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WORKFORCE OUTCOMES OF WIA-FUNDED
ON-THE-JOB TRAINING IN OHIO
Kristin Harlow, Research Associate
Center for Human Resource Research
I n f o r m i n g P o l i c y a n d P r a c t i c e t h r o u g h E d u c a t i o n a n d L a b o r M a r k e t s I C o l u m b u s , O H I F e b r u a r y 2 5 , 2 0 1 5
AGENDA
1. Research Question and Context
2. Workforce Investment Act (WIA)
3. On-the-Job Training (OJT)
4. Ohio Longitudinal Data Archive
5. Research Design
6. Results
2
Does WIA-funded on-the-job
training improve workforce
outcomes for trainees in Ohio?
Signs point to Yes
3
RESEARCH QUESTION
Sessions Today:
Workforce Success Measures Dashboard
Performance Measurement of Workforce
Development Programs
Longitudinal Data Systems
CONFERENCE CONTEXT
4
Federal workforce
development
funding
Ohio: WIB Regions
and County-level
OhioMeansJobs
Centers
5
WORKFORCE INVESTMENT ACT
Funding Streams
Adult
Dislocated Worker
Youth
(ages 14-21)
Levels of Service
Core
Intensive
Training
6
WORKFORCE INVESTMENT ACT
Reimburse an employer up to 50% of salary
for a maximum of:
 six months, or
$8,000
Match based on potential position’s skill
requirements and individual’s skill level
Used at the discretion of OhioMeansJobs
Centers
In 2012-13, approximately 20% of total WIA
trainees received OJT
7
WIA ON-THE-JOB TRAINING
WIA has been studied nationally over the
years
e.g. Heinrich et al., 2009; Hollenbeck et al., 2005
Studies have found WIA-funded training is
correlated with higher wages and
employment, in aggregate
8
WHY STUDY WIA OJT?
Job training often refers to formal training
Research finds employer-provided training is
at least as important for worker productivity
(see, e.g., Acemoglu & Pischke, 1999)
On-the-job training is squishy
Varies from firm to firm
Lack of systematic information
Particular benefit of OJT is intangible
9
WHY STUDY WIA OJT?
THEORETICAL MODEL
 Model of firm’s hiring decision informs
hypotheses regarding OJT impact
𝑃 ∗
𝑖=2
𝑛
𝑛𝑒𝑡 𝑟𝑒𝑣𝑒𝑛𝑢𝑒𝑠 𝑖
(1 + 𝑑𝑖𝑠𝑐𝑜𝑢𝑛𝑡 𝑟𝑎𝑡𝑒) 𝑖
− 𝑐𝑜𝑠𝑡 𝑜𝑓 ℎ𝑖𝑟𝑖𝑛𝑔 − 𝑠𝑒𝑡𝑡𝑙𝑖𝑛𝑔 𝑖𝑛 𝑐𝑜𝑠𝑡𝑠 ≥ 0
 Where:
 𝑛𝑒𝑡 𝑟𝑒𝑣𝑒𝑛𝑢𝑒𝑠 = 𝑚𝑎𝑟𝑔𝑖𝑛𝑎𝑙 𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑣𝑖𝑡𝑦 − 𝑤𝑎𝑔𝑒𝑠
 𝑠𝑒𝑡𝑡𝑙𝑖𝑛𝑔 𝑖𝑛 𝑐𝑜𝑠𝑡𝑠 = 𝑤𝑎𝑔𝑒𝑠1 − 𝑚𝑎𝑟𝑔𝑖𝑛𝑎𝑙 𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑣𝑖𝑡𝑦1
(Neubaumer, 2010)
10
 Do firms receiving OJT funding have an incentive to
provide high-quality training?
 Employee is hired at a certain fixed wage,
 If the probability of an employee staying with the firm
increases, it is of benefit to the firm to improve the
employee’s marginal productivity.
 Hypothesis: WIA-funded OJT will improve the value
of the worker, and will result in long-term wage and
employment benefits.
11
HYPOTHESIS
OLDA Administrative Data
Quasi-Experimental Design
12
HOW CAN WE TEST THE HYPOTHESIS?
OHIO LONGITUDINAL DATA ARCHIVE
Ohio
Longitudinal
Data Archive
(OLDA)
Ohio
Education
Research
Center
(OERC)
Center for
Human
Resource
Research
(CHRR)
Researchers
State Agents
Public Stakeholders
13
14
OHIO LONGITUDINAL DATA ARCHIVE
OLDA
Ohio Department
of Job and Family
Services
Individual Wages
and Employers
Employer
Information
Unemployment
Insurance Benefits
Workforce
Investment Act
Ohio Board of
Regents
Ohio Public Higher
Education
Student
Faculty
Ohio Technical
Centers
(formerly AWE)
Adult Basic and
Literacy Education
Ohio Department
of Education
Education
Management
Information
System (EMIS)
Student
Staff
District
Course
15
ADMINISTRATIVE DATA
Benefits:
Population data
No cost of data collection
Access to detailed measures without burden to
respondents
Concerns:
Not collected for research purposes
Quality
Does WIA-funded on-the-job
training improve workforce
outcomes for trainees in Ohio?
16
RESEARCH QUESTION - REMINDER
17
OLDA CAN BE USED TO STUDY THIS
QUESTION
OLDA
Ohio Department
of Job and Family
Services
Individual Wages
and Employers
Employer
Information
Unemployment
Insurance Benefits
Workforce
Investment Act
Ohio Board of
Regents
Ohio Public Higher
Education
Student
Faculty
Ohio Technical
Centers
(formerly AWE)
Adult Basic and
Literacy Education
Ohio Department
of Education
Education
Management
Information
System (EMIS)
Student
Staff
District
Course
Administrative records collected for Federal
reporting
Includes all individuals who receive any
services from OhioMeansJobs Centers
Variables include:
Services received, including OJT
Dates of service
Demographic variables
18
WIA DATA
All wages reported to Ohio Department of Job
and Family Services (ODJFS) for
unemployment insurance purposes
Wage data excludes the following:
Wages from federal employers
Earnings from self-employment
Wages from employment outside the state of Ohio
19
WAGE DATA
Identifies individuals who receive
unemployment insurance funds each
calendar quarter
20
UNEMPLOYMENT DATA
Randomized experiment – creates a treatment
and control groups that are statistically
similar
Propensity score matching – creates a
comparison group that is statistically similar
to the existing treatment group
Accounts for observed differences between the
groups
Statistically similar through matching rather than
randomization
21
RESEARCH DESIGN
OJT participants between 2006-2008
Literature indicates training benefits appear in the
long term (3 to 5 years)
Outcomes measured after 4 years:
Employment
Wages
22
RESEARCH DESIGN
 Propensity Score Matching
 Takes into account measured differences between groups
 Does not assume functional form
 Uses single combined propensity score to match, instead of
each individual variable
 Difference in Difference
 Takes into account unmeasured differences between groups
23
RESEARCH DESIGN
OJT (n=1,115) Comparison Pool (n=27,160)
Mean SD Mean SD
Age 37.35 11.47 39.28 12.11
Male 69.2% 46.2% 49.5% 50.0%
Nonwhite 16.5% 37.1% 43.5% 49.6%
Veteran 7.2% 25.8% 3.9% 19.2%
Dislocated Worker 32.5% 46.9% 16.6% 37.2%
Received UI 26.4% 44.1% 17.7% 38.2%
Conditional Earnings $6,080 $5,610 $5,429 $5,661
Earnings Dip 69.2% 46.2% 66.4% 47.2%
24
DESCRIPTIVE STATISTICS - UNMATCHED
25
OJT VS. NON-OJT
WIA PARTICIPANTS 2006-2008
 Group matched on:
 Demographics
 WIA funding stream and supportive services
 Variables describing employment in quarters 3 through 8 prior to
participation
 Variables describing employment/wage dip prior to participation
 Industry (1-digit NAICS code)
 Geographic region
 Quarter of participation
26
MATCHING
OJT (n=980) Comparison Pool (n=980)
Mean SD Mean SD
Age 37.54 11.64 37.23 11.75
Male 66.2% 47.3% 64.5% 47.9%
Nonwhite 17.4% 38.0% 17.6% 38.1%
Veteran 6.3% 24.4% 6.9% 25.4%
Dislocated Worker 27.0% 44.4% 27.4% 44.6%
Received UI 23.3% 42.3% 22.1% 41.5%
Conditional Earnings $5,925 $5,775 $5,722 $5,253
Earnings Dip 68.7% 46.4% 70.3% 45.7%
27
DESCRIPTIVE STATISTICS – MATCHED
28
RESULTS – PERCENT FOUND WORKING
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Comparison OJT
29
RESULTS - WAGES
$0
$1,000
$2,000
$3,000
$4,000
$5,000
$6,000
$7,000
$8,000
$9,000
$10,000
Comparison OJT
Difference in Difference Model
Beginning fourth quarter before participation
Average of quarters 15 through 18
Average 11.0 percentage point difference
in individuals found working
Average $1,100 difference in quarterly
wages
30
RESULTS –
DIFFERENCE IN DIFFERENCE MODELS
How do outcomes vary by industry?
By firm?
Are there best practices in OJT?
Which firms are retaining their
employees long-term?
31
NEXT STEPS
QUESTIONS?
harlow.19@osu.edu
connect@oerc.osu.edu |oerc.osu.edu
 Acemoglu, D., & Pischke, J.-S. (1999). Beyond Becker: Training in
Imperfect Labour Markets. The Economic Journal, F112-42.
 Heinrich, C.J., Mueser, P.R., Troske, K.R., Jeon, K.-S., &
Kahvecioglu, D.C. (2009). New Estimates of Public Employment
and Training Program Net Impacts: A Nonexperimental Evaluation
of the Workforce Investment Act Program. Bonn, Germany:
Institute for the Study of Labor.
 Hollenbeck, K., Schroeder, D., King, C.T., & Huang, W.-J. (2005).
Net Impact Estimates for Services Provided through the
Workforce Investment Act. Washington: U.S. Department of
Labor.
 Neubaumer, R. (2010). Can Training Programs or Rather Wage
Subsidies Bring the Unemployed Back to Work? A Theoretical and
Empirical Investigation for Germany. Bonn, Germany: The Open
Access Publication Server of the ZBW – Leibniz Information
Centre for Economics.
WORKS CITED
Race and/or gender are missing from about
5% of all individuals represented in the WIA
data set
Used multiple imputation to create basic
model of outcomes
Found no difference between model using
multiple imputation and model dropping
individuals with missing data
Going forward, dropping all individuals with
missing demographics
34
MISSING DATA
35
JOBSOHIO REGIONS
Employment Measures for quarters 3 through 8 prior to participation:
Employment Rate Percent of quarters employed
Conditional Earnings Average earnings for those quarter employed
Earnings Trend Slope of trend in earnings
Earnings Variation Variation in earnings
Employers per Quarter Average number of employers per quarter
Dip Measures:
Earnings Dip Categorical variable identifying earnings dip (quarter 1 or 2 is more than
20% less than any quarter through quarter 8 prior)
Quarter of Dip Number of quarters prior to participation that the dip occurred
Percent of Earnings Percent of pre-dip earnings that the dip represents
36
EMPLOYMENT CREATED VARIABLES
(Hollenbeck et al., 2005)

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WORKFORCE OUTCOMES OF WIA-FUNDED ON-THE-JOB TRAINING IN OHIO

  • 1. WORKFORCE OUTCOMES OF WIA-FUNDED ON-THE-JOB TRAINING IN OHIO Kristin Harlow, Research Associate Center for Human Resource Research I n f o r m i n g P o l i c y a n d P r a c t i c e t h r o u g h E d u c a t i o n a n d L a b o r M a r k e t s I C o l u m b u s , O H I F e b r u a r y 2 5 , 2 0 1 5
  • 2. AGENDA 1. Research Question and Context 2. Workforce Investment Act (WIA) 3. On-the-Job Training (OJT) 4. Ohio Longitudinal Data Archive 5. Research Design 6. Results 2
  • 3. Does WIA-funded on-the-job training improve workforce outcomes for trainees in Ohio? Signs point to Yes 3 RESEARCH QUESTION
  • 4. Sessions Today: Workforce Success Measures Dashboard Performance Measurement of Workforce Development Programs Longitudinal Data Systems CONFERENCE CONTEXT 4
  • 5. Federal workforce development funding Ohio: WIB Regions and County-level OhioMeansJobs Centers 5 WORKFORCE INVESTMENT ACT
  • 6. Funding Streams Adult Dislocated Worker Youth (ages 14-21) Levels of Service Core Intensive Training 6 WORKFORCE INVESTMENT ACT
  • 7. Reimburse an employer up to 50% of salary for a maximum of:  six months, or $8,000 Match based on potential position’s skill requirements and individual’s skill level Used at the discretion of OhioMeansJobs Centers In 2012-13, approximately 20% of total WIA trainees received OJT 7 WIA ON-THE-JOB TRAINING
  • 8. WIA has been studied nationally over the years e.g. Heinrich et al., 2009; Hollenbeck et al., 2005 Studies have found WIA-funded training is correlated with higher wages and employment, in aggregate 8 WHY STUDY WIA OJT?
  • 9. Job training often refers to formal training Research finds employer-provided training is at least as important for worker productivity (see, e.g., Acemoglu & Pischke, 1999) On-the-job training is squishy Varies from firm to firm Lack of systematic information Particular benefit of OJT is intangible 9 WHY STUDY WIA OJT?
  • 10. THEORETICAL MODEL  Model of firm’s hiring decision informs hypotheses regarding OJT impact 𝑃 ∗ 𝑖=2 𝑛 𝑛𝑒𝑡 𝑟𝑒𝑣𝑒𝑛𝑢𝑒𝑠 𝑖 (1 + 𝑑𝑖𝑠𝑐𝑜𝑢𝑛𝑡 𝑟𝑎𝑡𝑒) 𝑖 − 𝑐𝑜𝑠𝑡 𝑜𝑓 ℎ𝑖𝑟𝑖𝑛𝑔 − 𝑠𝑒𝑡𝑡𝑙𝑖𝑛𝑔 𝑖𝑛 𝑐𝑜𝑠𝑡𝑠 ≥ 0  Where:  𝑛𝑒𝑡 𝑟𝑒𝑣𝑒𝑛𝑢𝑒𝑠 = 𝑚𝑎𝑟𝑔𝑖𝑛𝑎𝑙 𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑣𝑖𝑡𝑦 − 𝑤𝑎𝑔𝑒𝑠  𝑠𝑒𝑡𝑡𝑙𝑖𝑛𝑔 𝑖𝑛 𝑐𝑜𝑠𝑡𝑠 = 𝑤𝑎𝑔𝑒𝑠1 − 𝑚𝑎𝑟𝑔𝑖𝑛𝑎𝑙 𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑣𝑖𝑡𝑦1 (Neubaumer, 2010) 10
  • 11.  Do firms receiving OJT funding have an incentive to provide high-quality training?  Employee is hired at a certain fixed wage,  If the probability of an employee staying with the firm increases, it is of benefit to the firm to improve the employee’s marginal productivity.  Hypothesis: WIA-funded OJT will improve the value of the worker, and will result in long-term wage and employment benefits. 11 HYPOTHESIS
  • 12. OLDA Administrative Data Quasi-Experimental Design 12 HOW CAN WE TEST THE HYPOTHESIS?
  • 13. OHIO LONGITUDINAL DATA ARCHIVE Ohio Longitudinal Data Archive (OLDA) Ohio Education Research Center (OERC) Center for Human Resource Research (CHRR) Researchers State Agents Public Stakeholders 13
  • 14. 14 OHIO LONGITUDINAL DATA ARCHIVE OLDA Ohio Department of Job and Family Services Individual Wages and Employers Employer Information Unemployment Insurance Benefits Workforce Investment Act Ohio Board of Regents Ohio Public Higher Education Student Faculty Ohio Technical Centers (formerly AWE) Adult Basic and Literacy Education Ohio Department of Education Education Management Information System (EMIS) Student Staff District Course
  • 15. 15 ADMINISTRATIVE DATA Benefits: Population data No cost of data collection Access to detailed measures without burden to respondents Concerns: Not collected for research purposes Quality
  • 16. Does WIA-funded on-the-job training improve workforce outcomes for trainees in Ohio? 16 RESEARCH QUESTION - REMINDER
  • 17. 17 OLDA CAN BE USED TO STUDY THIS QUESTION OLDA Ohio Department of Job and Family Services Individual Wages and Employers Employer Information Unemployment Insurance Benefits Workforce Investment Act Ohio Board of Regents Ohio Public Higher Education Student Faculty Ohio Technical Centers (formerly AWE) Adult Basic and Literacy Education Ohio Department of Education Education Management Information System (EMIS) Student Staff District Course
  • 18. Administrative records collected for Federal reporting Includes all individuals who receive any services from OhioMeansJobs Centers Variables include: Services received, including OJT Dates of service Demographic variables 18 WIA DATA
  • 19. All wages reported to Ohio Department of Job and Family Services (ODJFS) for unemployment insurance purposes Wage data excludes the following: Wages from federal employers Earnings from self-employment Wages from employment outside the state of Ohio 19 WAGE DATA
  • 20. Identifies individuals who receive unemployment insurance funds each calendar quarter 20 UNEMPLOYMENT DATA
  • 21. Randomized experiment – creates a treatment and control groups that are statistically similar Propensity score matching – creates a comparison group that is statistically similar to the existing treatment group Accounts for observed differences between the groups Statistically similar through matching rather than randomization 21 RESEARCH DESIGN
  • 22. OJT participants between 2006-2008 Literature indicates training benefits appear in the long term (3 to 5 years) Outcomes measured after 4 years: Employment Wages 22 RESEARCH DESIGN
  • 23.  Propensity Score Matching  Takes into account measured differences between groups  Does not assume functional form  Uses single combined propensity score to match, instead of each individual variable  Difference in Difference  Takes into account unmeasured differences between groups 23 RESEARCH DESIGN
  • 24. OJT (n=1,115) Comparison Pool (n=27,160) Mean SD Mean SD Age 37.35 11.47 39.28 12.11 Male 69.2% 46.2% 49.5% 50.0% Nonwhite 16.5% 37.1% 43.5% 49.6% Veteran 7.2% 25.8% 3.9% 19.2% Dislocated Worker 32.5% 46.9% 16.6% 37.2% Received UI 26.4% 44.1% 17.7% 38.2% Conditional Earnings $6,080 $5,610 $5,429 $5,661 Earnings Dip 69.2% 46.2% 66.4% 47.2% 24 DESCRIPTIVE STATISTICS - UNMATCHED
  • 25. 25 OJT VS. NON-OJT WIA PARTICIPANTS 2006-2008
  • 26.  Group matched on:  Demographics  WIA funding stream and supportive services  Variables describing employment in quarters 3 through 8 prior to participation  Variables describing employment/wage dip prior to participation  Industry (1-digit NAICS code)  Geographic region  Quarter of participation 26 MATCHING
  • 27. OJT (n=980) Comparison Pool (n=980) Mean SD Mean SD Age 37.54 11.64 37.23 11.75 Male 66.2% 47.3% 64.5% 47.9% Nonwhite 17.4% 38.0% 17.6% 38.1% Veteran 6.3% 24.4% 6.9% 25.4% Dislocated Worker 27.0% 44.4% 27.4% 44.6% Received UI 23.3% 42.3% 22.1% 41.5% Conditional Earnings $5,925 $5,775 $5,722 $5,253 Earnings Dip 68.7% 46.4% 70.3% 45.7% 27 DESCRIPTIVE STATISTICS – MATCHED
  • 28. 28 RESULTS – PERCENT FOUND WORKING 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Comparison OJT
  • 30. Difference in Difference Model Beginning fourth quarter before participation Average of quarters 15 through 18 Average 11.0 percentage point difference in individuals found working Average $1,100 difference in quarterly wages 30 RESULTS – DIFFERENCE IN DIFFERENCE MODELS
  • 31. How do outcomes vary by industry? By firm? Are there best practices in OJT? Which firms are retaining their employees long-term? 31 NEXT STEPS
  • 33.  Acemoglu, D., & Pischke, J.-S. (1999). Beyond Becker: Training in Imperfect Labour Markets. The Economic Journal, F112-42.  Heinrich, C.J., Mueser, P.R., Troske, K.R., Jeon, K.-S., & Kahvecioglu, D.C. (2009). New Estimates of Public Employment and Training Program Net Impacts: A Nonexperimental Evaluation of the Workforce Investment Act Program. Bonn, Germany: Institute for the Study of Labor.  Hollenbeck, K., Schroeder, D., King, C.T., & Huang, W.-J. (2005). Net Impact Estimates for Services Provided through the Workforce Investment Act. Washington: U.S. Department of Labor.  Neubaumer, R. (2010). Can Training Programs or Rather Wage Subsidies Bring the Unemployed Back to Work? A Theoretical and Empirical Investigation for Germany. Bonn, Germany: The Open Access Publication Server of the ZBW – Leibniz Information Centre for Economics. WORKS CITED
  • 34. Race and/or gender are missing from about 5% of all individuals represented in the WIA data set Used multiple imputation to create basic model of outcomes Found no difference between model using multiple imputation and model dropping individuals with missing data Going forward, dropping all individuals with missing demographics 34 MISSING DATA
  • 36. Employment Measures for quarters 3 through 8 prior to participation: Employment Rate Percent of quarters employed Conditional Earnings Average earnings for those quarter employed Earnings Trend Slope of trend in earnings Earnings Variation Variation in earnings Employers per Quarter Average number of employers per quarter Dip Measures: Earnings Dip Categorical variable identifying earnings dip (quarter 1 or 2 is more than 20% less than any quarter through quarter 8 prior) Quarter of Dip Number of quarters prior to participation that the dip occurred Percent of Earnings Percent of pre-dip earnings that the dip represents 36 EMPLOYMENT CREATED VARIABLES (Hollenbeck et al., 2005)

Editor's Notes

  1. Core Services – job search and placement assistance available to all customers (universal access) of the OhioMeansJobs Center. Core services include self-help services and services requiring minimal staff assistance as described under Section 134 (d) (2) of the Act Intensive Services - – Services available to adults and dislocated workers including individual career planning, resume preparation, job clubs, career counseling, internships, and comprehensive assessments. Basic education, ESL, and basic computer literacy are also sometimes considered intensive. Training - The education and employment training services to be offered at no cost to One-Stop system customers who have been unable to get a job after having received one or more core services and one or more intensive services (see also Individual Training Account -- ITA). Youth - Tutoring, alternative secondary school offerings, summer employment opportunities linked to academic and occupational learning, paid and unpaid work experiences, occupational skill training, leadership development opportunities, supportive services, mentoring, follow-up services, and comprehensive guidance and counseling.
  2. Present value of a worker less cost of hiring less cost in first period Training in general increases a worker’s marginal productivity, and increases the probability that the worker will reach the intended duration of the employment As n grows, benefits of training to the firm grow But, the amount of the benefits depend on the quality of the training OJT is unregulated, varying quality We hypothesize that given subsidized training, there is an incentive for high-quality training because the employee is hired at a certain fixed wage, and if the probability of an employee staying with the firm increases, it is of benefit to the firm to improve the employee’s marginal productivity. Further, OJT participants will benefit from higher quality training by being more employable and receiving higher wages over time.
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  4. DDD