U.S. Department of Labor (DOL) 

Employment and Training Administration (ETA) 

Does Occupational Training by the Trade Adjustment 

Assistance Program Really Help Reemployment?: 

Success Measured as Matching 

Prepared by: 

Jooyoun Park, Ph.D. 

Economics 

Kent State University 

Kent, Ohio 

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. 
Acknowledgements 

The author thanks Jeffrey Smith, Alan Deardorff, Linda Tesar, Magaret Levenstein, Jagadeesh 

Sivadasan, Kenneth Troske, Chris Jepson, Michelle Ennis, Francesco Renna, Sucharita Ghosh, 

and Shawn Rohlin for thoughtful comments and suggestions. The author has also benefited 

from participants of the Recovery and Reemployment Research Conference and the seminar at 

University of Akron. The author is also grateful to the Employment and Training 

Administration of the U.S. Department of Labor for the generous funding for this research. 
I. Introduction 

International trade has constantly increased throughout the second half of the 20th century, and the 

trend will continue well into the 21st century. International trade used to be mostly in finished goods; 

however, continuous technological advancement and the resulting reduction in transportation costs 

expanded international trade to include inputs (Yeats [1998], Hummels et al. [2001]), such as ball 

bearings for automobile or rubber soles for footwear. As virtually every good (and even services) 

becomes tradable, international trade is more active than ever. 

International trade has changed the competitive structure between developing and developed 

countries, and this affects employment in both groups of countries. First, goods from developed countries 

have to compete against cheaper goods from developing countries. This is a more traditional type of 

competition where firms compete with other firms with their final products. Since the goods from 

developed countries lose their market shares to goods from developing countries due to their high relative 

prices, employment in these countries decreases in the sectors that face high import competition (Kletzer, 

2002). Secondly, workers from developed countries have to compete against low-wage workers from 

developing countries. International trade and fragmentation together provide great cost-reduction 

opportunities for firms. Firms utilize the practice of offshore outsourcing, either by establishing their own 

subsidiaries in low wage countries or by using arm’s length contracts with foreign firms, to perform tasks 

that were previously performed by high-wage domestic workers. The tasks that are being outsourced 

offshore are not only production-related, but also include services (Amiti and Wei, 2005). This has both 

positive and negative effects on employment in the developed countries. Employment may rise as the 

efficiency of outsourcing firms increases, their prices fall, and market shares rise. Firms maintain some 

parts of their business process at home, and increases in sales will expand employment in the domestic 

part of their business as well as the foreign part. If sales gain is substantial, it is possible that employment 

in the domestic portion of those firms actually rises above the level prior to outsourcing. However, the 

workers whose tasks are replaced by activities of workers in developing countries lose their job. 

While most economists agree on there being a net welfare gain from freer trade through an 

increase in economic efficiency and aggregate income, larger variety of consumer products, and lower 

prices, they do not deny the fact that there are winners and losers. The biggest losers from international 

trade are the workers displaced due to the increase in competition from imports and offshoring. The U.S. 

Trade Adjustment Assistance (TAA) program is specifically designed to compensate these workers. 

Many studies find that TAA participants are, compared to a broader group of displaced workers 

such as Unemployment Insurance (UI) beneficiaries, more likely to have a harder time finding a job. 

Corson and Decker (1995) show that the majority (72% compared to 31% for UI exhaustees) are 

displaced due to plant/company closures; therefore they are less likely to be recalled by their previous 

employers.1

 Finding a similar job would not be easy for these workers since these layoffs occur in the 

import-competing sectors, which is suffering not only locally but also nationally from massive job 

destruction. These facts indicate that TAA participants may benefit from moving to different occupations, 

but the evidence shows that they are not very likely to have marketable skills. Decker and Corson, in the 

same study, find that TAA participants have higher tenure with previous employers, indicating a narrow 

 

1

 While only 23% of TAA participants reported that recall is likely, 43% of UI exhaustees reported so.

1 
job experience. Baicker and Rehavi (2004) show that TAA participants are older, less educated, and have 

a higher fraction of people without a proper level of English proficiency. For this reason, it is believed 

that training - and income support during training to promote training enrollment - should be the major 

benefit of the TAA program, and it has been the case since the Trade Act of 1974. Other benefits are 

summarized in Table 1. 

The performance of the TAA program is officially assessed by three performance measures: 

Average Earnings (which replaced Wage Replacement Rate since 2007), Reemployment Rate, and 

Retention Rate as of fiscal year 2010. Table 2 summarizes the performance of the TAA program of fiscal 

years 2003-2010. These performance measures provide a general idea of whether TAA participants find a 

job that replaces the job prior to participation reasonably well in monetary sense. However, these 

performance measures do not directly measure the efficacy of the training service in helping participants’ 

re-employment and post-exit economic welfare. 

An article in The New Yorker Magazine by Katherine Boo (2004)2

 tells a story about a woman 

who was laid off from the Fruit of the Loom plant located in Harlingen, TX, at the end of 2003. She 

received medical-assistant training, applied for twenty-nine positions, got three interviews, but ended up 

serving lunch at a nearby construction site. The article describes the reality of the training program in 

Cameron County as the following: 

In the past five years, more than a thousand displaced manufacturing workers had been retrained 

as medical assistants or air-conditioning repairmen or computer-maintenance technicians. ...The 

state workforce commission had predicted that twenty-five medical-assistant jobs would open in 

Cameron County in 2003, but it would be difficult to secure one. In one class of laid-off textile 

workers alone, eighty-five people had been trained for the profession. 

The New Yorker article above suggests that many participants receive training in occupations in which 

there are not enough job openings and end up in occupations unrelated to their training. There are studies 

investigating whether trainees do better after program exit compared to non-trainees in terms of three 

performance measures – see Corson and Decker (1995) for the TAA program, and Heckman, LaLonde, 

and Smith (1999) for federal training programs in general. However, how much obtaining skills for a 

specific occupation through classroom training contributed to their reemployment is not well explored. 

The information on TAA participants, services that they received, and the outcomes – whether 

they are reemployed after exiting the program, quarterly earnings at new jobs, and retention within three 

quarters from the program exit - are reported by the Trade Act Participant Report (TAPR). TAPR reports 

the 8-digit Occupational Skill Training Code (OSTC) and Occupational Code of Employment (OCE) for 

each participant who received training service. From a preliminary investigation of TAA participants who 

exited the program between July, 2004 and September, 2007, only 47.98% of people who received 

occupational skills training have identical codes for OSTC and OCE.3

 This might indicate that for 52% of 

trainees, the resources put into them did not achieve the goal. This is an inherent problem of federal 

training programs since the supply of trainees is only loosely linked to the demand for newly trained 

workers. The mismatch between supply and demand for trainees is not only a problem of the federal 

 

2

 “Letter from South Texas: The Churn,” The New Yorker Magazine, March 29th, 2004 3

 The number of observations is 143,300. 80,005 participants received occupational skills training, and 66,268 of them found a 

job after training. Among these 66,268 reemployed trainees, only 17,760 have valid 8-digit codes for both OSTC and OCE. 

2 
programs of the United States. Rasmussen and Westergaard-Nielsen (1999) found the apprenticeship 

systems in Germany and Denmark also have this mismatch. According to the U.S. General Accounting 

Office (GAO), the cost of training, i.e., tuition for training classes and Additional TRA payments, 

accounted for 48.36% of TAA expenditures for fiscal years 1995-1999.4

 The supply and demand 

mismatch issue indicates that there is significant room to improve the cost efficiency and program 

performances of training programs. For this reason, a good understanding of the mismatch problem is 

very important. 

In this paper, the efficacy of the training service provided through the TAA program will be 

investigated focusing on the match between OSTC and OCE reported in the TAPR. More specifically, 

this paper will examine what the probability of matching between OSTC and OCE is and how the 

matching is affected by the individual characteristics of participants and services that they received. 

Additionally, this paper will discuss whether matching improves the participants’ post-participation 

economic welfare measured by reemployment rates, wage replacement rates, average post-participation 

earnings, and retention rates by comparing the outcome measures for trainees with a match, trainees 

without a match, and non-trainees. 

 First, validity of a match as a measure of successful training is examined by testing the hypothesis 

that a match is a preferred option for employment after training. Under the alternative hypothesis, the 

probability of matching shows a bell-shaped curve over the ability level. Low ability trainees show low 

matching rates because they are not capable of succeeding in training to obtain a match. High ability 

trainees show low matching rates since voluntary withdrawal from a match is more likely because they 

have other marketable skills that would give them better pay and work environment. In the analysis with 

educational attainment as a proxy for the ability level, the matching rate is generally increasing in 

educational attainment rather than displaying a bell-shaped link in all specifications. Also the finding that 

exit years with favorable labor market are associated with higher matching rates supports the validity of 

matching as a measure of training success. 

Investigation of reemployment rates shows that receiving occupational skills training raises the 

chance of reemployment by 5 percentage points. Where training completion is separately included in the 

analysis, enrolling in skills training alone only raises the chance by 2 percentage points while skills 

trainees who complete the training enjoys the chance of reemployment that is 6 percentage points higher 

than that of non-trainees. On-the-job training (OJT) has a larger impact, 10 to 11 percentage points 

increase in the rate. The large impact of OJT is likely to be driven by the condition that the employers 

need to hire the participant to receive the benefits. Other worker characteristics, such as educational 

attainment, ethnicity, and age, affect reemployment rates by similar magnitude to the impacts of training 

programs. Participants with a bachelor’s degree show reemployment rates of 2 to 3 percentage points 

higher than those who did not complete high school. Reemployment rates decrease with age. Participants 

with age between 21 and 30 have reemployment rates that are 4.2 percentage points higher than those of 

participants of age between 41 and 50. Age group 51 to 60 show nearly 10 percentage points lower 

reemployment rates than participants of age between 41 and 50. The impact of old age is extremely 

 

4

 Training and Additional TRA accounted for 29.95% and 18.41%, respectively, of the total expenditure. Most of the rest of TAA 

expenditure was used to pay Basic TRA. Non-training-related services – reemployment services, job search allowances, and 

relocation allowances accounted for a negligible fraction of the total expenditure. 

3 
negative. Workers between ages 61 and 65 display the reemployment rates that are 41 percentage points 

lower than workers between ages 41 and 50, but this could be explained by their voluntary withdrawal 

from the labor market. 

 Matching has significant impacts on the wage replacement rates, raising the rates by 2 to 3 

percentage points compared to non-trainees and 4 to 5 percentage points compared to trainees without a 

match. Trainees without a match display the wage replacement rates lower than those of non-trainees. 

This is due to the limited skill sets that lead them into training enrollment in the first place. Only those 

who succeed in obtaining a match after the training show the wage replacement rates 3 percentage points 

higher than non-trainees. OJT again proves very beneficial to the participants by improving the rates by 4 

percentage points. The most important worker characteristic in determining the wage replacement rates is 

their age. Participants who received occupational skills training but failed to achieve a match experience 

lower post-participation earnings compared to non-trainees and this pattern is preserved even when preparticipation earnings are controlled. This again indicates the limited sets of skills that these trainees have 

other than the skills used in the previous employment. Obtaining a match eliminates the effect, indicating 

the success in occupational skills training compensates their lack of marketable skills. 

The retention rate is not influenced by matching but is influenced by various training program 

participation. Occupational skills training improves retention rates by 2.3 to 2.7 percentage points. 

Remedial training and OJT raise the rates by 1.0 and 3.7 percentage points, respectively. The impact of 

education is positive, approximately 1 to 2 percentage points higher for high school or more compared to 

participants with education of less than high school. Overall, retention is fairly stable across different 

training programs and various worker characteristics and is not drastically influenced by any. 

 The analyses of TAA participant data conducted for this paper supports that occupational skills 

training improve the post-participation outcomes of participants. In the absence of training, participants 

with limited skill sets suffers from higher adjustment costs such as low chances at reemployment and 

lower wage replacement rate. Training provision in the TAA program allows these participants to select 

into training while people with marketable skill sets are more likely opt out of training enrollment. 

Successful completion of the training program indicated by a match compensates (or more than 

compensates for some outcome measures) the negative impact of limited skill sets of these participants by 

providing the skills that generates good employment opportunities. The adjustment costs of these trainees 

– with a match – is lower than what it would have been if it were not for the training provision. The 

results from these analyses provide evidence that the focus of the TAA program on provision of training 

services important and would be more fruitful if it is accompanied by emphasis on choosing the right 

occupations for participants by conducting thorough career assessment and counseling. 

 The rest of the paper is organized as follows: Section II describes the TAA program in more 

detail; Section III introduces the data set and provides various descriptive statistics; Section IV presents 

the analytical methodology and results; and Section V provides conclusion. 

II. Trade Adjustment Assistance 

The TAA program is a dislocated worker program administered by the Employment and 

Training Administration of the U.S. Department of Labor (DOL). TAA was first established in 1962, but 

4 
it has only been actively implemented since the Trade Act of 1974. The North American Free Trade 

Agreement (NAFTA) Implementation Act of 1993 added a separate NAFTA-TAA program to help 

workers who are affected by the free trade agreement. The Trade Reform Act of 2002 integrated NAFTATAA into the regular TAA program. 

When layoffs occur at a certain establishment, a group of three or more workers from the 

establishment or any entity representing them may file a petition with DOL. The petitions are filed at the 

plant level. The Division of Trade Adjustment Assistance investigates the case and issues a certification if 

they find evidence that employment of the group of workers is adversely affected by any of the following: 

a shift in production to a foreign country, an increase in company imports, an increase in participant 

imports, or high and rising aggregate US imports. Once certified, all workers who are laid off from that 

establishment between the initial layoffs (impact date) and 2 years from the certification date are entitled 

to the services and benefits listed in Table 1. If a worker is over age 50, he/she may apply for the 

Alternative TAA (ATAA)5

 program instead of TAA. ATAA is a wage insurance program that subsidizes 

50% of the difference between the pre-layoff wage rate and the wage rate in the new job, up to $10,000 a 

year, in case where the worker obtains reemployment no later than 26 weeks from the date of the 

qualifying separation. ATAA was added by the TAA Reform Act of 2002. 

The most important benefits are training and income support. If career counseling determines that 

the participant does not have skills useful for reemployment, the worker may enroll in occupational skills 

training up to 104 weeks. If the participant lacks basic education such as English proficiency or high 

school education, the participant may enroll in remedial training for an additional 26 weeks in addition to 

the regular training. Training enrollment is permitted only if it is believed that the participant would 

benefit from training and has a higher chance of reemployment with training. If this is not the case, the 

participant may obtain a training requirement waiver. The training waiver is issued if the participant does 

not need training – if she has marketable skills or will soon be recalled by the previous employer – or is 

not able to take training – health issues or inability to find a suitable training program. While enrolled in 

training, TAA participants are entitled to an income support (Basic TRA, Additional TRA, and Remedial 

TRA). Participants who obtained a training waiver can receive the Basic TRA without enrolling in a 

training program. The Trade and Globalization Adjustment Assistance Act of 2009 (2009 Amendments) 

which is part of the American Recovery and Reinvestment Act of 2009 expanded the program benefits. It 

allows additional 26 weeks for training and TRA payments. It also raised the cap on many adjustment 

allowances such as ATAA maximum benefit, Job Search Allowances, and Relocation Allowances.6

These expanded benefits have decreased back to the normal benefits of before 2009 Amendments. 

 

Choice of training occupation is made by participants with the help of local TAA staff through a 

proper assessment of the worker’s ability. The ability assessment is measured based on an applicant’s 

education, work history, potential barriers to employment, basic skills capabilities, aptitudes, work skills, 

family situation, attitudes toward work, behavioral patterns, supportive service needs, and interests for 

careers and training as they relate to the local labor market. Information is gathered primarily using 

5

 The name has changed to Reemployment Trade Adjustment Assistance (RTAA) by The Trade and Globalization Adjustment 

Assistance Act of 2009. 6

 Job Search Allowance and Relocation Allowances were 90% of allowable costs up to $1,250 before 2009 Amendments, but 

they have changed to 100% of allowable costs up to $1,500. 2009 Amendments also allows part-time basis training and allows 

workers to begin the training program if there is a significant threat of displacement even if they are not yet laid off.

5 
questionnaires, individual interviews, paper and pencil tests, performance tests, behavioral observation, 

and career guidance instruments. 

III. Data 

 Since the initiation of the TAA program in 1962, a substantial number of workers have received 

various benefits through the program. However, the collection of participant data became obligatory only 

for participants who exited the program since July 1, 1999. Through the last quarter of 20087

, 314,964 

participant cases are reported on TAPR. Initially, OSTC and OCE were reported using various 

classification systems: the 8-digit O*

NET code, 5-digit OES code, and 9-digit DOT code. In 2005, 

TAPR’s coding system was revised so that reporting of OSTC and OCE were unified to the O*

NET 

code8

. Since OSTC and OCE are the main variables of interest, only the observations collected after 2005 

Revision were utilized for this study, specifically data reported since the fourth quarter of 2005, to avoid 

error due to imperfect classification. This restricts the sample size to 143,300. 

 The TAPR consists of three sections. The first section, Identification and Participant 

Characteristics, collects personal information of participants such as date of birth, gender, ethnicity, and 

education level. Any information regarding the qualifying separation (date of separation and tenure with 

previous employer) is also reported here. Section II, Activity and Service Record, summarizes the TAA 

benefits a participant received. If the participant received training, it reports what type of training he/she 

received, such as occupational skills training (along with OSTC), remedial training, OJT, or customized 

training. For all participants, receipt of financial assistance including Basic Trade Readjustment 

Allowance (TRA), Additional TRA, Remedial TRA, job search allowances, and relocation allowances – 

is reported. Finally, Section III, Outcomes, reports whether the participant is employed, which occupation 

he/she is employed in (OCE), and how much they earn during the three quarters following program exit. 

 Table 3 summarizes the data set. Columns 1 to 4 summarize data for different years of program 

exit. The last column summarizes the entire sample. Individual characteristics of the participants differ 

across exit years. Over time, the fraction of participants ages 55 and above increased while that of 

workers ages 30 to 44 noticeably decreased. One might think that participation of older workers is 

encouraged with the establishment of ATAA. However, with a little over 5%9

 of participants ages 50 or 

above participating in ATAA, the hypothesis is hardly plausible. 

Service delivery also changed over time. The fraction of trainees fell noticeably in 2006 and rose 

back to the previous level in 2007. However, the fraction of trainees who received occupational skills 

training continues to decrease in 2007. One potential explanation for this is the improvement in labor 

market situation over time.10 Occupation switching is less necessary for reemployment when there are 

more vacancies, reducing the demand for occupational skills training. This argument gains support from 

the rise in training waiver issuance in 2006 and 2007, which is driven by a rise in workers with 

 

7

 This is the reporting quarter. Each participant is monitored for three quarters from his/her date of program exit before being

reported on TAPR. The last program exit date reported by the end of 2008 is 9/30/2007. 8

 The structure of O*NET code is described later in this section. 

9

 The participation rates are 5.69% for 2006 and 6.35% for 2007. The participation rates for 2004 and 2005 are only 0.73% and 

2.43%, respectively. This is probably because the ATAA was not fully taken advantage of for those early participants. 

Participants of ATAA are also reported in the TAPR, and they are included in the sample of this study. 10 The national unemployment rate was 5.8-6% in 2002 and 2003. It started falling in 2004 (5.5%) and continued to decrease until 

the end of 2007. It was 5.1% in 2005,, 4.6% in 2006, and 4.5% in the first half of 2007. 

6 
marketable skills. Whether the participants’ existing skill sets are marketable depends on the prospect of 

reemployment. Another supporting evidence for this argument is the fall in Basic TRA take-up rate in 

2006 and 2007. Participants with a training waiver is entitle to Basic TRA, income support up to 26 

weeks from the date of UI benefit expiration. The decline in the take-up rate for Basic TRA despite of the 

large increase in training waiver issuance indicates that a large number of participants with a training 

waiver exited the program before their unemployment insurance benefit period ended. Despite of the 

decrease in enrollment in occupational skills training, the fraction of trainees among all participants 

remains high in 2007. This might be because of the trainees who received workshop-like training of very 

short duration. Among 90,503 trainees who reported valid dates for the first and last day of training, 6,972 

received training shorter than a week. 

 The main variable of interest in this paper is a match between OSTC and OCE. As discussed 

earlier, OSTC and OCE are coded with 8-digit O*NET occupation codes. O*NET codes are constructed 

in the following way. The first two digits represent 23 job families listed in Table 4. The next four digits 

represent different occupations within each job family. The last two digits represent any additional subcategories for each occupation.11 Therefore, the first six digits represent each occupation, and all eight 

digits together describe more detailed occupational categories. 

Reporting of occupation codes is far from perfect. Out of 143,300 TAA participants observed 

during the sample period, 80,005 participants received occupational skills training, but only 46,495 

observations have valid12 eight digits of occupational codes of training. Table 5 summarizes the fraction 

of trainees with valid 8-digit OSTC among occupational skills trainees for each state. The reporting 

quality – measured in the fraction of trainees with valid OSTC among occupational skills trainees – varies 

greatly from 0 percent to 100 percent across states. Also, 111,844 participants find a job during the first 

three quarters from the program exit, but only 28,386 observations are reported with valid 8-digit OCE.13 

 The subjects of the matching analyses are those who received occupational skills training and 

found a job after program exit. 66,268 participants satisfy this condition, but only 17,760 participants are 

reported with valid 8-digit codes for both OSTC and OCE. This number increases to 24,802 if only the 

first six digits are considered.14 Matching between OSTC and OCE are measured at 8-digit level using 

17,760 observations with valid 8 digits for both OSTC and OCE. Matching at 6-digit level are measured 

using 24,802 observations with valid six digits for both codes. Using all 8-digits provide more exact 

matching than using only the first 6-digits, but it reduces the sample size. 

 This paper also uses two definitions of matching between occupations of training and entered 

employment, first and second degree matching. First degree matching is identified by identical OSTC and 

OCE. Second degree matching allows matching to a related occupation. The O*NET system specifies 

related occupations for each occupation based on knowledge areas, skills, abilities, work environment, 

 

11 For instance, 29 is the job family of ‘Healthcare Practitioners and Technical’. 29-2099.00 is the code for ‘Health Technologists 

and Technicians, All Other’ This occupation has four sub-categories: 29-2099.01 is for ‘Electroneurodiagnostic Technologists,’

29-2099.02 is for ‘Hearing Aid Specialists,’ 29-2099.03 is for ‘Ophthalmic Medical Technologists and Technicians.’ 12 19,955 have zero codes, 12,795 observations have seven digits or less, and 177 observations have 100,000,000 as OSTC. 

Among 47,078 observations with eight digits of OSTC, 583 observations have invalid first two digits. 13 73,152 observations report zero as OCE, 8,598 observations have seven digits or less, and 1,207 observations have 

100,000,000. Among 28,887 observations with eight digits for OCE, 501 observations have invalid first two digits. 14 A large number of observations are reported only with six or seven digits for OSTC and OCE. (11,490 and 813 observations 

have six and seven digits, respectively, for OSTC. For OCE,7,796 and 22 observations have six and seven digits, respectively.) 

These digits correspond to the first six and seven digits of O*NET occupation codes. Instead of discarding these observations, 

I construct 6-digit OSTC and OCE using observations with valid six, seven, and eight digits of occupation codes.

7 
and work activities.15 It captures the idea that training in one occupation can help employment in related 

occupations. Since this type of matching is less direct, I define it as second degree matching. Second 

degree matching, however, represents a very small fraction of matches compared to first degree matching. 

At the level of 8-digit, only 437 out of 17,760 observations have a second degree match while 8,521 

observations have a first degree match. Table 6 summarizes the matching rates for four different 

definitions of matching: first and second degree matching at 6- and 8-digit levels. In panel (b), various 

matching rates are reported excluding the observations from Oklahoma and Virginia. Oklahoma and 

Virginia show 100% and 99.97% first-degree matching rates, respectively, at 6-digit level. These figures 

are likely results of miscoding. Observations from these two states accounts for 5.0% of the entire sample 

and 20.3% of the observations with valid OSTC and OCE. Excluding these observations, the matching 

rate by either degree decreases greatly from 46.40% to 37.28%. In the analyses that utilize the matching 

variable are carried out without observations from Oklahoma and Virginia. 

The outcome variables are summarized in Table 7. Panel (a) summarizes the outcome measures 

across different exit years, and Panel (b) summarizes them across different levels of pre-participation 

earnings. The earnings level is interesting because of its linkage to the participants’ potential outcomes. 

Outcome measures do not vary much across exit years except for the matching rate which increased over 

time from 33.32% in 2004 to 43.21% in 2007.16 Across all exit years, trainees display higher 

reemployment rates, wage replacement rates, and retention rates with variations in sizes of the differences 

across outcome measures and exit years. Such a trend is the most distinctive for reemployment rate. For 

all exit years, trainees show 10 percentage points higher reemployment rates.17 Trainees display higher 

wage replacement rates for all years, but the differences between those of trainees and non-trainees 

narrow over time. It is hard to find a distinctive pattern between outcome measures for trainees with and 

without a match. Wage replacement rates for trainees with a match are substantially higher than those 

without a match for exit year 2007 (by 7 percentage points); however the rates are higher for trainees 

without a match for exit years of 2004 and 2005. However, this lack of pattern is not enough of evidence 

against the hypothesis that a match improves the post-participation outcome. The labor market situation 

differs greatly across years, so the difficulty of achieving a match could be very different. The association 

between a match and outcome measures need thorough investigation. Retention rates do not differ much 

across exit years and across different groups of trainees. Trainees show slightly higher retention rates than 

non-trainees, and a larger fraction of trainees without a match stay employed after they are initially hired. 

However, the difference is very small. 

 Looking at reemployment rates, wage replacement rates, and retention rates across different preparticipation earnings levels provides another dimension to what can be observed across exit years. First 

of all, matching rates are higher for participants with low earnings levels. It could be because these 

 

15 For example, occupation code 51-4121.06 (Welders, Cutters, and Welder fitters) has ten related occupations. First six are: 

51-2011.00 Aircraft Structure, Surfaces, Rigging, and Systems Assemblers, 51-4031.00 Cutting, Punching, and Press Machine 

Setters, Operators, and Tenders, Metal and Plastic, 51-4032.00 Drilling and Boring Machine Tool Setters, Operators, and Tenders, 

Metal and Plastic, 51-4071.00 Foundry Mold and Coremakers, 51-4072.00 Molding, Coremaking, and Casting Machine Setters, 

Operators, and Tenders, Metal and Plastic, 51-4191.00 Heat Treating Equipment Setters, Operators, and Tenders, Metal and Plastic 16 Observations from Oklahoma and Virginia display 100% and 99.97% matching rates, respectively. This is more likely to be a 

coding error rather than perfect matching for those states. Observations from these two states are dropped for matching 

analysis. The number of observations with both OSTC and OCE codes also only includes observations from states other than 

Oklahoma and Virginia. 17 The figures for trainees and non-trainees are not statistically significantly different.

8 
workers have a very limited set of skills and as a result they are less likely to find a job outside their 

training occupations. Participants within the medium range of pre-participation earnings display a higher 

reemployment rate than the higher earnings group. This trend is shown by both trainees and non-trainees, 

but the rates are substantially higher for trainees at all earnings levels. 

Wage replacement rates show a very interesting pattern. First of all, the wage replacement rates 

for the first earnings category, quarterly earnings of $0 to $2,000 are extremely high. The TAPR reports 

the quarterly earnings of participants for three quarters prior to the date of participation rather than the 

date of separation. Therefore, these earnings records sometimes show the temporary positions that 

participants held between separation and participation and the wage rates for those positions tend to be 

low. The threshold $2,000 is quarterly earning if a person works for 30 hours at minimum wage rate, 

$5.15.18 Figures below $2,000 are likely to be indications of such positions; therefore, these earnings 

level does not reflect their potential earnings level after the program exit. The wage replacement rates for

this group of participants are not necessarily meaningful. Second, trainees with a match show higher wa

replacement rates for lower earnings level, but trainees without a match display higher rates for higher 

earnings level. The overall rates are higher for trainees with a match because lower earnings levels have 

larger numbers of observations. Higher wage replacement rates for higher earnings level could be driven 

by participants who have more desirable options outside the matching occupations. Workers with more 

options will choose non-matching occupations when they are better – higher pay, better work 

environment, and so on – creating association between non-match and higher wage-replacement rate. 

Trainees show higher rates of retention compared to non-trainees, but trainees with and without a match 

do not show a clear pattern. 

ge 

 

Participants’ choices of training occupations deserve a closer look. Some occupation groups such 

as Management are more generally applicable than others such as Building and Grounds Cleaning and 

Maintenance. Therefore, the probability of matching might vary across occupational groups. Table 4 

shows that the participants are widely distributed to most of the 23 occupation groups in their training 

choices, while employment is concentrated in a few groups. 

Tables 8 and 9 summarize individual characteristics of participants and the matching rate for each 

training occupation group and each employment occupation group, respectively. The first column shows 

that matching rates vary greatly across different occupation groups. Especially, the matching rates shown 

in Table 8 show that the majority of employees in some occupation groups, such as legal (23) and 

healthcare practitioners and technical (29), received training specific to the occupations, while training 

does not seem necessary to be employed in some occupations, such as sales and related (41) and farming, 

fishing, and forestry (45). 

Gender is very relevant for participants’ selection of occupational training according to Table 8. 

While some occupational groups for training, such as construction and extraction (47), installation, 

maintenance, and repair (49) , and transportation and material moving (53), are extremely male-oriented, 

some other occupation groups, such as legal (23), healthcare support (31), and office and administrative 

support (43), are highly female-oriented. This pattern is preserved for occupation groups of employment. 

The importance of English proficiency also differs greatly across occupations. In their training choices, 

18 The federal minimum wage rate increased to $5.85 in 2007 (then $6.55 in 2008 and $7.25 in 2009). Most participants in the 

sample (whose latest exit year is in 2007), $5.15 is the relevant minimum wage rate for earnings prior to participation. 

9 
people with limited English proficiency show high concentration in three occupation groups: food 

preparation and serving related (35), building and grounds cleaning and maintenance (37), and personal 

care and service (39). These three occupation groups employ more workers with limited English 

proficiency, but the farming, fishing, and forestry group (45) is the major employer of these workers. 

 Table 8 also shows that different ethnic groups concentrate their occupational choices differently. 

Training in farming, fishing, and forestry (45) is the most popular choice for white TAA participants 

while Asians choose to be trained for the occupation groups, building and grounds cleaning and 

maintenance (37) and personal care and service (39). African-American participants tend to choose 

community and social services (21) and education, training, and library (25), while Hispanic participants 

choose personal care and services (39) and office and administrative support (43). These choices are 

reflected in their occupations of employment as well. One noticeable thing, though, is the high 

concentration of Asian participants employed in architecture and engineering (17) and Hispanic 

participants in military specific (55). However, the military group is too small for this high concentration 

of Hispanic employees to be meaningful. Level of education is a very important factor in choosing 

occupations. Trainees with different education levels display distinctive choices of occupation groups. 

This pattern is preserved in their employment with higher intensity. 

 Table 10 summarizes the correlation between occupational choices and post-participation 

performance. Occupational choices certainly matter for the probability of reemployment after training. 

The reemployment rates range from 71.43% for farming, fishing, and forestry (45) to 88.79% for 

protective service (33). Once employed, retention rates and wage replacement differ across occupations of 

employment. Architecture and engineering (17) and healthcare practitioners and technical (29) show 

superior post-participation performance with high retention and wage replacement rates. Since choices of 

training occupation influence the post-participation outcomes, and different personal characteristics show 

different patterns of occupational choice, it is expected that gender, ethnicity, and education level are 

linked to the outcomes. 

IV. Analyses 

As shown in the previous section, matching between occupations of training and entered 

employment is linked with various personal characteristics of participants, and the outcomes of the 

program are not only correlated with matching but also with many other individual characteristics. In this 

section, the relationship between individual characteristics and matching and the relationship between 

matching and four outcome measures – reemployment, wage replacement, post-participation earnings 

level, and retention rate – are investigated. This is to improve the understanding of how matching is 

achieved and how the performance of the TAA program can improve through better matching. First, 

individual characteristics of participants and their influence on the probability of a match is discussed. 

Then, how matching and other characteristics of participants affect the outcome: the probability of 

reemployment, the wage replacement rate, post-participation earnings, and probability of remaining 

employed is examined. Matching used in the following analyses is first and second degree matching at 8-

digit level. 

10 
IV.1. What affects matching? 

 This paper assess the impact of successful training in various outcome measures – reemployment 

rate, wage replacement rate, post-participation earnings level, and retention rate – to see whether what 

really matters in achieving a better outcome is the skill sets acquired through the training program or 

merely the exposure to the intense federal assistance which involves a better access to job-related 

resources. Matching between occupations of training and entered employment is used as a measure of 

training success. In order for the analyses of the impacts of matching to be relevant, the validity of 

matching as a measure of successful training should be verified. 

 Matching would be a direct indication of training success if the occupation of training is the most 

preferred occupational choice for all trainees and the best trainees in each training program select into a 

match. In this case, there will be a clear positive relationship between the probability of matching and the 

ability level of the trainee. 

 However, the selection issue around matching is not as straightforward. There are two scenarios 

in which a trainee does not achieve a match. First, the trainee is not capable of acquiring the skills 

provided through the training program because the choice of training occupation is not adequate for the 

trainee’s ability level. One reason for this type of matching failure is poor worker assessment or 

counseling at the One Stop Center. This would provide a direct link between failure in training and failure 

to achieve a match, strengthening the validity of matching as a measure of training success. In the second 

scenario, a trainee voluntarily chooses to find a job in a non-match occupation despite his/her ability to 

successfully acquiring the skills set from training program because the non-match job provides a higher 

pay and/or better work environment. This is likely to happen to trainees with higher ability that tend to 

have other marketable skills. If this accounts for a significant portion of matching failure, the link 

between matching probability and the ability level would show a bell-shaped relationship. Controlling for 

the occupational choices of training19, low-ability trainees are more likely to fail in training program, so 

they tend to display low matching rates. High-ability trainees have marketable skills that are different 

from the training occupations, so they are more likely to choose a non-match occupation for their 

employment, showing low matching rates. Medium-ability trainees are more likely to succeed in training 

with limited outside options due to lack of marketable skills, they display higher matching rates. 

 The following regression analysis tests the hypothesis that matching is an outcome of successful 

training by looking at the importance of the variables related to ability level, such as educational 

attainment, in achieving a match. The outcome variable here is the indicator for matching with value ‘1’ 

implying a match and ‘0’ implying matching failure. Probit analysis is used for the following estimation: 

 (1) 

Education measured as degree attainment is used as a proxy for the ability level of each trainee. 

Five dummy variables for high school degree or equivalent, some college, associate degree, bachelor’s 

degree, and more than bachelor’s degree are used. Vector α will test the hypothesis whether matching is a 

 

19 Trainees with different ability level select into different occupations with different matching rates. This creates a link between 

ability level and the matching rate that is not necessarily the outcome of success/fail in training. 

11 
valid measure of training success. X is a vector of individual characteristics such as gender, ethnicity, 

educational attainment, completion of training, and age at program exit. OCCi is a vector of dummy 

variables for the training occupation groups listed in Table 5. ‘1’ indicates the occupation group of 

training. As shown in Tables 8 and 9, the probability of matching varies greatly across occupation group. 

Vector γ will capture such variation. D5i, D6i, and D7i are dummy variables for exit years to capture away 

the labor market situation as trainees finish training and start looking for a job. For this estimation, only 

the observations with both valid OSTC and OCE will be used, which requires receipt of occupational 

skills training. For this reason, investigation of the selection into a match will not be biased by selection 

issues around training enrollment. As mentioned earlier, observations from the states of Oklahoma and 

Virginia are omitted. 

There are eight specifications. Specification 1 uses only the personal characteristics included in 

vector X. The reference group for this specification is white females with less than high school education 

between ages 41 and 50. Specification 2 also includes dummy variables for years of exit, 2005, 2006, and 

2007. The reference year is 2004. Specification 3 includes 21 indicator variables for occupation groups 

of training. Group 11, Management, is used as a reference group. Occupation group 55, Military 

Specific, is omitted since training and hiring processes for military personnel are different from other 

occupation groups. Group 55 only accounts for 0.07%20 of the sample, so it would not affect the analysis 

in a significant manner. Specification 4 includes both years of program exit and occupation groups. 

Specifications 5 to 8 repeat specifications 1 through 4 with states of participants’ residency controlled. 

California is used as reference group. For each specification, I perform the analyses with or without the 

indicator variable for training completion. Table 12 presents the results. The numbers reported are the 

marginal effect of each variable on the chance of matching. 

As discussed above, if matching is not the preferred option for employment, the link between 

ability level and the probability of matching will display a bell-shaped relationship given that OSTCs are 

controlled. With degree attainment as the measure of ability level of trainees, bachelor’s degree and more 

are considered as high ability since it is safe to believe their skill sets are applicable to various 

occupations. High school degree, some college education, and associate degree are considered as 

medium-ability while less than high-school education is considered as low-ability. Under the hypothesis 

that matching is not the preferred option for employment, the medium-ability variables have positive 

coefficients while bachelor’s degree and more have coefficients smaller than those of medium-level 

education variables. 

The occupations of training are controlled under specifications 3, 4, 7, and 8. Under specifications 

3 and 4, the impacts of medium-level education are very small and highly educated trainees display 

matching rates that are much higher than low- or medium-ability trainees. Having a bachelor’s degree 

raises the probability of matching by 6-7 percentage points and some graduate school raises the matching 

rates by 11.5 percentage points compared to trainees with less than high school education. The bellshaped matching rates are not observed. Specifications 7 and 8 display a similar pattern. Medium-level 

education again shows very small impacts. Having a bachelor’s degree and some graduate work raise the 

 

20 This is different from the figure shown in the fifth column of Table 5 because it is calculated from the observations with valid 

codes for both OSTC and OCE.

12 
matching rates by 6 and 8 percentage points, respectively. In all four specifications, the probability of 

matching is generally increasing with the ability level measured as degree attainment rather than showing 

the bell-shaped relationship. This supports the hypothesis that matching is a preferred option for 

employment and proves that the voluntary withdrawals from matching because trainees prefer non-match 

jobs are not a significant portion of matching failure. 

Some might argue that training completion is a better indicator of training success. However, the 

endogeneity of training completion prevents us from achieving meaningful information about selection 

into matching. Whether a trainee withdraws from training program because he/she considers a match as 

undesirable (high-ability trainees) or unattainable (low-ability trainees), early withdrawal is highly 

correlated with matching failure. For this reason, I use training completion as a control variable rather 

than a proxy for training success. In all specifications, completion of training is very significant. 

Considering that the matching rate for the sample studied here is 37.82%, completion of training raising 

the chance of matching by 17 to 20 percentage points, depending on specifications, is substantial. 

However, whether training completion is controlled or not does not change the coefficients on educational 

degree attainments. The result that matching is the preferred option of employment is not affected. 

 Another piece of evidence that supports that matching is a preferred option for employment is the 

increasing coefficients on the exit years over time. Labor market was the least favorable in 2004 and 

gradually improved over time until 2007. Higher matching rates during favorable period imply that 

people take the matching job when it is more available. 

 Ethnicity is another important factor. Asians and African Americans show substantially lower 

matching rates than white participants while Hispanic participants have the matching rates 6 percentage 

points higher than white participants. This pattern becomes much weaker when the states of residency are 

controlled indicating a high concentration of certain ethnic groups in few states. The evidence can be 

found in Table 11. Hispanic participants account for a large fraction of participants in state of California, 

Colorado, Idaho, New Hampshire, New Jersey, New Mexico, and Nevada. These states, except for 

Colorado and New Jersey, have matching rates far higher than the matching rates for all trainees with 

valid OSTC and OCE. Table 11 also explains the weakening of the impacts of being black. Georgia and 

South Carolina have a large number of black participants and their matching rates are substantially lower 

than the matching rates for the valid trainee sample. However, Asian trainees still show matching rates 

about 10 percentage points lower than white trainees regardless of the state control. 

IV.2. Effects of training and training with match on Outcome Measures 

The conventional literature on the evaluation of training programs compares trainees to nontrainees. That comparison asks a simple question – Does training work? However, it does not answer why 

training works. If trainees’ outcome measures are significantly better than those of non-trainees, it could 

be because what they need is successful transition away from the previous occupations – for which any 

type of training suffices - or because they need transition into a specific occupation, so that which training 

you get becomes very relevant for reemployment. 

If the outcome measures of non-trainees do not differ from those of trainees, the failure might 

arise from three possible reasons. First, training was not necessary and all participants generally had 

13 
marketable skills already. Second, training does not work properly; that is, the training programs are 

poorly designed to teach the participants proper skill sets. Lastly, training works, but occupational choices 

are poorly made; that is, training did not help participants to earn the skills that are in demand – the 

occupations with vacancies. It is important to figure out which is causing the failure of training programs 

because each cause has different solutions. 

All these possible scenarios can be summarized by two questions: does training work in general?, 

and does a choice of training occupation affect the impact of training? For the first question, instead of 

simply comparing trainees and non-trainees, comparison will be made between trainees without a match 

and non-trainees to truly test the general validity of training. The second question can be answered by 

comparing trainees with a match and trainees without a match. Using the following estimation equation, 

both questions can be answered. 

 

(2) 

The outcome variable, Yi, is any one of the outcome measures – reemployment, post-participation 

earnings, wage replacement rate, and retention. Probit analysis is used for reemployment and retention 

and OLS is used for earnings and wage replacement rate. TR’s are indicator variables for various types of 

training. is 1 if participant i received occupational skills training. , , and are 

indicator variables for remedial training, OJT, and customized training, respectively. is the 

matching variable – 1 if matched, 0 otherwise. As in the previous section, first or second degree match at 

8-digit level is used for all analyses. X is a vector of individual characteristics of participants used in the 

first analysis. D4i, D5i, and D6i are dummy variables for exit years. 

α1 captures the impact of matching on the various outcome measures. α2, on the other hand, 

captures the effect of skill training without leading to a match. Therefore, the difference between α1 and 

α2 will provide the comparison between skill trainees with and without a match. α2 itself measure the 

impact of occupational skills training over no training. α3 to α5 capture the effect of remedial training, 

OJT, and customized training. Positive α1 implies that the choice of training occupation is relevant to the 

outcome, and the occupational choice should be made with caution. If neither α1 and α2 is significant, it 

comes down to either ‘occupational skills training is not necessary’ or ‘training programs are not a good 

source of new skills.’ Good outcome measures for both trainees and non-trainees would signal toward the 

former, and poor overall performance would signal the latter. 

As shown in the previous analyses, the impacts of various individual characteristics and programrelated variables differ greatly depending on whether states of residency are controlled or not. This is 

caused by differences in participants’ characteristics as described in Table 11. Different states also 

experience different labor market situations. Without controlling for the location of participants, the 

analyses will be influenced by the link between overall participant characteristics – such as educational 

attainment – and labor market situation of each state. In order to avoid such a bias, only specifications 5 

to 8 will be analyzed in this section. These four specifications will be shown with two different measures 

of education, degree attainment (tables marked with ‘a’) and years of schooling (tables marked with ‘b’). 

14 
a. Reemployment Rate 

The dependent variable is an indicator variable for employment. It takes the value 1 if a 

participant is employed for at least one quarter during three quarters following exit. In this analysis, the 

major comparison is between trainees and non-trainees using the Probit analysis. Matching should not be 

included in this analysis because the participants with match are necessarily all employed. Since matching 

variable is not included in the analysis, observations from Oklahoma and Virginia are included. Table 13 

summarizes the results for analyses with degree attainment and years of school as the measure of 

education. 

 First, all types of training programs influence the chance of reemployment substantially. The 

biggest effect comes from OJT. This is expected because employers can enjoy the benefit only by actually 

hiring the trainees. Receiving occupational skills training improves the chance of reemployment by 5 

percentage points. If a trainee does not complete the skills training, employment rate is only2 percentage 

points higher than those of non-trainees, completion of training raises the chance of reemployment by 

additional 4 percentage points. Customized training lowers the chance by 4 to 6 percentage points. 

Customized training is the training program that is specially designed for incumbent workers to meet the 

skills needs of a specific firm. That is, if a trainee is not hired by the firm, the skills acquired through this 

training may not be applicable to other jobs making reemployment rather difficult. 

 Educational attainment, ethnicity, and, age affect the reemployment rate by similar magnitude. 

While having associate degree is the most beneficial in terms of the chance of reemployment (6 

percentage points higher than less than high school), all educational level display similar reemployment 

rates, 3 to 4 percentage points higher that of trainees with less than high school education. The 

coefficients on age variables show a downward trend with age. Younger age (16 to 40) raises the chance 

of reemployment by approximately 3 percentage points compared to age group between 40 and 50, but 

the most significant impact of trainees’ age is the negative effect of old age. Especially, participants 

between the ages of 61 and 65 showing substantially lower reemployment rates compared to other age 

groups. This could be caused by voluntary withdrawal from the job market by participants who are close 

to retirement. Among 9,069 participants between 61 and 65 who obtained a training requirement waiver, 

810 (8.9%) reported retirement as a reason. This is surprisingly high considering that only 1.02% of 

training waivers issued, for all age groups, are issued for the reason of retirement. 

b. Wage Replacement Rates 

 Wage replacement rates are the ratio of post-participation earnings to pre-participation earnings. 

Each earnings level takes the average quarterly earnings during three quarters preceding participation and 

three quarters following exit. For the same reason as in the previous section, observations from 

Oklahoma and Virginia are excluded. Trainees who received occupational skills training without OSTC 

or are employed without OCE are also omitted. Again, each specification will be analyzed with or 

without pre-participation earnings. The results are summarized by Table 14. 

 The impacts of matching on the wage replacement rates are represented by the differences 

between the coefficients on matching and those on occupational skills training. The coefficient on the 

15 
occupational skills training will capture the impact of receiving the training and failing to obtain a match. 

Receiving occupational skills training without succeeding in getting a match has a negative impact on the 

wage replacement rates. This could be driven by the selection into skills training by trainees with limited 

skill sets. The impact of obtaining a match is substantial; it raises the wage replacement rates by 4 to 5 

percentage points compared to the trainees without a match. Where OSTCs are controlled, the impacts are 

reduced to 3 percentage points. Again, OJT is proved to be highly beneficial to trainees by raising the 

wage replacement rates by 4 percentage points over those of non-trainees. The effect of customized 

training is also positive. This implies that, though receiving customizing training is risk in the sense that it 

could lower the chance of reemployment by making the trainee’s skill sets too specific, the risk-taking is 

rewarded by higher wage replacement rate once hired. 

The large and negative coefficient on pre-participation earnings simply implies that it is hard to 

find a job with a comparable pay for high-earning (pre-layoff) workers and it is relatively easy to find one 

for low-earning workers. This indicates convergence in earnings after participation. The standard 

deviation of post-participation earnings is $4,011.10, which is 18.44% smaller than that of preparticipation earnings. On the same line of logic, it is interesting to see the coefficients on educational 

attainment. When pre-layoff earnings level is not controlled, the impacts of different levels of educational 

attainment are very small, with relatively larger positive impacts of lower educational attainment. Once 

the pre-layoff earnings are controlled, higher education has a huge impact on the wage replacement 

meaning highly educated workers find a job with a higher pay even if the wage replacement rate is not 

superior to that of less educated workers. 

 The most important worker characteristic in determining the wage replacement rates is their age. 

The negative impacts of old age are very large and are not due to the high pre-layoff tenure, which tends 

to imply a higher wage level, since the coefficients are not much affected by controlling for the level of 

pre-layoff earnings level. The positive impact of young age, though, is driven by low pre-layoff earnings 

level. When pre-layoff earnings level is not controlled, the impact of different level of educational 

attainment is very small. 

Gender is significant and very much affected by pre-participation earnings. Male participants 

display wage replacement rates one percentage point higher than those of female participants. This 

difference in wage replacement rates increase to 14.4 percentage points indicating that male participants 

were paid more before participating the TAA program. Hispanic and black participants show higher wage 

replacement rates compared to white participants, but this is due to low pre-participation earnings. This 

implies that they benefit more from the program participation. Asian participants display higher wage 

replacement rates regardless of controlling for pre-layoff earnings. Exit years also matter for the wage 

replacement rates. In years with a better labor market situation (2007), participants achieved higher wage 

replacement than they would have received in a relatively bad year (2004). 

c. Post-Participation Earnings 

 The dependent variable is the logarithm of average quarterly earnings during the three quarters 

following exit. This analysis utilizes the matching variable, so the observations from Oklahoma and 

Virginia are excluded. Participants who received occupational skills training but without OSTC and 

16 
participants who are employed after exit but without OCE are also excluded. For each specification, the 

analysis is attempted with or without the log of earnings prior to participation. Different ability levels will 

determine not only the post-participation earnings, but also the pre-participation earnings. So I focus more 

on the specifications with pre-participation earnings control. Training completion is dropped from 

analysis because the success of training is proxied by matching variable. Table 15 summarizes the results 

with two measures of educational attainment. 

 Again, the impacts of matching are represented by the differences between the coefficients on 

matching and occupational skills training. Receiving occupational skills training without obtaining a 

match has a negative impact on post-participation earnings and this is not affected by controlling for the 

pre-layoff earnings. This again indicates the limited skill sets that these trainees have other than the skills 

used in the previous employment. Obtaining a match eliminates the effect, indicating that a success in 

occupational skills training compensates the lack of marketable skills. This is exactly the objective of the 

training provision of the TAA program. This is the main proof of the importance of the training program 

in reducing the adjustment cost of these workers. 

Remedial training has negative impacts on the post-participation earnings. Participants who 

received remedial training represent the lower extreme of ability spectrum, hence lower earnings even 

with intensive training. The positive impacts of OJT are consistent with the findings from other outcome 

measures. Also, customized training does raise the potential of high earning once hired, making the risktaking worthwhile. 

Despite of the significance of training in affecting the participants’ post-participation earnings 

level, the earnings level is largely determined by the level of educational attainment. Controlling for prelayoff earnings do not change the power of educational attainment in predicting earnings determination. 

Also interesting to note is that the importance in educational attainment is not driven by different 

occupational choices by participants with different level of education. In specifications 7 and 8, where 

OSTCs are controlled, the coefficients are almost identical to the corresponding specifications 5 and 6. 

The influence of gender is also very large. Males in general have higher earnings than females, 

and the reduction in coefficient with inclusion of pre-participation earnings indicates that they also earned 

more than female workers prior to participation. Age variables show a bell-shaped link to the earnings 

level. Participants between the ages of 41 and 50 make the highest earnings, followed by the 31 to 40 age 

group. Younger workers and older workers generally have lower earning. This pattern is preserved when 

pre-participation earnings are controlled with a shift of the highest earnings group to participants between 

the ages of 31 and 40. Exit years also matter for earnings. In years with a better labor market situation, 

participants receive higher earnings than they would have received in a relatively bad year. 

d. Retention Rates 

The indicator variable for retention is 1 if a participant is still employed during the remainder of 

the observation period (up to three quarters) once he/she is employed and 0 otherwise.21 Probit analysis 

 

21 The retention variable is constructed using three employment indicator variables. The TAPR reports whether the participant is 

employed during each of three quarters of observation periods, providing three employment indicators. One caveat of this 

method of constructing the retention is that being reported as employed for two consecutive quarters does not necessarily mean 

that the participant is working for the same employer during the two quarters. 

17 
is used again for the analyses. Since the matching variable is used, observations from Oklahoma an

Virginia are omitted as well as the occupational skills trainees without OSTC and the trainees who are 

reemployed without OCE. Table 16 summarizes the results. The analyses with pre-participation earnings 

do not differ significantly from the ones without it, so are not reported. 

d 

While matching has little impacts on retention rates, various training program is largely beneficial 

to participants in terms or retention. Occupational skills training and OJT improves the retention rate, but 

the coefficients of occupational skills training become very small and insignificant as the occupation 

groups are controlled. This implies that occupational skills trainees choose occupations with more stable 

employment. The impacts of OJT are robust across specifications. High retention rates for OJT trainees 

can be attributed to the fact that they are trained in skills specific to the firms of reemployment. The 

impacts of ethnicity and gender are negligible for all specifications. Overall, the retention rate for the 

entire sample is quite high (around 90%), and the variables of individual characteristics and service 

received have very small effects even if they are significant. 

V. Conclusion 

Technology continues to advance and it becomes easier to utilize various resources in foreign 

countries by means of international trade. Competition from cheap imported products has been blamed for 

replacing domestic goods and jobs of workers who produced the goods that were replaced. With the rise 

of offshore outsourcing, such replacement of domestic workers with foreign workers has become more 

direct and massive. As most of labor-intensive manufacturing production no longer takes place in the 

U.S., many manufacturing workers go through painful layoffs. With the improvement of network 

technology, this trend is expanding to service workers. 

In order to reduce the adjustment costs of these workers, the DOL administers a dislocated 

worker program called TAA. Since the workers who are displaced by rising import competition tend to be 

less educated and have limited skill sets, TAA focuses on providing its participants the opportunities to 

acquire new skills that are in demand in order to improve their chance of reemployment. This paper 

investigates whether such occupational skills training truly helps participants to find stable employment 

that also pays relatively well. This paper measures this success of training provision by looking at the 

match between the occupations of training and entered employment. 

 First, the hypothesis that matching is the result of successful training is examined by measuring 

the impact of various individual characteristics of participants on the chance of matching. Under the 

alternative hypothesis where a match is not necessarily a preferred option of employment, the probability 

of matching shows a bell-shaped curve over the ability level. Low ability trainees show a low matching 

rate because they are not capable of succeed in training program to obtain a match. High ability trainees 

show low matching rate because they voluntarily withdraw from a match because they have other 

marketable skill that would give them better pay and work environment. In the analysis with educational 

attainment as a proxy for the ability level, the bell-shape pattern in the coefficients for educational 

attainment is not observed in any specification. I also find that exit years with favorable labor market 

situation are associated with higher matching rates, supporting the validity of matching as a measure of 

training success. 

18 
The main question of this paper is whether success in training, which is indicated by a match 

between the training occupation and the occupation of employment, would lead to a better outcome – 

measured by four performance measures: reemployment rate, post-participation earnings level, wage 

replacement rate, and retention rate. The analysis of impacts of various services on reemployment is 

based on comparison between trainees and non-trainees. Since construction of matching variable requires 

the occupation code of reemployment, the impact of matching on reemployment rate cannot be analyzed. 

All types of training programs influence the chance of reemployment substantially. The biggest effect 

comes from OJT, raising the reemployment rate by 10 to 11 percentage points. Occupational skills 

training raises the rates by 5 percentage points, but 4 percentage points are contingent on completion of 

training. Other worker characteristics, such as educational attainment, ethnicity, and age, affect the 

reemployment rates by similar magnitude to the impacts of training programs. 

 Matching affects the wage replacement rates significantly. Trainees with a match display wage 

replacement rates that are 4 to 5 percentage points higher than those of trainees without a match, 

indicating the importance of a match in achieving better post-participation outcome. Trainees without a 

match display the wage replacement rates lower than the rates of non-trainees. This is due to the limited 

skill sets that lead them into training enrollment in the first place. OJT again proves very beneficial to the 

participants by improving the rate by 4 percentage points. The most important worker characteristic in 

determining the wage replacement rates is their age. The negative impacts of old age are very large and 

are not due to the high pre-layoff tenure, hence higher pre-layoff earnings. 

 Participants who received occupational skills training but failed to achieve a match experience a 

lower post-participation earnings compared to non-trainees. This again indicates the limited skill sets that 

these trainees have other than the skills used in the previous employment. Obtaining a match eliminates 

these negative impacts, indicating the success in occupational skills training compensates their lack of 

marketable skills. This is the main proof of the importance of the training program (and succeeding in it) 

in reducing the adjustment cost of these low-skilled workers. Matching does not influence retention rate 

much. Retention rate is fairly stable across various individual characteristics and services rendered by the 

TAA program. Occupational skills training is relatively influential raising the retention rates by 2.3 to 2.7 

percentage points. However, these effects decrease substantially in magnitude when occupation groups of 

training are controlled. OJT has the largest impact of raising the retention rate by approximately 3 

percentage points. 

Overall, various training provision under the TAA program is effective in reducing adjustment 

cost of the participants by providing better employment opportunities. Receiving occupational skills 

training itself does not guarantee a good outcome. The skill trainees who do not complete the program 

display the reemployment rate only 2 percentage points higher than non-trainees. Skills trainees without a 

match tend to show post-participation earnings performance inferior to non-trainees potentially due to 

their limited skill sets. Success in training program indicated by a match offsets (more than offsets for 

some outcome measures) the negative impact of limited skill sets of these participants by providing the 

skills that generate good employment opportunities. This is the main goal of the training provision of the 

TAA program. The results from this analyses provide evidence that the TAA program’s focus on training 

program should continue and more effort should be placed on career assessment and counseling so that 

19 
participants can choose an occupation that is suitable for their needs and abilities which will directly lead 

to employment in that occupation. 

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Angrist, Joshua D., and Alan B. Krueger, ”Empirical Strategies in Labor Economics,” Handbook of 

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Baicker, Katherine, and M. Marit Rehavi, “Policy Watch: Trade Adjustment Assistance,” Journal of 

Economic Perspectives, Volume 18, No.2., 2004, pp.239-255 

Corson, Walter, and Paul T Decker, “International Trade ad Worker Displacement: Evaluation of the 

Trade Adjustment Assistance Program,” Industrial and Labor Relations Review, Vol.48, No.4. (Jul 1995), 

pp.758-774 

General Accounting Office (GAO), “Trade Adjustment Assistance: Trends, Outcomes, and Management 

Issues in Dislocated Worker Programs,” GAO-01-59, October 2000 

Heckman, James, Robert LaLonde, and Jeffrey Smith, “The Economics and Econometrics of Active 

Labor Market Programs,” Handbook of Labor Economics, Volume 3A, Chapter 31, 1999, pp. 1865-2097 

Hummels, David, Jun Ishii, Kei-Mu Yi, “The Nature and Growth of Vertical Specialization in World 

Trade”, The Journal of International Economics, 2001 

Kletzer, Lori G., “Imports, Exports, and Jobs: What Does Trade Mean for Employment and Job Loss?,” 

W.E. Upjohn Institute for Employment Research, 2002. 

Rasmussen, Anders Rue, & Niels Westergaard-Nielsen, “The Impact of Subsidies on Apprenticeship 

Training,” Aarhaus School of Business mimeo, 1999 

Yeats, Alexander J., “Just How Big is Global Production Sharing?,” Policy Research Working Paper 

Series No. 1871, the World Bank, 1998 

20 
Table 1. Benefits and Services Provided by the TAA Program 

Services and Benefits Description

Rapid Response Assistance Inform workers of various services available for them.

Available for all displaced workers, certification not necessary

Reemployment Services Assist workers with reemployment by providing career counseling and assessment, job search

related workshops, job search assistance and referrals. Career assessment determines whether

and which training is beneficial to each participant.

Relocation Allowance When a participant gets a job that requires moving, the program compensates 90% of moving

expenses with a stipend of three weeks’ wage. Maximum of $1,250(a)

Job Search Allowance Compensates 90% of the cost of job searches outside commuting area. Maximum of $1,250(a)

Training Participants are eligible for training up to 104 weeks.

To be eligible, the following criteria must be met:

    i) no suitable employment

    ii) training would be beneficial to the worker

    iii) training would lead to employment

    iv) training must be available

    v) the workers would be able to complete the training

    vi) training cost is reasonable

Training waiver may be issued to

a participant if  

    i) she will be recalled soon

    ii) she has marketable skills

    iii) she has a health problem

    iv) training is not available

    v) enrollment is not available

Classroom

Training

Targeted to obtain skill sets that are specific to an occupation of choice.  

Training provided by local community colleges or vocational training schools.

Remedial

Training

Eg. Literacy, English as a Second Language (ESL), and GED

Can occur concurrently with other training or during additional 26 weeks from

the end of regular training

On the Job

Training (OJT)

If a participant is employed under OJT, the TAA program pays 50% of the wage

rate to the employer during the training

Customized

Training

The training is customized to tasks of a specific firm, but the trainees are not

necessarily employed by this firm.

Trade Readjustment

Allowance (TRA)

A participant is eligible to receive income support for up to 104 weeks as the following:

     i) 26 weeks following separation: UI

     ii) 26 weeks following exhaustion of UI: Basic TRA

     iii) 52 weeks following exhaustion of Basic TRA: Additional TRA

Basic TRA During the first 26 weeks from exhaustion of UI. This requires training

enrollment unless(b)  

i) the participant has obtained a training waiver

ii) has completed approved training

Additional TRA During 52 weeks from exhaustion of Basic TRA. Training enrollment is

required without exception.

Remedial TRA Participants who are enrolled in remedial training qualify for 26 weeks of

21 
income support in addition to 104 weeks of UI, basic TRA, and additional TRA.  

Health Insurance Tax Credit

(HITC)(c)

This is a subsidy of 65% of the qualifying health insurance premium paid. The subsidy will be

paid as a Tax Credit. All TAA and NAFTA‐TAA participants all are eligible.

Source: Employment and Training Administration, US Department of Labor (http://www.doleta.gov/tradeact/benefits.cfm) 

(a) Max $800 prior to Reform Act of 2002 

(b) These exceptions do not apply to NAFTA-TAA participants. Training enrollment is required for NAFTA-TAA 

participants to receive basic TRA. 

(c) This is added to TAA benefits by 2002 Reform Act

Table 2. Summary of TAA Performance Goals and Outcomes 

Wage

Replacement

Rate (%)(ii)

Average 6‐month

Wages ($)(ii)

Reemployment

Rate (%)

Retention Rate

(%)

Fiscal

Year

Number of

TAA

Certifications

Issued

Estimated(i)

Number of

Workers

Covered by

Certifications Goal Outcome Goal Outcome Goal Outcome Goal Outcome

2003 1,894 197,748 90 73 78 62 90 86

2004 1,813 149,705 90 74 70 63 88 89

2005 1,564 118,024 80 76 70 70 89 91

2006 1,448 119,605 80 89 70 72 85 90

2007 1,465 146,898 12,000 13,915 70 70 85 88

2008 1,681 163,004 14,050 14,269 73 69 91 90

2009 3,131 337,071 13,386 15,077 65.2 69 87.5 88

2010 2,715 280,319 13,319 14,487 64.9 58.2 87.3 79.7

Source : ETA, US Department of Labor (http://www.doleta.gov/tradeact/taa_stats.cfm) 

(i) These figures are constructed from the number of workers indicated in petition forms. The report is supposed to be 

an approximate number; therefore, these figures differ from the actual number of workers covered by the petitions 

certified in each year. 

(ii) The performance measure Wage Replacement Rate is replaced by Average Annual Earnings since fiscal year 2007. 

22 
Table 3. Summary Statistics of TAA and NAFTA-TAA Participants across Years of Program Exit 

Year of Program Exit 2004 2005 2006 2007 All

Number of Participants 27,559(ii) 45,783 43,972 25,987(ii) 143,301

Participant Characteristics(i)

    Gender

        Male 49.51 51.36 52.37 53.21 51.65

        Female   50.49 48.64 47.63 46.79 48.35

    Age at Participation

        Under 30 10.25 9.53 9.32 9.11 9.53

        30‐44 39.88 38.13 35.99 33.51 36.97

        45‐54 32.17 33.15 33.57 34.89 33.40

        55 and more 17.70 19.20 21.12 22.50 20.10

        Mean Age at Participation (years) 44.02 44.58 45.12 45.62 44.83

    Ethnicity

        Hispanic/Latino 13.93 10.45 6.22 5.65 8.87

        American Indian/Alaska Native 0.99 0.77 1.00 0.94 0.92

        Asian 4.00 3.48 2.32 2.31 2.99

        Black or African American 16.07 13.79 15.14 16.13 15.07

        Hawaiian Native or Pacific Islander 0.44 0.35 0.29 0.28 0.33

        White 64.57 71.16 75.04 74.69 71.82

    Education

        Less than High School 19.88 18.45 20.16 20.49 19.63

        High School Graduate or Eqv. 54.34 55.73 55.95 55.43 55.49

        Some Post High School 19.83 19.61 17.81 17.37 18.70

        College Graduate or Eqv. 5.47 5.78 5.37 5.95 5.62

        Not Identified 0.48 0.43 0.71 0.76 0.56

    English Proficiency

        Not Proficient 5.13 5.23 4.20 4.13 4.70

Benefits & Services Received(i)

    Received Any Training 76.12 76.61 69.76 75.44 74.20

        Among Trainees  

           Occupational Skill Training 70.82 71.76 66.40 54.95 66.98

           On‐The‐Job Training 3.78 3.00 0.88 0.62 2.14

           Remedial Training 14.33 14.63 16.97 16.32 15.57

           Completed Training 68.90 65.82 59.12 48.96 61.39

           Average Weeks of Training 57.64 wks 61.42wks 63.67wks 61.25wks 61.19wks

           Rec’d Travel Allowance 11.55 14.57 17.93 13.34 14.72

           Rec’d Subsistence Allowance   1.91 1.32 1.14 0.72 1.28

    Training Waiver   56.07 57.21 70.48 76.73 64.60

           Recall 7.03 8.50 8.98 1.84 6.98

           Marketable Skills 36.93 43.77 59.76 72.45 54.16

           Retirement 0.97 1.16 1.91 2.11 1.58

           Health problem 1.88 0.89 0.27 0.58 0.78

23 
           Enrollment/Training Unavailable 23.68 20.39 19.30 20.30 20.55

           Reason unknown 29.50 25.28 9.77 2.72 15.94

    Basic TRA 61.29 63.52 61.08 56.83 61.13

    Additional TRA 30.40 35.57 30.87 26.18 31.43

    Job Search Allowance 1.07 1.12 1.59 1.99 1.41

    Relocation Allowance 1.03 1.14 1.47 1.46 1.28

(i) Units in % unless specified otherwise. 

(ii) Numbers of participants in 2004 and 2007 are smaller because the data are not collected throughout the whole year. 

Table 4. Occupation Categories in O*NET System 

Training Occupation

(OSTC)(i)

Reemployment

Occupation

(OCE)(ii)

Group Occupation Group Name Obs. % Obs. %

11 Management 2,889 4.91 1,168 3.23

13 Business and Financial Operations 1,701 2.89 644 1.78

15 Computer and Mathematical 3,848 6.54 1,284 3.55

17 Architecture and Engineering 2,170 3.69 1,326 3.66

19 Life, Physical and Social Science 404 0.69 155 0.43

21 Community and Social Services 943 1.6 327 0.9

23 Legal 444 0.76 124 0.34

25 Education, Training, and Library 1,892 3.22 810 2.24

27 Arts, Design, Entertainment, Sports, and Media 578 0.98 237 0.65

29 Healthcare Practitioners and Technical 5,876 9.99 2,127 5.88

31 Healthcare support 6,301 10.72 2,508 6.93

33 Protective Service 643 1.09 411 1.14

35 Food Preparation and Serving Related 500 0.85 580 1.6

37 Building and Grounds Cleaning and Maintenance 303 0.52 789 2.18

39 Personal Care and Service 1,661 2.82 698 1.93

41 Sales and Related 391 0.66 1,122 3.1

43 Office and Administrative Support 8,652 14.71 4,130 11.41

45 Farming, Fishing, and Forestry 88 0.15 230 0.64

47 Construction, and Extraction 1,814 3.09 1,145 3.16

49 Installation, Maintenance, and Repair 5,846 9.94 2,637 7.28

51 Production 7,122 12.11 10,520 29.06

53 Transportation and Material Moving 4,688 7.97 3,196 8.83

55 Military Specific 44 0.07 36 0.1

all 58,798 100.00 36,204 100.00

 (i) Figures related to OSTC are drawn from the observations with valid 6-digit occupation codes of training. 

(ii) Figures related to OCE are drawn from the observations with valid 6-digit occupation codes of entered employment. 

24 
Table 5. Occupation skills Training received and reporting quality by states 

States

            No. of

Obs.

% of all

sample

            No. of

OCC skills

trainees

% of state

observations

            No. of

obs with

valid OSTC

% of OCC

skill trainees  

AK 174 0.12 168 96.55 168 100.00   

AL 1,175 0.82 1,092 92.94 6 0.55   

AR 1,711 1.19 1,691 98.83 0 0.00   

AZ 1,220 0.85 520 42.62 0 0.00   

CA 3,295 2.3 3,024 91.78 3,024 100.00   

CO 1,447 1.01 806 55.70 708 87.84   

CT 1,652 1.15 757 45.82 708 93.53   

DE 115 0.08 79 68.70 17 21.52   

FL 934 0.65 869 93.04 633 72.84   

GA 3,653 2.55 2,759 75.53 1,645 59.62   

IA 1,151 0.8 1,003 87.14 927 92.42   

ID 1,304 0.91 640 49.08 557 87.03   

IL 4,594 3.21 3,208 69.83 558 17.39   

IN 8,007 5.59 3,105 38.78 900 28.99   

KS 1,164 0.81 811 69.67 675 83.23   

KY 3,222 2.25 1,683 52.23 1,581 93.94   

LA 116 0.08 90 77.59 85 94.44   

MA 3,704 2.58 2,506 67.66 1,076 42.94   

MD 960 0.67 591 61.56 119 20.14   

ME 3,295 2.3 1,806 54.81 1,773 98.17   

MI 10,123 7.06 3,263 32.23 3,262 99.97   

MN 1,570 1.1 1,146 72.99 106 9.25   

MO 1,750 1.22 1,216 69.49 964 79.28   

MS 2,892 2.02 1,477 51.07 1,477 100.00   

MT 454 0.32 189 41.63 111 58.73   

NC 13,917 9.71 12,254 88.05 0 0.00   

ND 66 0.05 41 62.12 41 100.00   

NE 249 0.17 188 75.50 182 96.81   

NH 809 0.56 314 38.81 217 69.11   

NJ 1,776 1.24 1,093 61.54 977 89.39   

NM 235 0.16 217 92.34 209 96.31   

NV 49 0.03 44 89.80 42 95.45   

NY 8,398 5.86 2,417 28.78 624 25.82   

OH 8,258 5.76 3,399 41.16 3,374 99.26   

OK 1,452 1.01 1,452 100.00 1,444 99.45   

OR 1,886 1.32 1,319 69.94 1,289 97.73   

RI 1,042 0.73 411 39.44 407 99.03   

SC 7,526 5.25 3,885 51.62 3,344 86.07   

SD 543 0.38 423 77.90 423 100.00   

TN 11,099 7.75 2,826 25.46 783 27.71   

TX 5,742 4.01 2,639 45.96 2,638 99.96   

UT 461 0.32 372 80.69 372 100.00   

VA 5,709 3.98 4,356 76.30 4,030 92.52   

VT 232 0.16 177 76.29 177 100.00   

WA 6,333 4.42 3,367 53.17 3,347 99.41   

WI 6,095 4.25 3,807 62.46 1,156 30.37   

WV 1,725 1.2 490 28.41 325 66.33   

WY 16 0.01 15 93.75 14 93.33   

25 
Total 143,300 100 80,005 100.00 46,495 58.12   

Table 6. Matching Rates at Various Definitions of Matching 

    (a) All valid observations

8 digit 6‐digit

Total sample Match (%) Total Sample Match (%)

First degree 17,760 47.98    24,802 44.33

Second degree 17,760 2.46    24,802 2.07

Either 17,760 50.44    24,802 46.40

   (b) Excluding observations from Oklahoma and Virginia

8 digit 6‐digit

Total sample Match (%) Total Sample Match ((%)

First degree 14,155 34.74    21,197 34.86

Second degree 14,155 3.09    21,197 2.42

Either 14,155 37.82    21,197 37.28

26 
27 
Table 7. Summary of the Performance Measures of the TAA Program across Exit years and Pre-participation Ea

a. Across Exit Years Reemployment Rate (%)(i) Wage Replacement Rate (%) 

Exit Year Number

of Obs.

%

Trainees

No of obs.

with both

OSTC&OCE

% Match

Non‐

Trainees TraineesiI

Non‐

trainees

Trainees

all

Trainees

w/

match(iii)

Trainees

w/o

match(iii)

Non‐

trainee

Tr

2004 27,559 76.12 4,739 33.32 68.44 82.27 77.71 86.97 89.08 90.19 88.57 2005 45,783 76.62 7,318 36.14 70.15 81.20 81.20 88.43 96.40 97.38 86.79 2006 43,971 69.76 6,196 38.85 71.07 79.96 83.53 88.76 98.49 97.27 89.00 2007 25,987 75.44 2,944 43.21 72.66 78.92 85.08 87.24 96.09 88.85 89.41 

All 143,300 74.20 21,197 37.28 70.61 80.64 82.15(iv) 88.00(iv) 95.49(iv) 94.46(iv) 88.36 

b. Across Pre‐Participation Earnings levels Reemployment Rate (%)(ii) Wage Replacement Rate (%) 

Quarterly Earnings

prior to

Participation ($)

Number

of Obs.

%

Trainees

No of obs.

with both

OSTC&OCE

% Match

Non‐

Trainees Trainees

Non‐

trainees

Trainees

all

Trainees

w/

match

Trainees

w/o

match

Non‐

trainees

Tr

0 – 2,000 6,515 76.59 1,323 36.96 60.92 79.14 2,699.08 2,135.60 1,821.17 1,356.97 84.53 

2,001 – 4,999 31,833 75.27 4,454 35.20 67.62 79.48 119.77 124.71 147.20 140.17 86.47 5,000 – 9,999 57,486 73.44 7,996 38.11 70.85 82.36 79.26 82.48 90.83 87.38 89.63 

10,000 – 19,999 29,682 71.44 4,590 38.69 75.42 83.03 64.38 65.52 66.87 72.80 88.73 20,000 – 29,999 3,955 63.03 503 35.59 71.89 80.43 50.39 50.29 45.88 57.22 88.82 30,000 – 39,999 1,008 55.06 106 33.96 62.91 75.86 37.53 38.90 27.82 38.55 85.04 

40,000 or higher 561 59.54 49 18.37 52.86 76.95 25.72 31.33 24.23 32.75 87.50 

All 131,040(v) 73.08 19,025 37.35 80.64 70.61 82.15(iv) 88.00(iv) 95.49(iv) 94.46(iv) 88.46 

(i) Employed at any point during the three quarters of observation following program exit. (ii) Trainees include participants who received training of all types. (iii) Trainees with or without a match only includes participants who received occupational skills training. (iv) The average wage-replacement rate for each group of participants is calculated excluding the observations with quarterly earnings of $2,000 or less prior to participa

for these participants, the average wage-replacement rate would be misleadingly high if they are included in calculation. The average wage-replacement rates includi

non-trainees, 191.24% for all trainees, 207.40% for trainees with a match, and 175.27% for trainees without a match. 

 (v) Total number of observation in panel (b) is smaller than that of panel (a) because 12,260 observations either do not have earnings record or have invalid entries.

28 
Table 8. Participant Characteristics and Matching Rates for Occupational Groups of Training 

Ethnicity (%) Education (%)

OSTC(i)

Group Occupation Group Name

%

Match(ii) % Male

%

Limited

English White Asian Black Hisp Other

Less

than

HS

High

Schl

11 Management 23.12 49.05 1.97 66.72 1.74 14.20 3.02 14.33 4.95 45.21 13 Business and Financial Operations 27.13 39.00 2.14 64.02 2.59 13.27 3.26 16.86 4.67 45.47 15 Computer and Mathematical 25.77 66.51 2.89 57.14 4.53 11.29 5.07 21.98 5.85 43.89 17 Architecture and Engineering 31.19 85.45 2.54 59.36 4.26 8.01 4.00 24.37 4.61 42.66 19 Life, Physical and Social Science 15.26 56.67 3.26 60.14 2.80 10.72 2.10 24.24 2.10 41.72 21 Community and Social Services 25.06 31.38 1.94 51.18 1.74 29.89 4.09 13.10 5.94 47.59 23 Legal 40.74 20.93 1.27 60.13 1.05 11.39 3.38 24.05 3.59 49.37 25 Education, Training, and Library 33.46 31.50 7.32 51.92 2.35 23.36 8.09 14.28 13.10 47.31 27 Arts, Design, Entertainment, Sports, and Media 26.72 53.91 4.56 53.93 4.87 8.49 4.72 27.99 5.97 43.40 29 Healthcare Practitioners and Technical 46.41 27.82 2.11 58.89 2.42 15.36 5.29 18.04 5.93 57.60 31 Healthcare support 53.32 11.14 7.92 51.70 2.28 20.67 11.94 13.41 16.54 64.90 33 Protective Service 22.19 65.14 1.47 60.62 0.59 16.37 3.54 18.88 6.19 62.98 35 Food Preparation and Serving Related 30.25 37.42 18.15 40.83 5.26 10.34 8.17 35.39 27.59 50.45 37 Building and Grounds Cleaning and Maintnc. 47.65 51.08 22.10 41.16 11.60 8.29 4.14 34.81 35.08 46.96 39 Personal Care and Service 41.80 16.20 18.65 31.32 11.31 17.74 14.55 25.07 24.84 54.63 41 Sales and Related 18.25 45.62 5.49 58.81 3.43 16.48 3.89 17.39 21.51 42.79 43 Office and Administrative Support 23.54 13.78 7.49 56.15 2.25 17.37 11.64 12.59 15.08 58.33 45 Farming, Fishing, and Forestry 6.45 45.05 3.30 74.73 2.20 7.69 2.20 13.19 12.09 48.35 47 Construction, and Extraction 36.62 95.09 4.42 62.11 1.56 10.60 9.20 16.53 12.16 60.40 49 Installation, Maintenance, and Repair 37.83 94.54 3.81 62.05 3.42 10.91 7.58 16.04 11.23 60.46 51 Production 33.28 48.84 3.98 64.13 1.93 18.88 4.65 10.42 32.13 50.58 53 Transportation and Material Moving 66.47 84.05 4.49 60.87 0.50 19.48 9.13 10.01 23.03 60.69 55 Military Specific 33.33 48.89 0.00 66.67 0.00 11.11 0.00 22.22 6.67 80.00 

Total 37.28 47.05 5.16 58.02 2.73 16.10 7.52 15.63 14.85 54.70 

(i) Occupation Skills Training Code 

(ii) First and Second degree matching at 6-digit level 

29 
Table 9. Participant Characteristics and Matching Rates for Occupational Groups of Employment 

Ethnicity (%) Education (%)

OCE(i)

Group Occupation Group Name

%

Match(ii) % Male

%

Limited

English White Asian Black Hisp Other

Less

than

HS

High

Schl

11 Management 38.60 56.25 1.39 70.32 1.70 6.26 1.78 19.94 5.33 37.71 13 Business and Financial Operations 44.30 40.68 1.97 61.94 1.69 6.18 2.39 27.81 4.49 32.72 15 Computer and Mathematical 54.09 67.11 2.18 52.78 3.87 5.63 2.67 35.05 5.00 34.98 17 Architecture and Engineering 40.41 83.88 2.36 49.86 7.43 4.65 2.64 35.42 3.82 28.82 19 Life, Physical and Social Science 28.00 65.87 1.80 59.28 0.60 5.99 1.80 32.34 2.99 37.13 21 Community and Social Services 42.86 38.57 3.71 62.29 0.86 19.43 4.00 13.43 7.43 46.00 23 Legal 80.21 19.85 0.76 54.55 0.76 8.33 4.55 31.82 5.30 42.42 25 Education, Training, and Library 39.43 34.81 1.39 59.35 0.81 20.44 2.21 17.19 3.83 38.10 27 Arts, Design, Entertainment, Sports, and Media 39.74 64.50 2.65 53.41 5.30 2.65 4.55 34.09 3.41 36.36 29 Healthcare Practitioners and Technical 68.75 30.69 1.72 64.83 1.85 7.59 3.07 22.65 4.43 54.16 31 Healthcare support 68.15 9.66 5.17 60.27 1.95 18.89 4.90 13.98 12.30 66.93 33 Protective Service 24.48 67.82 0.92 62.21 0.23 16.59 2.53 18.43 7.14 61.06 35 Food Preparation and Serving Related 18.55 28.16 12.66 55.36 5.84 15.58 4.22 18.99 23.38 56.17 37 Building and Grounds Cleaning and Maintnc. 16.95 51.15 15.46 56.09 2.46 14.29 7.38 19.79 33.14 51.52 39 Personal Care and Service 52.06 16.65 14.52 44.15 7.14 16.98 7.49 24.24 20.14 57.14 41 Sales and Related 4.04 37.76 2.44 64.06 1.39 12.63 3.10 18.83 12.31 54.20 43 Office and Administrative Support 24.59 21.68 3.86 63.08 2.11 11.13 5.39 18.29 8.34 56.31 45 Farming, Fishing, and Forestry 8.16 43.14 51.37 77.25 1.57 1.18 8.24 11.76 60.00 29.41 47 Construction, and Extraction 36.17 93.70 3.73 65.50 2.10 4.51 6.92 20.98 10.72 58.66 49 Installation, Maintenance, and Repair 51.11 93.64 3.07 62.84 3.18 6.88 4.89 22.21 10.41 57.11 51 Production 9.95 51.08 7.88 59.05 5.21 15.32 6.62 13.80 24.92 54.07 53 Transportation and Material Moving 54.73 76.36 4.72 61.95 1.24 16.06 5.93 14.82 22.63 58.46 55 Military Specific 33.33 56.10 2.38 61.90 0.00 7.14 9.52 21.43 9.52 59.52 

Total 37.28 50.60 5.56 60.36 3.33 12.39 5.16 18.76 15.58 52.72 

(i) Occupational Code of Employment (ii) First and Second degree matching at 6-digit level 

30 
31 

Table 10. Performance Measures for Occupational Groups of Training and Employment 

Among Trainees(i) Among Employees(ii)

Occupation

Group Occupation Group Name

Reemployment

Rate (%)

Retention Rate

(%)

Wage Replacement

rate (%)

A

11 Management 82.69 91.01 87.25 13 Business and Financial Operations 82.12 91.27 93.93 15 Computer and Mathematical 81.57 90.23 95.81 17 Architecture and Engineering 83.60 93.75 103.27 19 Life, Physical and Social Science 82.98 92.21 87.12 21 Community and Social Services 83.11 94.10 87.65 23 Legal 83.97 90.76 89.03 25 Education, Training, and Library 80.70 92.97 77.14 27 Arts, Design, Entertainment, Sports, and Media 76.10 89.61 93.33 29 Healthcare Practitioners and Technical 87.79 94.11 106.44 31 Healthcare support 87.09 92.80 85.83 33 Protective Service 88.79 94.09 90.11 35 Food Preparation and Serving Related 81.67 90.40 80.00 37 Building and Grounds Cleaning and Maintenance 77.62 90.00 88.39 39 Personal Care and Service 73.39 87.46 87.48 41 Sales and Related 75.29 90.13 78.20 43 Office and Administrative Support 80.80 91.79 84.20 45 Farming, Fishing, and Forestry 71.43 63.11 91.59 47 Construction, and Extraction 78.07 88.21 91.37 49 Installation, Maintenance, and Repair 83.97 91.78 89.99 51 Production 80.50 91.88 96.67 53 Transportation and Material Moving 85.84 89.44 94.58 55 Military Specific 73.33 91.43 82.94 

Total 82.85 91.37 91.48 

(i) For each occupation group of training. (ii) For each occupation group of reemployment. These observations include both trainees and non-trainees as long as occupation codes 

reemployment are reported. 
Table 11. States and Other Variables 

Ethnicity (%) Education (%)

State

No of

obs.

% of

sample

%

Match % Male

% Lmtd

English

Profic White Asian Black Hisp Other HS

Some

College Assoc Bachel

More

than B

AK 174 0.12 46.81 77.01 0.57 77.01 5.17 2.3 0.57 14.94 39.66 31.61 0 18.39 1.72

AL 1,175 0.83 0 41.28 0 70.21 1.45 25.28 0.51 2.55 54.81 28.34 0 5.45 1.28

AR 1,711 1.21 0 45.06 1.29 74.23 0.64 23.44 0.88 0.82 86.32 6.78 0 0.7 0.06

AZ 1,220 0.86 0 56.23 0.74 56.39 4.02 4.02 33.28 2.30 29.34 16.48 0 4.26 1.39

CA 3,295 1.91 47.37 47.95 23.64 0 0 0 37.27 62.73 25.64 27.74 0 7.62 1.7

CO 1,447 1.02 36.84 58.33 6.7 74.36 8.91 2.83 11.13 2.76 35.25 34.9 0 17.21 7.67

CT 1,652 1.16 0 48.01 8.23 56.66 4.96 10.65 18.34 9.38 24.88 14.65 0.06 3.57 0

DE 115 0.08 0 60.00 6.09 69.57 6.09 20.87 0.87 2.61 69.57 6.09 6.09 4.35 0

FL 934 0.66 0 36.62 11.46 38.87 3 14.56 38.87 4.71 66.49 12.42 0.64 4.6 1.07

GA 3,653 2.58 23.13 34.55 1.18 50.18 1.45 46.48 0.66 1.23 58.97 18.94 1.48 1.2 0.22

IA 1,151 0.81 25.00 48.39 2.26 94.87 0.87 0.78 0.78 2.69 58.3 31.54 0.26 5.65 1.48

ID 1,304 0.92 32.65 54.91 6.52 11.96 0.31 0.15 26.46 61.12 41.41 21.24 5.83 7.44 0

IL 4,594 3.23 24.47 52.47 4.55 78.3 2.68 8.69 5.75 4.59 64.74 15.98 0.15 5.68 1.15

IN 8,007 5.29 31.58 64.27 1.2 48.03 0.41 4.35 0.74 46.47 68.68 16.47 0.72 4.07 0.8

KS 1,164 0.82 27.58 59.62 1.12 79.38 4.04 10.91 2.75 2.92 52.41 27.23 3.61 7.82 2.06

KY 3,222 2.27 14.94 43.89 1.49 89.7 0.22 5.59 0.4 4.10 68.93 11.73 5.09 2.48 0.22

LA 116 0.08 66.67 39.66 0 61.21 1.72 31.9 0 5.17 68.97 10.34 0 7.76 0.86

MA 3,704 2.61 30.37 55.26 15.12 76.19 8.1 4.48 8.64 2.59 50.78 6.21 7.4 8.5 3.4

MD 960 0.67 0.00 42.81 1.67 63.13 1.25 30 0.73 4.90 71.35 11.67 0.94 1.88 0.31

ME 3,295 2.33 36.79 62.43 2.7 94.2 0.91 1.76 0.12 3.00 54.84 13.57 1.91 4.98 1.76

MI 10,123 7.14 45.03 60.75 3.9 86.9 1.86 10.02 0 1.22 61.97 10.13 0.94 8.11 2

MN 1,570 1.11 36.84 59.32 19.49 74.97 6.43 2.99 5.67 9.94 69.04 20.89 0 5.41 0.57

MO 1,750 1.23 27.99 42.29 0.17 83.26 0.57 8.4 0.8 6.97 69.89 14.17 1.26 1.54 0.29

MS 2,892 2.04 22.47 41.15 1.45 20.33 0.14 15.25 0.03 64.25 42.67 16.53 22.54 3.39 0.45

MT 454 0.26 45.31 74.86 0.88 94.71 0.22 0 0.88 4.19 66.3 22.03 0 2.64 0.66

NC 13,917 9.81 34.13 39.36 4.44 62.17 2.43 31.22 1.88 2.30 56.74 13.43 12.06 3.37 0.37

ND 66 0.05 0 43.94 0 98.48 0 0 0 1.52 43.94 21.21 27.27 6.06 0

NE 249 0.18 47.22 37.75 0.8 94.78 1.61 0.8 0 2.81 71.89 21.69 0 3.61 0

NH 809 0.57 43.82 60.32 13.1 51.3 4.7 1.11 12.36 30.53 61.43 10.14 0.37 9.02 2.1

NJ 1,776 1.24 10.14 50.80 16.39 38.68 4.5 15.93 30.69 10.19 65.48 9.74 3.27 4.05 1.13

NM 235 0.17 58.02 67.23 0.85 38.3 2.13 1.7 54.47 3.40 61.28 32.34 0 4.68 1.7

NV 49 0.03 73.33 77.55 4.08 71.43 0 10.2 14.29 4.08 36.73 26.53 2.04 18.37 8.16

NY 8,398 5.72 22.22 62.22 1.55 74.47 2.81 6.37 5.04 11.31 66.79 11.51 6.69 3.97 0.92

OH 8,258 5.82 50.22 59.93 0.02 84.22 0.52 11.92 1.32 2.02 68.15 18.15 0 3.11 1.86

OK 1,452 1.02 100.00 43.32 0.9 52 2.55 9.44 0.14 35.88 50.76 30.1 0 9.57 1.65

OR 1,886 1.33 71.06 62.46 10.13 46.5 6.26 0.8 9.07 37.38 51.33 20.47 6.68 8.38 3.02

RI 1,042 0.74 59.57 45.39 33.3 77.83 5.09 7.29 8.73 1.06 48.75 10.17 0.77 5.28 0.67

SC 7,526 5.31 13.81 44.80 0.85 50.47 1.29 39.37 0.54 8.33 58.42 14.27 0 3.16 0.5

SD 543 0.38 30.70 44.20 2.21 94.48 1.29 2.03 1.47 0.74 66.11 7.73 12.15 10.87 1.84

TN 11,099 7.83 0 45.54 0.97 88.32 0.41 9.18 0.11 1.97 46.5 2.45 1.04 0.82 0.26

TX 5,742 4.05 0 51.01 15.88 28.51 6.18 7.92 54.35 3.03 44.31 15.01 2.06 4.81 0.42

UT 461 0.33 45.78 53.15 19.96 79.18 13.45 0.65 1.95 4.77 46.2 27.55 0.22 7.38 1.08

VA 5,709 4.01 99.97 36.93 0 68.03 0.4 30.15 0.44 0.98 36.15 13.33 0.18 2.05 0.18

VT 232 0.16 25.00 50.00 1.29 96.12 0.86 0.43 0 2.59 53.02 21.55 0 11.21 1.29

WA 6,333 4.47 32.63 64.16 7.86 11.81 12.43 3.49 1.47 70.80 44.39 30.19 10.53 8.97 0.08

WI 6,095 4.30 33.33 53.52 3.94 89.58 1.18 5.61 1.62 2.00 65.43 18.75 0.31 5.04 0.82

WV 1,725 1.22 0.00 72.29 0.06 86.2 0.12 3.65 0 10.03 59.36 15.42 4 3.83 0.7

WY 16 0.01 75.00 81.25 0 100 0 0 0 0.00 31.25 50 12.5 6.25 0

Total 143,300 100.00 37.82 51.65 4.69 64.57 2.56 13.46 6.22 13.20 56.05 15.17 3.53 4.64 0.98

32 
Table 12. What Affects Matching? Education as Degree Attainment 

1 2 3 4 5 6 7 

ind. Variable dy/dx dy/dx dy/dx dy/dx dy/dx dy/dx dy/dx dy/dx dy/dx dy/dx dy/dx dy/dx dy/dx 

Male 0.018* 0.014 0.020* 0.016 0.026* 0.021 0.028* 0.023* 0.004 0.000 0.006 0.001 0.007 

(0.008) (0.008) (0.008) (0.008) (0.011) (0.011) (0.011) (0.011) (0.009) (0.009) (0.009) (0.009) (0.011) 

Limited Engl Proficiency 0.016 0.019 0.014 0.017 0.004 0.007 0.004 0.006 ‐0.021 ‐0.019 ‐0.022 ‐0.021 ‐0.033 

(0.021) (0.021) (0.021) (0.021) (0.021) (0.021) (0.021) (0.021) (0.021) (0.021) (0.021) (0.021) (0.021) 

Eth: Hispanic 0.054** 0.061** 0.060** 0.066** 0.057** 0.062** 0.061** 0.065** 0.011 0.015 0.010 0.014 0.000 

(0.016) (0.016) (0.016) (0.016) (0.016) (0.016) (0.016) (0.016) (0.018) (0.018) (0.018) (0.018) (0.018) 

Eth: Asian ‐0.094** ‐0.106** ‐0.074** ‐0.088** ‐0.090** ‐0.103** ‐0.074** ‐0.088** ‐0.093** ‐0.098** ‐0.088** ‐0.094** ‐0.101** 

(0.022) (0.021) (0.022) (0.022) (0.022) (0.022) (0.023) (0.022) (0.023) (0.023) (0.023) (0.023) (0.023) 

Eth: Black ‐0.074** ‐0.087** ‐0.067** ‐0.081** ‐0.091** ‐0.103** ‐0.085** ‐0.097** ‐0.023 ‐0.033 ‐0.027 ‐0.036 ‐0.037 

(0.018) (0.018) (0.019) (0.018) (0.018) (0.018) (0.018) (0.018) (0.021) (0.020) (0.021) (0.020) (0.021) 

Eth: Others 0.036** 0.030** 0.051** 0.043** 0.045** 0.040** 0.057** 0.050** ‐0.014 ‐0.013 ‐0.016 ‐0.014 ‐0.024 

(0.010) (0.010) (0.010) (0.010) (0.010) (0.010) (0.011) (0.010) (0.015) (0.015) (0.015) (0.015) (0.015) 

Edu: High School ‐0.039** ‐0.044** ‐0.034* ‐0.040** ‐0.025 ‐0.029 ‐0.021 ‐0.025 ‐0.013 ‐0.015 ‐0.012 ‐0.014 ‐0.002 

(0.015) (0.015) (0.015) (0.015) (0.015) (0.015) (0.015) (0.015) (0.015) (0.015) (0.015) (0.015) (0.015) 

Edu: Some College ‐0.033* ‐0.036* ‐0.024 ‐0.028 0.013 0.013 0.021 0.020 ‐0.021 ‐0.022 ‐0.017 ‐0.019 0.022 

(0.016) (0.016) (0.016) (0.016) (0.017) (0.017) (0.017) (0.017) (0.017) (0.016) (0.017) (0.017) (0.017) 

Edu: Associate ‐0.076** ‐0.080** ‐0.069** ‐0.073** ‐0.016 ‐0.017 ‐0.010 ‐0.011 ‐0.028 ‐0.025 ‐0.030 ‐0.027 0.032 

(0.023) (0.023) (0.023) (0.023) (0.025) (0.025) (0.026) (0.026) (0.026) (0.026) (0.026) (0.026) (0.028) 

Edu: Bachelor’s ‐0.012 ‐0.016 ‐0.009 ‐0.013 0.064** 0.065** 0.066** 0.067** ‐0.014 ‐0.016 ‐0.013 ‐0.016 0.060** 

(0.021) (0.021) (0.021) (0.021) (0.023) (0.023) (0.023) (0.023) (0.021) (0.021) (0.021) (0.021) (0.023) 

Edu: More than B 0.016 0.011 0.018 0.011 0.114** 0.114** 0.115** 0.114** ‐0.016 ‐0.025 ‐0.014 ‐0.024 0.080 

(0.042) (0.041) (0.042) (0.041) (0.044) (0.044) (0.044) (0.044) (0.041) (0.041) (0.041) (0.041) (0.045) 

Age: 16_20 0.059 0.066 0.056 0.063 0.057 0.061 0.054 0.057 0.059 0.063 0.051 0.056 0.045 

(0.119) (0.118) (0.119) (0.118) (0.118) (0.117) (0.118) (0.117) (0.120) (0.118) (0.119) (0.118) (0.118) 

Age: 21_30 0.026 0.025 0.026 0.025 0.043** 0.043** 0.043** 0.044** 0.007 0.007 0.010 0.009 0.025 

(0.015) (0.015) (0.015) (0.015) (0.015) (0.015) (0.015) (0.015) (0.015) (0.015) (0.015) (0.015) (0.016) 

Age: 31_40 0.019 0.019 0.018 0.019 0.024* 0.025* 0.023* 0.024* 0.015 0.016 0.016 0.017 0.021 

(0.011) (0.011) (0.011) (0.011) (0.011) (0.011) (0.011) (0.011) (0.011) (0.011) (0.011) (0.011) (0.011) 

Age: 51_60 ‐0.039** ‐0.036** ‐0.042** ‐0.039** ‐0.044** ‐0.042** ‐0.046** ‐0.044** ‐0.045** ‐0.042** ‐0.047** ‐0.044** ‐0.049** 

(0.011) (0.011) (0.011) (0.011) (0.011) (0.011) (0.011) (0.011) (0.011) (0.011) (0.011) (0.011) (0.011) 

Age: 60_65 ‐0.021 ‐0.016 ‐0.027 ‐0.022 ‐0.004 0.000 ‐0.011 ‐0.006 ‐0.020 ‐0.017 ‐0.025 ‐0.021 ‐0.006 

(0.029) (0.029) (0.029) (0.029) (0.030) (0.030) (0.030) (0.030) (0.030) (0.030) (0.030) (0.030) (0.031) 

Training Completed 0.200** 0.203** 0.184** 0.187** 0.183** 0.189** 0.167** 

(0.009) (0.009) (0.010) (0.010) (0.010) (0.010) (0.010) 

Exit Year 2005 0.033** 0.041** 0.033** 0.041** 0.017 0.024* 

(0.012) (0.011) (0.012) (0.012) (0.012) (0.012) 

Exit Year 2006 0.088** 0.086** 0.082** 0.080** 0.067** 0.061** 

(0.012) (0.012) (0.013) (0.012) (0.013) (0.013) 

Exit Year 2007 0.121** 0.118** 0.103** 0.099** 0.120** 0.111** 

(0.014) (0.014) (0.014) (0.014) (0.016) (0.016) 

33 
13.Business & Financial 0.000 0.006 0.000 0.006 0.009 

(0.033) (0.033) (0.033) (0.033) (0.034) 

15.Computer, Math 0.061* 0.069** 0.064* 0.071** 0.079** 

(0.027) (0.027) (0.027) (0.027) (0.028) 

17.Architecture 0.101** 0.107** 0.101** 0.107** 0.112** 

(0.029) (0.029) (0.029) (0.029) (0.030) 

19.Science ‐0.080 ‐0.073 ‐0.077 ‐0.069   ‐0.060 

(0.049) (0.050) (0.050) (0.050) (0.052) 

21.Community Service 0.139** 0.145** 0.142** 0.147** 0.122** 

(0.040) (0.040) (0.041) (0.040) (0.042) 

0.261

**

0.263

**

0.267

**

0.267

**

0.296

**

(0.049) (0.048) (0.049) (0.048) (0.048) 

23.Legal

0.163

**

0.169

**

0.163

**

0.169

**

0.189

**

(0.034) (0.033) (0.034) (0.034) (0.035) 

25.Education, Training27.Arts, Design 0.055 0.068 0.057 0.069 0.070 

(0.044) (0.044) (0.044) (0.044) (0.045) 

0.315

**

0.317

**

0.313

**

0.315

**

0.342

**

(0.023) (0.023) (0.023) (0.023) (0.024) 

29.Healthcare Pract’r

0.349

**

0.363

**

0.347

**

0.362

**

0.362

**

(0.023) (0.023) (0.023) (0.023) (0.024) 

31.Healthcare Support33.Protective Service ‐0.051 ‐0.068 ‐0.052 ‐0.070   ‐0.074 

(0.043) (0.042) (0.043) (0.042) (0.042) 

35.Food Prep & Serving 0.152** 0.145** 0.143** 0.136** 0.152** 

(0.048) (0.047) (0.048) (0.047) (0.049) 

37.Building Maintenance 0.334** 0.340** 0.331** 0.337** 0.336** 

(0.049) (0.048) (0.049) (0.048) (0.050) 

39.Personal Care&Service 0.274** 0.289** 0.272** 0.287** 0.266** 

(0.032) (0.031) (0.032) (0.031) (0.033) 

41.Sales and Related 0.089 0.094 0.094 0.100 0.084 

(0.065) (0.065) (0.065) (0.065) (0.067) 

43.AdministrativeSupport 0.100** 0.110** 0.103** 0.113** 0.106** 

(0.025) (0.025) (0.026) (0.025) (0.026) 

45.Farming & Fishing ‐0.192 ‐0.187 ‐0.191 ‐0.185   ‐0.172 

(0.138) (0.140) (0.139) (0.140) (0.146) 

47.Construction 0.197** 0.209** 0.197** 0.209** 0.190** 

(0.031) (0.031) (0.031) (0.031) (0.032) 

49.Installation & Repair 0.181** 0.194** 0.182** 0.195** 0.214** 

(0.025) (0.025) (0.025) (0.025) (0.026) 

51.Production 0.128** 0.138** 0.126** 0.135** 0.136** 

(0.027) (0.027) (0.027) (0.027) (0.029) 

53.Transportation 0.429** 0.447** 0.425** 0.444** 0.451** 

(0.021) (0.020) (0.022) (0.021) (0.021) 

States YES YES YES YES YES Number of obs 13,800 13,800 13,800 13,800 13,800 13,800 13,800 13,800 13,800 13,800 13,800 13,800 13,800 LR chi2 508.9 132.41 609.47 223.46 1,417.45 1,115.38 1,490.22 1,180.61 1,274.82 980.57 1,351.38 1,041.31 2,223.3 Prob >chi2 0 0 0 0 0 0 0 0 0 0 0 0 0 Pseudo R2 0.0278 0.0072 0.0333 0.0122 0.0774 0.0609 0.0814 0.0645 0.0697 0.0536 0.0738 0.0569 0.1215 



* and ** indicate significance at the 95% and 99% level, respectively. Numbers in the parentheses are standard errors. 

34 
Table 13. Reemployment Rates 

Education: Degree Completion Education: Years of Schooling

5a 6a 7a 8a 5b 6b 7b 

ind. Variable with without with without with without with without with without with without with Male ‐0.001 ‐0.002 ‐0.001 ‐0.002 0.000 ‐0.001 0.000 ‐0.001 ‐0.001 ‐0.002 ‐0.001 ‐0.002 0.000 

(0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002

Limited Engl Proficiency ‐0.014* ‐0.012* ‐0.014* ‐0.012* ‐0.013* ‐0.011 ‐0.013* ‐0.011 ‐0.018** ‐0.016** ‐0.018** ‐0.015** ‐0.016*

(0.006) (0.006) (0.006) (0.006) (0.006) (0.006) (0.006) (0.006) (0.006) (0.006) (0.006) (0.006) (0.006

Eth: Hispanic 0.002 0.003 0.003 0.003 0.002 0.002 0.002 0.002 ‐0.002 ‐0.002 ‐0.002 ‐0.001 ‐0.003 

(0.005) (0.005) (0.005) (0.005) (0.005) (0.005) (0.005) (0.005) (0.005) (0.005) (0.005) (0.005) (0.005

Eth: Asian ‐0.049** ‐0.049** ‐0.048** ‐0.048** ‐0.044** ‐0.045** ‐0.044** ‐0.044** ‐0.050** ‐0.050** ‐0.050** ‐0.050** ‐0.046*

(0.008) (0.008) (0.008) (0.008) (0.008) (0.008) (0.008) (0.008) (0.008) (0.008) (0.008) (0.008) (0.008

Eth: Black 0.018** 0.016** 0.018** 0.016** 0.017** 0.015** 0.017** 0.015** 0.017** 0.016** 0.017** 0.015** 0.017*

(0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003

Eth: Others ‐0.023** ‐0.024** ‐0.022** ‐0.023** ‐0.024** ‐0.025** ‐0.023** ‐0.024** ‐0.022** ‐0.024** ‐0.021** ‐0.023** ‐0.023*

(0.004) (0.004) (0.004) (0.004) (0.004) (0.004) (0.004) (0.004) (0.004) (0.004) (0.004) (0.004) (0.004

Edu: High School 0.038** 0.038** 0.038** 0.038** 0.038** 0.038** 0.038** 0.038**

(0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003)

Edu: Some College 0.041** 0.042** 0.041** 0.042** 0.043** 0.044** 0.043** 0.044**

(0.004) (0.004) (0.004) (0.004) (0.004) (0.004) (0.004) (0.004)

Edu: Associate 0.055** 0.060** 0.055** 0.060** 0.059** 0.063** 0.058** 0.063**

(0.006) (0.006) (0.006) (0.006) (0.006) (0.006) (0.006) (0.006)

Edu: Bachelor’s 0.027** 0.027** 0.027** 0.028** 0.030** 0.032** 0.031** 0.032**

(0.005) (0.005) (0.005) (0.005) (0.005) (0.005) (0.005) (0.005)

Edu: More than B 0.038** 0.038** 0.038** 0.038** 0.040** 0.040** 0.041** 0.041**

(0.010) (0.010) (0.010) (0.010) (0.010) (0.010) (0.010) (0.010)

Schooling 0.003** 0.003** 0.003** 0.003** 0.003*

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

Age: 16_20 0.031 0.028 0.032 0.029 0.031 0.028 0.032 0.029 0.030 0.027 0.031 0.028 0.030 

(0.023) (0.023) (0.023) (0.023) (0.023) (0.023) (0.023) (0.023) (0.023) (0.023) (0.023) (0.023) (0.023

Age: 21_30 0.043** 0.042** 0.043** 0.042** 0.043** 0.042** 0.043** 0.042** 0.043** 0.042** 0.043** 0.043** 0.043*

(0.004) (0.004) (0.004) (0.004) (0.004) (0.004) (0.004) (0.004) (0.004) (0.004) (0.004) (0.004) (0.004

Age: 31_40 0.028** 0.028** 0.028** 0.028** 0.028** 0.028** 0.028** 0.028** 0.029** 0.029** 0.029** 0.029** 0.028*

(0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003

Age: 51_60 ‐0.098** ‐0.099** ‐0.098** ‐0.099** ‐0.097** ‐0.098** ‐0.098** ‐0.098** ‐0.099** ‐0.100** ‐0.099** ‐0.100** ‐0.098*

(0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003

Age: 61_65 ‐0.404** ‐0.406** ‐0.404** ‐0.406** ‐0.402** ‐0.404** ‐0.402** ‐0.404** ‐0.406** ‐0.408** ‐0.407** ‐0.409** ‐0.405*

(0.006) (0.006) (0.006) (0.006) (0.006) (0.006) (0.006) (0.006) (0.006) (0.006) (0.006) (0.006) (0.006

Train: Occupational 0.020** 0.048** 0.020** 0.048** 0.014** 0.036** 0.014** 0.036** 0.023** 0.051** 0.023** 0.052** 0.017*

(0.003) (0.003) (0.003) (0.003) (0.004) (0.003) (0.004) (0.003) (0.003) (0.003) (0.003) (0.003) (0.004

Train: Remedial ‐0.015** ‐0.008 ‐0.016** ‐0.008* ‐0.015** ‐0.008* ‐0.016** ‐0.009* ‐0.023** ‐0.015** ‐0.024** ‐0.015** ‐0.023*

(0.004) (0.004) (0.004) (0.004) (0.004) (0.004) (0.004) (0.004) (0.004) (0.004) (0.004) (0.004) (0.004

Train : OJT 0.108** 0.116** 0.109** 0.117** 0.109** 0.117** 0.110** 0.117** 0.109** 0.117** 0.110** 0.118** 0.109*

(0.008) (0.007) (0.008) (0.007) (0.008) (0.007) (0.008) (0.007) (0.008) (0.007) (0.008) (0.007) (0.008

Train: Customized ‐0.052** ‐0.066** ‐0.050** ‐0.064** ‐0.045** ‐0.055** ‐0.043* ‐0.054** ‐0.047** ‐0.061** ‐0.044** ‐0.059** ‐0.040

(0.017) (0.018) (0.017) (0.018) (0.017) (0.017) (0.017) (0.017) (0.017) (0.017) (0.017) (0.017) (0.017

Marketable Skills 0.011** 0.010** 0.009** 0.008** 0.011** 0.010** 0.009** 0.008** 0.012** 0.010** 0.009** 0.008** 0.012*

(0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003

Training Completed 0.044** 0.045** 0.041** 0.042** 0.045** 0.045** 0.042*

(0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003

35 
Exit Year 2005 0.002 0.001 0.002 0.001 0.002 0.002 

(0.003) (0.003) (0.003) (0.003) (0.003) (0.003) 

Exit Year 2006 0.005 0.003 0.004 0.003 0.006 0.004 

(0.003) (0.003) (0.003) (0.003) (0.003) (0.003) 

Exit Year 2007 0.015** 0.014** 0.015** 0.014** 0.016** 0.015** 

(0.004) (0.004) (0.004) (0.004) (0.004) (0.004) 

13.Business & Financial ‐0.011 ‐0.007 ‐0.011 ‐0.006 ‐0.010 

(0.011) (0.011) (0.011) (0.011) (0.011

15.Computer, Math ‐0.021** ‐0.016* ‐0.020** ‐0.015 ‐0.021*

(0.008) (0.008) (0.008) (0.008) (0.008

17.Architecture ‐0.007 ‐0.002 ‐0.006 ‐0.002 ‐0.005 

(0.010) (0.009) (0.010) (0.009) (0.010

19.Science ‐0.020 ‐0.014 ‐0.019 ‐0.014 ‐0.019 

(0.022) (0.021) (0.022) (0.021) (0.022

21.Community Service ‐0.015 ‐0.010 ‐0.015 ‐0.010 ‐0.012 

(0.014) (0.014) (0.014) (0.014) (0.014

23.Legal ‐0.018 ‐0.011 ‐0.017 ‐0.011 ‐0.015 

(0.021) (0.020) (0.021) (0.020) (0.021

25.Education, Training ‐0.003 0.003 ‐0.002 0.004 ‐0.003 

(0.009) (0.009) (0.009) (0.009) (0.009

27.Arts, Design ‐0.069** ‐0.063** ‐0.068** ‐0.063** ‐0.067*

(0.019) (0.019) (0.019) (0.019) (0.019

29.Healthcare Pract’r 0.040** 0.043** 0.040** 0.044** 0.041*

(0.006) (0.006) (0.006) (0.006) (0.006

31.Healthcare Support 0.046** 0.053** 0.047** 0.053** 0.046*

(0.006) (0.005) (0.006) (0.005) (0.006

33.Protective Service 0.032* 0.034* 0.032* 0.034* 0.033

(0.016) (0.016) (0.016) (0.016) (0.016

35.Food Prep & Serving 0.017 0.018 0.017 0.018 0.016 

(0.018) (0.018) (0.018) (0.018) (0.018

37.Building Maintenance ‐0.023 ‐0.018 ‐0.023 ‐0.018 ‐0.025 

(0.024) (0.023) (0.024) (0.023) (0.024

39.Personal Care&Service ‐0.088** ‐0.080** ‐0.088** ‐0.080** ‐0.088*

(0.012) (0.012) (0.012) (0.012) (0.012

41.Sales and Related ‐0.036 ‐0.034 ‐0.035 ‐0.033 ‐0.038 

(0.021) (0.021) (0.021) (0.021) (0.021

43.AdministrativeSupport 0.005 0.009 0.005 0.009 0.005 

(0.005) (0.005) (0.005) (0.005) (0.005

45.Farming & Fishing ‐0.098 ‐0.094 ‐0.096 ‐0.092 ‐0.099 

(0.051) (0.050) (0.050) (0.050) (0.051

47.Construction ‐0.045** ‐0.037** ‐0.044** ‐0.037** ‐0.045*

(0.011) (0.011) (0.011) (0.011) (0.011

49.Installation & Repair 0.013* 0.018** 0.013* 0.019** 0.013

(0.006) (0.006) (0.006) (0.006) (0.006

51.Production 0.028** 0.034** 0.029** 0.034** 0.028*

(0.006) (0.005) (0.006) (0.005) (0.006

53.Transportation 0.044** 0.054** 0.044** 0.054** 0.042*

(0.006) (0.006) (0.006) (0.006) (0.006

States YES YES YES YES YES YES YES YES YES YES YES YES YES Number of obs 139,291 139,291 139,291 139,291 139,291 139,291 139,291 139,291 139,291 139,291 139,291 139,291 139,291 LR chi2 12,165 11,957 12,186 11,974 12,497 12,319 12,517 12,337 12,049 11,832 12,071 11,851 12,376 Prob >chi2 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Pseudo R2 0.0855 0.083 0.085 0.083 0.087 0.086 0.087 0.086 0.084 0.082 0.084 0.083 0.086 



* and ** indicate significance at the 95% and 99% level, respectively. Numbers in the parentheses are standard errors. 

36 
Table 14. Wage Replacement Rates 

Education: Degree Attainment Education: Years of Schooling

5a 6a 7a 8a 5b 6b 7b 

ind. Variable without with without with without with without with without with without with withouMale 0.018** 0.144** 0.018** 0.144** 0.010 0.135** 0.010* 0.135** 0.020** 0.146** 0.020** 0.146** 0.012

(0.005) (0.005) (0.005) (0.005) (0.005) (0.005) (0.005) (0.005) (0.005) (0.005) (0.005) (0.005) (0.005

Limited Engl Proficiency 0.021 ‐0.021 0.020 ‐0.021 0.022 ‐0.018 0.022 ‐0.018 0.023 ‐0.024* 0.023 ‐0.025* 0.025 

(0.013) (0.011) (0.013) (0.011) (0.013) (0.011) (0.013) (0.011) (0.013) (0.011) (0.013) (0.011) (0.013

Eth: Hispanic 0.066** ‐0.016 0.065** ‐0.016 0.065** ‐0.017 0.065** ‐0.017 0.069** ‐0.019* 0.068** ‐0.019* 0.068*

(0.010) (0.009) (0.010) (0.009) (0.010) (0.009) (0.010) (0.009) (0.010) (0.009) (0.010) (0.009) (0.010

Eth: Asian 0.045** 0.036** 0.044** 0.036** 0.045** 0.037** 0.044** 0.037** 0.047** 0.043** 0.046** 0.043** 0.047*

(0.014) (0.012) (0.014) (0.012) (0.014) (0.012) (0.014) (0.013) (0.014) (0.013) (0.014) (0.013) (0.014

Eth: Black 0.040** 0.002 0.040** 0.002 0.040** 0.002 0.040** 0.002 0.039** 0.001 0.039** 0.001 0.039*

(0.009) (0.008) (0.009) (0.008) (0.009) (0.008) (0.009) (0.008) (0.009) (0.008) (0.009) (0.008) (0.009

Eth: Others 0.045** 0.013 0.045** 0.013 0.045** 0.012 0.044** 0.012 0.045** 0.016* 0.045** 0.016* 0.045*

(0.008) (0.008) (0.008) (0.008) (0.008) (0.007) (0.008) (0.008) (0.008) (0.008) (0.008) (0.008) (0.008

Edu: High School  ‐0.037** 0.015* ‐0.037** 0.015* ‐0.037** 0.015* ‐0.036** 0.016*

(0.007) (0.006) (0.007) (0.006) (0.007) (0.006) (0.007) (0.006)

Edu: Some College  ‐0.022* 0.097** ‐0.021* 0.098** ‐0.023* 0.096** ‐0.022* 0.096**

(0.009) (0.008) (0.009) (0.008) (0.009) (0.008) (0.009) (0.008)

Edu: Associate ‐ 0.150** 0.002 ‐0.002 0.149** ‐0.002 0.149** ‐0.002 0.148**

(0.015) (0.014) (0.015) (0.014) (0.015) (0.014) (0.015) (0.014)

Edu: Bachelor’s 0.012 0.207** 0.013 0.207** 0.012 0.206** 0.013 0.207**

(0.012) (0.011) (0.012) (0.011) (0.012) (0.011) (0.012) (0.011)

Edu: More than B 0.021 0.283** 0.022 0.284** 0.020 0.282** 0.022 0.283**

(0.021) (0.019) (0.021) (0.019) (0.021) (0.019) (0.021) (0.019)

Schooling 0.000 0.009** 0.000 0.009** 0.000 

(0.001) (0.001) (0.001) (0.001) (0.001

Age: 16_20 0.267

**

‐0.029 0.262

**

‐0.033 0.268

**

‐0.029 0.263

**

‐0.033 0.266

**

‐0.019 0.260

**

‐0.024 0.267

*

(0.049) (0.044) (0.049) (0.044) (0.049) (0.044) (0.049) (0.044) (0.049) (0.044) (0.049) (0.044) (0.049

Age: 21_30 0.142

**

0.011 0.142

**

0.011 0.143

**

0.012 0.143

**

0.012 0.140

**

0.015 0.141

**

0.015 0.142

*

(0.009) (0.008) (0.009) (0.008) (0.009) (0.008) (0.009) (0.008) (0.009) (0.008) (0.009) (0.008) (0.009

Age: 31_40 0.061

**

0.021

**

0.061

**

0.022

**

0.061

**

0.021

**

0.062

**

0.022

**

0.061

**

0.025

**

0.062

**

0.026

**

0.061

*

(0.006) (0.006) (0.006) (0.006) (0.006) (0.006) (0.006) (0.006) (0.006) (0.006) (0.006) (0.006) (0.006

Age: 51_60  ‐0.081

**

‐0.068

**

‐0.082

**

‐0.069

**

‐0.081

**

‐0.068

**

‐0.082

**

‐0.068

**

‐0.080

**

‐0.066

**

‐0.081

**

‐0.067

**

‐0.079

*

(0.006) (0.005) (0.006) (0.005) (0.006) (0.005) (0.006) (0.005) (0.006) (0.005) (0.006) (0.005) (0.006

Age: 61_65 ‐0.162** ‐0.185** ‐0.164** ‐0.187** ‐0.162** ‐0.184** ‐0.163** ‐0.186** ‐0.161** ‐0.183** ‐0.162** ‐0.185** ‐0.160*

(0.014) (0.012) (0.014) (0.012) (0.014) (0.012) (0.014) (0.012) (0.014) (0.012) (0.014) (0.012) (0.014

Match 0.029** 0.020* 0.027** 0.019* 0.027** 0.019* 0.025* 0.017 0.028** 0.019* 0.027** 0.017 0.027*

(0.010) (0.009) (0.010) (0.009) (0.010) (0.009) (0.010) (0.009) (0.010) (0.009) (0.010) (0.009) (0.010

Train: Occupational  ‐0.016* ‐0.027** ‐0.013 ‐0.024** 0.001 ‐0.013 0.000 ‐0.014 ‐0.019* ‐0.027** ‐0.016* ‐0.024** 0.000 

(0.008) (0.007) (0.008) (0.007) (0.012) (0.011) (0.012) (0.011) (0.008) (0.007) (0.008) (0.007) (0.012

Train: Remedial 0.017 ‐0.053** 0.016 ‐0.055** 0.019 ‐0.049** 0.017 ‐0.051** 0.022* ‐0.061** 0.021* ‐0.062** 0.025

(0.011) (0.009) (0.011) (0.009) (0.011) (0.010) (0.011) (0.010) (0.010) (0.009) (0.010) (0.009) (0.011

Train : OJT 0.043

*

0.037

*

0.043

*

0.038

*

0.045

*

0.040

*

0.044

*

0.039

*

0.039 0.027 0.040 0.028 0.043

(0.020) (0.018) (0.020) (0.018) (0.021) (0.018) (0.021) (0.018) (0.020) (0.018) (0.020) (0.018) (0.021

Train: Customized 0.023 0.076 0.022 0.075 0.027 0.078 0.025 0.076 0.023 0.086* 0.022 0.085* 0.028 

(0.047) (0.042) (0.047) (0.042) (0.048) (0.042) (0.048) (0.042) (0.048) (0.042) (0.048) (0.042) (0.048

Marketable Skills  ‐0.022** ‐0.003 ‐0.023** ‐0.005 ‐0.022** ‐0.002 ‐0.022** ‐0.004 ‐0.022** ‐0.001 ‐0.023** ‐0.003 ‐0.021*

(0.006) (0.006) (0.007) (0.006) (0.006) (0.006) (0.007) (0.006) (0.006) (0.006) (0.007) (0.006) (0.006

log(Prev Earning)   ‐0.478** ‐0.478** ‐0.479** ‐0.479** ‐0.453** ‐0.453** 

(0.005) (0.005) (0.005) (0.005) (0.005) (0.005) 

37 
Exit Year 2005 0.041

**

0.031

**

0.041

**

0.031

**

0.042

**

0.034

**

(0.008) (0.007) (0.008) (0.007) (0.008) (0.007) 

Exit Year 2006 0.038

**

0.036

**

0.037

**

0.036

**

0.039

**

0.040

**

(0.008) (0.007) (0.008) (0.007) (0.008) (0.007) 

Exit Year 2007 0.041

**

0.039

**

0.040

**

0.038

**

0.041

**

0.041

**

(0.009) (0.008) (0.009) (0.008) (0.009) (0.008) 

13.Business & Financial ‐0.076** ‐0.047 ‐0.070* ‐0.041 ‐0.074*

(0.028) (0.025) (0.028) (0.025) (0.028

15.Computer, Math ‐0.020 ‐0.012 ‐0.014 ‐0.005 ‐0.018 

(0.019) (0.016) (0.019) (0.016) (0.019

17.Architecture 0.041 0.057** 0.048* 0.064** 0.044

(0.022) (0.019) (0.022) (0.019) (0.022

19.Science ‐0.039 0.009 ‐0.034 0.014 ‐0.036 

(0.047) (0.041) (0.047) (0.041) (0.047

21.Community Service ‐0.030 ‐0.072

*

‐0.025 ‐0.066

*

‐0.032 

(0.034) (0.030) (0.034) (0.030) (0.034

23.Legal ‐0.039 0.005 ‐0.035 0.010 ‐0.040 

(0.043) (0.038) (0.043) (0.038) (0.043

25.Education, Training ‐0.099** ‐0.118** ‐0.094** ‐0.114** ‐0.090*

(0.027) (0.023) (0.027) (0.023) (0.026

27.Arts, Design 0.051 0.038 0.058 0.045 0.051 

(0.036) (0.032) (0.036) (0.032) (0.036

29.Healthcare Pract’r 0.057** 0.066** 0.062** 0.072** 0.054*

(0.017) (0.015) (0.017) (0.015) (0.017

31.Healthcare Support ‐0.092** ‐0.096** ‐0.087** ‐0.090** ‐0.097*

(0.018) (0.016) (0.018) (0.016) (0.018

33.Protective Service ‐0.034 ‐0.005 ‐0.032 ‐0.002 ‐0.038 

(0.037) (0.033) (0.037) (0.033) (0.037

35.Food Prep & Serving ‐0.024 ‐0.069 ‐0.020 ‐0.064 ‐0.030 

(0.041) (0.036) (0.041) (0.036) (0.041

37.Building Maintenance 0.020 ‐0.001 0.024 0.003 0.020 

(0.053) (0.047) (0.053) (0.047) (0.053

39.Personal Care&Service ‐0.039 ‐0.065* ‐0.035 ‐0.060* ‐0.044 

(0.029) (0.026) (0.029) (0.026) (0.029

41.Sales and Related ‐0.097 ‐0.048 ‐0.088 ‐0.039 ‐0.099 

(0.053) (0.047) (0.053) (0.047) (0.053

43.AdministrativeSupport ‐0.056** ‐0.060** ‐0.050** ‐0.054** ‐0.061*

(0.017) (0.015) (0.017) (0.015) (0.017

45.Farming & Fishing ‐0.152 ‐0.098 ‐0.154 ‐0.097 ‐0.154 

(0.175) (0.155) (0.175) (0.155) (0.175

47.Construction 0.000 0.003 0.006 0.009 ‐0.006 

(0.025) (0.022) (0.025) (0.022) (0.025

49.Installation & Repair ‐0.027 ‐0.011 ‐0.022 ‐0.005 ‐0.032 

(0.017) (0.015) (0.017) (0.015) (0.017

51.Production 0.022 0.006 0.026 0.010 0.017 

(0.018) (0.016) (0.018) (0.016) (0.018

53.Transportation 0.001 0.003 0.006 0.009 ‐0.003 

(0.019) (0.017) (0.020) (0.017) (0.019

Constant 0.897

**

5.171

**

0.863

**

5.140 0.903

**

5.192

**

0.870

**

5.161

**

0.884

**

4.898

**

0.848

**

4.863

**

0.891

*

(0.017) (0.048) (0.018) (0.048) (0.018) (0.048) (0.019) (0.048) (0.019) (0.048) (0.020) (0.048) (0.020

States YES YES YES YES YES YES YES YES YES YES YES YES YES Number of obs 32559 32559 32559 32559 32559 32559 32559 32559 32559 32559 32559 32559 32559 F 38.03 236.06 36.42 222.72 27.48 163.93 26.8 157.66 40.35 239.22 38.45 224.62 28.37 Prob > F 0 0 0 0 0 0 0 0 0 0 0 0 0 R‐squared 0.0511 0.2544 0.052 0.2552 0.0544 0.2582 0.0553 0.2589 0.0495 0.2403 0.0505 0.2412 0.0529 Adj R‐squared 0.0497 0.2534 0.0506 0.254 0.0524 0.2567 0.0533 0.2573 0.0483 0.2393 0.0492 0.2401 0.0511 

38 
Table 15. Post-Participation Earnings 

Education: Degree Attainment Education: Years of Schooling5a 6a 7a 8a 5b 6b 7b 

ind. Variable without with without with without with without with without with without with without Male 0.274** 0.152** 0.274** 0.152** 0.263** 0.141** 0.263** 0.142** 0.290** 0.154** 0.290** 0.154** 0.279** 

(0.004) (0.005) (0.004) (0.005) (0.005) (0.005) (0.005) (0.005) (0.004) (0.005) (0.004) (0.005) (0.005) 

Limited Engl Proficiency ‐0.069** ‐0.019 ‐0.069** ‐0.020 ‐0.063** ‐0.016 ‐0.063** ‐0.016 ‐0.085** ‐0.023 ‐0.084** ‐0.023* ‐0.077** 

(0.011) (0.012) (0.011) (0.012) (0.011) (0.012) (0.011) (0.012) (0.011) (0.012) (0.011) (0.012) (0.011) 

Eth: Hispanic ‐0.093** ‐0.010 ‐0.092** ‐0.011 ‐0.096** ‐0.013 ‐0.094** ‐0.013 ‐0.112** ‐0.014 ‐0.111** ‐0.014 ‐0.114** 

(0.009) (0.009) (0.009) (0.009) (0.009) (0.009) (0.009) (0.009) (0.009) (0.009) (0.009) (0.009) (0.009) 

Eth: Asian 0.040** 0.044** 0.043** 0.044** 0.042** 0.045** 0.044** 0.045** 0.053** 0.052** 0.056** 0.052** 0.056** 

(0.012) (0.013) (0.012) (0.013) (0.012) (0.013) (0.012) (0.013) (0.013) (0.013) (0.013) (0.013) (0.013) 

Eth: Black ‐0.054** ‐0.014 ‐0.055** ‐0.014 ‐0.054** ‐0.014 ‐0.054** ‐0.014 ‐0.059** ‐0.015 ‐0.060** ‐0.015 ‐0.059** 

(0.008) (0.008) (0.008) (0.008) (0.008) (0.008) (0.008) (0.008) (0.008) (0.008) (0.008) (0.008) (0.008) 

Eth: Others ‐0.029** 0.003 ‐0.026** 0.004 ‐0.030** 0.003 ‐0.027** 0.003 ‐0.028** 0.007 ‐0.025** 0.007 ‐0.030** 

(0.007) (0.008) (0.007) (0.008) (0.007) (0.008) (0.007) (0.008) (0.008) (0.008) (0.008) (0.008) (0.008) 

Edu: High School 0.053** 0.011 0.054** 0.012 0.053** 0.012 0.054** 0.012

(0.006) (0.007) (0.006) (0.007) (0.006) (0.007) (0.006) (0.007)

Edu: Some College 0.202** 0.101** 0.202** 0.101** 0.198** 0.099** 0.198** 0.100**

(0.008) (0.008) (0.008) (0.008) (0.008) (0.008) (0.008) (0.008)

Edu: Associate 0.287** 0.164** 0.286** 0.163** 0.282** 0.162** 0.282** 0.161**

(0.013) (0.014) (0.013) (0.014) (0.013) (0.014) (0.013) (0.014)

Edu: Bachelor’s 0.397** 0.217** 0.398** 0.217** 0.393** 0.217** 0.395** 0.217**

(0.010) (0.011) (0.010) (0.011) (0.010) (0.011) (0.010) (0.011)

Edu: More than B 0.562** 0.317** 0.564** 0.319** 0.558** 0.317** 0.560** 0.318**

(0.019) (0.020) (0.019) (0.020) (0.019) (0.020) (0.019) (0.020)

Schooling 0.015** 0.009** 0.016** 0.009** 0.015** 

(0.001) (0.001) (0.001) (0.001) (0.001) 

Age: 16_20 ‐0.324** ‐0.071 ‐0.327** ‐0.077 ‐0.327** ‐0.072 ‐0.330** ‐0.078 ‐0.343** ‐0.061 ‐0.346** ‐0.067 ‐0.345** 

(0.040) (0.046) (0.040) (0.046) (0.040) (0.046) (0.040) (0.046) (0.041) (0.046) (0.041) (0.046) (0.041) 

Age: 21_30 ‐0.124** ‐0.008 ‐0.124** ‐0.007 ‐0.124** ‐0.007 ‐0.124** ‐0.006 ‐0.131** ‐0.003 ‐0.131** ‐0.003 ‐0.131** 

(0.008) (0.009) (0.008) (0.009) (0.008) (0.009) (0.008) (0.009) (0.008) (0.009) (0.008) (0.009) (0.008) 

Age: 31_40 ‐0.016** 0.023** ‐0.016** 0.024** ‐0.017** 0.023** ‐0.017** 0.024** ‐0.013* 0.027** ‐0.013* 0.028** ‐0.015* 

(0.006) (0.006) (0.006) (0.006) (0.006) (0.006) (0.006) (0.006) (0.006) (0.006) (0.006) (0.006) (0.006) 

Age: 51_60 ‐0.083** ‐0.098** ‐0.084** ‐0.099** ‐0.082** ‐0.097** ‐0.083** ‐0.098** ‐0.077** ‐0.095** ‐0.079** ‐0.096** ‐0.076** 

(0.005) (0.005) (0.005) (0.005) (0.005) (0.005) (0.005) (0.005) (0.005) (0.005) (0.005) (0.005) (0.005) 

Age: 61_65 ‐0.304** ‐0.277** ‐0.305** ‐0.279** ‐0.301** ‐0.276** ‐0.302** ‐0.277** ‐0.301** ‐0.274** ‐0.302** ‐0.276** ‐0.299** 

(0.012) (0.013) (0.012) (0.013) (0.012) (0.013) (0.012) (0.013) (0.012) (0.013) (0.012) (0.013) (0.012) 

Match ‐0.008 0.003 ‐0.010 0.001 ‐0.006 0.004 ‐0.008 0.002 ‐0.008 0.001 ‐0.011 ‐0.001 ‐0.001 

(0.009) (0.009) (0.009) (0.009) (0.009) (0.009) (0.009) (0.009) (0.009) (0.009) (0.009) (0.009) (0.009) 

Train: Occupational ‐0.047** ‐0.047** ‐0.043** ‐0.043** ‐0.025* ‐0.027* ‐0.026* ‐0.029** ‐0.045** ‐0.048** ‐0.041** ‐0.044** ‐0.015 

(0.007) (0.007) (0.007) (0.007) (0.010) (0.011) (0.010) (0.011) (0.007) (0.007) (0.007) (0.007) (0.011) 

Train: Remedial ‐0.117** ‐0.059** ‐0.119** ‐0.061** ‐0.107** ‐0.053** ‐0.109** ‐0.055** ‐0.148** ‐0.067** ‐0.150** ‐0.068** ‐0.135** 

(0.009) (0.010) (0.009) (0.010) (0.009) (0.010) (0.009) (0.010) (0.009) (0.010) (0.009) (0.010) (0.009) 

Train : OJT 0.058** 0.071** 0.062** 0.072** 0.069** 0.076** 0.070** 0.075** 0.045** 0.059** 0.048** 0.061** 0.066** 

(0.017) (0.019) (0.017) (0.019) (0.017) (0.019) (0.017) (0.019) (0.017) (0.019) (0.017) (0.019) (0.018) 

Train: Customized 0.151** 0.113** 0.149** 0.113** 0.151** 0.113** 0.149** 0.111* 0.182** 0.125** 0.180** 0.124** 0.187** 

(0.041) (0.044) (0.041) (0.044) (0.041) (0.044) (0.041) (0.044) (0.042) (0.044) (0.042) (0.044) (0.042) 

Marketable Skills 0.022** ‐0.002 0.016** ‐0.004 0.023** ‐0.001 0.017** ‐0.004 0.029** 0.001 0.022** ‐0.002 0.030** 

(0.005) (0.006) (0.005) (0.006) (0.005) (0.006) (0.005) (0.006) (0.005) (0.006) (0.006) (0.006) (0.005) 

log(Prev Earning) 0.465** 0.465** 0.464** 0.464** 0.493** 0.493** 

(0.005) (0.005) (0.005) (0.005) (0.005) (0.005) 

39 
Exit Year 2005 0.019** 0.040** 0.018** 0.039** 0.020** 0.043** 

(0.006) (0.007) (0.006) (0.007) (0.007) (0.007) 

Exit Year 2006 0.034** 0.041** 0.033** 0.040** 0.036** 0.045** 

(0.007) (0.007) (0.007) (0.007) (0.007) (0.007) 

Exit Year 2007 0.055** 0.051** 0.054** 0.050** 0.057** 0.053** 

(0.007) (0.008) (0.007) (0.008) (0.008) (0.008) 

13.Business & Financial ‐0.011 ‐0.061* ‐0.004 ‐0.054* 0.017  

(0.024) (0.026) (0.024) (0.026) (0.024) 

15.Computer, Math ‐0.002 0.000 0.006 0.008 0.017 

(0.016) (0.017) (0.016) (0.017) (0.017) 

17.Architecture 0.085** 0.076** 0.092** 0.084** 0.106** 

(0.019) (0.020) (0.019) (0.020) (0.019) 

19.Science 0.043 0.025 0.050 0.032 0.074 

(0.040) (0.043) (0.040) (0.043) (0.041) 

21.Community Service ‐0.139** ‐0.099** ‐0.132** ‐0.093** ‐0.131** 

(0.030) (0.031) (0.030) (0.031) (0.031) 

23.Legal 0.024 ‐0.023 0.033 ‐0.017 0.031  

(0.040) (0.040) (0.040) (0.040) (0.041) 

25.Education, Training ‐0.169** ‐0.175** ‐0.164** ‐0.170** ‐0.119** 

(0.022) (0.024) (0.022) (0.024) (0.023) 

27.Arts, Design 0.022 0.030 0.032 0.040 0.028 

(0.032) (0.033) (0.032) (0.033) (0.033) 

29.Healthcare Pract’r 0.061** 0.036* 0.067** 0.043** 0.061** 

(0.015) (0.016) (0.015) (0.016) (0.015) 

31.Healthcare Support ‐0.104** ‐0.099** ‐0.097** ‐0.092** ‐0.135** 

(0.015) (0.016) (0.015) (0.016) (0.016) 

33.Protective Service 0.022 0.011 0.027 0.014 0.002  

(0.032) (0.034) (0.032) (0.034) (0.033) 

35.Food Prep & Serving ‐0.123** ‐0.086* ‐0.118** ‐0.080* ‐0.150** 

(0.038) (0.037) (0.038) (0.037) (0.039) 

37.Building Maintenance ‐0.153** ‐0.072 ‐0.150** ‐0.068 ‐0.182** 

(0.044) (0.049) (0.044) (0.049) (0.045) 

39.Personal Care&Service ‐0.118** ‐0.086** ‐0.112** ‐0.080** ‐0.157** 

(0.025) (0.027) (0.025) (0.027) (0.026) 

41.Sales and Related ‐0.054 ‐0.090 ‐0.046 ‐0.079 ‐0.060 

(0.047) (0.049) (0.047) (0.049) (0.048) 

43.AdministrativeSupport ‐0.084** ‐0.062** ‐0.077** ‐0.055** ‐0.106** 

(0.014) (0.015) (0.014) (0.015) (0.015) 

45.Farming & Fishing ‐0.050 ‐0.192 ‐0.043 ‐0.191 ‐0.037 

(0.138) (0.162) (0.138) (0.162) (0.142) 

47.Construction ‐0.027 ‐0.014 ‐0.020 ‐0.007 ‐0.054* 

(0.022) (0.023) (0.022) (0.023) (0.023) 

49.Installation & Repair ‐0.008 ‐0.013 ‐0.001 ‐0.007 ‐0.041** 

(0.015) (0.015) (0.015) (0.015) (0.015) 

51.Production ‐0.023 ‐0.004 ‐0.017 0.002 ‐0.049** 

(0.015) (0.016) (0.016) (0.016) (0.016) 

53.Transportation ‐0.026 ‐0.005 ‐0.020 0.001 ‐0.070** 

(0.017) (0.018) (0.017) (0.018) (0.017) 

Constant 8.775** 4.622** 8.746** 4.584** 8.797** 4.648** 8.767** 4.609** 8.724** 4.326** 8.690** 4.284** 8.752** 

(0.016) (0.050) (0.017) (0.050) (0.016) (0.050) (0.017) (0.050) (0.018) (0.050) (0.019) (0.050) (0.018) 

States YES YES YES YES YES YES YES YES YES YES YES YES YES Number of obs 44871 32559 44871 32559 44871 32559 44871 32559 44871 32559 44871 32559 44871 F 358.87 474.19 338.92 447.23 250.04 327.21 240.69 314.6 322.78 491.97 303.42 461.65 220.8 Prob > F 0 0 0 0 0 0 0 0 0 0 0 0 0 R‐squared 0.2734 0.4067 0.2744 0.4075 0.278 0.41 0.2789 0.4108 0.2364 0.3942 0.2374 0.3951 0.2426 Adj R‐squared 0.2727 0.4059 0.2736 0.4066 0.2769 0.4087 0.2778 0.4095 0.2357 0.3934 0.2367 0.3942 0.2415 

40 
Table 16. Retention Rates 

Education: Degree Attainment Education: Years of Schooling

5a 6a 7a 8a 5b 6b 7b 

ind. Variable without with without with without with without with without with without with withouMale ‐ ‐0.006* 0.002 ‐0.002 ‐0.006* 0.000 ‐0.006 0.000 ‐0.005 ‐0.001 ‐0.006* ‐0.001 ‐0.006 0.000 

(0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003

Limited Engl Proficiency ‐0.021** ‐0.013 ‐0.020** ‐0.013 ‐0.018* ‐0.010 ‐0.017* ‐0.010 ‐0.023** ‐0.015 ‐0.021** ‐0.015 ‐0.020*

(0.007) (0.008) (0.007) (0.008) (0.007) (0.008) (0.007) (0.008) (0.007) (0.008) (0.007) (0.008) (0.007

Eth: Hispanic ‐0.007 ‐0.002 ‐0.006 ‐0.001 ‐0.008 ‐0.003 ‐0.006 ‐0.002 ‐0.010 ‐0.004 ‐0.008 ‐0.002 ‐0.010 

(0.006) (0.006) (0.006) (0.006) (0.006) (0.006) (0.006) (0.006) (0.006) (0.006) (0.006) (0.006) (0.006

Eth: Asian 0.011 0.003 0.012 0.005 0.011 0.003 0.013 0.005 0.011 0.003 0.012 0.005 0.011 

(0.007) (0.009) (0.007) (0.009) (0.007) (0.009) (0.007) (0.009) (0.007) (0.009) (0.007) (0.009) (0.007

Eth: Black ‐0.003 ‐0.002 ‐0.003 ‐0.003 ‐0.002 ‐0.002 ‐0.003 ‐0.003 ‐0.003 ‐0.002 ‐0.003 ‐0.003 ‐0.002 

(0.005) (0.006) (0.005) (0.006) (0.005) (0.006) (0.005) (0.006) (0.005) (0.006) (0.005) (0.006) (0.005

Eth: Others ‐0.001 ‐0.005 0.001 ‐0.003 ‐0.001 ‐0.005 0.001 ‐0.003 0.000 ‐0.005 0.002 ‐0.003 ‐0.001 

(0.005) (0.005) (0.005) (0.005) (0.005) (0.005) (0.005) (0.005) (0.005) (0.005) (0.005) (0.005) (0.005

Edu: High School 0.016** 0.009* 0.016** 0.009* 0.015** 0.009** 0.016** 0.009*

(0.004) (0.004) (0.004) (0.004) (0.004) (0.004) (0.004) (0.004)

Edu: Some College 0.024** 0.017** 0.023** 0.017** 0.022** 0.015** 0.022** 0.015**

(0.004) (0.005) (0.004) (0.005) (0.004) (0.005) (0.004) (0.005)

Edu: Associate 0.017* 0.010 0.017* 0.009 0.016* 0.008 0.015* 0.007

(0.008) (0.009) (0.008) (0.009) (0.008) (0.009) (0.008) (0.009)

Edu: Bachelor’s 0.009 0.000 0.010 0.001 0.007 ‐0.001 0.008 ‐0.001

(0.006) (0.007) (0.006) (0.007) (0.006) (0.008) (0.006) .

Edu: More than B 0.024* 0.013 0.025** 0.014 0.023* 0.012 0.024* 0.013

(0.010) (0.012) (0.010) (0.012) (0.010) (0.012) (0.010) (0.012)

Schooling 0.001** 0.000 0.001** 0.000 0.001*

(0.000) (0.001) (0.000) (0.001) (0.000

Age: 16_20 ‐0.010 0.013 ‐0.006 0.014 ‐0.009 0.012 ‐0.005 0.013 ‐0.010 0.013 ‐0.006 0.014 ‐0.009 

(0.025) (0.028) (0.024) (0.028) (0.025) (0.029) (0.024) (0.028) (0.025) (0.028) (0.024) (0.028) (0.025

Age: 21_30 ‐0.013 ‐0.006

*

‐0.013

*

‐0.005 ‐0.013

*

‐0.006 ‐0.013

*

‐0.006 ‐0.013

*

‐0.005 ‐0.013

*

‐0.005 ‐0.013

(0.005) (0.006) (0.005) (0.006) (0.005) (0.006) (0.005) (0.006) (0.005) (0.006) (0.005) (0.006) (0.005

Age: 31_40 0.004 0.002 0.004 0.002 0.003 0.001 0.003 0.001 0.004 0.002 0.004 0.002 0.004 

(0.004) (0.004) (0.004) (0.004) (0.004) (0.004) (0.004) (0.004) (0.004) (0.004) (0.004) (0.004) (0.004

Age: 51_60  ‐0.016

**

‐0.022

**

‐0.017

**

‐0.022

**

‐0.016

**

‐0.021

**

‐0.016

**

‐0.022

**

‐0.017

**

‐0.022

**

‐0.017

**

‐0.023

**

‐0.016

*

(0.003) (0.004) (0.003) (0.004) (0.003) (0.004) (0.003) (0.004) (0.003) (0.004) (0.003) (0.004) (0.003

Age: 61_65 ‐0.103** ‐0.100** ‐0.104** ‐0.100** ‐0.103** ‐0.099** ‐0.103** ‐0.100** ‐0.105** ‐0.101** ‐0.105** ‐0.101** ‐0.104*

(0.009) (0.011) (0.009) (0.011) (0.009) (0.011) (0.009) (0.011) (0.009) (0.011) (0.009) (0.011) (0.009

Match ‐0.005 ‐0.007 ‐0.006 ‐0.007 ‐0.004 ‐0.007 ‐0.005 ‐0.008 ‐0.005 ‐0.007 ‐0.006 ‐0.008 ‐0.005 

(0.006) (0.007) (0.006) (0.007) (0.006) (0.007) (0.006) (0.007) (0.006) (0.007) (0.006) (0.007) (0.006

Train: Occupational 0.026** 0.023** 0.027** 0.023** 0.011 ‐0.001 0.009 ‐0.002 0.028** 0.024** 0.028** 0.024** 0.012 

(0.004) (0.005) (0.004) (0.005) (0.007) (0.008) (0.007) (0.008) (0.004) (0.005) (0.004) (0.005) (0.007

Train: Remedial 0.010 0.011 0.009 0.010 0.009 0.009 0.007 0.007 0.007 0.009 0.005 0.008 0.005 

(0.005) (0.006) (0.005) (0.006) (0.006) (0.006) (0.006) (0.006) (0.005) (0.006) (0.005) (0.006) (0.006

Train : OJT 0.031

**

0.036

**

0.033

**

0.037

**

0.026

**

0.029

**

0.028

**

0.030

**

0.031

**

0.036

**

0.033

**

0.037

**

0.027

*

(0.009) (0.010) (0.009) (0.010) (0.010) (0.011) (0.010) (0.011) (0.009) (0.010) (0.009) (0.010) (0.010

Train: Customized 0.010 0.030 0.010 0.031 0.008 0.027 0.007 0.027 0.011 0.031 0.011 0.032 0.008 

(0.024) (0.024) (0.024) (0.024) (0.025) (0.025) (0.025) (0.025) (0.024) (0.024) (0.024) (0.023) (0.025

Marketable Skills 0.009** 0.005 0.003 0.000 0.009** 0.005 0.003 0.000 0.009** 0.005 0.003 0.000 0.010*

(0.003) (0.004) (0.003) (0.004) (0.003) (0.004) (0.003) (0.004) (0.003) (0.004) (0.003) (0.004) (0.003

log(Prev Earning) 0.013** 0.013** 0.013** 0.013** 0.014** 0.014** 

(0.003) (0.003) (0.003) (0.003) (0.003) (0.003) 

41 
42 

Exit Year 2005   ‐0.016

**

‐0.013

**

‐0.015

**

‐0.012

*

‐0.016

**

‐0.013

**

(0.004) (0.005) (0.004) (0.005) (0.004) (0.005) 

Exit Year 2006 0.006 0.004 0.007 0.006 0.006 0.003 

(0.004) (0.005) (0.004) (0.005) (0.004) (0.005) 

Exit Year 2007 0.015

**

0.015

**

0.016

**

0.017

**

0.015

**

0.015

**

(0.004) (0.005) (0.004) (0.005) (0.004) (0.005) 

13.Business & Financial 0.027* 0.028 0.029* 0.030* 0.027

(0.013) (0.015) (0.013) (0.014) (0.013

15.Computer, Math 0.028** 0.033** 0.030** 0.036** 0.028*

(0.009) (0.010) (0.009) (0.009) (0.009

17.Architecture 0.014 0.028 0.017 0.030** 0.015 

(0.012) (0.012) (0.011) (0.011) (0.012

19.Science 0.025 0.038 0.027 0.039 0.025 

(0.023) (0.023) (0.023) (0.022) (0.023

21.Community Service 0.016 0.020 0.019 0.022 0.017 

(0.018) (0.019) (0.018) (0.019) (0.018

23.Legal 0.056** 0.052** 0.058** 0.054** 0.056*

(0.019) (0.020) (0.018) (0.019) (0.018

25.Education, Training 0.021 0.040** 0.022 0.040** 0.020 

(0.012) (0.013) (0.012) (0.012) (0.012

27.Arts, Design 0.024 0.033 0.026 0.034 0.025 

(0.018) (0.018) (0.018) (0.018) (0.018

29.Healthcare Pract’r 0.035** 0.039** 0.037** 0.041** 0.036*

(0.008) (0.008) (0.008) (0.008) (0.008

31.Healthcare Support 0.032** 0.033** 0.034** 0.036** 0.032*

(0.008) (0.009) (0.008) (0.009) (0.008

33.Protective Service 0.019 0.014 0.023 0.017 0.020 

(0.019) (0.022) (0.018) (0.021) (0.019

35.Food Prep & Serving ‐0.046 ‐0.027 ‐0.045 ‐0.026 ‐0.046 

(0.027) (0.027) (0.026) (0.026) (0.027

37.Building Maintenance ‐0.025 ‐0.022 ‐0.023 ‐0.021 ‐0.027 

(0.029) (0.033) (0.029) (0.033) (0.029

39.Personal Care&Service ‐0.029 ‐0.004 ‐0.026 ‐0.002 ‐0.029 

(0.017) (0.017) (0.017) (0.017) (0.017

41.Sales and Related 0.026 0.055* 0.028 0.056* 0.028 

(0.028) (0.023) (0.027) (0.022) (0.027

43.AdministrativeSupport 0.013 0.024** 0.016 0.026** 0.014 

(0.009) (0.009) (0.009) (0.009) (0.009

45.Farming & Fishing 0.020 0.029* 0.025 0.031* 0.019 

(0.080) (0.013) (0.076) (0.013) (0.080

47.Construction 0.013 0.028

**

0.015 0.030

**

0.014 

(0.013) (0.009) (0.013) (0.009) (0.013

49.Installation & Repair 0.017 0.025** 0.019* 0.028** 0.018

(0.009) (0.009) (0.009) (0.009) (0.009

51.Production 0.021* 0.010 0.024** 0.012 0.022

(0.009) (0.011) (0.009) (0.011) (0.009

53.Transportation ‐0.004 0.002 ‐0.002 0.000 ‐0.005 

(0.011) (0.013) (0.011) (0.013) (0.011

States YES YES YES YES YES YES YES YES YES YES YES YES YES 

Number of obs 55136 39331 55136 39331 55134 39324 55134 39324 55136 39331 55136 39331 55134 LR chi2 660.35 478.46 730.86 516.56 714.73 519.42 789.4 561.59 637.65 466.35 709.42 505.46 694.26 Prob >chi2 0 0 0 0 0 0 0 0 0 0 0 0 0 Pseudo R2 0.0178 0.019 0.0198 0.0205 0.0193 0.0206 0.0213 0.0223 0.0172 0.0185 0.0192 0.02 0.0188 



* and ** indicate significance at the 95% and 99% level, respectively. Numbers in the parentheses are standard errors.