experienced discrimination from DPS or a prime due to the race, ethnicity, or gender of the company’s owner(s)?

Yes (1) No (2)

a. Race/ethnicity

b. Gender

c. Both

25a. If yes, explain why. (Ask if they have documented evidence to support their response)



26. Do you believe your company experienced discrimination with other public sector agencies or the private sector in the Denver region?

Yes (1) No (2)

a. Race/ethnicity

b. Gender

c. Both

26a. If yes, explain why (Ask if they have documented evidence to support their response).

27. In your opinion are there challenges that M/WBE’s face that non-M/WBE firms do not face?

Yes 1 No 2

27a. If so, what?________________________________________________________

28. Have you experienced access to capital as being an impediment to securing DPS contracts?

Yes 1 No 2

28a. If yes, describe how?

29. Have you experienced bonding as being an impediment to obtaining DPS contracts (if applicable)?

Yes 1 No 2

29a. If yes, describe how?

30. What were your company’s approximate gross revenues for calendar year 2013?

$________________________

[If respondent does not provide an answer, read following ranges for respondent to select one.]

Up to $50,000? 1 $50,001 to $100,000? 2 $100,001 to $300,000? 3 $300,001 to $500,000? 4 $500,001 to $1 million? 5 $1,000,001 to $3 million? 6 $3,000,001 to $5 million? 7 $5,000,001 to $10 million? 8 Over $10 million? 9 Don’t Know 99

31. Is there anything that we have not covered that you feel will be helpful to this study?



AFFIDAVIT

__________________________________________ (interviewee) HEREBY ACKNOWLEDGE THAT THE TESTIMONY I GAVE IS TRUE AND AN ACCURATE REFLECTION OF MY PAST EXPERIENCES IN PROCUREMENT AND BUSINESS OPPORTUNITIES WITH DENVER PUBLIC SCHOOLS (DPS).

ADDITIONALLY, THIS TESTIMONY WAS GIVEN FREELY AND I HAVE NOT BEEN COERCED OR RECEIVED ANY REMUNERATION FOR MY COMMENTS.

_____________________________________________

SIGNATURE

_________________________

DATE

_____________________________________________

SIGNATURE OF INTERVIEWER AS WITNESS

_________________________

DATE


APPENDIX H

APPENDIX H: NEXUS BETWEEN COMMERCIAL AND PUBLIC CONSTRUCTION

MGT staff utilized two data sets to compare the utilization of firms. The first data set contained a listing of permits issued to contractors in the market area. The second data set contained firms at the prime level utilized on Denver Public Schools (DPS) public sector construction projects from January 1, 2009 through December 31, 2013, as well as subcontracting activity in 2008 and 2012. The goal of this analysis is to examine public sector and private sector contracting patterns for construction. In doing so, we compare the public sector utilization of firms on construction DPS-issued projects with the private sector commercial permitting data. The general questions to be answered include:

To what extent do utilized prime contractors that appear in DPS construction data set also appear in the private sector permitting data for commercial construction projects?

What is the utilization of subcontractors that are in the DPS construction data set that are also in the permitting data set for commercial construction projects?

Figure H-1 presents that when contractors on DPS public construction projects were cross referenced with the commercial construction projects, a total of 10 M/WBE contractors from DPS public construction projects at the prime level were also found in the commercial construction projects.

FIGURE H-1 NEXUS ANALYSIS OF CONSTRUCTION FIRMS

BY RACE, ETHNICITY, AND GENDER PARTICIPATING IN BOTH DPS CONSTRUCTION AND COMMERCIAL PROJECTS, PRIME LEVEL

Sources: MGT developed a Master Commercial Private sector Database based on commercial permitting data awarded between January 1, 2009 and December 31, 2013. MGT developed a Master Contract database based on DPS construction awards and expenditures between January 1, 2009 and December 31, 2013.

2

0

4

1

7

3

10

0 2 4 6 8 10 12

African American

Asian American

Hispanic American

Native American

Total MBE Firms

Nonminority Female

Total M/WBE Firms

Denver Public Schools Draft Report Appendix H October 2014 H-1

NEXUS BETWEEN COMMERCIAL AND PUBLIC CONSTRUCTION

Figure H-2 presents that when contractors on DPS public construction projects were cross referenced with the commercial construction projects, a total of 28 M/WBE contractors from DPS public construction projects at the subcontractor level were also found in the commercial construction projects.

FIGURE H-1 NEXUS ANALYSIS OF CONSTRUCTION FIRMS

BY RACE, ETHNICITY, AND GENDER PARTICIPATING IN BOTH DPS CONSTRUCTION AND COMMERCIAL PROJECTS, SUBCONTRACTOR LEVEL

Sources: MGT developed a Master Commercial Private sector Database based on commercial permitting data awarded between January 1, 2009 and December 31, 2013. MGT developed a Master subcontract database based on DPS construction contracting (2008 and 2012).

3

2

15

0

20

8

28

0 5 10 15 20 25 30

African American

Asian American

Hispanic American

Native American

Total MBE Firms

Nonminority Female

Total M/WBE Firms

Denver Public Schools Draft Report Appendix H October 2014 H-2

APPENDIX I

APPENDIX I: PUMS REGRESSION ANALYSES

PART I, RESULTS OF LOGISTIC REGRESSION

EXPLANATION OF RESULTS AND VARIABLES LOGISTIC REGRESSION OUTPUT

Below, variable names and operational definitions are provided. When interpreting Exhibits I-1 to I-5, the third column— Exp (B) — is the most informative index with regard to the influence of the independent variables on the likelihood of being self-employed. From the inverse of this value, we can interpret a likelihood value of its effect on self-employment. For example the Exp (B) for an African American is .410 from Exhibit I-1, the inverse of this is 2.45. This means that a nonminority male is 2.45 times more likely to be self-employed than an African American. Columns A and B are reported as a matter of convention to give the reader another indicator of both the magnitude of the variable’s effect and the direction of the effect (“-“ suggests the greater the negative B value the more it depresses the likelihood of being self-employed, and vice versa for a positive B value. It is noteworthy that theoretically “race-neutral” variables (e.g., marital status) tend to impact the likelihood of self-employment positively and that the race, ethnicity, and gender variables, in general, tend to have a negative effect on self-employment.

VARIABLES

Race, ethnicity, and gender indicator variables: African American Asian American Hispanic American Native American Gender: Nonminority woman or not

Other indicator variables: Marital Status: Married or not Age Age2: age squared. Used to acknowledge the positive, curvilinear relationship between each year of age and self-employment. Disability: Individuals self-reported health-related disabilities. Tenure: Owns their own home Value: Household property value. Mortgage: Monthly total mortgage payments. Unearn: Unearned income, such as interests and dividends. Resdinc: Household income less individuals’ personal income. P65: Number of individuals over the age of 65 living in the household. P18: Number of children under the age of 18 living in the household. Some College: Some college education College Graduate: College degree More than College: Professional or graduate degree

Denver Public Schools Draft Report Appendix I October 2014 I-1


EXHIBIT I-1 RESULTS OF LOGISTIC REGRESSION, OVERALL

VARIABLE B Sig. Exp (B) African American -0.892 0.017 0.410 Hispanic American -0.672 0.000 0.511 Asian American -0.434 0.104 0.648 Native American 0.295 0.430 1.344 Gender (1=Female) -0.653 0.000 0.521 Marital Status (1=Married) 0.388 0.000 1.474 Age 0.114 0.000 1.121 Age2 -0.001 0.009 0.999 Disability (1=Yes) -0.144 0.478 0.866 Tenure (1=Yes) 0.632 0.000 1.882 Value 0.000 0.171 1.000 Mortgage 0.000 0.000 1.000 Unearn 0.000 0.000 1.000 Resdinc 0.000 0.703 1.000 P65 -0.003 0.979 0.997 P18 -0.062 0.603 0.940 Some College (1=Yes) -0.087 0.870 0.917 College Graduate (1=Yes) -0.173 0.777 0.841 More than College (1=Yes) -0.009 0.938 0.991 Number of Observations 6797 Chi-squared statistic (df=19) 481.04 Log Likelihood -3656

Source: The Public Use Microdata Samples (PUMS) data from 2010 American Community Survey and calculations using SPSS. Note: BOLD statistically significant at p < .05. Estimation was conducted using the Binary Logistic command on SPSS. The Binary Logistic command performs binary logistic regressions and reports estimated coefficients and odds ratios that measure the effect on the probability of each one-unit increase in the included variables.



EXHIBIT I-2 RESULTS OF LOGISTIC REGRESSION, CONSTRUCTION

VARIABLE B Sig. Exp (B) African American -0.159 0.883 0.853 Hispanic American -0.440 0.168 0.644 Asian American -0.487 0.656 0.615 Native American 1.238 0.092 3.450 Gender (1=Female) -0.228 0.404 0.796 Marital Status (1=Married) 0.647 0.007 1.910 Age 0.159 0.017 1.173 Age2 -0.001 0.058 0.999 Disability (1=Yes) -0.877 0.063 0.416 Tenure (1=Yes) 0.998 0.002 2.712 Value 0.000 0.142 1.000 Mortgage 0.000 0.000 1.000 Unearn 0.000 0.012 1.000 Resdinc 0.000 0.096 1.000 P65 -0.015 0.955 0.985 P18 0.096 0.688 1.101 Some College (1=Yes) 0.187 0.776 1.206 College Graduate (1=Yes) -0.671 0.525 0.511 More than College (1=Yes) 0.296 0.186 1.345 Number of Observations 994 Chi-squared statistic (df=19) 114.489 Log Likelihood -707.957




EXHIBIT I-3 RESULTS OF LOGISTIC REGRESSION, PROFESSIONAL SERVICES

VARIABLE B Sig. Exp (B) African American -1.436 0.052 0.238 Hispanic American -1.793 0.001 0.167 Asian American -2.394 0.024 0.091 Native American -0.658 0.536 0.518 Gender (1=Female) -1.191 0.000 0.304 Marital Status (1=Married) 0.040 0.850 1.040 Age 0.121 0.056 1.128 Age2 -0.001 0.225 0.999 Disability (1=Yes) 0.449 0.247 1.567 Tenure (1=Yes) 0.772 0.010 2.165 Value 0.000 0.410 1.000 Mortgage 0.000 0.000 1.000 Unearn 0.000 0.001 1.000 Resdinc 0.000 0.833 1.000 P65 -0.087 0.738 0.917 P18 -0.538 0.049 0.584 Some College (1=Yes) -17.692 0.999 0.000 College Graduate (1=Yes) -18.157 0.999 0.000 More than College (1=Yes) -1.028 0.017 0.358 Number of Observations 2359 Chi-squared statistic (df=19) 192.722 Log Likelihood -945.543




EXHIBIT I-4 RESULTS OF LOGISTIC REGRESSION, OTHER SERVICES

VARIABLE B Sig. Exp (B) African American -0.658 0.217 0.518 Hispanic American -0.475 0.070 0.622 Asian American 0.286 0.378 1.331 Native American 0.168 0.765 1.182 Gender (1=Female) -0.147 0.330 0.863 Marital Status (1=Married) 0.452 0.005 1.572 Age 0.127 0.004 1.135 Age2 -0.001 0.044 0.999 Disability (1=Yes) -0.252 0.425 0.777 Tenure (1=Yes) 0.479 0.048 1.615 Value 0.000 0.603 1.000 Mortgage 0.000 0.000 1.000 Unearn 0.000 0.008 1.000 Resdinc 0.000 0.339 1.000 P65 0.044 0.821 1.045 P18 0.015 0.935 1.015 Some College (1=Yes) -0.953 0.354 0.386 College Graduate (1=Yes) 0.998 0.211 2.713 More than College (1=Yes) 0.017 0.917 1.017 Number of Observations 2712 Chi-squared statistic (df=19) 220.467 Log Likelihood -1581.14




EXHIBIT I-5 RESULTS OF LOGISTIC REGRESSION, GOODS & SUPPLIES

VARIABLE B SIG. EXP (B) African American -0.054 0.961 0.948 Hispanic American -0.022 0.972 0.979 Asian American 0.025 0.977 1.025 Native American 0.930 0.409 2.534 Gender (1=Female) -0.251 0.530 0.778 Marital Status (1=Married) 0.610 0.153 1.840 Age 0.028 0.797 1.028 Age2 0.000 0.958 1.000 Disability (1=Yes) 0.181 0.798 1.199 Tenure (1=Yes) 0.368 0.580 1.445 Value 0.000 0.695 1.000 Mortgage 0.000 0.225 1.000 Unearn 0.000 0.544 1.000 Resdinc 0.000 0.030 1.000 P65 0.011 0.981 1.011 P18 -0.128 0.784 0.880 College Graduate (1=Yes) -18.392 0.999 0.000 More than College (1=Yes) -1.608 0.032 0.200 Number of Observations 732 Chi-squared statistic (df=19) 38.238 Log Likelihood -266.359




PART II , RESULTS OF LOGISTIC REGRESSION

EXPLANATION OF RESULTS AND VARIABLES LOGISTIC REGRESSION OUTPUT

Below, variable names and operational definitions are provided. When interpreting the linear regression in Exhibits I-6 to I-10, the first column— Unstandardized B — is the most informative index with regard to the influence of the independent variables on earnings. Each number in this column represents a percent change in earnings. For example the corresponding number for a nonminority woman is -.229, from Exhibit I-6, meaning that a nonminority woman will earn 22.9 percent less than a nonminority male. The other four columns are reported in order to give the reader another indicator of both the magnitude of the variable’s effect and the direction of the effect. Std. Error reports the standard deviation in the sampling distribution. Standardized B reports the standard deviation change in the dependent variable from on standard deviation increase in the independent variable. The t and Sig. columns simply report the level and strength of a variable’s significance.

VARIABLES

Race, ethnicity, and gender indicator variables: African American Asian American Hispanic American Native American Gender: Nonminority woman or not

Other indicator variables: Marital Status: Married or not Age Age2: age squared. Used to acknowledge the positive, curvilinear relationship between each year of age and self-employment. Disability: Individuals self-reported health-related disabilities. Tenure: Owns their own home Value: Household property value. Mortgage: Monthly total mortgage payments. Unearn: Unearned income, such as interests and dividends. Resdinc: Household income less individuals’ personal income. P65: Number of individuals over the age of 65 living in the household. P18: Number of children under the age of 18 living in the household. Some College: Some college education College Graduate: College degree More than College: Professional or graduate degree



EXHIBIT I-6 RESULTS OF LOGISTIC REGRESSION, OVERALL

VARIABLE UNSTANDARDIZED STANDARDIZED

B STD. ERROR B T SIG.

African American -0.411 0.042 -0.085 -9.806 0.000 Hispanic American -0.415 0.023 -0.183 -18.414 0.000 Asian American -0.245 0.039 -0.057 -6.236 0.000 Native American -0.288 0.065 -0.038 -4.436 0.000 Nonminority Women (1=Female) -0.229 0.016 -0.133 -14.558 0.000

Marital Status (1=Married) 0.229 0.015 0.139 15.491 0.000 Disability (1=Yes) -0.259 0.032 -0.068 -8.036 0.000 Age 0.085 0.003 1.350 24.651 0.000 Age2 -0.001 0.000 -1.181 -21.745 0.000 Speaks English Well (1=Yes) -0.031 0.024 -0.012 -1.286 0.198 Some College (1=Yes) -0.509 0.056 -0.078 -9.054 0.000 College Graduate (1=Yes) -0.436 0.070 -0.053 -6.245 0.000 More than College (1=Yes) -0.342 0.017 -0.169 -19.668 0.000 Constant 8.786 0.072 121.752 0.000

Source: The Public Use Microdata Samples (PUMS) data from 2010 American Community Survey and calculations using SPSS. Note: BOLD statistically significant at p < .05.



EXHIBIT I-7 RESULTS OF LOGISTIC REGRESSION, CONSTRUCTION



African American -0.298 0.156 -0.042 -1.910 0.056 Hispanic American -0.443 0.056 -0.219 -7.954 0.000 Asian American -0.175 0.162 -0.024 -1.083 0.279 Native American -0.170 0.176 -0.021 -0.969 0.333 Nonminority Women (1=Female) -0.106 0.055 -0.044 -1.931 0.054

Marital Status (1=Married) 0.237 0.040 0.138 5.955 0.000 Disability (1=Yes) -0.083 0.083 -0.022 -0.996 0.320 Age 0.072 0.009 1.112 7.915 0.000 Age2 -0.001 0.000 -1.016 -7.268 0.000 Speaks English Well (1=Yes) 0.036 0.066 0.014 0.553 0.580 Some College (1=Yes) -0.376 0.098 -0.089 -3.848 0.000 College Graduate (1=Yes) -0.351 0.126 -0.062 -2.784 0.005 More than College (1=Yes) -0.292 0.041 -0.160 -7.100 0.000 Constant 9.059 0.190 - 47.647 0.000




EXHIBIT I-8 RESULTS OF LOGISTIC REGRESSION, PROFESSIONAL SERVICES



African American -0.526 0.070 -0.113 -7.469 0.000 Hispanic American -0.550 0.044 -0.215 -12.617 0.000 Asian American -0.312 0.063 -0.079 -4.977 0.000 Native American -0.708 0.126 -0.083 -5.626 0.000 Nonminority Women (1=Female) -0.381 0.026 -0.242 -14.645 0.000 Marital Status (1=Married) 0.187 0.025 0.115 7.532 0.000 Disability (1=Yes) -0.265 0.059 -0.065 -4.453 0.000 Age 0.091 0.006 1.442 15.196 0.000 Age2 -0.001 0.000 -1.263 -13.369 0.000 Speaks English Well (1=Yes) -0.083 0.041 -0.032 -2.011 0.044 More than College (1=Yes) -0.383 0.036 -0.156 -10.506 0.000 Constant 8.900 0.129 68.739 0.000




EXHIBIT I-9 RESULTS OF LOGISTIC REGRESSION, OTHER SERVICES



African American -0.514 0.067 -0.113 -7.700 0.000 Hispanic American -0.435 0.038 -0.194 -11.576 0.000 Asian American -0.329 0.064 -0.081 -5.176 0.000 Native American -0.148 0.101 -0.021 -1.465 0.143 Nonminority Women (1=Female) -0.240 0.027 -0.135 -8.758 0.000 Marital Status (1=Married) 0.238 0.025 0.145 9.364 0.000 Disability (1=Yes) -0.304 0.053 -0.083 -5.767 0.000 Age 0.081 0.006 1.311 14.070 0.000 Age2 -0.001 0.000 -1.150 -12.466 0.000 Speaks English Well (1=Yes) 0.026 0.039 0.011 0.665 0.506 Some College (1=Yes) -0.423 0.087 -0.071 -4.860 0.000 College Graduate (1=Yes) -0.371 0.125 -0.042 -2.957 0.003 More than College (1=Yes) -0.287 0.028 -0.146 -10.072 0.000 Constant 8.782 0.120 73.341 0.000




EXHIBIT I-10 RESULTS OF LOGISTIC REGRESSION, GOODS & SUPPLIES



African American -0.175 0.090 -0.041 -1.945 0.052 Hispanic American -0.249 0.047 -0.127 -5.302 0.000 Asian American -0.149 0.089 -0.037 -1.685 0.092 Native American -0.140 0.138 -0.021 -1.014 0.311 Nonminority Women (1=Female) -0.266 0.036 -0.163 -7.327 0.000 Marital Status (1=Married) 0.222 0.033 0.148 6.714 0.000 Disability (1=Yes) -0.199 0.069 -0.060 -2.890 0.004 Age 0.093 0.008 1.630 11.896 0.000 Age2 -0.001 0.000 -1.453 -10.715 0.000 Speaks English Well (1=Yes) -0.082 0.052 -0.036 -1.564 0.118 College Graduate (1=Yes) -0.338 0.146 -0.048 -2.322 0.020 More than College (1=Yes) -0.223 0.036 -0.128 -6.121 0.000 Constant 8.536 0.160 53.513 0.000



DISPARITY STUDY FOR ENVER PUBLIC SCHOOLS RAFT REPORTfile… · In February 2014, the Denver Public Schools Board of Education (DPS) contracted with MGT of America, Inc., (MGT) to

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