U.S. Department of Labor (DOL) 

Employment and Training Administration (ETA) 

Earnings Progression Among Workforce Development 

Participants: Evidence from Washington State 

Prepared by: 

Colleen K. Chrisinger, Ph.D. 

Department of Planning, Public Policy and Management
 

University of Oregon 

This paper was funded, either wholly or in part, with Federal funds from the U.S. Department of 

Labor, Employment and Training Administration (ETA) under Contract Number DOLJ061A20380. 

The contents of this publication do not necessarily reflect the views or policies of the Department, 

nor does mention of trade names, commercial products, or organizations imply endorsement of 

same by the U.S. Government. This paper was one of seven prepared under the 2009 ETA 

Research Papers Program, which competitively awarded doctoral and post-doctoral students 

funding to conduct original research and prepare scholarly papers on topics relating to the 

workforce investment system. 
Acknowledgments

I am very grateful to Robert Plotnick, Mark Long, William Zumeta, David Harrison, Joaquin 

Herranz, Jr., Anna Haley-Lock, Jennifer Romich, Michelle Ennis, Kenneth Troske, Christopher 

Jepsen, Ernst Stromsdorfer, numerous seminar participants, and the staff of the Washington 

workforce agencies for their advice and comments. I also gratefully acknowledge funding from 

the 2009 Employment and Training Administration Research Papers Program. 
1 Introduction

The United States government has funded workforce development programs since the passage of the WagnerPeyser Act in 1933. The broad goal of workforce development and job training is to provide individuals with 

skills to enable them to obtain and maintain employment (O’Leary et al., 2004). Publicly funded workforce 

training encompasses a range of activities, complementing and, at times, including employer-provided job 

training and training through the public community and technical college system. 

The most recent major federal workforce policy innovation was the Workforce Investment Act (WIA) 

of 1998. It mandated the local availability of One-Stop Career Centers across the United States, which are 

open to all individuals, regardless of prior work experience or education. WIA includes programs for adults, 

dislocated workers, and youth. The Department of Labor required states to implement most provisions of 

WIA by July 1, 2000 (U.S. Government Accountability Office, 2005). 

This research is a case study of selected workforce development programs in the state of Washington. 

Washington has a highly developed workforce policy system, clear strategic goals, and a commitment to 

research and evaluation. Although workforce programs are implemented differently from state to state, the 

essential elements of the programs in this evaluation are the same across all states. 

In Washington, One-Stop Career Centers are known as WorkSource centers. The centers provide services 

within the following categories: core, intensive, training, follow-up, and support. Core services include 

skill assessment, labor market information, and job search and placement assistance. Intensive services are 

available to individuals who are unable to obtain a job solely through the use of core services, and include 

specialized assessments and counseling. Training services, available only to individuals who have also 

received core and intensive services, attempt to enhance skills with the goal of preparing individuals for local 

job opportunities. For individuals who have obtained employment, follow-up services are available for at 

least 12 months and may include additional career counseling, contact with employers, and peer support 

groups. Finally, support services may include assistance with child care, transportation, or work-related 

expenses. 

State and local Workforce Investment Boards (WIBs) oversee the implementation of WIA. The WIBs 

include representatives of business, labor and government. By law, the majority of WIB members are business 

representatives, who are charged with ensuring that workforce development programs respond to the needs 

1
 
of local employers. In Washington, the statewide WIB is called the Workforce Training and Education 

Coordinating Board. The state has twelve local WIBs, which are called Workforce Development Councils 

(WDCs). Except in the most populated counties such as King County, which includes Seattle, each WDC 

covers several counties. 

Approximately 3,700 individuals exited WIA Title I-B Adult staff-assisted programs in Washington 

during the 2006 calendar year (Workforce Training and Education Coordinating Board, 2008). Thousands 

more adults each year receive WorkSource services that are either not funded by the Title I-B program or 

are not assisted by Title I-B staff members (but may be assisted by non-Title I-B staff). This group is called 

Labor Exchange, Employment Service, or Wagner-Peyser. Labor Exchange services include self-assisted 

services, facilitated self-assisted services, and staff-assisted services. Substantial overlap occurs between 

Labor Exchange services and WIA core and intensive services (Eberts & Holzer, 2004). One distinction is 

that state employees directly provide Labor Exchange services, while private companies or community-based 

organizations provide WIA services under contract at many One-Stop Career Centers. 

For purposes of this research, Labor Exchange clients are those who did not receive a WIA Adult 

service but did receive a service coded as Labor Exchange, Wagner-Peyser, or Claimant Placement Program.1 

Individuals who received only a self-service through Washington’s Go2WorkSource website are not part of 

either the treatment or comparison group for this analysis. In addition, WIA training services are not included 

in Labor Exchange programs here, as they are in federal reporting. Approximately half of WIA participants 

in Washington have received training services, according to data used in this analysis. 

Although WIA services are available to individuals of all income and education levels, participants are 

more disadvantaged than state residents as a whole. The median annual earnings of adult program participants 

who exited in 2005-06 and worked in the third quarter after the program was $20,3732 (Workforce Training 

and Education Coordinating Board, 2008). The statewide median earnings among workers in 2007 was 

$30,535 (U.S. Census Bureau, 2009). 

Given the relatively low earnings associated with participants’ initial job placements, wage and earnings 

progression among training recipients are of substantial concern to workforce investment practitioners, 

1

The Claimant Placement Program serves recipients of Unemployment Insurance in the state of Washington. 

2

For comparison, the poverty threshold for a family of three in 2007 was $17,170 (U.S. Department of Health and Human 

Services, 2007). 

2 
policy makers, and antipoverty scholars. This research measures earnings progression among workforce 

development participants by comparing earnings progression among individuals served by WIA Title I-B to 

earnings progression among individuals served by the Labor Exchange program. The chapter uses propensity 

score methods for purposes of equating observable characteristics across the two groups. 

The structure of the chapter is as follows. First, I review the existing literature on earnings progression 

among disadvantaged workers. Then I describe the specific research questions, data, and methods before 

presenting descriptive statistics, analytical results, and a discussion of their implications. 

2 Background

State officials in Washington are keenly interested in preparing workers for jobs that have demonstrated career 

ladders and lead to earnings increases over time. The premise is that publicly funded training, first, provides 

the foundation for connecting workers with jobs, and, second, enhances the ability of individuals to advance 

to positions with greater compensation as they add work experience and obtain additional, often privatelyprovided, training.3 Of course, individuals might not achieve earnings gains even with publicly funded job 

training, for reasons such as training ineffectiveness, labor competition, layoffs, weak labor markets, or 

insurmountable personal barriers. Other potential reasons include a lack of additional employer-provided 

training or a lack of returns to work experience. In these cases, we would expect to observe stagnant wages 

over time. 

Similar concerns arise in research on Temporary Assistance for Needy Families (TANF) and the economic 

safety net in general. A central debate among welfare researchers is whether low-income parents are better 

served by “work first” programs that promote immediate entry into the labor market or by education and 

training prior to job application (Brown, 1997). Advocates of education argue that workers can become stuck 

in “dead-end” jobs without it, while others suggest that simply being employed is a major advancement for 

disadvantaged individuals (Mead, 1993). Ultimately, assessment of whether immediate or deferred placement 

is more appropriate economically depends upon earnings levels and earnings growth among individuals who 

follow each strategy. 

3

This view is related to the larger theory of human capital, which suggests that investments in knowledge, skills, health and 

values enable individuals to receive financial returns in the labor market that would not have been available without such investments 

(Becker, 1993). 

3 
Previous research on wage and earnings progression can be categorized by the population under study: 

1) less skilled workers in general, 2) recipients of TANF or the Earned Income Tax Credit (EITC), and 3) 

participants in workforce development programs. Research on less skilled workers4 has generally found 

relatively modest rates of earnings growth that are similar to those of other workers in the population. Current 

Population Survey data indicate comparable rates of wage growth across educational groups, with a possible 

exception for young high school dropouts who have relatively lower and more volatile wage growth, especially 

during recessions (French et al., 2006). In general, wage growth varies over time in accordance with economic 

conditions, and tends to be highest during the early years of a worker’s career. 

Data from the National Longitudinal Survey of Youth (NLSY) 1979 panel suggests that wage growth 

among less skilled workers no longer attending school would be approximately 4 to 6 percent per year of 

full-time work, and that this rate is similar to that for medium-skilled workers (Gladden & Taber, 2000).5 

The finding that wage growth of high school dropouts in the NLSY as a function of work experience is 

approximately comparable to that of high school graduates suggests that neither the theory of “dead-end” 

jobs nor of rapid advancement with human capital is fully accurate (Gladden & Taber, 2000). 

Despite similar returns to experience, there still may be differences in observed earnings growth if high 

school dropouts work more sporadically than high school graduates. In the CPS data presented by French 

et al. (2006)6, observed growth is contingent on labor market conditions as well as educational attainment. 

While wage growth as a function of year and experience are both important to less skilled workers, the 

results from the NLSY—that wage growth in response to experience is approximately equal for high school 

graduates and dropouts—are more relevant for this study. 

Several experimental studies have tested the effects of special programs on wage growth. The Employment 

Retention and Advancement (ERA) project was implemented in 16 locations across the United States between 

1999 and 2009 to promote career advancement among low-wage workers. Evaluations by MDRC indicate that 

the program’s effectiveness varied by site. In Cleveland, the program was not well integrated into participants’ 

lives and had minimal effects (Miller et al., 2008). In Riverside County, California, the program appeared to 

4

Less skilled workers are usually defined as having an educational credential no greater than a high school diploma. 

5

Unless otherwise specified, all research on this topic uses the logarithm of wages as the dependent variable rather than direct 

wages, in order to correct for skewness in the wage distribution. 6

Growth is defined as the percentage increase in the hourly wage among individuals in the survey wave who were working both 

then and during the previous year’s outgoing rotation group. 

4 
contribute to increased earnings in the initial job as well as a greater probability of obtaining subsequent jobs 

that provided for earnings increases (Navarro et al., 2007). Overall, relatively little is known about effective 

strategies for promoting advancement among less skilled workers. 

TANF recipients, who are predominately female, generally fare much worse in the labor market than 

women in the population at large. Only 25 percent of women ages 26-27 who had previously enrolled in 

welfare had ever worked at least 35 hours per week in a job that paid at least $8 per hour, compared to 73 

percent of women who had not received welfare, according to data from the NLSY (Pavetti & Acs, 1997). 

Among former TANF recipients, data from the Survey of Income and Program Participation indicate that 

increases in the logarithm of median annual earnings were 3.8 percent during 1996-1997, 2.8 percent during 

2001-2002 and -1.4 percent during 2002-2003 (Chrisinger et al., 2008). 

The Earned Income Tax Credit attempts to encourage low-income individuals such as former TANF 

recipients to engage in paid work. While some observers have worried that the EITC subsidizes low-quality 

jobs, Dahl et al. (2009) find that earnings growth rates were as high or higher among single mothers who had 

received the EITC than among those who hadn’t. 

Research specifically on workforce development addresses the impacts of program participation on 

earnings and employment, both in the short term and long term. Some studies measure rates of earnings 

growth as well. A large, multi-state nonexperimental WIA impact study by Heinrich et al. (2008) finds that 

benefits to recipients of core or intensive services during 2003-2005 were $100-200 per quarter above what 

non-participants (UI recipients or Labor Exchange participants) experienced, and that training participants 

received up to $400 more per quarter than they otherwise would have. The researchers use propensity score 

matching methods to account for different characteristics among the treatment and comparison groups. 

Similarly, an analysis of Washington administrative data from 2001 through 2004 suggests that WIA Title 

I-B Adult programs added approximately $403 to participants’ long-term average quarterly earnings and 

were associated with a 6.6 percent increase in the long-term number of quarters employed, relative to a Labor 

Exchange comparison group (Hollenbeck & Huang, 2006). In this case, the researchers define long-term as 

9-12 quarters after exit. Regression-adjusted difference-in-difference methods account for self-selection in to 

the program. Overall, the impact of WIA on earnings and employment outcomes appears to be positive, but 

modest, in most states. 

5 
Researchers have also extensively evaluated the predecessor programs to WIA: the Job Training Partnership Act (JTPA) of 1984-1999, the Comprehensive Employment and Training Act (CETA) of 1974-1984, 

and the Manpower Development Training Act (MDTA) of 1962-1973. In general, the results suggest that 

training programs are associated with modest increases in earnings that are greater for women than men and 

that fade out over time (Holzer, 2009). A common interpretation is that “wage gains from these programs 

tend to be quite modest (although the costs are often also small)” (Gladden & Taber, 2000, p. 160). Indeed, 

federal spending on employment and training programs totals approximately 0.1 percent of GDP, and has 

decreased substantially since the late 1970s (Holzer, 2009). An additional challenge is that training funds 

are used to support a diverse set of activities, so that pinpointing successful elements through a national 

evaluation becomes difficult. Previous research has, in general, also not addressed adequately the rates of 

earnings growth on the job or through changing jobs in order to understand whether particular programs or 

career paths are more promising than others. 

In the most closely related previous study of earnings growth rates, Mueser & Stevens (2003) use 

data from six states (Florida, Georgia, Illinois, Maryland, Missouri and Texas) and calculate that quarterly 

earnings rose faster for adult WIA clients who received training than those receiving core or intensive services, 

especially in the period immediately surrounding the clients’ exit from the program. The earnings increase in 

the year after participation was 69 percent in the training group compared to 22 percent in the core group. In 

the subsequent year, the rates were equal. The follow-up period in the study was only two years. 

In summary, there is some evidence that federally funded training is associated with earnings growth in 

the short term. Research on wage growth and skills in general implies that long-term earnings gains, above 

those experienced by other groups, might be more difficult to achieve. Overall, however, there have been very 

few studies of long-term earnings progression among WIA participants. 

3 Research Questions and Hypotheses

The primary research question addressed in this analysis is: what is the difference in earnings progression 

between individuals who received any WIA Title I-B Adult service (core, intensive, and training) and those 

who received only Labor Exchange services? 

Underlying the central question are two hypotheses: 

6 
(H1) Achieving a Steeper Path: General Human Capital 

Generalized investments in knowledge, skills, and values, such as that provided in training course 

attendance, enable WIA enrollees to achieve higher earnings growth rates than non-enrollees with the 

same educational background. 

(H2) Achieving a Steeper Path: Specific Human Capital 

Specific components of WIA, such as personalized training and service recommendations, position 

participants to climb the earnings ladder within a firm or sector at a faster rate than their comparable 

counterparts who do not receive specialized services. 

4 Data

The data for this research come from two types of statewide administrative records maintained by the State 

of Washington. Data on WIA program participation and participant demographic information come from 

the Services, Knowledge and Information Exchange System (SKIES), which is the statewide workforce 

investment case management system that was first implemented in 2002. Data on earnings and employment 

come from Unemployment Insurance (UI) records. The Washington Employment Security Department 

matched these datasets on Social Security number (SSN) and provided the combined dataset with an 

alternative individual identification number in lieu of the SSN for protection of participants’ privacy. 

SKIES contains detailed information on the characteristics of job seekers and the services they receive. 

The participant characteristics are available for a single point in time and include gender, race, primary 

language spoken, zip code of residence, TANF recipiency status, schedule availability for work, date of birth, 

veteran status, Unemployment Insurance status (whether currently or formerly receiving UI), and highest 

degree completed. Employees of WorkSource Washington enter this information into SKIES at the point of 

contact with participants in One-Stop Career Centers. SKIES also contains records of the services provided 

to job seekers. SKIES includes the start and end dates of participation, and whether the participant completed 

the relevant course, program, or degree. The name of the service provider is included in many cases, but the 

details of training programs, such as the skill and industry focus of the training, are not available. 

7
 
Quarterly earnings, hours worked, and industry codes from the North American Industry Classification 

System (NAICS) come from the UI dataset. Employers that are covered by the UI system must report 

quarterly wages of all employees for purposes of administration of the UI program. Coverage includes most 

state and local government employees, employees of private firms employing any workers for a minimum of 

20 weeks per calendar year, and employees of agricultural firms that pay at least $20,000 cash annually to 

agricultural laborers (Bureau of Labor Statistics, 2008). UI does not cover domestic workers, self-employed 

farmers, railroad workers, and self-employed nonagricultural workers. 

This research uses SKIES and UI data from 2001 through the first quarter of 2009. The data include 

earnings for the entire study period to allow comparison of earnings before, during and after program 

participation. Table 1 shows the dates of coverage. The total number of individuals in the analysis is 39,981. 

Of these, 13,261 received a WIA Adult service. 

Table 1: Coverage Dates in the Analysis 

Data Type Dates Included
 

Program Records January 1, 2002 - June 30, 2008 

Earnings Records January 1, 2001 - March 31, 2009 

Individuals may begin service at any point within the dataset. For example, someone might have his or 

her first contact with the workforce development system in 2005 and would only be in program records after 

that date. Once an individual ever receives a WIA Adult service, that individual is always in the treatment 

group for all future quarters, whether or not a Labor Exchange service follows a WIA service. The data 

include complete earnings records for all individuals from 2001 forward. 

The SKIES/UI administrative dataset is a unique resource that provides rich and up-to-date information on 

labor market experiences and workforce development program participation for the entire state of Washington. 

Compared to data from surveys, administrative data have the advantage of complete coverage of participating 

individuals, some of whom might be difficult to reach for a survey for reasons such as lacking a stable 

residence or a telephone. Administrative data are also better sources of precise dates of program participation, 

whereas survey respondents may have difficulty recalling exact dates and wage information over time. In 

addition, the processing time required for data from surveys, sometimes even if administered on the Internet, 

implies that very recent data often are not available. 

8 
Nevertheless, administrative data appear to undercount certain types of earnings relative to survey data. 

Specifically, individuals who have earned tips, who work for UI-exempt employers, who work outside of 

their state of residence, or whose employer did not report earnings correctly, often have lower UI-reported 

than survey-reported earnings (Wallace & Haveman, 2007). Another disadvantage of administrative data 

is that they were originally collected for a purpose other than research. Data fields that are important 

to researchers might be less important to program administrators and, as a consequence, contain a high 

proportion of ambiguities, errors, or missing values. As an example, the hours field in the UI data does not 

allow researchers to distinguish between individuals who work part time for an entire quarter and those who 

work full time for only a month. 

Despite these limitations, the SKIES/UI dataset provides a superior resource over survey data for the 

purposes of addressing the proposed research question. The data allow for a complete enumeration of 

all program participants in the state and a comprehensive reporting of quarterly wages from all covered 

employment. Other researchers have successfully used workforce administrative data after engaging in data 

cleaning and the creation of standard definitions to bring consistency to their analysis (Heinrich et al., 2008; 

Hollenbeck & Huang, 2006). 

The results of this research are not strictly generalizable to other states in the United States. In Washington, 

WIA Adult participants had 12.4 mean years of education and were on average 36.7 years old at registration 

(Hollenbeck & Huang, 2006). Approximately 57 percent were female. In the twelve states analyzed by 

Heinrich et al. (2008), the mean years of education were 12.3 and the average age was 32.7. Approximately 

58 percent of participants were female. Nearly 45 percent of the sampled individuals in the multi-state 

study were black and 3 percent were Hispanic, while only 31.7 percent of the Washington participants were 

members of a minority group.7 Average quarterly earnings in the year prior to participation (in 2008 dollars) 

were $2,970 in Washington, compared to only $2,162 in the multi-state study. In addition to these differences 

in participant composition, industry types differ in Washington from other states, as do the specific training 

programs and support services provided by the workforce development agencies. For example, Washington 

has a higher concentration of employment in the aerospace and aluminum industries than the U.S. as a whole 

(Sommers, 2001). 

7

SKIES does not include Hispanic as a separate racial category, and an ethnicity variable was not part of the dataset. 

9 
5 Research Design and Methods

The goal of this research is to measure the association of participant exposure to federally funded workforce 

development programs with earnings progression. The estimate of interest is the effect of the workforce 

program treatment on the treated individuals, although I do not assume causal relationships. The research 

uses a nonexperimental, ex post evaluation design. Most prior evaluations of workforce development have 

used similar approaches (Hollenbeck & Huang, 2006; Heinrich et al., 2008; Sianesi, 2004). 

An alternative to the current nonexperimental design is an experimental evaluation. The advantage of an 

experimental design is that researchers randomly assign individuals either to participate or not participate 

in the program. This randomization ensures that, given a sufficient sample size, the treatment and control 

groups have a similar composition, and that estimates of the program’s effects are not subject to selection 

bias. With a nonrandomized evaluation, the primary concern is that people who participate are different 

from those who do not. In that case, the observed effects could be due to differences in treatment and 

comparison group composition rather than the program itself. Offsetting these advantages is that randomized 

experiments are costly and time-consuming to conduct. Randomized trials also are subject to concerns 

about applicability of findings outside of the experimental setting, for reasons such as randomization bias 

and contamination. Econometric methods can sometimes approximate the results of an experiment, but 

nonexperimental estimates generally still suffer from some degree of selection bias (Pirog et al., 2009). If 

an imperfect estimate of program impact is still desirable to policy makers, as it often is, one of the main 

research tasks is to minimize selection bias. 

Bearing in mind concerns about selection, I use several models to estimate the relationship of interest. 

The outcome variable, Eit, is the quarterly earnings of individual i in quarter t.

8 I include earnings from all 

UI-covered jobs worked in Washington during the quarter. All dollar amounts are expressed in 2008 prices. 

To achieve comparability with U.S. Department of Labor performance measures and Hollenbeck & 

Huang (2006), I define individuals as members of the treatment group if they last received services from a 

selected program during the relevant fiscal year. Consistent with DOL protocol, the exit date is measured as 

8

This response variable was chosen rather than the earnings growth rate because, with the inclusion of quarters in which earnings 

are zero, the value of the earnings growth rate can range from -1 to nearly infinity. Due to the greater number of quarters in which the 

WIA Adult population is unemployed prior to participation, their earnings growth rate after program exit is higher for the treatment 

than the comparison group virtually by construction. 

10 
90 days after the end of the most recent service. Several other studies instead use the program entry date 

because of concerns that the program exit event is not independent of program outcomes, and that any low 

earnings during program participation are also relevant to consider. These concerns are valid. I chose the exit 

rather than entry date as the identifying point because of my interest in post-program earnings progression, 

which would be representative of a long-term outcome in the absence of a continuing intervention. In the 

analysis, I drop earnings observations in the exit quarter and two quarters on either side of the exit date, in 

part to address the “Ashenfelter dip”9 and in part to minimize the influence of short-term differences between 

the treatment and comparison groups associated with nonrandom exit dates. 

The models estimate quarterly earnings as a function of several covariates. The variable Qt is the time 

difference between the current quarter and the quarter of program exit. It ranges from -29 to 29. For example, 

the count would be 2 in the third quarter of 2006 for someone who exited in the first quarter of 2006. It would 

be -29 during the first quarter of 2001 if an individual exited during the second quarter of 2008. 

The variable PostWIAit takes the value of 1 in quarters after WIA Adult program exit. It is 0 otherwise, 

and always 0 for individuals who have never participated in WIA Adult programs. A similar variable exists 

for the comparison group. PostLEit is 1 after Labor Exchange exit and 0 otherwise. If a Labor Exchange 

participant subsequently exits from WIA Adult, that person is counted only as a WIA Adult participant 

in order to maintain mutually exclusive treatment and comparison groups, under the assumption that WIA 

provides additional and more intensive services than Labor Exchange. The quarterly county non-seasonally 

adjusted unemployment rate is included as Uit to account for macroeconomic influences. 

The primary model is a series of individual-level ordinary least squares (OLS) regressions whose 

coefficients are later weighted and averaged. The model takes the following form for each person. 

Eit = β0i + β1iQt + β2iPostWIAit + β3iPostWIAitQt + β4iUit + β5iPostWIAitUit + �it (1) 

Similarly for Labor Exchange participants, the individual specification is as follows. 

9

A concern throughout research on training programs is that earnings usually are lower in the years immediately before and 

during program participation relative to earnings in the years on either side of program participation (Ashenfelter, 1978). 

11 
Eit = γ0i + γ1iQt + γ2iPostLEit + γ3iPostLEitQt + γ4iUit + γ5iPostLEitUit + �it (2) 

The coefficient of most interest is on the interaction of post-participation with time. This enables a 

comparison of post-participation earnings slopes. The coefficient on Qt captures the overall trend in earnings 

prior to program participation. The coefficient on the post-participation dummy allows for a one-time shift in 

earnings coincident with participation. Controlling for county unemployment permits a measurement of the 

reduction in earnings that might be associated with working in a high-unemployment county. The interaction 

of the unemployment rate and participation indicates the additional association with earnings of the county 

unemployment rate after participation. If positive, the coefficient on this interaction could suggest that WIA 

participants might not be as strongly harmed by high unemployment as non-participants, or that training 

might be more effective during recessions (Lechner & Wunsch, 2009). 

I use individual-level regression because it allows all of the coefficients to vary by individual. The analysis 

estimates regressions separately for each individual, and then averages coefficients across individuals for each 

variable. I average the β3i and compare them to the averaged γ3i. A function of each individual’s propensity 

score, which will be described below, serves as a weight to account for selection. 

An individual approach accounts for heterogeneity across people who receive workforce services and 

may differ from the results of a pooled regression. The individual approach does not permit inclusion of 

time-invariant personal characteristics, so the constant and other coefficients in the model subsume these 

effects. 

For comparison, I also present a pooled model. In this approach, clustered standard errors account for 

the repeated observations by person. The vector Zit includes demographic controls for age, gender, race, 

educational attainment, single parenthood, veteran status, geographic location (Workforce Development 

Council region), unemployment insurance receipt at baseline, primary language, and disability status. The 

mutually exclusive categories for educational attainment are less than high school, high school diploma or 

GED, some college, associate’s degree, bachelor’s degree, or graduate degree. The specification follows. 

12
 
�

Eit =β0 + β1Qt + β2PostWIAit + γ2PostLEit + β3PostWIAitQt + γ3PostLEitQt+

β4Uit + β5PostWIAitUit + γ5PostLEitUit + β6Zit + it (3) 

Again, none of the estimates include the five quarters immediately surrounding program exit. 

5.1 Addressing Selection Bias

The primary approach this study takes to reduce selection bias is propensity score weighting. In general, 

propensity score methods first use a logit or probit regression to estimate the probability of program participation on the basis of observed characteristics for each individual in the sample, whether he or she participated 

or not. Researchers then use this conditional probability, or propensity score, in a variety of ways to reduce 

bias due to observed covariates (Rosenbaum & Rubin, 1983). The propensity score is denoted p(Xi) for 

individual i with observed characteristics Xi. If D is a binary indicator of membership in the treatment group, 

p(Xi) = P r(Di = 1 | Xi). 

In propensity score weighting, the propensity score weights take the value of 1 if the individual is in 

the treatment group and p(Xi)/(1 − p(Xi)) if the individual is in the comparison group, in order to form a 

comparison group that is observationally similar to the treatment group. These weights permit computation of 

the average treatment effect on the treated (ATT), rather than the overall average treatment effect (ATE) that 

might be observed were any individual from the population at large to participate in the programs (Gelman & 

Hill, 2007). The weights account for differential probability of treatment by weighting individuals from the 

comparison group who have similar observable characteristics as treatment group members more heavily 

than comparison group members who are dissimilar. For more details on these weights, also known as the 

Horvitz-Thompson estimator, see, for example, Rosenbaum (1987), Imbens (2000), Frolich (2004), and Guo 

& Fraser (2010). 

An important component of generating propensity scores is to perform balancing tests. These tests 

verify that characteristics of treatment and comparison group members are similar for each region of the 

propensity score distribution, and that program group membership is independent of observed characteristics, 

13
 
so that D ⊥ X | p(X). If conditional distributions of characteristics are approximately equal, the propensity 

score balances the covariates in expectation (Smith & Zhang, 2009). Although Smith & Zhang (2009) 

recommend the regression test, they note that it is difficult to satisfy. Less stringent tests, used here, require 

only approximately equal means across the program groups (Dehejia & Wahba, 1999). These tests include a 

univariate t test for differences in means, a multivariate analog, the Hotelling T-squared test, and a standardized 

differences test. The standardized differences test computes the following statistic, where xk,D=1 is the mean 

of the variable xk for the treated group and xk,D=0 is the mean for the weighted comparison group. 

100(xk,D=1 − xk,D=0) SD = (4) (var(xk,D=1) + var(xk,D=0))/2

The value of SD should be less than 20 (Rosenbaum & Rubin, 1985). For the t tests, the t statistic should 

be less than 1.96. If none of these were to hold, the propensity score prediction model would have to be 

adjusted to include additional terms (Becker & Ichino, 2002), but that is not the case here. 

The assumptions of propensity score approaches include the following: program selection is only on 

the basis of observable characteristics (called ignorability), unobservables do not determine selection or the 

program outcome, there is some positive probability of participation for all values of the predictors, and 

the treatment does not influence the outcome for people who didn’t receive it (Rubin, 1978; Cameron & 

Trivedi, 2005). Further, propensity score methods work best when treated and control group members are 

in geographic proximity to each other, when the propensity score is based on a large number of predictors, 

and when program definitions and data quality are similar for the treated and control groups (Heckman et al., 

1997). Many of these assumptions appear to hold in this study, with the possible exception of the influence of 

unobservable characteristics. 

Propensity scores for each group are shown in the results section. In addition, I show demographic 

characteristics with and without adjustment for propensity scores. The propensity scores account for 

differences in observable characteristics in the propensity function of treatment and comparison groups. 

14
 
6 Results

The total number of individuals in the analysis is 39,981, including 13,261 WIA Adult participants and 

26,720 Labor Exchange participants. Table 2 provides details of the time period in which individuals in the 

dataset first joined the treatment group. Since the data cover 33 quarters (January 2001 to March 2009), the 

analysis includes up to 1,319,373 person-quarters. To focus on the traditional working-age population, the 

results include only individuals who are between the ages of 18 and 64 during the entire time period. The 

sample size for analysis shrinks to 5,677 WIA participants and 12,360 Labor Exchange participants after 

missing data are considered. One reason is that historical information, such as earnings in the 10th quarter 

prior to exit, which I use to balance the treatment and comparison groups, is not available for all individuals, 

since it excludes those who exited early in the analysis period. I also rely on industry of employment for 

balancing, excluding individuals who were never employed during the study period. As a result, this sample 

is potentially more advantaged than the entire workforce development participant population. 

Table 2: Participant Counts by Cohort of First Exit in Data 

Cohort WIA Adult Labor Ex. Total 

Jan. 2002 - June 2002 525 2,065 2,590 

July 2002 - June 2003 2,242 6,876 9,118 

July 2003 - June 2004 2,439 5,450 7,889 

July 2004 - June 2005 2,151 3,982 6,133 

July 2005 - June 2006 2,166 3,358 5,524 

July 2006 - June 2007 1,938 2,591 4,529 

July 2007 - June 2008 1,800 2,398 4,198 

Total 13,261 26,720 39,981 

Table 3 shows characteristics of the treatment and comparison groups prior to adjustment for the 

probability of participation. Almost all averages were significantly different across the treatment and 

comparison groups. The individuals who received a WIA Adult service had average quarterly earnings 

during the 10th quarter prior to exit of $5,643 in 2008 dollars, compared to $10,169 for Labor Exchange. 

These amounts include individuals with no covered work during that quarter. On average, individuals who 

participated in WIA Adult were employed (had earnings of at least $100) during approximately 9.9 of the 

quarters in the data prior to their first exit, while Labor Exchange participants were employed during 10.8 

of the quarters in the data prior to their first exit. WIA participants were younger than Labor Exchange 

15
 
Table 3: Demographic Composition of Sample, Unweighted
 

WIA Adult Labor Ex. 

Mean/N 1 Std. Err. Mean/N 2 Std. Err. 

Quarters Employed Prior to Exit 9.9∗∗∗ (0.1) 10.8∗∗∗ (0.1) 

Earnings in 10th Quarter Before Exit 5643.5∗∗∗ (74.0) 10169.1∗∗∗ (69.4) 

Earnings in 7th Quarter Before Exit 6231.8∗∗∗ (72.1) 10763.8∗∗∗ (63.8) 

Earnings in 5th Quarter Before Exit 5276.7∗∗∗ (83.2) 10027.5∗∗∗ (67.1) 

Age 36.1∗∗∗ (0.1) 39.5∗∗∗ (0.1) 

County Unemp. Rate in 2nd Qtr. Before Exit 6.0∗∗∗ (0.0) 6.2∗∗∗ (0.0) 

Employed in 6th Qtr Before Exit 87.4∗∗∗ (0.4) 94.1∗∗∗ (0.2) 

Less than HS 10.3∗∗∗ (0.4) 5.6∗∗∗ (0.2) 

HS or GED 40.2 (0.7) 39.2 (0.4) 

Some College 28.2 (0.6) 27.5 (0.4) 

Associate’s Degree 9.4∗∗ (0.4) 10.8∗∗ (0.3) 

Bachelor’s Degree 9.2∗∗∗ (0.4) 13.5∗∗∗ (0.3) 

Graduate Degree 2.7∗ (0.2) 3.4∗ (0.2) 

Nonwhite 27.8∗∗∗ (0.6) 22.8∗∗∗ (0.4) 

Female 58.4∗∗∗ (0.7) 46.5∗∗∗ (0.4) 

Veteran 29.8∗∗∗ (0.6) 25.6∗∗∗ (0.4) 

UI Claimant 23.5∗∗∗ (0.6) 30.8∗∗∗ (0.4) 

Single Parent 25.1∗∗∗ (0.6) 11.4∗∗∗ (0.3) 

Primary Language Not English 8.4∗∗∗ (0.4) 4.6∗∗∗ (0.2) 

Has a Disability 3.5∗∗ (0.2) 2.6∗∗ (0.1) 

Accommodation and Food Services 8.5∗∗∗ (0.4) 2.8∗∗∗ (0.1) 

Admin. and Support and Waste Management 11.9∗∗∗ (0.4) 7.3∗∗∗ (0.2) 

Agriculture, Forestry, Fishing and Hunt 2.7∗ (0.2) 2.1∗ (0.1) 

Arts, Entertainment, and Recreation 1.9∗∗∗ (0.2) 1.0∗∗∗ (0.1) 

Construction 4.0 (0.3) 3.5 (0.2) 

Educational Services 3.3∗∗ (0.2) 2.5∗∗ (0.1) 

Finance and Insurance 3.2∗∗∗ (0.2) 5.4∗∗∗ (0.2) 

Health Care and Social Assistance 16.3∗∗∗ (0.5) 6.7∗∗∗ (0.2) 

Information 1.9∗∗∗ (0.2) 4.1∗∗∗ (0.2) 

Management of Companies and Enterprises 0.1 (0.0) 0.2 (0.0) 

Manufacturing 13.9∗∗∗ (0.5) 35.2∗∗∗ (0.4) 

Mining 0.1∗∗∗ (0.0) 0.9∗∗∗ (0.1) 

Other Services (except Public Admin.) 5.4∗∗∗ (0.3) 3.1∗∗∗ (0.2) 

Professional, Scientific, and Technical 3.6∗∗∗ (0.2) 5.0∗∗∗ (0.2) 

Public Administration 2.8 (0.2) 2.4 (0.1) 

Real Estate and Rental and Leasing 2.0∗∗ (0.2) 1.4∗∗ (0.1) 

Retail Trade 12.0∗∗∗ (0.4) 7.9∗∗∗ (0.2) 

Transportation and Warehousing 3.0∗∗∗ (0.2) 4.2∗∗∗ (0.2) 

Utilities 0.5 (0.1) 0.4 (0.1) 

Wholesale Trade 3.0∗∗ (0.2) 3.9∗∗ (0.2) 

5677 12360 

Numbers are percentages except for quarters employed, earnings, and age 

Asterisks indicate significant differences from other group using a two-tailed t test 

* p < 0.05, ** p < 0.01, *** p < 0.001

16 
participants and more likely to be nonwhite, female, veterans, single parents, disabled, or not to speak English 

as a primary language, compared to Labor Exchange clients. Educational attainment was less for WIA than 

for the Labor Exchange group, as measured by the percentage of the group members who had earned a 

college degree. 

The industry in which individuals worked during the 7th quarter prior to exit, if employed, varied 

substantially across the groups. Compared to Labor Exchange, WIA participants were more frequently 

working in accommodation and food services, administration, arts, education, health care and social assistance, 

retail, and other services. They were less frequently represented in finance, information, manufacturing, 

professional occupations, transportation, and wholesale trade. 

Figure 1: Estimated Probabilities of Participation (Propensity Scores) 

17
 
Table 5 shows the factors that were used to predict participation in the WIA Adult program. These 

include age, gender, race, number of hours worked during the in seventh quarter prior to exit, educational 

attainment, earnings in the fifth and tenth quarters prior to program exit, single parent status, Unemployment 

Insurance status at first program entry, the number of total quarters employed prior to program exit, primary 

language, geographic location (Workforce Development Council area), veteran status, disability status, the 

county-level unemployment rate, and industry of employment in the 7th quarter prior to exit. Based on the 

logistic regression, veterans, people with disabilities, single parents, and people of minority ethnic groups 

were more likely to enroll in the WIA Adult program, while UI claimants were less likely. 

Figure 1 shows predicted probabilities of participating in the two programs. As expected, the chart 

shows that propensity scores were lower for Labor Exchange clients than for WIA Adult clients. The mean 

propensity score was 0.45 among WIA Adult participants and 0.24 among Labor Exchange participants. A 

small number of Labor Exchange participants had a high probability of participating in WIA but did not do 

so. The remaining analysis excludes individuals with the highest and lowest five percent of propensity scores 

to maximize overlap between the groups. 

As described previously, I used propensity score weights to adjust for compositional differences. Table 4 

shows the demographic comparison of groups after this adjustment. WIA and Labor Exchange are statistically 

similar on all variables. The specification passes all three balancing tests previously described, with scores 

well below 20 for the standardized differences test. Thus, the resulting adjusted sample contains very similar 

groups that are suitable for comparison. 

For reference, the quarterly state and national unemployment rates (not seasonally adjusted) during this 

time are shown in Figure 2. County-level unemployment varied substantially from the statewide average, 

with some counties experiencing official unemployment rates as high as 16 percent during the study period, 

and even higher rates when marginally attached workers are considered. 

Average quarterly earnings as a function of the number of quarters from the time of program exit are 

shown in Figure 3. The quarter count is not aligned with calendar time because the first quarter after 

participation occurred at different times for different individuals. Without any adjustments for economic 

conditions or participant characteristics, earnings are higher among the Labor Exchange group than among 

18
 
Table 4: Demographic Composition of Sample, Propensity Score Weighted
 

WIA Adult Labor Ex. 

Mean Std. Err. Mean Std. Err. SD Test 

Quarters Employed Prior to Exit 10.1 (0.1) 9.9 (0.1) 2.6 

Earnings in 10th Quarter Before Exit 6139.1 (80.6) 6181.1 (54.6) -0.7 

Earnings in 7th Quarter Before Exit 6700.2 (76.4) 6826.9 (53.2) -2.3 

Earnings in 5th Quarter Before Exit 5741.3 (89.5) 5868.0 (58.2) -2.0 

Hours Worked in 7th Quarter Before Exit 380.2 (2.9) 380.6 (1.9) -0.2 

Age in 2001 37.0 (0.2) 37.1 (0.1) -1.0 

County Unemp. Rate in 2nd Qtr. Before Exit 5.99 (0.0) 6.0 (0.0) 1.2 

Employed in 6th Quarter Before Exit 89.0 (0.4) 89.2 (0.3) -0.9 

Less than HS 8.8 (0.4) 8.8 (0.3) 0.2 

HS or GED 39.6 (0.7) 39.5 (0.5) 0.2 

Some College 28.8 (0.6) 28.9 (0.4) -0.4 

Associate’s Degree 9.8 (0.4) 9.4 (0.3) 1.2 

Bachelor’s Degree 10.1 (0.4) 10.2 (0.3) -0.3 

Graduate Degree 2.9 (0.2) 3.1 (0.2) -1.4 

Nonwhite 27.0 (0.6) 26.2 (0.4) 1.7 

Female 56.2 (0.7) 55.5 (0.5) 1.6 

Veteran 28.4 (0.6) 27.0 (0.4) 3.1 

UI Claimant 24.9 (0.6) 26.1 (0.4) -2.6 

Single Parent 20.9 (0.6) 21.2 (0.4) -0.8 

Primary Language not English 7.9 (0.4) 8.3 (0.3) -1.5 

Has a Disability 3.5 (0.3) 3.4 (0.2) 0.5 

Accommodation and Food Services 7.0 (0.4) 7.2 (0.2) -0.9 

Admin and Support and Waste Mgmt. 11.9 (0.5) 11.6 (0.3) 1.1 

Ag., Forestry, Fishing, and Hunting 2.7 (0.2) 3.0 (0.2) -2.0 

Arts, Entertainment, and Recreation 1.8 (0.2) 1.7 (0.1) 0.6 

Construction 4.2 (0.3) 4.4 (0.2) -1.1 

Educational Services 3.4 (0.3) 3.1 (0.2) 1.7 

Finance and Insurance 3.6 (0.3) 3.5 (0.2) 0.7 

Health Care and Social Assistance 14.3 (0.5) 13.6 (0.3) 1.9 

Information 2.1 (0.2) 2.2 (0.1) -0.6 

Mgmt. of Companies and Enterprises 0.1 (0.0) 0.1 (0.0) -0.6 

Manufacturing 15.7 (0.5) 15.7 (0.3) 0.2 

Mining 0.1 (0.0) 0.1 (0.0) 0.2 

Other Services (except Public Admin.) 5.2 (0.3) 5.1 (0.2) 0.1 

Professional, Scientific, and Technical 3.9 (0.3) 4.3 (0.2) -2.1 

Public Administration 3.0 (0.2) 3.1 (0.2) -0.6 

Real Estate and Rental and Leasing 2.1 (0.2) 2.1 (0.1) 0.2 

Retail Trade 11.8 (0.5) 11.9 (0.3) 0.0 

Transportation and Warehousing 3.4 (0.3) 3.4 (0.2) -0.2 

Utilities 0.4 (0.1) 0.3 (0.1) 0.8 

Wholesale Trade 3.3 (0.3) 3.5 (0.2) -1.4 

Observations 4903 11377 

Numbers are percentages except for quarters employed, earnings and age 

Hotelling’s T-squared=53.72, F=1.07 (p=0.34) 

No values diff. from LE at p < 0.05

19 
Figure 2: Quarterly State & National Unemployment Rate, 2001-2009Q1 (Bureau of Labor Statistics, 2009)
 

Figure 3: Average Earnings for Treatment and Comparison Groups
 

20
 
Figure 4: Average Earnings Residual Controlling for County Unemployment Rate
 

Figure 5: Residual Earnings in Treatment and Comparison, Propensity Score Weighted
 

21
 
Table 5: Propensity Score Prediction Logit Model
 

Odds Ratio Std. Error 

County Unemp. Rate in 2nd Qtr. Before Exit 0.856*** (0.00) 

Age in 2001 0.960*** (0.00) 

Age in 2001, Squared 1.000*** (0.00) 

Female 0.974 (0.03) 

Nonwhite 1.254*** (0.04) 

Hours Worked in 7th Quarter Before Exit 1.001*** (0.00) 

Hours Worked in 7th Qtr. Before Exit, Squared 1.000 (0.00) 

Less than HS 1.408** (0.17) 

HS or GED 1.112 (0.12) 

Some College 0.971 (0.11) 

Associate’s Degree 0.874 (0.10) 

Bachelor’s Degree 0.557*** (0.07) 

Earnings in 5th Quarter Before Exit 1.000*** (0.00) 

Earnings in 5th Qtr. Before Exit, Squared 1.000*** (0.00) 

Earnings in 5th Qtr. Before Exit, Cubed 1.000*** (0.00) 

Earnings in 7th Quarter Before Exit 1.000*** (0.00) 

Earnings in 7th Qtr. Before Exit, Squared 1.000*** (0.00) 

Earnings in 7th Qtr. Before Exit, Cubed 1.000*** (0.00) 

Earnings in 10th Quarter Before Exit 1.000*** (0.00) 

Earnings in 10th Qtr. Before Exit, Squared 1.000*** (0.00) 

Employed in 6th Quarter Before Exit 1.053*** (0.01) 

Single Parent 1.859*** (0.02) 

Earnings in 10th Qtr. Before Exit, Cubed 1.000*** (0.00) 

UI Claimant 0.708*** (0.01) 

Quarters Employed Prior to Exit 0.973*** (0.00) 

Quarters Employed Before Exit, Squared 1.001*** (0.00) 

Primary Language not English 1.503*** (0.02) 

wdc==Benton-Franklin 3.546*** (0.07) 

wdc==Eastern Washington Partnership 0.406*** (0.01) 

wdc==North Central 9.674*** (0.24) 

wdc==Northwest 0.826*** (0.02) 

wdc==Olympic 2.137*** (0.04) 

wdc==Pacific Mountain 1.767*** (0.03) 

wdc==Seattle-King County 1.355*** (0.02) 

wdc==Snohomish 0.964 (0.02) 

wdc==South Central 0.552*** (0.01) 

wdc==Southwest Washington 3.023*** (0.06) 

wdc==Spokane 1.978*** (0.04) 

Veteran 1.427*** (0.01) 

Has a Disability 1.398*** (0.03) 

Age in 2001*Female 1 (0.00) 

Continued on Next Page. . .
 

22
 
Table 5 (Continued)
 

Odds Ratio Std. Error 

Age in 2001*Nonwhite 0.997*** (0.00) 

Age in 2001*Less Than HS 0.988*** (0.00) 

Age in 2001*HS/GED 0.989*** (0.00) 

Age in 2001*Some College 0.995* (0.00) 

Age in 2001*Associate’s 0.994* (0.00) 

Age in 2001*Bach 1.007** (0.00) 

Age in 2001*Earnings in 5th Qtr. Before Exit 1.000*** (0.00) 

Age in 2001*Earnings in 7th Qtr. Before Exit 1.000*** (0.00) 

Age in 2001*Earnings in 7th Qtr. Before Exit, Squared 1.000*** (0.00) 

Age in 2001*Earnings in 10th Qtr. Before Exit 1.000* (0.00) 

Veteran*Has a Disability 0.967 (0.04) 

industry7==Accommodation and Food Services 1.583*** (0.09) 

industry7==Admin. and Support and Waste Management 1.190** (0.06) 

industry7==Agriculture, Forestry, Fishing and Hunting 0.733*** (0.04) 

industry7==Arts, Entertainment, and Recreation 1.226*** (0.07) 

industry7==Construction 1.111 (0.06) 

industry7==Educational Services 1.05 (0.06) 

industry7==Finance and Insurance 0.929 (0.05) 

industry7==Health Care and Social Assistance 1.970*** (0.11) 

industry7==Information 0.855** (0.05) 

industry7==Management of Companies and Enterprises 1.054 (0.11) 

industry7==Manufacturing 0.587*** (0.03) 

industry7==Mining 0.492*** (0.04) 

industry7==Other Services (except Public Administration) 1.386*** (0.08) 

industry7==Professional, Scientific, and Technical Services 0.974 (0.05) 

industry7==Public Administration 1.163** (0.07) 

industry7==Real Estate and Rental and Leasing 1.263*** (0.07) 

industry7==Retail Trade 1.123* (0.06) 

industry7==Transportation and Warehousing 0.810*** (0.05) 

industry7==Wholesale Trade 0.962 0.053 

Observations 595221 

χ2(71) 169205.1 

Exponentiated coefficients 

Reference categories: Graduate degree, Tacoma-Pierce WDC, utilities industry 

Asterisks indicate significant predictor; * p < 0.05, ** p < 0.01, *** p < 0.001

the WIA Adult group. For the Labor Exchange group, earnings are substantially lower near the quarter of 

program exit than during other quarters. 

23
 
To take into account the potential association of macroeconomic factors with quarterly earnings, Figure 

4 shows residual earnings after an OLS regression of the county-level quarterly unemployment rate on 

individual earnings. This adjustment removes the downturn in earnings that had been noticeable at the end of 

the observation period for individuals who had exited the program approximately 26 quarters previously. 

An “Ashenfelter dip” is still present, particularly among the Labor Exchange group, even after controlling for the unemployment rate. Earnings declines are often the reason that individuals seek workforce 

development services in the first place. 

Figure 5 shows residual earnings after weighting by p(Xi)/(1 − p(Xi)). Virtually all of the pre-program 

difference in earnings has been addressed by the propensity score procedure. Earnings for WIA participants 

begin to increase prior to exit because of their receipt of additional services after obtaining employment, 

unlike for most Labor Exchange participants. 

The remaining tables show regression results from a variety of specifications. As a baseline, Table 6 is an 

unweighted OLS specification that does not account for selection. These unweighted results suggest that WIA 

participants experienced lower earnings growth following participation than Labor Exchange participants. 

In addition, WIA participants experienced a one-time decrease in earnings around the time of program exit. 

Examining Figure 5 reveals that this decrease should be understood as the shortfall in post-program earnings 

relative to pre-program averages. Program participants experienced substantial gains relative to the lowest 

level of earnings in the pre-exit dip, but on the whole these gains were insufficient to reach the same earnings 

levels as prior to any contact with the program. 

Table 7 shows the results from the pooled OLS specification, weighted to account for selection. In the 

weighted results, the primary coefficient of interest is -$17.48 for WIA Adult and $15.22 for Labor Exchange. 

These coefficients are not statistically significantly different from zero or each other, revealing that earnings 

growth after program exit was approximately equal for the two groups. The other coefficients indicate that 

earnings were increasing slightly before individuals received services, by about $49.02 per quarter. The sum 

shows that, after exit, the average quarterly increase in earnings was $31.54 for WIA participants ($49.02 ­

$17.48) and $64.24 for Labor Exchange participants ($49.02 + $15.22). 

The other coefficients in this specification show that the county unemployment rate was negatively 

associated with earnings prior to WIA participation and that individuals with greater levels of educational 

24
 
Table 6: Pooled OLS Results, Unweighted Baseline (Unweighted Equation (3))
 

(1) 

Quarterly earnings in 2008 dollars 

Quarters after program exit 79.27∗∗∗ (5.180) 

WIA exit indicator -5022.8∗∗∗ (223.2) 

Labor Exchange exit indicator -3429.8∗∗∗ (192.2) 

WIA Adult*Quarters after program exit -49.77∗∗∗ (12.90) 

Labor Exchange*Quarters after program exit 56.18∗∗∗ (10.72) 

County unemployment rate -277.9∗∗∗ (22.09) 

WIA Adult*County unemployment rate 72.65∗ (34.93) 

Labor Exchange*County unemployment rate -223.6∗∗∗ (31.08) 

Age in years 87.12∗∗∗ (3.629) 

Female -1973.1∗∗∗ (81.70) 

Nonwhite -98.10 (89.37) 

HS or GED 1132.5∗∗∗ (125.9) 

Some College 1629.1∗∗∗ (136.7) 

Associate’s Degree 1569.6∗∗∗ (161.0) 

Bachelor’s Degree 3265.3∗∗∗ (184.5) 

Graduate Degree 5163.0∗∗∗ (388.4) 

Single Parent -314.1∗∗ (98.18) 

UI Claimant 191.0∗ (81.87) 

Primary Language not English -1455.5∗∗∗ (129.6) 

Veteran 62.31 (92.13) 

Has a Disability -1355.0∗∗∗ (214.1) 

Constant 7197.4∗∗∗ (255.3) 

Observations 360183 

Clustered standard errors in parentheses 

∗ ∗∗ ∗∗∗ p < 0.05, p < 0.01, p < 0.001

25
 
Table 7: Pooled OLS Results, Propensity Score Weighted (Weighted Equation (3))
 

(1) 

Quarterly earnings in 2008 dollars 

Quarters after program exit 49.02∗∗∗ (5.85) 

WIA exit indicator -1877.8∗∗∗ (231.9) 

Labor Exchange exit indicator -1678.7∗∗∗ (227.6) 

WIA Adult*Quarters after program exit -17.48 (12.94) 

Labor Exchange*Quarters after program exit 15.22 (12.04) 

County unemployment rate -153.8∗∗∗ (28.48) 

WIA Adult*County unemployment rate -48.03 (37.56) 

Labor Exchange*County unemployment rate -141.9∗∗∗ (37.80) 

Age in years 65.09∗∗∗ (3.85) 

Female -1259.4∗∗∗ (88.99) 

Nonwhite -79.60 (99.09) 

HS or GED 656.3∗∗∗ (122.0) 

Some College 1145.7∗∗∗ (135.1) 

Associate’s Degree 1194.3∗∗∗ (162.3) 

Bachelor’s Degree 2549.9∗∗∗ (201.8) 

Graduate Degree 4179.7∗∗∗ (546.9) 

Single Parent -163.9 (96.40) 

UI Claimant 276.7∗∗ (88.97) 

Primary Language not English -866.0∗∗∗ (124.8) 

Veteran 38.98 (101.5) 

Has a Disability -1071.3∗∗∗ (210.0) 

Constant 4910.7∗∗∗ (287.5) 

Observations 360183 

Clustered standard errors in parentheses 

∗ ∗∗ ∗∗∗ p < 0.05, p < 0.01, p < 0.001

26
 
 

Table 8: Averaged Individual-Level OLS Results, Propensity Score Weighted (Equations (1) and (2))
 

WIA Adult Std. Err. Labor Ex. Std. Err. 

Quarter 75.45*** (9.79) 27.04 (7.20) 

Post -2141.36 (327.45) -2243.28 (199.13) 

Post*Quarter -17.24 (36.01) 4.22 (19.86) 

Unemp -236.98* (35.35) -341.67 (22.48) 

Post*Unemp 20.78 (43.72) 118.47 (26.31) 

Constant 8402.1 (295.27) 8771.97 (186.10) 

Observations 4903 11377 

Diff. from LE * p < 0.05, ** p < 0.01, *** p < 0.001.
 

The standard errors are the average of individual-level standard
 

rrors, weighted with the propensity score weight. The standard


rror for the average coefficient

e

e 1 Σβi is 1 Σvar[βi].
 n n

attainment had higher earnings than those with less than a high school education. In particular, individuals 

with a bachelor’s degree had quarterly earnings that were approximately $2,549 more on average than those 

of individuals without a high school diploma. UI claimants earned an average of approximately $277 more 

than non-claimants. On average, women earned $1,259 less than men per quarter, people with disabilities 

earned $1,071 less than people without disabilities, and individuals whose primary language was not English 

earned $866 less than English speakers. Differences by ethnic background were not statistically significant. 

Table 8 provides the averaged individual-level OLS results. The slope terms of interest have the value 

of -$17.24 for WIA Adult participants and $4.22 for Labor Exchange participants, and are not statistically 

significantly different from each other. The conclusion is consistent with the pooled results that earnings 

growth was not statistically significantly different among individuals who received less intensive workforce 

services and those who received WIA-funded services. The overall average earnings growth after exit is 

$58.21 for WIA ($75.45 - $17.24) and $31.26 for Labor Exchange ($27.04 + $4.22). 

Table 9 provides averaged individual-level OLS results using the logarithm of quarterly earnings as the 

response variable. The slope coefficients are not different between the WIA Adult and Labor Exchange 

groups in a two-tailed t test. This result matches the previous finding that earnings progression was not 

statistically significantly different among individuals who received less intensive workforce services and 

those who received WIA-funded services. I also present the pooled model using the logarithm of quarterly 

earnings in Table 10. The individual-level specification is more appropriate in this case, however, because of 

its flexibility to account for heterogeneity among individuals who receive workforce development services. 

27
 
 

Table 9: Averaged Individual-Level OLS Results, Propensity Score Weighted, Log Earnings
 

WIA Adult Std. Err. Labor Ex. Std. Err. 

Quarter 0.14 (0.01) 0.14 (0.00) 

Post -2.31*** (0.19) -3.25 (0.13) 

Post*Quarter -0.16 (0.03) -0.16 (0.02) 

Unemp -0.2*** (0.02) -0.38 (0.01) 

Post*Unemp 0.02*** (0.02) 0.17 (0.02) 

Constant 10*** (0.16) 10.96 (0.12) 

Observations 4903 11377 

Diff. from LE * p < 0.05, ** p < 0.01, *** p < 0.001.
 

The standard errors are the average of individual-level standard
 

errors, weighted with the propensity score weight. The standard
 

error for the average coefficient 1 Σβi is 1 Σvar[βi].
 n n

Table 10: Pooled OLS Results, Propensity Score Weighted, Log Earnings 

(1) 

Log of quarterly earnings in 2008 dollars 

Quarters After Program Exit 0.11∗∗∗ (0.0041) 

WIA Exit Indicator -1.81∗∗∗ (0.16) 

Labor Exchange Exit Indicator -1.92∗∗∗ (0.16) 

WIA Adult*Quarters After Program Exit -0.14∗∗∗ (0.0095) 

Labor Exchange*Quarters After Program Exit -0.11∗∗∗ (0.0095) 

County Unemployment Rate -0.074∗∗∗ (0.015) 

WIA Adult*County Unemployment Rate -0.046 (0.025) 

Labor Exchange*County Unemployment Rate -0.090∗∗∗ (0.025) 

Age 0.018∗∗∗ (0.0025) 

Female 0.013 (0.052) 

Nonwhite -0.053 (0.058) 

HS or GED 0.18 (0.098) 

Some College 0.27∗∗ (0.10) 

Associate’s Degree 0.33∗∗ (0.12) 

Bachelor’s Degree 0.32∗∗ (0.12) 

Graduate Degree 0.095 (0.18) 

Single Parent -0.0067 (0.067) 

UI Claimant 0.67∗∗∗ (0.051) 

Primary Language not English -0.088 (0.099) 

Veteran -0.32∗∗∗ (0.062) 

Has a Disability -0.40∗∗ (0.14) 

Constant 7.89∗∗∗ (0.18) 

Observations 360183 

Clustered standard errors in parentheses 

∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

28 
Figures 6-8 provide kernel density estimates of the weighted individual coefficients. For the coefficients 

on post interacted with quarter and post interacted with the county unemployment rate, there is a slightly wider 

spread among WIA participants than Labor Exchange participants. Overall, the distribution of individual 

coefficients is quite similar across the groups. 

Figure 6: Kernel Density for Individual Post Coefficient 

7 Conclusion

This research contributes to the literature and practice of workforce development by analyzing whether 

a large sample of individuals who obtained specialized career services experienced different patterns of 

earnings progression than individuals who had not. This question has received relatively little attention in 

labor economics (Gladden & Taber, 2000), and is of substantial importance to scholars, policy makers, and 

practitioners who seek to promote economic self-sufficiency among the poor. 

These findings indicate that, once observable differences in the two groups were minimized, WIA 

participants in Washington State experienced approximately the same or lower earnings growth as their Labor 

Exchange counterparts who received less intensive services. This is consistent with the results of Mueser & 

29
 
Figure 7: Kernel Density for Individual Post*Quarter Coefficient
 

Figure 8: Kernel Density for Individual Post*County Unemployment Coefficient
 

30
 
Stevens (2003) that any initial differences in earnings growth across the WIA and Labor Exchange groups 

were quickly equalized. An important consideration is that WIA participants are more disadvantaged, on 

average, than Labor Exchange clients. Nevertheless, the results are somewhat surprising considering the 

resource investments in training and placement, as well as explicit efforts to assist workers obtain good jobs 

and higher incomes. It is possible that unobservable factors differentiated the comparison group from the 

treatment group and were not captured in the analysis. 

The limitations of this study include a lack of wage data on workers not covered by UI, lack of random 

assignment to WIA and Labor Exchange services, and variation in program delivery that might not be 

adequately captured by geographic control variables. For future research, alternate designs should include a 

random assignment study as well as a differentiation of WIA and Labor Exchange services and more equal 

distribution of participants to allow for more comparability between treatment and comparison groups to 

assess the Workforce Investment Act more validly. Although workforce agencies do not directly control the 

quantity and quality of jobs that are available in their jurisdictions, and they have expended considerable 

effort identifying high-paying vacancies, this analysis suggests that patterns of earnings progression following 

existing WIA services are not distinguishable from those following the receipt of Labor Exchange services. 

31
 
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