Andrew Weaver Massachusetts Institute of Technology IST/Lisbon June 25, 2014 (joint with Paul Osterman) Manufacturing Skills and Skill Gaps following Volatility.

Andrew WeaverMassachusetts Institute of Technology

IST/LisbonJune 25, 2014

(joint with Paul Osterman)

Manufacturing Skills and Skill Gaps following Volatility and High

Unemployment

What are the Issues?

2

BackgroundHigh and persistent unemploymentFirms complain they can’t find skilled workers

QuestionsDoes mismatch/gap exist between employer

demands and the supply of skills in the marketplace?

If so, is it a simple/mechanical result of inadequate worker skills, or are other more complex factors to blame (cyclical demand, corporate strategy, communication among economic actors, etc.)?

Relation to Uncertainty and Industrialization Patterns

3

Loss of domestic mfg. raises questions about trajectory of industrial growth, economic development, job quality

Policymakers need to understand this issue in order to foster economic growth and improve economic outcomes for workersCommon skill-biased technical change (SBTC) narrative

leads to focus on supply-side labor market frictions If problem is just structural/skills gap: long-term ed. attainment and

worker behaviorIf other factors matter, SBTC narrative may be misleading

and other interventions may be necessaryExhortations to increase STEM education may not solve the

problem Institutional approaches may be required: making connections with

local labor market intermediaries, solving coordination/communication failures, etc.

Presentation Goals

4

Set boundaries on incidence of skill gapsDemonstrate simple skill mismatch story is

inadequatePoint to importance of intermediaries and

institutions in addressing challenges in skill supplies

Shortcomings of Existing Research

5

Takes place at very abstract level without direct measurement

Unemployment-vacancy indices (Sahin et al. 2012; Canon, Chen and Marifian 2013)Are sensitive to changes in firm strategy (recruitment,

wages)Are sensitive to cyclicalityVague measure: hides mechanism (geography? skills?)Only measure inter-industry mismatch (Modestino 2010;

Lazear and Spletzer 2012)Supply-Demand indices (Estevau and Tsounta 2011;

Rothwell 2012)Use education as proxy

Distorts demand measurement: college-educated barista Ignores within-education variation in skillsProxy on both sides: any regional or intra-industry variation generates

mismatch

Manufacturing Puzzle

6

For manufacturing, important facts are inconsistent with skill gap claims

Deloitte and the National Association of Manufacturers (2011) report survey results:600,000 unfilled jobs due to lack of qualified

workers74% of manufacturers report lack of skilled

production workers had significant negative impact

If demand exceeds supply for high-skilled manufacturing workers, we would expect wages to increase

Manufacturing Wage Trends

7

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 20111.05

1.10

1.15

1.20

1.25

1.30

1.35

Community College Wage Premium by Industry Sector

Mfg. PremiumNon-Mfg. Premium

Year

Rati

o o

f A

A t

o H

S W

ages

Source: CPS MORG (NBER) data.

Approach

8

To answer questions about skill and mismatch, it’s necessary to gather direct evidence on skill demands:What skills do employers demand?Which establishments demand high levels of skill?Do establishments, particularly those with high skill

demands, have trouble finding workers with these skills?

To really narrow in on skills, important to focus on industry/industry sector

Paul Osterman and I designed and administered a nationally representative survey of manufacturing plants to answer these questions

We conducted extensive fieldwork to identify critical factors relating to institutions, regional eco-system

Our Survey

9

Administered in late 2012, early 2013Random sample—Dun & Bradstreet databasen=90336% response rateFocus on “core” production workers (Ben-Ner

and Urtasun 2013, Osterman 1995)—62% of estab. employment

Concrete skill questions: does this job require reading complex technical manuals? algebra? geometry? etc.

Defined skill gaps as prolonged core worker vacancies (> 3 mos.)

Basic Skill Demands

10

Basic Skill Demands for Core Production Jobs

 All 

Establishments

Basic reading (ability to read basic instruction manuals) 75.6%

Basic writing (ability to write short notes, memos, reports less than one page long) 60.5%

Basic math (ability to perform all of math categories below) 74.0%

Addition and subtractionMultiplication and divisionFractions, decimals, or percentages

Require basic reading, writing, and math 42.4%

Require use of computers several times per week or more frequently 62.3%

Ability to use word processing software or ability to search Internet for information 41.7%

Interpersonal/Problem-Solving/Soft Skill Demands

11

Percent of Establishments Citing Interpersonal, Problem-Solving, and Other Soft Skills as Very or Moderately Important for Core Jobs

  Very Important

Very or Moderately Important

Cooperation with other employees 81.2% 99.3%Ability to evaluate quality of output 71.0% 95.8%

Ability to take appropriate action if quality is not acceptable 76.3% 97.7%

Ability to work in teams 64.2% 91.1%Ability to learn new skills 50.1% 89.3%

Ability to independently organize time or prioritize tasks 45.6% 84.4%

Ability to solve unfamiliar problems 38.8% 83.0%

Ability to critically evaluate different options 35.7% 74.1%

Ability to initiate new tasks without guidance from management 35.2% 80.9%

Extended Skill Demands

12

Extended Skill Demands for Core Production Jobs

  All Establishments

Extended reading (docs > 5pg.; trade jrn.; tech. docs) 52.6%Extended writing (>1pg.) 22.1%Extended math (ability to perform any of three math categories below) 38.0%

Algebra, geometry, or trigonometry 31.5%Probability or statistics 13.6%Calculus or other advanced mathematics 7.4%

Extended computer 41.9%Use CAD/CAM 28.4%

Use other engineering or manufacturing software 29.2%

Ability to write computer programs (such as program a CNC machine for a new piece, etc.) 18.6%

Unique skill 25.9%

Skill Gap Evidence

13

No vacancies More than zero, less than or equal to 5%

More than 5%, less than or

equal to 10%

More than 10%0.0%

10.0%20.0%30.0%40.0%50.0%60.0%70.0%80.0%90.0%

64.9%

10.2% 7.4%

17.4%

76.3%

7.6% 5.6%10.6%

Vacancies

Any Vacancies Long-Term Vacancies

Vacancy Measure as a Percent of an Establishment's Core Employees

Perc

en

t of

Est

ab

lish

men

ts

What Skill Demands are Associated with Hiring Difficulties?

14

Demands for higher level reading, math, and unique skills are significant predictors of long-term vacancies

Computer and soft skills/problem-solving/initiative skills are not

So is this relationship between skill demands and hiring problems an automatic/mechanical one?Examine high skill-demanding

establishments

Which Establishments Demand High Skills?

15

Establishments that demand extended skills are characterized by:high-techcluster membershiphigh-performance work organization (TQM/self-

managed team)frequent process (not product) innovationmore foreign competition

If the simple skill mismatch story is accurate, these establishments should have significantly higher levels of hiring difficulties

Long-Term Vacancies: Estab. Characteristics Models

16

 Pct. LT vac. LTV--Logit Pct. LT vac.--

RFPct. LT vac.--RF+wage

LTV--Logit--RF

LTV--Logit--RF+wage

High-tech -0.01 -0.052 -0.014** -0.017** -0.068* -0.072*(0.007) (0.038) (0.007) (0.007) (0.037) (0.039)

Above-avg. tech. -0.001 -0.019 -0.001 -0.001 -0.024 -0.026(0.006) (0.033) (0.006) (0.006) (0.033) (0.034)

TQM pct. 0.000 0.001 0.002 0.003 0.004 0.005(0.000) 0.000  0.000  0.000  0.000  0.000 

Self team pct. 0.000 0.001 0.002 0.003 0.004 0.005(0.000) (0.001) (0.000) (0.000) (0.001) (0.001)

Product innovation 0.002 0.019 0.001 0.003 0.018 0.022(0.007) (0.038) (0.007) (0.007) (0.039) (0.040)

Process innovation 0 0.005 0.002 0.003 0.013 0.021(0.007) (0.038) (0.007) (0.007) (0.038) (0.039)

Industry cluster 0.017*** 0.119*** 0.014** 0.013** 0.117*** 0.117***(0.006) (0.032) (0.006) (0.006) (0.032) (0.033)

Part of larger firm 0.003 0.024 0.003 0.001 0.032 0.031(0.007) (0.037) (0.006) (0.007) (0.037) (0.038)

More foreign comp. 0.002 0.019 0.001 0.001 0.027 0.025(0.007) (0.038) (0.007) (0.007) (0.039) (0.039)

Long-Term Vacancies: Red. Form Cont’d

17

County pop. density 0.000 0.000 0.000 0.000

(0.000) (0.000) (0.000) (0.000)

County unemp. rate (2011) -0.148 -0.129 -0.795 -0.695

(0.122) (0.124) (0.711) (0.721)

Pct. change in core emp. last 2 yrs. -0.011*** -0.012*** 0.016 0.016

(0.004) (0.004) (0.024) (0.025)

Standardized division wage 0.003 0.005

(0.003) (0.017)

Low wage 0.121*** 0.18

        (0.032)   (0.160)

R-Squared 0.036 0.034 0.050 0.073 0.040 0.038

N 783 784 766 738 766 738

Source: PIE Manufacturing Survey. * p<0.10, ** p<0.05, *** p<0.01

Summary of Results

18

No widespread problem with skill gapsIt is worth paying attention to the minority of

establishments reporting difficultiesSkills are important

Extended math is importantExtended reading is surprisingly prominentUnique skills: may reflect internal training decline

However, many establishment characteristics associated with higher skill demands (high-tech, HPWS, process innovation) are not associated with hiring difficulties

This implies no simple/mechanical relationship between higher skill demands and hiring problems: other factors mediate relationship

What’s Going On?

19

Skills are critical, but skill gap formulation is not necessarily the best way to frame the issue

American skill production system has been changing

Decline in mfg. establishment size (Holmes 2011; Henly and Sanchez 2009)

Small firms provide less internal training (Lynch and Black 1998)

External training actors like community colleges are more important than they once were

But system is disaggregatedMore potential for coordination failures and

underinvestment in public goods

Intermediaries/Institutions are Important

20

Rochester storyKodakMonroe Community CollegeRochester Regional Photonics Cluster (RRPC)Addressed coordination failure

Intermediaries and institutions are critical for matching supply and demand, as well as coordinating increases in skill demands and supplies

Challenge coming from volatility/uncertainty: Simple SBTC story says high returns to education/skills will

provide should provide incentive for supply side of labor mkt.

However, volatilty may destroy the very institutions and intermediaries necessary to raise skill levels on both supply and demand side

21

Thank You

Andrew [email protected]

Our Survey

22

Production in the Innovation Economy (PIE) projectAdministered in late 2012, early 2013Dun & Bradstreet databaseEstablishment approach: Bloom and Van Reenen 2007,

Lynch and Black 1998Manufacturing establishments, excluding baking,

printing, and publishingRandom sample, stratified by estab. size (>10 emp.)Targeted plant managers (identified appropriate

person)$10 incentiven=90336% response rate

Survey Design

23

Focus on “core” production workers (Ben-Ner and Urtasun 2013, Osterman 1995)—62% of estab. employment

Battery of concrete skill questions (>30)Examples

ReadingBasic: Does this job require reading basic instruction manuals?Extended: Does this job require reading complex technical

documents or manuals? Any document that is longer than five pages? etc.

MathBasic: Does this job require mathematical operations involving

multiplication and division?Extended: Does this job require mathematical operations

involving probability and statistics? Algebra, geometry, or trigonometry? etc.

Survey Design (2)

24

Concrete questions re skill gapsDefined skill gaps as prolonged core worker

vacancies (> 3 mos.)Background data on establishment and

workforceIndustryEmployment/financial trendsInnovationTrainingAge structureSex compositionetc.

Hypotheses

25

H1: If skill gaps are a widespread problem in manufacturing, then long-term vacancies will be a widespread problemBenchmark from Deloitte survey:

74% of mfg. firms suffered from lack of skilled production workers

H2: If demand for higher skills mechanically leads to hiring problems, the establishments characterized by the highest skill demands should experience greater problemsHigher level skill demands (math, reading, etc.) should

be associated with hiring problemsIf high-tech or other types of establishments have

higher skill demands, they should have more severe hiring problems

Analysis of Establishments with Hiring Difficulties

26

Two dependent variables: Long-term vacancies as a percentage of total core workers

(OLS)Indicator for long-term vacancies (Logit)

First estimate models with skill variables as regressors, then with high-skill establishment characteristics as regressors

Reduced form controlsSupply: county unemployment rate (2011), county

population densityDemand: change in core workers over past two yearsWage measures (mgmt. strategy): standardized by Census

geographic division (2011 to avoid simultaneity)All models control for establishment size

Long-Term Vacancies: Skill Models

27

Pct. LT vac. LTVPct. LT vac.--detailed skills

LTV--detailedPct. LT vac.--detailed, red. form

LTV--detailed, red. form

Any extended skill 0.016*** 0.073**(0.006) (0.035)

Extended reading 0.012** 0.465*** 0.011* 0.513***(0.006) (0.178) (0.006) (0.189)

Extended writing -0.002 -0.12 -0.003 -0.143(0.007) (0.205) (0.007) (0.215)

Extended math 0.017*** 0.531*** 0.019*** 0.608***-0.006 -0.187 -0.006 -0.197

Extended computer 0.007 -0.11 0.007 -0.062(0.006) (0.179) (0.006) (0.185)

Unique skill 0.013** 0.442** 0.015** 0.553***(0.006) (0.177) (0.006) (0.185)

New skills -0.002 0.263 -0.003 0.224(0.006) (0.172) (0.006) (0.180)

Evaluate quality 0.001 -0.252 0.002 -0.282      (0.006) (0.187) (0.006) (0.192)R-Squared/Pseudo R2 0.025 0.023 0.052 0.053 0.082 0.060N 869 870 831 832 778 778* p<0.10, ** p<0.05, *** p<0.01

Implications

28

Demanding high skill levels is not necessarily a ticket to trouble

A wider range of institutional policies responses may be relevant

Targeted policies may have the potential to affect hiring outcomes even holding current worker skill levels constantInstitutional relationships (e.g., between firms and

community colleges or labor market intermediaries)Incentives for firm-level human resource/training

policyPolicies that reduce risk of mfg. career for job

applicants

Further Research Agenda

29

PIE SurveyInstitutional and other factors that mediate

skill and hiring problemsCommunity college density: determinants

and implications for economic growthLPNs and job laddersSocial entrepreneurship and philanthropic

capital markets

Extended Skill Demands: Logit Analysis

30

Any Extended 

SkillExtended Reading

Extended Math

Extended Computer Unique Skill Extended 

Writing

High-tech industry 0.172*** 0.288*** 0.026 0.143*** 0.006 0.025(0.032) (0.038) (0.039) (0.043) (0.037) (0.036)

Above-average tech. 0.019 0.039 0.001 0.059* 0.087*** -0.018(0.032) (0.035) (0.033) (0.036) (0.032) (0.030)

TQM pct. 0.001*** 0.001 0.0003 0.001*** 0.001* -0.00010.000  0.000  0.001  0.000  0.000  0.000 

Self team pct. 0.001 0.002*** 0.001** 0.0005 0.001** 0.001(0.001) (0.001) (0.001) (0.001) 0.000  0.000 

Frequent product innovation 0.032 0.006 -0.06 -0.025 -0.014 0.038

(0.037) (0.041) (0.040) (0.041) (0.038) (0.034)

Frequent process innovation 0.078** 0.081** 0.076** 0.125*** 0.036 0.02

(0.037) (0.040) (0.037) (0.040) (0.036) (0.034)Industry cluster 0.073** 0.062* 0.066** 0.093*** 0.095*** -0.023

(0.030) (0.034) (0.032) (0.034) (0.031) (0.029)Part of larger firm -0.025 0.01 -0.089** -0.088** -0.039 0.029

(0.035) (0.039) (0.038) (0.039) (0.036) (0.033)More foreign competition 0.059* 0.062 0.112*** 0.119*** 0.027 -0.019  (0.035) (0.040) (0.040) (0.041) (0.037) (0.033)Pseudo R-Squared 0.066 0.082 0.043 0.061 0.038 0.013N 804 797 796 795 800 792

* p<0.10, ** p<0.05, ***p<0.01

Size Distribution

31

<20 20-99 100-249 250-499 500+0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

PIECounty Business Patterns 2010

Weighting and Validation

32

Small establishments somewhat more likely to respond than large estab.

For all descriptive statistics we use size weights based on the employment-weighted proportion of establishments of various size classes in the Census Bureau’s County Business Patterns (CBP) data

Validate aggregate workforce data with CPS: close match

Validation with CPS

33

 PIE CPS (2012)

Hourly wage 16.95 16.49*

Union 18.1% 13.7%*

Female 26.7% 26.6%

Age 30 or less 20.6% 21.3%

Age 31-40 27.5% 22%*

Age 41-55 35.8% 38.8%*

Age 56 plus 16.1% 17.9%*

*=significant differences at 95 percent level or higher.

Geographic Distribution

34

Northeast21%

Midwest35%South

26%

West18%

PIE Survey

Geographic Comparison

35

Northeast Midwest South West0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

PIE surveyCounty Business Patterns 2011

Industry Distribution

36

NAICS NAICS 3-Digit Industry PIE Survey Pct. of Estab.

311 Food mfg. 4.5%312 Beverage and tobacco product mfg. 1.2%313 Textile mills 0.6%314 Textile product mills 2.4%315 Apparel mfg. 1.3%316 Leather and allied product mfg. 0.1%321 Wood product mfg. 3.4%322 Paper mfg. 1.4%323 Printing and related support activities 3.6%324 Petroleum and coal products mfg. 0.8%325 Chemical mfg. 6.4%326 Plastics and rubber products mfg. 6.0%327 Nonmetallic mineral product mfg. 3.4%331 Primary metal mfg. 3.4%332 Fabricated metal product mfg. 22.2%333 Machinery mfg. 12.0%334 Computer and electronic product mfg. 7.7%335 Electrical equip, appliance, and comp. mfg. 3.1%336 Transportation equipment mfg. 5.4%337 Furniture and related product mfg. 3.0%339 Miscellaneous mfg. + other 8.4%

Industry Comparison

37

NAICS NAICS 3-Digit Industry PIE Survey Pct. of Estab.

CBP Pct. of Estab. 2011 PIE-CBP

311 Food mfg. 4.5% 8.6% -4.1%312 Beverage and tobacco product mfg. 1.2% 1.7% -0.5%313 Textile mills 0.6% 0.8% -0.2%314 Textile product mills 2.4% 2.1% 0.3%315 Apparel mfg. 1.3% 2.4% -1.2%316 Leather and allied product mfg. 0.1% 0.4% -0.3%321 Wood product mfg. 3.4% 4.7% -1.3%322 Paper mfg. 1.4% 1.5% -0.1%

323 Printing and related support activities 3.6% 9.4% -5.8%324 Petroleum and coal products mfg. 0.8% 0.8% 0.0%325 Chemical mfg. 6.4% 4.4% 2.0%326 Plastics and rubber products mfg. 6.0% 4.3% 1.6%327 Nonmetallic mineral product mfg. 3.4% 5.2% -1.8%331 Primary metal mfg. 3.4% 1.6% 1.8%332 Fabricated metal product mfg. 22.2% 18.8% 3.4%333 Machinery mfg. 12.0% 8.1% 3.9%

334Computer and electronic product mfg. 7.7% 4.4% 3.3%

335Electrical equip, appliance, and comp. mfg. 3.1% 2.0% 1.1%

336 Transportation equipment mfg. 5.4% 3.9% 1.5%337 Furniture and related product mfg. 3.0% 5.6% -2.6%339 Miscellaneous mfg. + other 8.4% 9.3% -0.9%

Occupational Wage Comparison

38

Average Hourly Wages by Selected Manufacturing

Occupations      

  2008 2011 % change

Production occupations 15.87 16.74 5.5%

Machinists 18.17 19.51 7.4%

Industrial Engineering Technicians 22.89 24.42 6.7%

Mechanical Engineering Technicians 23.74 24.92 5.0%

Industrial Engineers 35.47 37.56 5.9%

Source: BLS Occupational Employment Statistics.

Extended Skill Demand Models (2)

39

  Extended Skill Index Pr(index=3)

Extended Skill Index Pr(index=3) Gen. Ord. Logit

High tech 0.095*** 0.097***(0.021) (0.020)

Above-avg. tech 0.016 0.0004(0.016) (0.015)

TQM pct. 0.0004** 0.0004**(0.0002) (0.0002)

Self team pct. 0.001** 0.001***(0.0003) (0.0002)

Frequent prod. innovation (g) -0.012 -0.065***(0.019) (0.024)

Frequent process innovation 0.047*** 0.051***(0.018) (0.018)

Industry cluster 0.034** 0.035**(0.015) (0.015)

Part of larger firm (g) -0.025 -0.060***(0.018) (0.018)

More foreign comp. 0.048** 0.049***(0.020) (0.018)

Employment size FE (g-2nd) x xR-Squared 0.031 0.042N 778 778

LR test (Chi2) p-value--prop. odds 0.158 0.801

Brant test p-value 0.096  * p<0.10, ** p<0.05,  ***p<0.01

Quantifying the Skill Gap

40

Core vacancies as pct. of total estab. emp. 1.1%Core long-term vacancies as pct. of total estab. emp. 0.5%Core long-term vac. as pct. of total vac. 48.4%Core workers as pct. of total estab. emp. 62.0%

Est. total PIE vac. pct. if vac. are proportional 1.8%JOLTS vacancies as pct. of total CES mfg. emp. (Aug. 2011) 2.0%

JOLTS vacancies as pct. of total CES mfg. emp. (4Q 2012) 2.0%

Deloitte implied vacancies as pct. of total CES mfg. emp. (Aug. '11) 5.1%

Implied long-term vac./emp. (based on long-term vac. pct. of total vac.)PIE 0.9%JOLTS (4Q 2012) 1.0%Deloitte 2.4%

Implied total PIE mfg. vacancies 213,712

Implied PIE long-term mfg. vacancies 103,390

PIE long-term vacancies as pct. of mfg. unemployed (4Q 2012) 11.6%

Skill Gap Robustness (1)

41

What if we miss skill gaps because we’re looking at a point in time

Hiring funnel (based on attempt to hire in last two years)Hiring Funnel for Core Workers    

Mean Median 75th-weeks 

25th-others

Weeks required to recruit and hire applicant (start of process to extension of offer) 5.9 4.0 6.0

Typical number of applications received per open core position 23.8 10.0 5.0

Typical number of interviews conducted per open core position 5.9 5.0 3.0

Acceptance rate by applicants who are extended an offer 85.4% 95.0% 80.0%

Source: PIE Manufacturing Survey.

Skill Gap Robustness (2)

42

Alternative Measures  

Ever reduced production due to vacancies 17.7%

Have vacancy and ever reduced prod. 7.6%

Long hire times over past two yrs. (>=3 mos.) 10.9%

Long-term vac. OR vac. + reduced prod. 25.6%

Long-term vac. OR prior long hire times 34.1%

Why Manufacturing?

43

Manufacturing is interesting test case for structural mismatch

Arguments about mismatch/spiking skill demands are commonly applied to manufacturingDeloitte and the National Association of Manufacturers (2011)

report survey results: 600,000 unfilled jobs due to lack of qualified workers 74% of manufacturers report lack of skilled production workers had

significant negative impact

Capital intensive / sensitive to technology shocksKey theories: tech. shocks drive mismatch (Brynjolfsson and

McAfee 2012, Autor, Levy, and Murnane 2003)STEM skills are important Idea of vacancies in industry with millions of laid off

workers implies structural gapBroad sector with a lot of variation (high-tech/low-tech,

domestic/export, etc.)12% of GDP; 70% of industry R&D