Association for Education Finance and Policy · Web viewMostly harmless econometrics: An empiricist’s companion. Princeton, NJ: Princeton University Press. Bosman, J. (2015, June

Running head: WHO BEARS THE COSTS OF DISTRICT FUNDING CUTS? 1

Who Bears the Costs of District Funding Cuts?

Reducing Inequality in the Distribution of Teacher Layoffs

David S. Knight

Center for Education Research and Policy Studies

University of Texas at El Paso

Katharine O. Strunk

Rossier School of Education and Sol Price School of Public Policy

University of Southern California

The research presented here utilizes confidential data from the Los Angeles Unified School District (LAUSD). We gratefully acknowledge the receipt of these data and we wish to thank Justo Avila and William Bass of LAUSD for their assistance with data or insights into the layoff process. The views expressed in this paper do not necessarily reflect those of the University of Southern California or LAUSD. Responsibility for any and all errors rests solely with the authors.

WHO BEARS THE COST OF DISTRICT FUNDING CUTS?

Abstract

Massive funding cuts to public education took place around the country following the Great

Recession. Many school districts were forced to conduct teacher layoffs at a larger scale than any

other time in recent history. We show that prior to the district’s intervention, the layoff process

disproportionately impacted historically disadvantaged students in the Los Angeles Unified

School District. We then demonstrate the success of a policy designed to reduce inequality in the

distribution of teacher layoffs.

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Who Bears the Costs of District Funding Cuts?

Reducing Inequality in the Distribution of Teacher Layoffs

The Great Recession of 2008 led to unprecedented reductions in public education funding

in the United States. As a direct result of recessionary spending cuts, more districts were forced

to lay off teachers in the years during and following the Recession than at any other time in

recent history (Goldhaber & Theobald, 2013; Shierholz, 2013). Research has documented that

layoffs harm both teachers and students by increasing class sizes, damaging school culture and

morale, contributing to school-level teacher churn, and decreasing individual teachers’

productivity (Goldhaber, Strunk, Brown & Knight, 2015; Guin, 2011; Kraft, 2015; Strunk,

Goldhaber, Knight & Brown, 2015).

These harmful effects are concerning, particularly because teacher layoffs often

disproportionately affect historically disadvantaged students, who bear an uneven share of the

consequences of recessionary staffing reductions (e.g., UCLA/IDEA, 2009). This primarily

occurs for two reasons. First, many state school finance systems do not provide equitable levels

of funding across districts (Baker, Sciarra, & Farrie, 2015; Education Trust, 2015), causing

layoffs to be concentrated in districts with lower allocations of state financial support and greater

proportions of low-income students and students of color (Estrada, 2012; Plecki, Elfers &

Finster, 2010). Second, when layoffs are determined by districtwide seniority, such policies can

concentrate layoffs at particular schools within districts (Goldhaber & Theobald, 2013; Levinson

& Theisen-Homer, 2015), and novice teachers are often concentrated in the highest-poverty

schools (Clotfelter, Ladd, Wheeler & Vigdor, 2006; Darling-Hammond, 2004; 2000; Goldhaber,

Lavery & Theobald, 2015).1

1 Most state and district policies take into account teachers’ endorsement areas and special skills or training. However, districtwide seniority is typically the most important factor in determining layoffs (National Center on Teacher Quality, 2010; Thomsen, 2014).

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Some school districts have experimented with policies designed to mitigate the

inequitable distribution of layoffs. For instance, the layoff process in the Charlotte-Mecklenburg

school district was conducted on a school-by-school basis during 2008-09 and 2009-10

(Sawchuk, 2015). By first taking into account changes in enrollment and natural teacher attrition

at each school, district administrators created a system that allocated budget-based layoffs

equitably across all schools in the district (Kraft, 2015).

A different type of policy intervention was implemented in the Los Angeles Unified

School District (LAUSD) when layoffs took place in 2010-11 and 2011-12. After an

unprecedented staffing reduction in which almost 5,000 reduction-in-force (RIF) notices were

distributed to teachers in March of 2009, the American Civil Liberties Union (ACLU) filed a

class action lawsuit alleging that the layoff process in LAUSD disproportionally affected

students in South Los Angeles, where schools have the highest concentrations of English

language learners and students of color and in poverty (Reed v. State of California, 2010). A

second round of teacher layoffs occurred the following school year, before the State Supreme

Court made any decision in the Reed case. Ultimately, the involved parties agreed on a

settlement that required LAUSD to redirect layoffs in a set of 45 schools with high staff turnover

for the third and fourth years of layoffs (2010-11 and 2011-12).

Using longitudinal data from LAUSD, we examine how layoffs in the district impacted

different kinds of students both before and after the implementation of the Reed settlement.

Using a difference-in-difference analysis, we show that Reed dramatically reduced the extent to

which students of color and low-income students were disproportionately impacted. In particular,

we find that while the Reed policy significantly reduced inequities in the way layoffs were

distributed across impacted students and schools, the small scale of the intervention (only 6.5%

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of district schools and 8.7% of students) meant that the district did not ultimately achieve equity

in the distribution of layoffs. The results of this study have important policy implications as

districts around the country continue to struggle to meet budget obligations and teacher layoffs

have continued into the 2014-15 school year (Bosman, 2015; Schulte, 2015; Superville, 2016).

In the next section we provide additional background on the implications of widescale

layoffs for schools, teachers, and students. We then review the policy context under which large-

scale teacher layoffs took place in LAUSD as well as the Reed v. State of California settlement

that prevented budget-based layoffs in high-need schools. The subsequent section provides an

overview of our data and analytic approach and in the final two sections, we discuss findings and

policy implications.

Background Literature

Decades of research document the inequitable access to highly qualified, experienced,

and effective teachers across students’ race/ethnicity, socioeconomic status, English language

proficiency, and achievement levels (e.g., Darling-Hammond, 2004; 2000; 1998; Goldhaber,

Lavery & Theobald, 2015; Ingersoll, 1999; Isenberg et al., 2013; Lankford, Loeb & Wyckoff,

2002). As a result, seniority-based layoffs, which assign layoffs primarily in reverse order of

district seniority (and are required by law in California and many other states around the country;

Thomsen, 2014), may disproportionately harm low-income students of color (e.g., Dowell,

Whitmore, Hodgman, Littlefield, & Tracey, 2011; Hahnel, Barondess & Ramanathan, 2011). To

this end, several recent policy briefs use aggregate student data to argue that, given the

distribution of average teacher experience across schools in LAUSD and California, seniority-

based layoffs must be concentrated in high-poverty, high-minority schools (Sepe & Roza, 2010;

UCLA/IDEA, 2009).

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Two studies simulate how layoffs would be distributed under alternate layoff policies

(Boyd et al., 2011; Goldhaber & Theobald, 2013).2 Boyd et al. (2011) find that, in New York

City, layoff policies based solely on either seniority or value-added measures would result in laid

off teachers coming from schools with approximately 80% of low-income students, whereas the

district average is approximately 72%. In contrast, simulations based on Washington State data

suggest that layoffs polices based solely on value-added lead to more equitable distributions than

the layoff policies that were actually used, which relied heavily on seniority (Goldhaber &

Theobald, 2013). The divergent findings in layoff simulation studies results in part from

differences in the way teacher experience is distributed across schools in each context.

Only one prior study that we know of examines how layoffs are distributed across

students and schools when districts use selection criteria other than seniority and teaching

credentials. Kraft (2015) examines layoffs in Charlotte Mecklenburg Schools, where district

administrators determined the number of teacher layoffs at each school based on the difference

between the full-time equivalent (FTE) staffing levels generated from the district’s student-

teacher ratio policies and the number of FTE teachers employed at each school. Kraft did not

find substantial differences in the likelihood of being laid off by student race/ethnicity or

students’ family income level. Although teachers in schools with greater proportions of African-

American students, lower achievement scores, and higher rates of student absenteeism were

slightly more likely to be laid off, the school-by-school determinations of the number of layoffs

at each school prevented substantial inequities.

We build off this past work first by drawing on student-level proprietary data to

demonstrate the inequitable distribution of seniority-based layoffs in a large urban school

2 Kraft (2015) also simulates alternate layoff policies, but does not report how each policy affects student groups.

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district. We then examine the impacts of a policy aimed at equalizing the harmful effects of

layoffs.

Policy Context in LAUSD

The diverse and highly segregated student population in LAUSD makes it an ideal and

important district to assess the equity implications of budget-based layoffs. Three-quarters of

students in LAUSD identify as Latina/o, about 9% as Black, 6% Asian, 8% White, and under 1%

as Native American, Pacific Islander, or more than one race. Over 85% of students qualified for

free or reduced priced meals in at least one year of our data and approximately 70% are emergent

bilinguals. Thus when dramatic funding cuts stemming from the Great Recession required

LAUSD to lay off teachers based predominantly on district seniority every year from 2008-09

through 2011-12, the district faced serious equity implications in terms of which schools and

students would be affected.

The California Education Code requires that districts lay off teachers in reverse order of

seniority within each teaching subject (e.g., math, English, elementary). However, exceptions are

allowed for any certificated employee who has “special training and experience necessary to

teach [a specific course of study], which others with more seniority do not possess” (Cal. Educ.

Code §44955d). Districts can also deviate from seniority within teaching area “for purposes of

maintaining or achieving compliance with constitutional requirements related to equal protection

of the laws” (Cal. Educ. Code §44955d). The Education Code also requires that teachers receive

a RIF notice by March 15 that warns the teacher of a possible layoff. By May 15, districts must

notify teachers of whether their RIF notice was rescinded, or whether they will be laid off at the

end of the school year.3 From 2008-09 to 2011-12, 13.7% of teachers received initial RIF notices

3 We distinguish between reduction-in-force (RIF) notices, which provide teachers with an early warning that their position is at risk of being eliminated, and layoff notices, which are distributed later in the school year and imply that the teacher has officially lost her or his teaching position.

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and 4.5% were laid off.4 Whether a teacher is RIF-rescinded, laid off or not impacted by RIFs is

important because past research indicates that RIF-rescinded and laid off teachers have lower

retention rates and are less productive in the following year (Goldhaber et al., 2015; Strunk et al.,

2015).

We split recessionary layoffs in LAUSD into two phases. In Phase I, 2008-09 and 2009-

10, layoffs were dictated solely by state law, which requires seniority-based layoffs. In Phase II,

2010-11 and 2011-12, LAUSD implemented an intervention as a result of the settlement in Reed

v. California that prevented budget-based layoffs at a subset of schools with the highest levels of

teacher turnover (described in greater detail below). The Phase I layoff process exemplifies how

layoffs are distributed in large districts under typical seniority-based systems; Phase II

demonstrates the distribution of layoffs under a policy intervention intended to stem the

inequities resulting from traditional layoff processes.

Phase I: Layoffs During the Pre-Intervention Period

California laws requiring seniority-based layoffs are reflected in the LAUSD

administrative data. Table 1 provides descriptive statistics of teachers for each layoff category

outlined above for Phase I layoffs. The first panel demonstrates the strong correlation between

seniority and teachers’ likelihood of receiving a RIF or layoff notice. Only 53% of novice

teachers (with between 0-2 years of experience), compared to 97% of veteran teachers (9 or more

years of experience), did not receive a RIF notice. Approximately 28% of novice teachers were

laid off, while 3.4% of mid-career teachers (3-8 years) and 0.6% of veteran teachers received

final layoff notices.

4 These figures differ slightly from those reported in prior work because in this study we consider only teachers of record that we can link to students. See the online Appendix Table A1 for additional information on the proportion of students and teachers impacted by layoffs over time.

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The second panel of Table 1 shows that less than 4% of special education teachers

received either an initial RIF notice or a layoff notice. Teachers with math or science credentials

were more likely to be RIF-rescinded, relative to the overall average, but less likely to be laid

off. In addition to sending RIF and eventual layoff notices to the least experienced teachers

within each teaching area, the district also targeted teachers who did not have the appropriate

credentials for their teaching area during the first year of layoffs (i.e., were not NCLB-

compliant). This is reflected both by the fact that teachers with other non-elementary credentials

had the highest proportion of layoffs and that a considerable amount of mid-career teachers

received RIF and layoff notices.

Phase II: Layoffs During the Post-Intervention Period

In February of 2010, the ACLU filed a class action lawsuit asserting that three middle

schools in LAUSD incurred a disproportionate level of layoffs (ACLU, 2011). The parties

involved in the lawsuit agreed to prevent budget-based layoffs at 45 schools with high levels of

teacher turnover during the third and fourth years in which layoffs took place in LAUSD (2010-

11 and 2011-12). The selection criteria for Reed schools focused on new schools and schools that

were low-performing, but making gains over the past three years, as measured by Academic

Performance Index (API, a composite measure of student test score performance used in

California).

As Table 2 shows, 1.7% of all elementary students were enrolled in Reed schools in both

2010-11 and 2011-12, with Black and Latina/o students overrepresented and White students

underrepresented. Non-native English speaking students were also more heavily represented in

Reed schools at the elementary level, while elementary students eligible for free or reduced-price

lunches were roughly equally represented in Reed and non-Reed schools. For middle and high

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school grades, 7.1% of students attended Reed schools in both years, while an additional 4.0%

and 6.9% were enrolled in Reed schools in just 2011 and 2012, respectively. Reed schools at the

middle and high school level also included higher proportions of students of color, non-native

English speakers, and low-income students compared to non-Reed schools.

Data and Analytic Approach

We ask two research questions about the distribution of layoffs in LAUSD resulting from

the Great Recession: 1) how are RIFs and layoffs distributed under typical seniority-based layoff

policies (in Phase I); and 2) how did the Reed Settlement impact the distribution of RIFs and

layoffs across students (in Phase II)?

Data

We draw on LAUSD administrative data that link students to teachers and schools over a

five-year window of observation, from 2008-9 to 2012-13. The student-level data include

information on student demographics and are linked to the teacher-level datasets, which include

information on teachers’ experience, educational attainment, endorsement areas, contract status

(e.g., temporary, probationary, permanent/tenured, etc.), courses taught each semester, and layoff

status. These proprietary data are merged with public-use school-level data accessed through the

California Department of Education.

Analytic Approach

We examine the following outcome measures: (a) whether a student’s teacher did not

receive a RIF notice, (b) whether a student’s teacher received a RIF notice that was rescinded

(“RIF-rescinded”), and (c) whether a student’s teacher received a layoff notice. We also include

in our analyses lagged versions of each of these outcome measures. That is, for each layoff

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category we also examine how students’ current teachers were impacted by layoffs in the prior

year. At the elementary level, we examine the likelihood that various student groups see their

teacher impacted by RIFs or layoffs. For middle and high school students, the outcome measures

are the proportion of a student’s teachers who are impacted by RIFs or layoffs. Outcomes for

middle and high school students are calculated differently because these students attend non-self-

contained classes and therefore have multiple teachers throughout the school day. In particular,

we examine how results differ when we consider the proportion of a student’s teachers that are

laid off, the likelihood a student has at least one teacher laid off, or the likelihood a student has

the majority of her or his teachers laid off.

To examine how layoffs are distributed under typical seniority-based policies, we use

descriptive analyses to assess the extent to which RIFs and layoffs disproportionately affected

various student groups in the years prior to the Reed intervention. We disaggregate by

race/ethnicity, English language status, FRL status, and special education enrollment.5

We use two strategies to address the question of how Reed impacted the distribution of

RIFs and layoffs across students. First, we calculate the same outcome measures for each student

group for the two years after the Reed intervention was implemented (2010-11 and 2011-12). We

then compare the odds ratios that particular students’ teachers were exposed to RIFs or layoffs

before and after implementation of Reed. This approach shows whether, for example, low-

income students or students of color became less likely to have their teacher laid off, compared

to their higher income and/or white peers when Reed layoff protections were in place.

5 The district classifies emergent bilingual students into three categories: limited English proficiency (LEP), non-native English speakers who were fluent in English when they initially enrolled, and students who are reclassified as fluent. We refer to each of these student categories throughout this paper as LEP, fluent, non-native English speakers or fluent NNE, and reclassified. We also collectively refer to the district’s emergent bilingual students as non-native English speakers (NNE).

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This first approach is limited because it fails to consider confounding factors that may

take place within the same timeframe as the policy intervention. We therefore use a difference-

in-difference (DID) framework, which relies on a group of comparison schools that do not

receive the treatment to serve as a counterfactual for the treatment group (Angrist & Krueger,

1999; Angrist & Pischke, 2009; Ashenfelter & Card, 1985; Imbens & Wooldridge, 2009; Strunk,

McEachin & Westover, 2012). We select as our non-treated group schools that were nearly

selected as Reed schools, using the same selection mechanisms as the district did. Because the

treatment schools and comparison schools had approximately the same proportion of teachers

RIFed and laid off before the Reed intervention, the comparison schools provide a counterfactual

for what would have happened in the treatment schools in the absence of treatment.6 In short, the

DID analysis compares the pre- and post-intervention outcomes in treated schools (the first

difference) with the pre- and post-intervention outcomes in non-treated schools (the second

difference).

For the DID analysis, we focus on the three RIF and layoff outcomes that prior research

has linked to higher teacher turnover and lower teacher effectiveness (i.e., Goldhaber et al.,

2015; Strunk et al., 2015): (a) whether a teacher receives an initial RIF notice in the current year,

(b) whether a teacher is laid off in the current year, and (c) whether a teacher was laid off in the

prior year, but rehired to teach for the current year.7 We estimate the following model:

Pr(RIFit) = β0 + β1 REED_2011it + β2 REED_2011it * YEARit + β3 REED_2012it

6 It is important to note that as part of the Reed decision, the district could only send redirected RIF notices to schools in which the proportion of teachers receiving notices was below the district-average (ACLU, 2011). See Appendix Table A2 for summary statistics of Reed and comparison schools.7 The first outcome, whether a student’s teacher received a RIF notice (or for secondary students, the proportion receiving RIF notices) includes teachers who received a RIF notice that was rescinded and teachers who received a final layoff notice. We combine the RIF-rescinded and layoff outcomes because no teachers in Reed schools were RIF-rescinded – of the teachers that received a RIF notice, all received a final layoff notice. Discussions with district administrators revealed that layoffs at Reed schools took place solely due to changes in enrollment (personal communication, 2014). Finally, we exclude models that use RIF-rescinded in the prior year because past research shows this particular variable is unrelated to changes in teacher turnover or effectiveness in LAUSD.

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+ β4 REED_2012it * YEARit + β5 SCHOOL_LEVELit + εi + μit (1)

Where Pr(RIFit) is the probability that student i's teacher received a RIF notice in year t. We run

the same models predicting the likelihood a students’ teacher was laid off or was laid off the

prior year, but rehired. For middle and high school students, the outcomes are continuous

variables showing the proportion of a student’s teachers that received a RIF notice, received a

layoff notice, or were laid off in the prior year. The Reed indicators (REED_2011 and

REED_2012) are time-invariant variables indicating whether the student will be enrolled in a

Reed school in 2011 and in 2012, respectively.8 YEARit and SCHOOL_LEVELit are sets of

dummy variables indicating the school year and the school level (elementary, middle, high

school, or span school), respectively. Finally, the error term is divided into two components, εi

and μit, which represent unobserved factors related to individual students and individual student-

year observations, respectively (i.e., standard errors are clustered at the student level).

The models described above provide causal estimates of the extent to which Reed

impacted the likelihood that students’ teachers were affected by layoffs. In order to test how the

general effects of Reed varied across students groups, we rerun each of the Reed models for each

student subgroup by race/ethnicity, free/reduced price lunch eligibility, and language status.

Findings

Teacher Layoffs Prior to the Reed Intervention

Table 3 shows that layoffs were inequitably distributed across student groups during

Phase I. We report odds ratios of the likelihood a student in a particular subgroup had a teacher

8 Note that most Reed schools were treated in both 2010-11 and 2011-12 (35 of the 58 ever selected). The DID estimate for students in schools treated in both years is given by β1 + β2, while the effect of Reed for students treated only in 2010-11 or only in 2011-12 is given by β1 and β2, respectively. For models in which the outcome measure is whether the student’s teacher was laid off and rehired in the prior year, the Reed indicators are time-invariant variables indicating whether the student’s current year school was a Reed school in the prior year. We make this slight adjustment because the likelihood of being assigned to a teacher who was laid off in the prior year (but rehired) is most directly affected by whether the school was protected by Reed in the prior year.

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who is not RIFed, RIF-rescinded, or laid off. Significance tests show whether odds ratios are

significantly different than one (which implies the student group is as likely as the reference

group to have their teacher impacted). Reference categories for each set of odds ratios were

selected based on the group that is generally most privileged in educational settings: White,

native English speakers, never eligible for free/reduced price lunch, and not eligible for special

education.

The first three columns of Table 3 show the results for elementary students. Latina/o

students were equally likely to have their teacher RIF-rescinded, but 24.5% more likely to see

their teacher laid off, compared to White elementary students. Black elementary students were

41.4% and 67.9% more likely to see their teacher RIF-rescinded and laid off, respectively,

compared to White students. Both Asian and Native American students were less likely than

White students to have a teacher RIF-rescinded, but more likely to see their teacher laid off.

Non-native English speakers (NNE) were assigned to teachers who were 2.5% more likely to be

RIF-rescinded and 4.9% more likely to be laid off, compared to native English speakers.

Surprisingly, we find that elementary students ever eligible for free/reduced price lunch

were less likely to see their teacher RIF-rescinded or laid off, compared to those never eligible

(the same is true when we consider those eligible in a particular year). This is largely because

teacher experience in the elementary grades is roughly evenly distributed across low-income and

non-low-income students (Appendix Figure A1). Finally, we find that special education students

are less likely to have their teacher RIF-rescinded or laid off, compared to those not enrolled in

special education. This finding is consistent across all special education categories (not shown).

Results for middle and high school students are shown in the last three columns of Table

3. We find that Phase I RIF and layoffs notices were distributed even more inequitably across

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student race, income level, and language status in the upper grades. Black and Latina/o students

were both over twice as likely to see their teacher laid off. NNE students and students ever

eligible for free/reduced-price lunch were also more likely than native English speakers and non-

eligible students to have teachers who were RIF-rescinded or laid off. Disparities are greater

when we consider students who had a majority of their teachers RIF-rescinded or laid off, and

about equal across students with at least one teacher impacted by the layoffs (Appendix Table

A3). Finally, consistent with the elementary grades, special education students were less likely to

be exposed to teachers who were RIFed or laid off.9

Teacher Layoffs During the Reed Intervention

Results for the Phase II layoffs for elementary students are shown in the first three

columns of Table 4. For every one of the elementary groups who were disproportionately

affected during the Phase I layoffs, we find that the distribution of teachers who were RIF-

rescinded and laid off was more equitable with Reed protections in place. For example, Latina/o

students in elementary grades became less likely than White students to see their teacher RIF-

rescinded or laid off. Although Black students in elementary grades were still 15.4% and 17.4%

more likely to have their teacher RIF-rescinded or laid off in Phase II, the respective figures for

the Phase I layoffs were far higher. Elementary students who identify in other race/ethnicity

categories and non-native English speakers also experienced dramatic reductions in the extent to

which they were disproportionately impacted by the RIF process.

Changes in the distribution of RIF and layoff notices between the first and second phase

of layoffs were even more substantial for secondary students. The last three columns of Table 4

9 We report the raw percentages used to compute odds ratios for Phase I layoffs in Appendix Tables A4 and A5. Our results are generally consistent when we consider teachers’ lagged RIF and layoff outcomes (Appendix Tables A6 and A7). Raw percentages for Phase II layoffs for elementary and secondary students are shown in Appendix Tables A8 and A9, respectively. Finally, Appendix Tables A10 and A11 show results for teachers’ lagged RIF and layoff outcomes during the Phase II layoffs.

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show the results for middle and high school students. Every race/ethnicity category had a lower

proportion of teachers RIF-rescinded compared to White students (with the exception of Pacific

Islanders, who had a roughly equal proportion). These student groups maintained a higher

proportion of teachers laid off compared to White students during the Phase II layoff period, but

the degree of inequality reduced substantially. For example, Black and Latina/o students had an

average of 16.5% and 21.0% more of their teachers laid off than White students, but these

figures were down from over double the number of layoffs as White students during Phase I.

Both middle and high school non-native English speakers and students who qualified for free or

reduced-price lunch had fewer teachers RIF-rescinded than their more advantaged peers during

the Phase II layoffs, but still had a slightly greater proportion of their teachers laid off.

The Effect of Reed Layoff Protections

It is likely that the Reed intervention caused much of the changes in disadvantaged

students’ exposure to the layoff process discussed above. Next we present the results of our DID

analyses that test this hypothesis. Table 5 shows that for elementary students in Reed schools, the

likelihood of having a teacher who received a RIF notice decreased by between 24.7 and 27.8

percentage points each year, and the likelihood of having a teacher who received a layoff notice

declined by between 7.0 and 8.7 percentage points. Finally, Reed lowered the likelihood that

elementary students were taught by a teacher who was laid off in the prior year and rehired by

between 2.5 and 3.8 percentage points.10 The results are generally similar whether we focus on

students treated in just the first year, the second year, or both (shown in the first, second and

third rows of Table 5).

10 Recall that the DID treatment indicators for this third outcome is slightly different (although the interpretation does not change): we use time-invariant variables indicating whether the student’s current year school was a Reed school in the prior year (as opposed to the current year). Also note that the estimated Reed effects for students treated in both years are the average for the two years, while the estimated Reed effects for students treated in just one year is the effect of Reed based on that one year of treatment.

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Similarly, we find that Reed substantially lowered middle and high school students’

exposure to the layoff process. In particular, Reed reduced the average proportion of a student’s

teachers that were RIFed, laid off, and laid off in the prior year but rehired, by 15.6, 6.3, and 3.1

percentage points, respectively (for students enrolled in Reed schools in both years). As shown in

the bottom two rows of Table 5, the effects of Reed for middle and high school students enrolled

in Reed for only one year were similar to the yearly effects for students treated in both years.

Discussion and Implications for Policy

This paper contributes to our understanding of the impacts of education funding cuts that

result in teacher layoffs. First, seniority-based layoff policies used in LAUSD caused RIF and

layoff notices to be inequitably distributed, disadvantaging students of color, low-income

students (in middle and high schools), and emergent bilingual students. This finding is generally

consistent with that found in other contexts such as Washington State or New York City, where

layoffs conducted under seniority-based policy were found to disproportionately harm

disadvantaged students. Given the well-documented within-district disparities in access to well-

qualified and effective teachers across many measures of teacher quality (e.g., Goldhaber

Lavery, & Theobald, 2015), a layoff system that uses any districtwide teacher characteristic (as

opposed to determining layoffs on a school-by-school basis) runs the risk of concentrating

layoffs in high-need schools (Boyd et al., 2011).

More importantly, this paper demonstrates that districts can implement policies that

protect disadvantaged students from the inequitable distribution of layoffs. The Reed policy

accomplished its goal of lowering the likelihood that teachers in Reed schools received RIF and

layoff notices, thus protecting the students in these schools from having teachers impacted by the

layoff process. This is particularly important in light of the recent research showing that

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exposure to teacher layoffs can substantially reduce students’ access to educational opportunity

(Goldhaber et al., 2015; Strunk et al., 2015).

This paper also highlights another potential benefit of instituting layoff protections in

high-need schools. When districts must conduct budget-based layoffs, protection from layoffs in

a subset of schools can serve as a recruitment or retention incentive in hard-to-staff schools by

providing teachers in such schools with increased job security. Indeed, Strunk et al. (2015) show

that teachers in Reed schools, because of the schoolwide protections from budget-based layoffs,

were more likely to return to their teaching positions the following year than were otherwise

similar teachers in non-Reed schools. Anecdotally, we found that protected schools highlighted

their Reed status in teacher job calls. These types of recruitment and retention incentives would

not only reduce teacher turnover at little or no direct expense to the district, but as this paper

shows, also would dramatically improve access to equal educational opportunities for

disadvantaged students.

Given the success of the policy, there may be reasons to consider how it would have

fared if taken to a broader scale, protecting more schools with particularly disadvantaged

populations that were extensively impacted by the layoff process. Additionally, we might expect

cost savings to the district if the intervention is scaled up because layoff protections in high-need

schools might reduce the costs associated with teacher layoffs. By targeting more heavily the

most junior and least expensive teachers in a district, seniority-based layoffs increase the number

of teachers that must be let go in order to achieve a given reduction in district expenditures

(Goldhaber & Theobald, 2009; Boyd et al., 2011). Reed necessarily shifted RIFs and layoffs to a

more experienced population of teachers who draw higher salaries and are more expensive.

However, the intervention would have to be undertaken at a far wider scale: an analysis of the

18


potential cost savings associated with Reed shows that LAUSD did not see substantial savings as

a result of Reed.11

In addition, while the policy was a substantial improvement over the first two years of

layoffs, our analysis shows that even with Reed protections, most traditionally disadvantaged

student groups still faced a disproportionate amount of layoff notices (although not in Reed

schools). Again, these results are not surprising given the scale of the Reed intervention, which

impacted only 3.6% of elementary students and 14.0% of middle and high school students (8.7%

of all students). Because the settlement only called for layoff protection in 45 schools in a district

with 696 traditional K-12 public schools, the overall impact of the Reed policy was not as

substantial as it could have been if a larger number of schools were targeted.

Of course, expanding the intervention to more schools might create unanticipated general

equilibrium effects. However, districts might scale up layoff protections in other ways that may

cause fewer adverse consequences. For instance, many districts (including LAUSD) are allowed

to protect teachers from layoffs if they have special skills or training that more senior staff do not

possess (Cal. Educ. Code §44955d). Districts also often avoid conducting layoffs in certain

shortage areas such as special education, math and science, choosing instead to target teachers

credentialed in elementary education, arts, and humanities. It might also be feasible for districts

to grant protections for teachers with experience working with students of color or in high-

poverty schools. As a result, low-income students of color would be less likely to experience a

disparate impact of layoffs. Such an intervention may also help districts recruit and retain

teachers with experience working in high-poverty / high-minority schools, which is a challenge

11 This is not surprising given the size of LAUSD (over 23,000 teachers in 2011-12 across 696 schools). Redirecting layoffs in just 45 schools to more senior teachers does not have a substantial impact on the average experience of laid off teachers. Specific results of the cost-benefit analysis are available upon request.

19


in many large urban districts across the country (Darling-Hammond, 2004; Ingersoll & May,

2011; Quiocho & Rios, 2000; Villegas & Lucas, 2004).

Conclusion

The analyses shown here demonstrate important facets of layoff polices generally, and

seniority-based policies in large urban districts in particular. Although seniority-based layoffs

can create inequitable distributions of layoffs, a relatively simple mechanism to address this

problem is to prevent budget-based layoffs from taking place at the highest need schools. By

selecting schools with the greatest needs and ensuring that a sufficient number of schools are

protected, districts can prevent particular students groups from bearing an uneven share of the

costs associated with district funding cuts. In the often-unavoidable circumstances that force

districts to conduct budget-based layoffs, averting inequitable distributions is an important and

laudable objective. This study demonstrates the potential for substantial inequities to occur under

standard layoff policies, but shows how districts can work to mitigate this problem.

20


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25


Tables

TABLE 1

Summary statistics for teachers by layoff threat (teacher-year observations), 2008-09 to 2009-10

% Overall No RIFRIF

RIF-rescinded Laid off-

All Teachers 52,40645,571 4,688 2,14787.0% 8.9% 4.1%

Experience / educationNovice teachers (1st - 3rd year) 10.4% 52.7% 19.7% 27.5%Mid - career teachers (4th - 8th year) 24.3% 75.4% 21.2% 3.4%Veteran teachers (9th year or above) 65.3% 96.7% 2.7% 0.6%Mean years of experience 9.3 10.1 4.7 2.5Master’s degree or higher 34.2% 88.5% 8.4% 3.0%

Endorsement areaSpecial Education 12.6% 96.0% 1.7% 2.2%Math or Science 12.9% 86.8% 11.8% 1.4%Other non-elementary 29.3% 87.2% 7.0% 5.8%Elementary 45.2% 84.3% 11.4% 4.3%

Note: “No RIF” refers to teacher who did not receive a reduction-in-force (RIF) notice; “RIF-rescinded” implies the teacher received a RIF-notice, but it was later rescinded; “Laid off-return” means the teacher was laid off, but returns in the following year; and “Laid off-no return” refers to teachers that were laid off and do not return the following year. The % overall column shows the overall proportion for each teacher characteristic districtwide (within categories, columns sum to 100%). The next four columns show how those characteristics are distributed across four RIF/layoff categories (rows sum to 100%). For example, 10.4% of teachers are novice and of those, 52.7% were not RIFed.

26


TABLE 2

Summary statistics of student characteristics by Reed treatment group, 2010-11 to 2011-12

OverallReed 2011 / 2012

Reed 2011 schools

Reed 2012 schools

Comparable schools

All other schools

Elementary student observations 543,943

9,516 11,203 11,744 127,152 384,3281.7% 2.1% 2.2% 23.4% 70.7%

Race / ethnicityBlack 8.2% 3.0% 4.2% 3.4% 30.2% 59.2%Latina/o 75.3% 2.0% 2.1% 2.4% 25.3% 68.2%Other race/ethnicity 7.0% 0.2% 1.3% 0.7% 12.3% 85.5%White 9.5% 0.1% 0.3% 0.2% 10.7% 88.7%

English language statusNon-native Eng. speakers 63.1% 2.1% 2.3% 2.6% 26.1% 66.9%Native English speaker 36.9% 1.2% 1.6% 1.4% 18.7% 77.1%

Family incomeEver enrolled in FRL 88.6% 1.5% 2.1% 2.3% 23.6% 70.5%Never enrolled in FRL 11.4% 1.8% 2.1% 2.1% 23.3% 70.7%

Special educationAny SPED classification 10.8% 8.0% 4.0% 7.2% 33.9% 46.9%No SPED classification 89.2% 7.0% 4.0% 6.9% 31.9% 50.2%

Middle and high school student obs. 524,551

37,185 21,109 36,295 168,370 261,5927.1% 4.0% 6.9% 32.1% 49.9%

Race / EthnicityBlack 9.1% 9.9% 5.5% 10.8% 34.4% 39.3%Latina/o 75.9% 7.8% 4.3% 7.5% 34.0% 46.4%Other race/ethnicity 7.6% 2.5% 2.9% 2.4% 28.8% 63.5%White 7.4% 0.7% 0.9% 1.3% 12.8% 84.4%

ELL StatusAll non-native Eng. speakers 70.8% 7.8% 4.3% 7.4% 34.1% 46.4%Native English speaker 29.2% 5.3% 3.4% 5.8% 27.2% 58.3%



Note: The Reed 2011/2012 schools column indicates students that were enrolled in Reed school during both the 2010-11 and 2011-12 school years. Reed 2011 schools and Reed 2012 schools refer to students enrolled in Reed school only in 2010-11 or 2011-12, respectively. A total of 32 schools were selected for Reed protection in both years, while 13 schools were selected for Reed in just the 2010-11 and 2011-12 school years. We identified comparison schools using the same criteria the district used to identify Reed schools. Thus comparison schools were next in line for Reed protection and would have been targeted for Reed if the intervention included more schools. The first column, labeled “overall,” shows the proportion of elementary and secondary students that fall into each category. In the Reed treatment categories, shown in next five columns, rows sum to 100%. For example, 8.2% of elementary students are Black and of those, 3.0% were enrolled in Reed schools during both the 2010-11 and 2011-12 school years. Other race/ethnicity includes the categories shown in Tables 4 and 5.

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TABLE 3

Odds ratios for the likelihood of having a teacher who did not receive a reduction-in-force (RIF) notice, received a RIF notice that was rescinded, or received a final layoff notice, prior to Reed intervention, 2008-09 to 2009-10 (Phase I layoffs)

Elementary students Middle and high school students

No RIF RIF-rescinded Layoff No RIF RIF-

rescinded Layoff

Total student-observations

464,864 62,234 24,501 506,144 42,601 22,61684.3% 11.3% 4.4% 88.6% 7.5% 4.0%

Race / Ethnicity (odds ratios are relative to White)Black 0.919*** 1.414*** 1.679*** 0.955*** 1.334*** 2.065***Latina/o 0.990*** 1.000 1.245*** 0.955*** 1.355*** 2.025***Multiple 1.002 0.952 1.095 0.955*** 1.390*** 1.913***Native Amer. 1.007 0.849* 1.295* 0.978*** 1.098 1.685***Pac. Isl. 1.000 1.073 0.785 0.970*** 1.297*** 1.507***Asian 1.000 0.942** 1.184*** 1.007*** 0.889*** 1.003White 1.000 1.000 1.000 1.000 1.000 1.000English language status (odds ratios are relative to NES)LEP 0.997* 1.002 1.062*** 0.965*** 1.252*** 1.419***Reclassified 0.980*** 1.132*** 1.045*** 0.989*** 1.048*** 1.191***Fluent, NNE 1.003 0.971* 1.013 0.996*** 1.058*** 0.990All NNE 0.994 1.025** 1.049*** 0.982*** 1.115*** 1.239***Native Eng. 1.000 1.000 1.000 1.000 1.000 1.000Family income (relative to never FRL)Ever FRL 1.015*** 0.925*** 0.931*** 0.970*** 1.199*** 1.520***Never FRL 1.000 1.000 1.000 1.000 1.000 1.000Special Education (relative to non-SPED)Any SPED 1.063*** 0.673*** 0.679*** 1.043*** 0.682*** 0.675***Non-SPED 1.000 1.000 1.000 1.000 1.000 1.000

Note: Multiple refers to students who report multiple race/ethnicities across years. LEP stands for limited English proficiency; reclassified refers to students who entered the district with LEP, but were reclassified as fluent; fluent NNE refers to students who are non-native English speakers (NNE), but entered the district fluent in English; NES refers to native English speakers. FRL refers to students eligible for free and reduced price meals. ED stands for emotional disturbance; SLD stands for specific learning disability, and the "other" disability category includes deafness, developmental delay, established medical disability, hard of hearing, mentally retarded, multiple disabilities, orthopedic impairment, other health impairment, traumatic brain injury, and visual impairment; any SPED refers to any student enrolled in special education. +p<0.10, * p<0.05, ** p<0.01, *** p<0.001.

28


TABLE 4

Odds ratios for the likelihood of having a teacher who did not receive a reduction-in-force (RIF) notice, received a RIF notice that was rescinded, or received a final layoff notice, prior to Reed intervention, 2010-11 to 2011-12 (Phase II layoffs)


No RIF RIF-rescinded Layoff No RIF RIF-

rescinded Layoff

Total student-observations

443,779 72,571 27,593 500,181 38,501 32,67981.6% 13.3% 5.1% 87.5% 6.7% 5.7%

Race / Ethnicity (odds ratios are relative to White)Black 0.962*** 1.154*** 1.174*** 1.016*** 0.728*** 1.165***Latina/o 1.017*** 0.956*** 0.862*** 1.003*** 0.835*** 1.210***Multiple 1.004 0.973 1.005 1.017*** 0.782*** 1.048Native Amer. 1.019 0.933 0.887 1.001 0.918* 1.124*Pac. Isl. 1.005 1.109+ 0.663*** 0.987** 1.002 1.224***Asian 0.987*** 1.103** 0.940* 1.15*** 0.878*** 0.935***White 1.000 1.000 1.000 1.000 1.000 1.000English lang. status (odds ratios are relative to NES)LEP 1.011* 0.926 1.019*** 1.004*** 0.906*** 1.062***Reclassified 0.968*** 1.175*** 1.042 1.000 0.939*** 1.085***Fluent, NNE 1.005*** 1.004*** 0.906** 0.996*** 1.049** 0.998All NNE 0.999 1.004 1.009 1.000 0.947*** 1.064***Native Eng. 1.000 1.000 1.000 1.000 1.000 1.000Family income (relative to never FRLEver FRL 1.025*** 0.904*** 0.888*** 1.011*** 0.815*** 1.100***Never FRL 1.000 1.000 1.000 1.000 1.000 1.000Special Education (relative to non-SPED)Any SPED 1.067*** 0.762*** 0.860*** 1.041*** 0.725*** 0.724***Non-SPED 1.000 1.000 1.000 1.000 1.000 1.000

Note: All notes from Table 3 also apply to this table. +p<0.10, * p<0.05, ** p<0.01, *** p<0.001.

29


TABLE 5

Difference-in-difference estimates of the effect of the Reed intervention on the likelihood a students’ teacher receives a RIF notice, receives a layoff notice, or received a layoff notice in the prior year and was rehired to the district

RIF notice Layoff Layoff in t-1

Elementary students

Students impacted by Reed in 2010-11 and 2011-12 -27.8%*** -7.5%*** -3.0%***

2012 only Reed schools -24.7%*** -7.0%*** -3.8%***


Middle and high school students

Students impacted by Reed in 2010-11 and 2011-12 -15.6%*** -6.3%*** -3.1%***


2011 only Reed schools -16.3%*** -7.1%*** -5.2%***Note: RIF stands for reduction-in-force and refers to students whose teacher received an initial RIF notice (a warning of potential layoff). Layoff refers to students whose teacher received both a RIF notice and a final layoff notice. The outcome “Layoff in t-1” is defined as having a teacher who was laid off in the prior year and returned to the district. For middle and high school students, who have more than one teacher throughout the day, we use the average proportion of a student’s teacher that was RIFed, laid off, or laid off in t-1. For both elementary and middle and high school students, there are three different treatment effects for each of the three different treatment groups: (a) students affected by Reed in both years (2011/2012 Reed schools), (b) those affected by Reed in just 2012 (2012 only Reed schools); and (c) those affected by Reed in just 2011 (2011 only Reed schools). Treatment indicators for the RIF and layoff variable identify students who are in Reed schools while Reed is in place. In models predicting whether a student’s teacher was laid off in t-1 (i.e. laid off and rehired in the prior year), the treatment indicators are changed to reflect students who were ever enrolled in a school that was classified as a Reed school in the prior year. *** p<0.001.

30


Online Appendix A: Additional Figures and Tables

APPENDIX FIGURE A1

Panel A: Average experience by percent of students eligible for free/reduced price lunch (FRL) at the school level (with percent FRL in quintiles), elementary and secondary levels

Panel B: Average experience by percent of students eligible for free/reduced price lunch (FRL) at the classroom level (with percent FRL in quintiles), elementary and secondary levels

31


APPENDIX TABLE A1

Total students and proportion affected by layoffs over time

2008-09 2009-10 2010-11 2011-12

Elementary teachers 13,815 12,646 12,521 12,138No RIF 81.7% 88.7% 82.0% 82.9%RIF-rescinded 10.7% 10.5% 12.8% 12.5%Laid off 7.6% 0.8% 5.2% 4.6%

Middle and high school teachers 13,388 12,564 12,152 11,340No RIF 83.6% 94.5% 88.0% 89.8%RIF-rescinded 11.0% 3.3% 6.4% 6.1%Laid off 5.4% 2.1% 5.6% 4.2%

Elementary students 275,161 276,438 274,416 269,527No RIF 80.5% 88.0% 81.0% 82.2%RIF-rescinded 11.4% 11.1% 13.6% 13.1%Laid off 8.0% 0.9% 5.5% 4.7%

Middle and high school students 289,993 281,368 267,586 256,965Average % not RIFed 83.0% 94.5% 86.4% 88.8%Average % RIF-rescinded 11.4% 3.3% 7.0% 6.4%Average % Laid off 5.6% 2.2% 6.6% 4.8%

Note: “No RIF” refers to a student whose teacher did not receive a reduction-in-force (RIF) notice. “RIF-rescinded” implies the student’s teacher received a RIF-notice, but it was later rescinded. Laid off means the student’s teacher was laid off. For non-self-contained classrooms in middle and high schools, because each student has multiple teachers, I report the average proportion of a student’s teacher that is RIF-rescinded, laid off, or not RIFed. The sample of teachers shown here includes only classroom teachers that report grades for students. The total sample size differs from that reported in other studies based on the same datasets because in that work, we include any employee classified as a teacher who was subject to the RIF process. Enrollment for elementary students as a proportion of total enrollment increases beginning in 2009-10 because in that year it became more common for grade 6 to be housed in elementary schools, rather than middle schools.

32


APPENDIX TABLE A3

Descriptive statistics for Reed schools, comparable schools, and all other schools

2009-10 School year 2010-11 School year 2011-12 School year

Reed 2011

schools

Reed 2011 / 2012

Reed 2012

schools

Comp-arable school

s

All other

schools

Reed 2011

schools

Reed 2011 / 2012

Reed 2012

schools

Comp-arable

schools

All other

schools

Reed 2011

schools

Reed 2011 / 2012

Reed 2012

schools

Comp-arable school

s

All other

schools

Student char.

% FRL 88.5%92.3% 85.9% 88.1% 78.0% 87.4% 90.2% 84.0% 88.4% 78.1% 88.3% 92.6% 87.2% 89.5% 78.3%

% LEP 44.5%31.3% 31.5% 34.7% 29.6% 44.5% 29.9% 29.3% 32.5% 28.1% 44.8% 28.0% 28.9% 30.6% 26.7%

% non-White 96.4%98.6% 97.4% 95.9% 87.8% 97.1% 98.8% 98.1% 96.3% 87.9% 97.3% 98.8% 98.6% 96.2% 88.2%

API 723 627 605 682 756 716 661 619 702 770 737 680 655 718 782% top quin API 6.7% 0.0% 0.0% 1.5% 19.9% 5.1% 0.0% 0.0% 1.5% 20.6% 5.0% 0.0% 0.0% 2.2% 21.6%

% bot. quin API 24.7%88.5% 91.1% 50.9% 16.7% 39.9% 69.8% 91.0% 44.4% 17.1% 38.0% 66.5% 100.0% 40.1% 15.7%

Mean enroll. 551 1,895 1,920 1,213 1,047 556 1,406 1,791 1,081 994 549 1,205 1,430 956 921Teacher char.

Mean exp. 9.5 8.6 8.6 9.5 10.5 9.5 8.7 9.0 10.0 11.1 9.7 9.1 9.0 10.7 11.7

% novice tchrs 8.6%13.5% 10.7% 7.8% 4.7% 8.3% 12.9% 7.4% 5.1% 3.0% 3.2% 9.1% 11.4% 3.0% 1.8%

% MA / Doc 43.5%41.1% 42.4% 37.8% 35.2% 44.9% 41.6% 42.4% 38.8% 36.3% 47.4% 42.1% 41.6% 40.1% 37.5%

% NBPTS 3.5% 2.3% 2.0% 3.2% 3.8% 5.4% 2.5% 2.1% 3.3% 4.3% 6.7% 2.7% 2.5% 3.9% 4.6%RIF variables

% Not RIFed 16.9% 8.3% 5.5% 8.7% 7.9% 0.0% 0.5% 15.8% 17.3% 14.9% 26.6% 0.7% 0.7% 15.4% 14.2%% RIF-re. 14.1% 5.9% 3.2% 6.9% 7.0% 0.0% 0.5% 8.3% 10.3% 10.7% 12.6% 0.4% 0.3% 9.9% 10.6%% Laid off 2.7% 2.4% 2.3% 1.8% 0.9% 0.3% 2.3% 7.5% 7.1% 4.2% 14.0% 2.2% 1.4% 5.6% 3.6%Num of tch. 255 1,992 951 7,122 16,438 336 1,819 868 6,902 15,907 342 1,655 734 6,484 15,367Num of sch. 9 27 10 143 420 13 32 10 155 427 13 32 13 164 436

Note: FRL stands for free/reduced price lunch and indicates students participating in the federal school lunch program. LEP indicates students with limited English proficiency. API stands for Academic Performance Index, a measurement of a school’s academic achievement on reading and math standardized exams. This table demonstrates that comparable schools had similar characteristics as Reed schools.

33

APPENDIX TABLE A3

Distribution of RIFs and layoffs across secondary students, using three definitions, 2008-09 to 2009-10 (Phase I layoffs)

Proportion of teachers

Majority of teachers

At least one teacher

RIF-rescinde

dLayoff

RIF-rescinde

dLayoff

RIF-rescinde

dLayoff

Total 7.5% 4.0% 2.5% 0.9% 31.9% 20.8%Race / Ethnicity

Black 7.7% 4.3% 2.5% 0.9% 33.6% 22.9%Latina/o 7.8% 4.2% 2.7% 1.0% 32.9% 22.2%More than one race/ethnicity 8.0% 4.0% 2.5% 1.2% 33.0% 19.0%Native American 6.3% 3.5% 1.8% 0.7% 28.4% 19.1%Pacific Islander 7.5% 3.2% 3.1% 0.7% 29.3% 17.7%Asian 5.1% 2.1% 1.4% 0.3% 23.2% 11.7%White 5.8% 2.1% 1.5% 0.3% 26.9% 11.6%

ELL StatusLimited English proficiency 8.6% 4.8% 3.3% 1.2% 35.7% 24.2%Reclassified English proficient 7.2% 4.0% 2.3% 0.9% 31.3% 21.4%Fluent, non-native English 7.3% 3.3% 2.7% 0.7% 30.0% 17.5%Native English speaker 7.7% 4.2% 2.6% 1.0% 32.6% 21.8%All non-native English speakers 6.9% 3.4% 2.2% 0.7% 30.0% 18.2%



Note: More than one race/ethnicity refers to students who report multiple race/ethnicities across years. Reclassified English proficient refers to students who entered the district with limited English proficiency, but were reclassified as fluent; and fluent non-native English refers to students who are non-native English speakers, but entered the district fluent in English. FRL refers to students eligible for free and reduced price meals. The "other disability” category includes deafness, developmental delay, established medical disability, hard of hearing, mentally retarded, multiple disabilities, orthopedic impairment, other health impairment, traumatic brain injury, and visual impairment; any SPED refers to any student enrolled in special education.

APPENDIX TABLE A4

Percent of elementary students in each demographic category (% overall column) and the percent of those students whose teachers fall into each RIF / layoff category, 2008-09 to 2009-10 (Phase I layoffs)

% Overall No RIF

RIF Odds ratiosRIF-

rescinded

Laid off No RIFRIF-

rescinded

Layoff

Total student-obs. 551,599

464,864 62,234 24,50184.3% 11.3% 4.4%

Race / Ethnicity Relative to WhiteBlack 8.7% 78.6% 15.5% 5.9% 0.919*** 1.414*** 1.679***Latina/o 75.6% 84.7% 10.9% 4.4% 0.990*** 1.000 1.245***Multiple 0.5% 85.7% 10.4% 3.9% 1.002 0.952 1.095Nat. Am. 0.3% 86.1% 9.3% 4.6% 1.007 0.849* 1.295*Pac. Isl. 0.3% 85.5% 11.7% 2.8% 1.000 1.073 0.785Asian 5.9% 85.5% 10.3% 4.2% 1.000 0.942** 1.184***White 8.8% 85.5% 10.9% 3.5% 1.000 1.000 1.000

English language status Relative to NESLEP 39.1% 84.3% 11.1% 4.6% 0.997* 1.002 1.062***Reclassified 14.6% 82.9% 12.6% 4.5% 0.980*** 1.132*** 1.045***Fluent, NNE 11.8% 84.9% 10.8% 4.4% 1.003 0.971* 1.013All NNE 65.5% 84.1% 11.4% 4.5% 0.994 1.025** 1.049***Native Eng. 34.5% 84.6% 11.1% 4.3% 1.000 1.000 1.000

Family income Relative to non-FRLEver FRL 86.0% 84.4% 11.2% 4.4% 1.015*** 0.925*** 0.931***Never FRL 14.0% 83.2% 12.1% 4.7% 1.000 1.000 1.000

Special Education (SPED) Relative to non-SPEDAny SPED 10.6% 89.0% 7.9% 3.1% 1.063*** 0.673*** 0.679***Non-SPED 89.4% 83.7% 11.7% 4.6% 1.000 1.000 1.000

Note: Multiple refers to students who report multiple race/ethnicities across years. LEP stands for limited English proficiency; reclassified refers to students who entered the district with LEP, but were reclassified as fluent; fluent NNE refers to students who are non-native English speakers (NNE), but entered the district fluent in English; NES refers to native English speakers. FRL refers to students eligible for free and reduced price meals. ED stands for emotional disturbance; SLD stands for specific learning disability, and the "other" disability category includes deafness, developmental delay, established medical disability, hard of hearing, mentally retarded, multiple disabilities, orthopedic impairment, other health impairment, traumatic brain injury, and visual impairment; any SPED refers to student enrolled in special education. +p<0.10, * p<0.05, ** p<0.01, *** p<0.001.APPENDIX

APPENDIX TABLE A5

Percent of middle and high school students in each demographic category (% overall column) and the average percent of their teachers in each RIF / layoff category, 2008-09 to 2009-10 (Phase I layoffs)

% Overall No RIF

RIF Odds ratiosRIF-

rescinded

Laid off No RIFRIF-

rescinded

Layoff

Total student-obs. 571,361 506,144 42,601 22,616

88.6% 7.5% 4.0%Race / Ethnicity Relative to White

Black 9.7% 88.0% 7.7% 4.3% 0.955*** 1.334*** 2.065***Latina/o 75.8% 88.0% 7.8% 4.2% 0.955*** 1.355*** 2.025***Multiple 0.3% 88.0% 8.0% 4.0% 0.955*** 1.390*** 1.913***Nat. Am. 0.3% 90.2% 6.3% 3.5% 0.978*** 1.098 1.685***Pac. Isl. 0.3% 89.4% 7.5% 3.2% 0.970*** 1.297*** 1.507***Asian 6.4% 92.8% 5.1% 2.1% 1.007*** 0.889*** 1.003White 7.2% 92.2% 5.8% 2.1% 1.000 1.000 1.000

English language status Relative to NESLEP 23.5% 86.6% 8.6% 4.8% 0.965*** 1.252*** 1.419***Reclassified 39.8% 88.8% 7.2% 4.0% 0.989*** 1.048*** 1.191***Fluent, NNE 9.5% 89.4% 7.3% 3.3% 0.996*** 1.058*** 0.990All NNE 72.8% 88.2% 7.7% 4.2% 0.982*** 1.115*** 1.239***Native Eng. 27.2% 89.7% 6.9% 3.4% 1.000 1.000 1.000



Note: Multiple refers to students who report multiple race/ethnicities across years. LEP stands for limited English proficiency; reclassified refers to students who entered the district with LEP, but were reclassified as fluent; fluent NNE refers to students who are non-native English speakers (NNE), but entered the district fluent in English; NES refers to native English speakers. FRL refers to students eligible for free and reduced price meals. ED stands for emotional disturbance; SLD stands for specific learning disability, and the "other" disability category includes deafness, developmental delay, established medical disability, hard of hearing, mentally retarded, multiple disabilities, orthopedic impairment, other health impairment, traumatic brain injury, and visual impairment; any SPED refers to any student enrolled in special education. +p<0.10, * p<0.05, ** p<0.01, *** p<0.001.

APPENDIX TABLE A6

Odds ratios predicting whether each elementary student’s teacher falls into each lagged RIF / layoff category, 2009-10 to 2010-11 (next year results of Phase I layoffs)

No RIF in prior year

RIF-rescinded in prior year

Layoff in prior year

Not present in prior year

550,854 477,099 62,761 5,538 5,45686.6% 11.4% 1.0% 1.0%

Race / EthnicityBlack 0.938*** 1.358*** 1.320*** 2.339***Latina/o 0.101*** 0.949*** 0.742*** 1.635***Multiple 1.005 0.961 0.619* 1.741**Native American 1.015 0.860* 0.794 1.973**Pacific Islander 0.996 1.034 0.863 1.239Asian 1.010*** 0.937** 0.837** 1.124White 1.000 1.000 1.000 1.000English language statusLEP 1.001 0.966*** 0.968 1.358***Reclassified 0.991*** 1.107*** 0.809*** 0.771*Fluent, NNE 1.013*** 0.937*** 0.691*** 0.907+All NNE 1.001 0.996 0.854*** 1.171***Native Eng. 1.000 1.000 1.000 1.000Family incomeEver FRL 1.006*** 0.935*** 0.784*** 2.073***Never FRL 1.000 1.000 1.000 1.000Special EducationAny SPED 1.027*** 0.698*** 1.440*** 1.864***Non-SPED 1.000 1.000 1.000 1.000

Note: Odds ratios are relative to the group shown in the bottom row of each panel (i.e., White students, native English speaking students, never FRL students, and non-SPED students).

APPENDIX TABLE A7

Odds ratios predicting the proportion of each middle and high school student’s teachers that fall into each lagged RIF / layoff category, 2009-10 to 2010-11 (next year results of Phase I layoffs)





548,954 478,968 38,546 14,530 16,91187.3% 7.0% 2.6% 3.1%

Race / EthnicityBlack 0.942*** 1.204*** 3.048*** 2.036***Latina/o 0.945*** 1.256*** 2.879*** 2.003***Multiple 0.958*** 1.182*** 2.635*** 1.699***Native American 0.975*** 1.071 1.840*** 1.645***Pacific Islander 0.965*** 1.224*** 2.033*** 1.562***Asian 1.003+ 0.870*** 1.284*** 1.148***White 1.000 1.000 1.000 1.000English language statusLEP 0.962*** 1.171*** 1.551*** 1.377***Reclassified 0.987*** 1.054*** 1.279*** 1.051***Fluent, NNE 1.002* 1.075*** 0.954** 0.799All NNE 0.982*** 1.092*** 1.311*** 1.110***Native Eng. 1.000 1.000 1.000 1.000Family incomeEver FRL 0.956*** 1.230*** 2.040*** 1.642***Never FRL 1.000 1.000 1.000 1.000Special Education (SPED)Any SPED 1.033*** 0.678*** 0.724*** 1.084***Non-SPED 1.000 1.000 1.000 1.000


APPENDIX TABLE A8

Percent of elementary students in each demographic category (% overall column) and the percent of those students whose teachers fall into each RIF / layoff category, 2010-11 to 2011-12 (Phase II layoffs)

% Overall No RIF

RIF Odds ratiosRIF-

rescinded Laid off No RIF RIF-rescinded Layoff

Total student-obs. 543,943

443,779 72,571 27,59381.6% 13.3% 5.1%

Race / Ethnicity Relative to WhiteBlack 8.2% 77.8% 15.6% 6.6% 0.962*** 1.154*** 1.174***Latina/o 75.3% 82.2% 12.9% 4.8% 1.017*** 0.956*** 0.862***Multiple 0.6% 81.2% 13.2% 5.6% 1.004 0.973 1.005Nat. Am. 0.3% 82.4% 12.6% 5.0% 1.019 0.933 0.887Pac. Isl. 0.3% 81.3% 15.0% 3.7% 1.005 1.109+ 0.663***Asian 5.8% 79.8% 14.9% 5.3% 0.987*** 1.103** 0.940*White 9.5% 80.9% 13.5% 5.6% 1.000 1.000 1.000

English language status Relative to NESLEP 36.6% 82.5% 12.3% 5.1% 1.011* 0.926 1.019***Reclassified 16.5% 79.1% 15.6% 5.3% 0.968*** 1.175*** 1.042Fluent, NNE 10.0% 82.1% 13.4% 4.6% 1.005*** 1.004*** 0.906**All NNE 63.1% 81.6% 13.4% 5.1% 0.999 1.004 1.009Native Eng. 36.9% 81.6% 13.3% 5.0% 1.000 1.000 1.000



Note: Multiple refers to students who report multiple race/ethnicities across years. LEP stands for limited English proficiency; reclassified refers to students who entered the district with LEP, but were reclassified as fluent; fluent NNE refers to students who are non-native English speakers (NNE), but entered the district fluent in English; NES refers to native English speakers. FRL refers to students eligible for free and reduced price meals. ED stands for emotional disturbance; SLD stands for specific learning disability, and the "other" disability category includes deafness, developmental delay, established medical disability, hard of hearing, mentally retarded, multiple disabilities, orthopedic impairment, other health impairment, traumatic brain injury, and visual impairment; any SPED refers to student enrolled in special education. +p<0.10, * p<0.05, ** p<0.01, *** p<0.001.

APPENDIX TABLE A9

Percent of middle and high school students in each demographic category (% overall column) and the average percent of their teachers in each RIF / layoff category, 2010-11 to 2011-12 (Phase II layoffs)

% Overall No RIF

RIF Odds ratiosRIF-

rescinded Laid off No RIF RIF-rescinded Layoff

Total student-obs. 524,551 500,181 38,501 32,679

87.5% 6.7% 5.7%Race / Ethnicity Relative to White

Black 9.1% 88.5% 5.8% 5.7% 1.016*** 0.728*** 1.165***Latina/o 75.9% 87.4% 6.7% 5.9% 1.003*** 0.835*** 1.210***Multiple 0.4% 88.6% 6.3% 5.1% 1.017*** 0.782*** 1.048Nat. Am. 0.3% 87.2% 7.4% 5.5% 1.001 0.918* 1.124*Pac. Isl. 0.4% 86.0% 8.0% 6.0% 0.987** 1.002 1.224***Asian 6.6% 88.4% 7.0% 4.6% 1.15*** 0.878*** 0.935***White 7.4% 87.1% 8.0% 4.9% 1.000 1.000 1.000

English language status Relative to NESLEP 19.0% 87.9% 6.3% 5.8% 1.004*** 0.906*** 1.062***Reclassified 40.3% 87.5% 6.6% 5.9% 1.000 0.939*** 1.085***Fluent, NNE 11.5% 87.2% 7.3% 5.5% 0.996*** 1.049** 0.998All NNE 70.8% 87.5% 6.6% 5.8% 1.000 0.947*** 1.064***Native Eng. 29.2% 87.5% 7.0% 5.5% 1.000 1.000 1.000



Note: Multiple refers to students who report multiple race/ethnicities across years. LEP stands for limited English proficiency; reclassified refers to students who entered the district with LEP, but were reclassified as fluent; fluent NNE refers to students who are non-native English speakers (NNE), but entered the district fluent in English; NES refers to native English speakers. FRL refers to students eligible for free and reduced price meals. ED stands for emotional disturbance; SLD stands for specific learning disability, and the "other" disability category includes deafness, developmental delay, established medical disability, hard of hearing, mentally retarded, multiple disabilities, orthopedic impairment, other health impairment, traumatic brain injury, and visual impairment; any SPED refers to any student enrolled in special education. +p<0.10, * p<0.05, ** p<0.01, *** p<0.001

APPENDIX TABLE A10

Odds ratios predicting whether each elementary student’s teacher falls into each lagged RIF / layoff category, 2011-12 to 2012-13 (next year results of Phase II layoffs)





534,961 433,633 71,801 24,458 5,06981.1% 13.4% 4.6% 0.9%

Race / EthnicityBlack 0.984*** 1.017 1.125*** 1.496***Latina/o 1.033*** 0.859*** 0.872*** 1.134*Multiple 1.000 0.976 1.0403 1.220Native American 1.011 0.941 0.989 1.072Pacific Islander 1.001 1.073 0.835 0.584Asian 0.985*** 1.054** 1.042 1.182*White 1.000 1.000 1.000 1.000English language statusLEP 1.013 0.890*** 1.051 1.216Reclassified 0.977 1.159*** 0.995 0.695Fluent, NNE 1.012 0.974* 0.915 0.775All NNE 1.003* 0.978** 1.015 1.005Native Eng. 1.000 1.000 1.000 1.000Family incomeEver FRL 1.038*** 0.836*** 0.883*** 1.292***Never FRL 1.000 1.000 1.000 1.000Special EducationAny SPED 1.046*** 0.695*** 0.745*** 3.210***Non-SPED 1.000 1.000 1.000 1.000


APPENDIX TABLE A11

Odds ratios predicting the proportion of each middle and high school student’s teachers that fall into each lagged RIF / layoff category, 2011-12 to 2012-13 (next year results of Phase II layoffs)





503,953 478,396 36,311 21,512 12,73587.1% 6.6% 3.9% 2.3%

Race / Ethnicity Relative to WhiteBlack 0.999 0.721*** 1.073*** 2.448***Latina/o 0.996*** 0.837*** 1.178*** 1.699***Multiple 1.005 0.730*** 1.132*** 1.913***Native American 0.996 0.881** 1.135** 1.563***Pacific Islander 0.995 0.995 1.120 1.022Asian 1.017*** 0.843*** 0.970*** 0.910*White 1.000 1.000 1.000 1.000English language status Relative to NESLEP 0.999+ 0.902*** 1.081*** 1.204***Reclassified 1.002*** 0.964*** 1.062*** 0.914***Fluent, NNE 1.006*** 1.015+ 0.979+ 0.787***All NNE 1.002*** 0.957*** 1.052*** 0.963***Native Eng. 1.000 1.000 1.000 1.000Family income Relative to non-FRLEver FRL 1.010*** 0.779*** 1.049*** 1.485***Never FRL 1.000 1.000 1.000 1.000Special Education (SPED) Relative to non-SPEDAny SPED 1.021*** 0.753*** 0.737*** 1.404***Non-SPED 1.000 1.000 1.000 1.000


APPENDIX TABLE A12

Difference-in-difference estimates of the likelihood a students’ teacher receives a RIF notice, layoff notice, or was laid off in the prior year and returned to the district

Elementary Middle & high school

RIF Layoff Layoff in t-1 RIF Layoff Layoff in t-1

Treatment indicators2012 Reed school x 2013 year FE

-1.106*** -0.024***(0.141) (0.001)

2011 Reed school x 2013 year FE

0.343** -0.011***(0.109) (0.002)


-3.005*** -2.135*** 0.270* -0.116*** -0.039*** 0.022***(0.085) (0.101) (0.111) (0.002) (0.001) (0.002)


0.253*** 0.759*** -0.688*** -0.056*** -0.039*** -0.058***(0.041) (0.055) (0.108) (0.002) (0.002) (0.002)


0.065* 0.207*** 0.601*** 0.006** 0.009*** 0.004*(0.032) (0.046) (0.157) (0.002) (0.002) (0.001)


-4.185*** -3.311*** 0.738*** -0.185*** -0.096*** -0.011***(0.118) (0.141) (0.151) (0.002) (0.002) (0.002)


0.030 -0.337*** 0.005* 0.009***(0.031) (0.097) (0.002) (0.001)


0.002 0.361*** -0.043*** -0.048***(0.033) (0.094) (0.002) (0.002)

2012 Reed school 0.310*** 0.212*** -0.274** 0.005** -0.004** (0.001)(0.020) (0.029) (0.097) (0.002) (0.001) (0.001)

2011 Reed school 0.136*** 0.145*** 0.384*** 0.052*** 0.047*** 0.018***(0.022) (0.031) (0.091) (0.002) (0.002) (0.002)

School types

Middle school 0.490*** 0.172 0.800*** -0.059*** 0.013*** 0.004(0.070) (0.121) (0.218) (0.006) (0.003) (0.003)

High school -0.102*** 0.006+ 0.001(0.006) (0.003) (0.003)

K-12 school (ref. cat. is Elementary)

0.225*** 0.274*** 0.623*** -0.070*** 0.007+ 0.004(0.037) (0.057) (0.061) (0.007) (0.004) (0.003)

Year fixed effects

2013 1.196*** -0.002***0.04 0.001

2012 -0.025+ -0.349*** 1.394*** -0.067*** -0.010*** 0.011***0.014 0.022 0.038 0.001 0.001 0.001

2011 0.138*** -0.041* -1.137*** -0.044*** 0.012*** -0.022***(0.013) (0.020) (0.067) (0.001) (0.001) (0.001)

2010 -0.533*** -2.198*** -0.143*** -0.046***(0.014) (0.041) (0.001) (0.001)

Constant -1.330*** -2.386*** -4.104*** 0.287*** 0.064*** 0.041***(0.010) (0.014) (0.034) (0.006) (0.003) (0.003)

N 335,689 335,689 258,289 457,458 457,458 356,618R-squared - - - 0.154 0.055 0.023

Note: Models for elementary grades are logisitic regressions and models for secondary grades are OLS regressions (predicting the percent of a student’s teachers that were RIFed or laid off. . +p<0.10, * p<0.05, ** p<0.01, *** p<0.001

APPENDIX TABLE A13

Alternative measures of difference-in-difference estimates of the likelihood a students’ teacher receives a RIF notice, receives a layoff notice, or received a layoff notice in the prior year (and was rehired to the district)

RIF Layoff Layoff in t-1

Elementary students2011/2012 Reed schools2009-10 to 2010-11 (2010-11 to 2011-12 for layoff in t-1) -29.2%*** -8.1%*** -3.3%***

2009-10 to 2011-12 (2010-11 to 2012-13 for layoff in t-1) -24.7%*** -4.3%*** -4.2%***

Mean of two years of pre-treatment to mean of two years of treatment -27.8%*** -7.5%*** -3.0%***

2012 only Reed schools2009-10 to 2011-12 (2010-11 to 2012-13 for layoff in t-1) -23.4%*** -5.1%*** -4.1%***2010-11 to 2011-12 (2011-12 to 2012-13 for layoff in t-1) -26.2%*** -8.9%*** -3.9%***Mean of three years of pre-treatment to treatment year -24.7%*** -7.0%*** -3.8%***2011 only Reed schools2009-10 to 2010-11 (2010-11 to 2011-12 for layoff in t-1) -24.5%*** -8.4%*** -2.7%***2010-11 to 2011-12 (2011-12 to 2012-13 for layoff in t-1) 29.9%*** 15.5%*** 6.6%***Mean of two years of pre-treatment to treatment year -24.9%*** -8.7%*** -2.5%***

Middle and high school students2011/2012 Reed schools2009-10 to 2010-11 (2010-11 to 2011-12 for layoff in t-1) -14.0%*** -4.7%*** -2.8%***

2009-10 to 2011-12 (2010-11 to 2012-13 for layoff in t-1) -13.4%*** -3.8%*** -2.7%***

Mean of two years of pre-treatment to mean of two years of treatment -15.6%*** -6.3%*** -3.1%***

2012 only Reed schools2009-10 to 2011-12 (2010-11 to 2012-13 for layoff in t-1) -12.1%*** -4.7%*** -2.8%***2010-11 to 2011-12 (2011-12 to 2012-13 for layoff in t-1) -12.3%*** -4.8%*** -4.6%***Mean of three years of pre-treatment to treatment year -12.0%*** -4.4%*** -3.2%***2011 only Reed schools2009-10 to 2010-11 (2010-11 to 2011-12 for layoff in t-1) -14.2%*** -4.7%*** -4.6%***2010-11 to 2011-12 (2011-12 to 2012-13 for layoff in t-1) 12.9%*** 5.6%*** 4.7%***Mean of two years of pre-treatment to treatment year -16.3%*** -7.1%*** -5.2%***

Note: the outcome “Layoff in t-1” is defined as having a teacher who was laid off in the prior year and returned to the district. For middle and high school students, who have more than one teacher throughout the day, I use the average proportion of a student’s teacher that was RIFed, laid off, or laid off in t-1. +p<0.10, * p<0.05, ** p<0.01, *** p<0.001

APPENDIX TABLE A14

Difference-in-difference estimates of the likelihood a students’ teacher receives a RIF notice, receives a layoff notice, or received a layoff notice in the prior year (and was rehired to the district), by student demographic categories


RIF Layoff Layoff in t-1 RIF Layoff Layoff in t-1

All students

Students in Reed schools in 2010-11 and 2011-12 -27.8%*** -7.5%*** -3.0%*** -15.6%*** -6.3%*** -3.1%***

Students in Reed schools in 2011-12 only -24.7%*** -7.0%*** -3.8%*** -12.0%*** -4.4%*** -3.2%***

Students in Reed schools in 2010-11 only -24.9%*** -8.7%*** -2.5%*** -16.3%*** -7.1%*** -5.2%***Black students


Students in Reed schools in 2011-12 only -25.7%*** -6.7%*** -4.4%*** -9.8%*** -4.6%*** -0.4%

Students in Reed schools in 2010-11 only -28.3%*** -10.5%*** -2.9%** -15.2%*** -7.0%*** -3.8%***Latina/o students


Students in Reed schools in 2011-12 only -24.9%*** -7.1%*** -3.8%*** -12.2%*** -4.3%*** -3.6%***

Students in Reed schools in 2010-11 only -24.3%*** -8.5%*** -2.4%*** -16.2%*** -7.2%*** -5.5%***Native American, Pacific Islander, and students reporting multiple race/ethnicities across years

Students in Reed schools in 2010-11 and 2011-12 -15.2%* 2.7% -1.1% -20.7%*** -8.7%*** -8.0%***

Students in Reed schools in 2011-12 only -12.8% -2.7% 6.9% -11.7%*** -5.2%*** -2.6%*

Students in Reed schools in 2010-11 only N/A N/A -1.2% -20.5%*** -7.4%*** -6.8%***White / Asian students

Students in Reed schools in 2010-11 and 2011-12 -24.3%*** -7.5%*** 1.6%* -20.7%*** -5.2%*** -3.1%***

Students in Reed schools in 2011-12 only N/A N/A -2.3% -13.6%*** -5.4%*** -2.4%***

Students in Reed schools in 2010-11 only -26.6%*** -8.2%*** -3.8%* -21.5%*** -7.3%*** -4.3%***Note: Each column and each panel refers to a different difference-in-difference regression. For example, the first regression shown is for all elementary student and that model provides results for students treated in both years, in 2011-12 only, and in 2010-11 only. The second regression is for just Black students in elementary grades. N/A refers to models that STATA can not estimate because of empty cells. This happens when, for example, no White / East Asian elementary students who were enrolled in Reed schools in just 2011-12 had a teacher RIFed or laid off in 2011-12. *** p<0.001, ** p<.0.01, * p<.05

Online Appendix B: Additional information on the policy context and methods

In this Appendix, we first provide additional information on the policy context in

LAUSD. We then offer greater detail on how Reed comparison schools were selected.

Policy context

Approximately 10% to 13% of elementary teachers received RIF notices that were

rescinded, and other than the 2009-10 school year about 5% to 8% were laid off each year

(shown in the top panel of Appendix Table A1). A slightly lower proportion of middle and high

school teachers were RIF-rescinded or laid off each year (shown in the second panel of

Appendix Table A1). The bottom two panels of Appendix Table A1 show the extent to which

students’ teachers were affected by RIFs and layoffs. Because RIF-rescinded and laid off

elementary teachers had slightly larger class sizes than non-RIFed teachers, a greater proportion

of elementary students were exposed to the layoff process compared to the proportion of

teachers.

In the bottom panel of Appendix Table A1, we show the average percent of middle and

high school students’ teachers that were RIF-rescinded and laid off. For example, in 2008-09,

students in middle and high schools saw an average of 11.4% of their teachers RIF-rescinded and

5.6% laid off. Again, because non-RIFed secondary teachers generally taught smaller class sizes,

the proportion of middle and high school students exposed to RIFs and layoffs is greater than

that of teachers. Non-RIFed teachers generally have smaller class sizes in part because special

education teachers are typically assigned to smaller class sizes at all school levels, and these

teachers are more protected from layoffs than other teachers. A second factor relates to

California’s K-3 class size reduction program. At the elementary level, teachers in grades K-3

generally have more experience compared to teachers in grades 4-5, which may happen if more

senior teachers transfer from grades 4-5 to K-3 over time (because they prefer the smaller class

sizes in K-3, compared to 4-5). As a result, when elementary teachers are targeted for layoffs,

they tend to fall disproportionately on grades 4-5 because those teachers are slightly less senior.

Thus grade 4-5 teachers teach larger class sizes and are more likely to be RIFed than K-3

teachers.

Selection of Reed Schools and Comparison Schools

The selection process for Reed schools was determined during a series of meetings

between district administrators and representatives of United Teachers Los Angeles (UTLA, the

local teachers’ union). The parties agreed that there would be two ways in which a school could

be selected for Reed protection. The first included the 25 schools with the highest teacher

turnover rates that were also in the bottom 30% of Academic Performance Index (API, a

composite measure of student test score performance), but demonstrating positive API growth

over the past three years, and with at least 15 teachers. The second set included 20 schools that

were established within two years of September 1 of the current school year that would be most

adversely affected by layoffs.

Although information on the Reed selection criteria is available publically, we met with

human resource directors to confirm how the selection process was implemented and how we

should identify comparison schools. To ensure that no non-Reed school experienced a

substantially greater proportion of layoffs as a result of the Reed policy, a final provision of the

Reed decision required that redirected RIF notices could only be sent to schools in which the

proportion of teachers receiving notices was below the district-average (see ACLU, 2011). For

the first year Reed was implemented, the district and union ultimately agreed to protect 35

schools under the first set of criteria and 10 schools under the “new school” criteria. During the

second year of Reed, 36 and 9 schools were selected under each set of criteria, respectively. By

September 1, 2010, a total of 49 schools qualified as a “new school” (i.e., opened within the prior

two years). A total of 38 schools were considered new for the second year of Reed protection. Of

the 45 schools that were protected in the first year, 32 were again selected during the second

year, while 13 schools were only targeted during either the first or second year of Reed. Based

on the selection criteria, greater proportion of middle and high schools were targeted for Reed

protection.

Association for Education Finance and Policy · Web viewMostly harmless econometrics: An empiricist’s companion. Princeton, NJ: Princeton University Press. Bosman, J. (2015, June

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