does trade adjustment assistance make a...

DOES TRADE ADJUSTMENT ASSISTANCE MAKE A DIFFERENCE?

KARA M. REYNOLDS and JOHN S. PALATUCCI∗

The U.S. Trade Adjustment Assistance (TAA) program provides workers who havelost their jobs due to increased trade with income support and training, job search, andrelocation benefits. This paper uses data collected by the Department of Labor on TAAbeneficiaries to provide the most recent econometric evaluation of the effectiveness ofthe TAA program. Summary statistics suggest that the TAA program successfully targetsdisplaced workers who have a greater difficulty finding new employment. However,using propensity score matching techniques we find that while the required trainingcomponent of the program improves the employment outcomes of beneficiaries, onaverage the TAA program has no discernible impact on the employment outcomes ofthe participants. (JEL F16)

I. INTRODUCTION

For over 30 years, the U.S. Trade Adjust-ment Assistance (TAA) program has providedworkers who can show that they have lost theirjobs due to increased imports with income sup-port, training, job search, and relocation bene-fits. The United States spent $855.1 million toassist approximately 150,000 TAA beneficiariesin 2007. This amount is expected to increasedramatically in the future as the program wassignificantly expanded by the American Recov-ery and Reinvestment Act of 2009.

The TAA program receives a great deal ofsupport from policy makers; one of the unstatedgoals of the program is to decrease politicalresistance to new trade liberalization efforts andCongress has approved additional funding forthe TAA program with virtually every newfree trade agreement that has been implementedsince the program’s inception.1 There is littleevidence, however, regarding whether workershave actually been helped by the TAA program.

∗The authors thank participants of American Univer-sity’s Department of Economics Seminar and anonymousreferees for helpful suggestions.Reynolds: Association Professor, Department of Economics,

American University, Washington, DC 20016. Phone(202) 885-3768, Fax (202) 885-3790, E-mail [email protected]

Palatucci: Student, Department of Economics, AmericanUniversity, Washington, DC 20016. Phone (202) 885-3768, Fax (202) 885-3790.

1. Critics of the TAA program sometimes argue thatthere is no justification for funding a program that helpsworkers displaced from their jobs due to increased importswhile not funding similar programs for workers displacedbecause of technological change or industrial restructuring.

On the contrary, in 2007 the Office of Man-agement and Budget (OMB) rated the program“ineffective” because it failed to demonstrate thecost effectiveness of achieving its goals.

The Department of Labor reports simplestatistics that measure, for example, the per-centage of beneficiaries who are able to findemployment following their participation in theTAA program. Statistics such as these fail totake into account the fact that these participantsmay have found the same employment absentparticipation in the TAA program. The moreappropriate measure of a program’s effective-ness is to what extent the program changed theemployment outcome of TAA participants.

This paper provides an in-depth statisticalevaluation of the effectiveness of the TAA pro-gram. Summary statistics suggest that the TAAprogram successfully targets displaced work-ers who have greater difficulty finding newemployment, but we find no statistical evidencethat the TAA program actually improves the

ABBREVIATIONS

ATAA: Alternative TAAATE: Average Treatment EffectCIA: Conditional Independence AssumptionCPS: Current Population SurveyGAO: General Accounting OfficeNAICS: North American Industry ClassificationOMB: Office of Management and BudgetTAA: U.S. Trade Adjustment AssistanceTAPR: Trade Act Participant ReportsTRA: Trade Readjustment AllowanceUI: Unemployment Insurance

43Contemporary Economic Policy (ISSN 1465-7287)Vol. 30, No. 1, January 2012, 43–59Online Early publication February 7, 2011

doi:10.1111/j.1465-7287.2010.00247.x© 2011 Western Economic Association International

44 CONTEMPORARY ECONOMIC POLICY

employment outcomes of beneficiaries. We dofind strong evidence, however, suggesting thatthose workers who participate in TAA-fundedtraining opportunities are more likely to obtainreemployment, and at higher wages, when com-pared to TAA beneficiaries who do not partici-pate in training. The results suggest that policymakers should require that the TAA programundergoes a comprehensive evaluation as to howit can more effectively assist displaced workers.

II. THE TRADE ADJUSTMENT ASSISTANCEPROGRAM

The U.S. TAA program, first developedin 1962 and amended repeatedly since thattime, compensates workers harmed by increasedimport competition.2 The goals of the programare threefold: encourage the rapid reemploymentof participants; provide training and income sup-port to allow participants to achieve reemploy-ment; and assist participants to obtain reemploy-ment in fields where they are “likely to remainemployed and earn wages comparable to theirprior jobs.”3

To be eligible for the TAA program, a com-pany affiliate must file a petition with theDepartment of Labor alleging that workers inthe firm lost their jobs (or suffered a reductionin hours and/or wages) as a result of increasedimports or shifts in production outside of theUnited States.4,5 The Department of Labor mustthen decide whether the firm is eligible for theprogram under current guidelines.

Once a firm is certified, any worker fromthe firm laid off up to 1 year before or 2 yearsafter the petition was filed is eligible for TAAbenefits. TAA benefits fall into five categories:training, income support, relocation allowances,job search allowances, and a health coverage taxcredit.6 Specifically, the TAA program will pay

2. For an excellent history of the TAA program, seeBaicker and Marit Rehavi (2004).

3. “Overview of the TAA program,” Department ofLabor.

4. State labor agencies may also file a petition on behalfof the workers. The program rules described in this sectionare those in place between 2002 and 2008. The AmericanRecovery and Reinvestment Act of 2009 made a number ofimportant changes, including expanding TRA payments to130 weeks.

5. Service workers were ineligible for the TAA programprior to May 18, 2009. The firm’s shift in production musteither be (1) to a country that receives preferential tarifftreatment or (2) likely to result in an increase in imports.

6. The Trade Act of 2002 created an additional TAAprogram for workers over 50 years old which will not

for up to 104 weeks of any basic training pro-gram, or up to 130 weeks if the worker is in needof remedial education. Participants are eligiblefor an additional 26 weeks of unemploymentinsurance, known as the Trade ReadjustmentAllowance (TRA). Workers must be enrolled inor have completed training to receive TRA ben-efits, although some workers may be eligible fora waiver of the training requirement.7 Workerswho are participating in a training program mayreceive 52 weeks of “additional” TRA, and thoseworkers enrolled in remedial education may beeligible for remedial TRA payments.

Other benefits include a job search and relo-cation allowance; the maximum payment foreach allowance is $1,250. Finally, TAA-eligibleworkers qualify for the Health Coverage TaxCredit, which pays 65% of the premium forqualified health insurance plans.

III. LITERATURE REVIEW

In part because of a lack of data, there havebeen few empirical evaluations of the TAA pro-gram. Early evaluations, including Corson andNicholson (1981) and the U.S. General Account-ing Office (GAO) (1980) found that 70% ofTAA support went to workers who eventuallyreturned to work with their previous employers.

More recently, Decker and Corson (1995)used a survey of TAA participants between 1988and 1989 to evaluate the impact of the 1988amendments to the program that mandated par-ticipation in a training program. The authorsfind that the 1988 changes in the TAA programincreased participation in training programs,reduced the amount of TRA collected by bene-ficiaries, and led to a decline in the duration ofunemployment. The authors conclude, however,that training does not have a substantial positiveeffect on the earnings of TAA participants.

Marcal (2001) uses the same data as Deckerand Corson (1995) to study whether the TAAprogram increases the earnings of beneficia-ries over comparable unemployment insurance(UI) exhaustees. Marcal (2001) also finds littleevidence that the TAA program improves the

be analyzed in this paper: the Alternative TAA (ATAA)program.

7. Workers who show that they are subject to recall, inpoor health, near retirement, or already possess marketableskills can obtain a waiver of the training requirement.Waivers are also available to workers who can prove thattraining is either unavailable or they are unable to enroll intraining.

REYNOLDS & PALATUCCI: DOES TAA MAKE A DIFFERENCE? 45

earnings of displaced workers, although TAAbeneficiaries that participate in training pro-grams were employed more on average thanboth UI exhaustees and TAA beneficiaries whodid not participate in a training program.

The only recent evaluations of the TAA pro-gram have been conducted by federal agenciessuch as the GAO. In the study most relevantto this paper, GAO (2006b) conducted a sur-vey of workers from five trade-related plantclosures. They found that reemployment ratesat these plants ranged from 33% to 60%. Thestudy further found that the majority of reem-ployed workers were earning less than they hadpreviously.

This paper improves upon the most recentevaluations of the TAA program in a numberof ways. Perhaps most importantly, rather thanrelying on summary statistics of the employmentoutcomes of TAA beneficiaries, we use propen-sity score matching econometric techniques toestimate the actual impact of the TAA programon the participants.

IV. EMPIRICAL METHODOLOGY AND DATA

As described in Heckman, LaLonde, andSmith (1999), there is an extremely large lit-erature devoted to the evaluation of variouslabor market programs. To accurately evalu-ate the impact of a program such as TAAon workers, the researcher must compare theoutcome of interest for program participantswith the outcome for a “comparable” group ofnonparticipants.

Ideally, one would want to compare theoutcome of interest (i.e., whether or not reem-ployed) for displaced workers when they partic-ipate in the TAA program to the outcome forthese same workers when they do not partici-pate. The problem is that the researcher neverobserves how the TAA participant would havefared if they chose not to participate in theprogram.

Alternatively, one could compare the out-come of displaced workers who enrolled in theTAA program with the outcome of those whowere ineligible or chose not to enroll in theTAA program. Unfortunately, estimators suchas these suffer from selection bias if those whochoose to participate in the program are system-atically different from those who are ineligibleor chose not to participate. Although controllingfor differences in observable characteristics canhelp alleviate this problem, selection bias will

still remain if those who participate are system-atically different from nonparticipants in theirunobservable characteristics.

Selection bias in the case of the TAAprogram can arise at three separate points.First, an eligible entity (firm, union, etc.) mustchoose to apply for TAA certification. Next, theDepartment of Labor must certify the firm aseligible for the TAA program. Finally, the indi-vidual worker must choose to apply for TAAbenefits. According to the Department of LaborStatistics, only 38.7% of those workers eligiblefor TAA benefits between 2003 and 2005 choseto participate in the program.8

As discussed in Heckman and Navarro-Lozano (2004), a number of econometric meth-ods have been developed to deal with selectionbias of this nature, including control functionmethods such as the Heckman selection model.9

Control function methods explicitly model thestochastic dependence of the unobservable char-acteristics in the outcome equation on theobservable characteristics. To do this, thesemethods typically require specification of thefunctional form of the outcome equation, andthus can be susceptible to misspecification.

Heckman, Ichimura, and Todd (1997, 1998)proposed another solution to the selection biasproblem called propensity score matching inwhich each participant in the labor programis matched with a “control” observation froman alternative dataset using a propensity score;the outcome variable of the participant is thenmatched with the outcome of the control obser-vation.10 Specifically, propensity score matchingtechniques use the estimates from a logistic orprobit regression analysis to generate the pre-dicted probability of program participation foreach observation based on observed character-istics such as age, gender, and education level.The impact of the program, commonly known asthe average treatment effect (ATE), is calculatedas the average difference in the outcome variablebetween the program participants and the con-trol observations that they are matched to based

8. This figure was calculated by dividing the number ofnew workers enrolling in the TAA by the estimated numberof workers covered by new TAA certifications.

9. This was the method used in the Marcal (2001)analysis of the TAA program.

10. Although the method was initially developed inRosenbaum and Rubin (1983), it became a popular methodof evaluating labor market policies following the work ofHeckman, Ichimura, and Todd (1997, 1998).


on the similarity of the predicted probability orpropensity score.11

Propensity score matching models rely onthe assumption that conditional on observablecharacteristics the decision to participate ina program is completely independent of theunobservable characteristics. In other words, itassumes that after conditioning on observablesone can assume that the decision to participatein a program is completely random. Althoughthis is a strong assumption, we choose to usethis method to analyze the impact of the TAAprogram because we believe it is preferable tospecifying a functional form for the outcomeequation. Specifically, we correct for potentialselection bias by comparing the employmentoutcomes of TAA participants to the employ-ment outcomes of displaced workers who didnot participate in the TAA program, but haveobservable characteristics similar to TAA par-ticipants as measured by their propensity score.

To conduct this evaluation of the TAA pro-gram, we utilize data from the Department ofLabor’s Trade Act Participant Reports (TAPR).Since 1999, each state has had to submit TAPRreports to the Department of Labor every quar-ter with data on individuals who exited the TAAprogram.12 TAPR reports include data on theTAA participant, including their gender and edu-cation level, and the services they received underthe TAA program. Outcome variables in theTAPR reports include whether the participantwas employed in the first three quarters afterexit and the worker’s earnings in these quarters.

Unfortunately, the TAPR data have a num-ber of weaknesses. The U.S. GAO (2006a)notes that only half of the states reported thatthe data they submit in the TAPR include allTAA participants who exit the program. Otherstates are inaccurately recording some work-ers’ employment status due to limited infor-mation technology systems. Nevertheless, the

11. Some evaluations of the propensity score matchingtechniques have questioned the ability of the econometrictechnique to effectively control for selection bias. Smith andTodd (2005) found that results from propensity score tech-niques are highly sensitive to the choice of the comparisonsample and the set of variables included in the estimation.Dehijia (2005) responded that propensity score matchingtechniques can be a powerful tool as long as researchersexamine the sensitivity of their results to small changes inthe specification of their propensity score.

12. Participants are deemed to have exited the programif (1) they have a known date of completion for all TAA-funded services or (2) they have not received any TAA-funded services for 90 days and are not scheduled for futureservices.

TAPR database is the best data available regard-ing participants in the TAA program.

Our original TAPR dataset included dataon 286,840 individuals who exited the TAAprogram between the final quarter of 2000 andthe first quarter of 2008. In order to observe theindustry of the worker, we limit the dataset tothose observations which reported a valid TAAPetition Number.

We include only manufacturing sector work-ers in the final dataset; program rules in placeprior to 2009 ensured that virtually all TAAbeneficiaries were employed in the manufactur-ing sector. Note that slightly over 80% of thepetitions were filed by firms in the manufactur-ing sector, with the largest number filed by theapparel, electronics, and motor vehicle indus-tries. Moreover, GAO (2007) reported that 44%of the 2,500 petitions denied TAA certificationin Fiscal Year 2006 were denied because work-ers were not involved in producing a product.13

Finally, we limit our sample to those workerswho were displaced between 2003 and 2005 andwho exited the TAA program in the fourth quar-ter of 2005.14 The number of TAA participantsin the final dataset is 5,125.

Table 1 summarizes the characteristics of theTAA beneficiaries in our sample, and lists thespecific TAA program provisions that these indi-viduals profited from. We observe an over-whelming majority (77.5%) of workers enrolledin a TAA-funded training program. Slightlymore than 90% of the workers who chose notto participate in a training program receiveda waiver from the training requirement, thuswere still eligible to receive TRA payments. Ofthose that received a waiver, at least one-quarterqualified for a waiver because they possessed“marketable skills,” while approximately 10%qualified because training was unavailable intheir area.15

A much smaller percentage of beneficiaries(59%) in the sample received TRA payments.Of those workers who did not receive TRA

13. An analysis of certifications between 2003 and 2005suggests a similar pattern; of the 2,962 petitions deniedcertification, 43% were in nonmanufacturing industries.

14. We limit the sample in this way in order to bettermatch the period during which we observe the controlobservations in the CPS dataset. We also exclude fromthe analysis 48 participants who reported that they werechoosing to participate in the ATAA program and 13individuals for which we had no employment outcome data.

15. The percentage of waivers attributed to each reasonis likely much lower than the true percentage of workerswho received a waiver due to these reasons. Officials failedto record a reason for the waiver in 62% of observations.


TABLE 1Summary Statistics

Trade Act Participant CPS Displaced Worker

Mean Standard Deviation Mean Standard Deviation

Male 0.563 0.496 0.631 0.483Age 47.136 11.083 45.134 11.768Education (highest level)

Years of education 11.872 2.378 13.373 2.634High school graduate 0.606 0.488 0.388 0.487Some college 0.134 0.340 0.272 0.445BA+ 0.068 0.251 0.217 0.412

Tenure (in years) 9.502 9.933 8.836 8.903Import sensitivity 0.325 0.164 0.295 0.185One-year growth, imports 0.089 0.045 0.087 0.061Intraindustry trade 0.670 0.281 0.661 0.247Industry unionization 0.118 0.116 0.116 0.113Average industry wage 14.181 2.703 14.356 2.784Industry layoffs 56.232 37.266 55.233 44.764State unemployment rate 0.059 0.008 0.056 0.010Average state wage 15.152 1.692 15.618 1.878State layoffs 19.370 15.900 21.223 19.006TAA benefits

Training 0.775 0.417 — —Job search 0.013 0.113 — —Relocation 0.014 0.116 — —TRA 0.589 0.492 — —

PostdisplacementEmployed (% sampled) 0.789 0.407 0.850 0.356Change in wages −0.305 0.740 −0.179 0.499

Number of observations 5,125 469

payments, 66% were still receiving basic unem-ployment insurance. In other words, they exitedthe TAA program prior to exhausting their basic26 weeks of unemployment insurance. Less than2% of program beneficiaries collected job searchor relocation benefits.

To conduct an evaluation of the TAA program,we need to compare the employment outcomesof the TAA participants to the employmentoutcomes of a group of control observations.We use workers from the January 2006 Dis-placed Worker, Employee Tenure, and Occupa-tional Mobility Supplement File of the U.S. Cen-sus Bureau’s Current Population Survey (CPS).Like the TAPR data, the CPS Displaced WorkerSurvey includes information on workers wholost their job because their company closed ormoved, their position was abolished, or therewas insufficient work to support their position,although these workers did not necessarily losetheir job due to increased imports.16

16. We exclude from the analysis those workers whoreported they were displaced due to the completion of a

The Displaced Worker Survey includes dataon 5,611 workers who reported that they weredisplaced from their job between 2003 and 2005;nearly 80% of these workers were employed inconstruction or a service industry. Because theTAA program was targeted to workers displacedfrom firms that produce a product, we limitour sample to manufacturing workers; the finalCPS comparison sample includes data on 469individuals.

Table 2 shows some disparity between theindustries represented in the TAA sample ofworkers when compared to the manufacturingworkers included in the CPS. For example, 20%of TAA participants were displaced from the tex-tile, apparel, and leather manufacturing sectorscompared to only 7% of CPS workers. The dif-ferences in the worker’s industry of employment

seasonal job or the failure of a self-operated business. We donot observe whether workers in the CPS sample participatedin the TAA or other labor adjustment program. To the degreethat there is overlap in the two samples, our results wouldbe biased toward finding no impact of the TAA program onworkers.


TABLE 2Top Industries of Lost Employment

Industry

Percentage ofTAA Participant

Sample

Percentage ofCPS Control

Sample

Nonmetallic minerals 3.8 1.7Primary metals/fabricated

metal products9.0 13.6

Machinery 14.2 8.1Computer and electronic

products17.2 18.9

Electrical equipment 5.9 4.2Transportation equipment 5.6 9.6Wood products 2.4 3.4Furniture manufacturing 3.7 4.2Miscellaneous

manufacturing4.2 6.1

Food, beverage, andtobacco

1.6 7.7

Textile, apparel, andleather

19.5 7.5

Paper and printing 5.7 6.6Chemicals 1.7 5.3Plastics and rubber

products5.3 2.8

are also reflected in the average import penetra-tion ratio of the industry from which the workerswere displaced; the average import penetrationratio in industries displacing TAA workers was32.5% compared to 29.5% in the CPS sample.

Table 1 shows several other distinctions be-tween the two groups. TAA participants aremore likely to be female and from states withlower manufacturing wages and higher unem-ployment rates. The participants are also slightlyolder and had more experience at the job fromwhich they were displaced when compared totheir CPS counterparts.

Workers from the CPS sample are more edu-cated than TAA beneficiaries: 80.8% of the TAAbeneficiaries in our sample have a high schooldiploma compared to 87.7% of those sampledfrom the CPS. Similarly, only 7% of TAA ben-eficiaries have a college degree compared to22% of the CPS sample of displaced workers.The CPS sample is more reflective of the edu-cational attainment of the U.S. population asa whole—87% of the U.S. population has ahigh school diploma while 29% has a collegedegree. According to GAO (2001), officials incommunities with a large number of TAA partic-ipants reported needing to “improve local edu-cational systems, which often had high schooldropout rates much higher than the nationalaverage.”

The most dramatic differences between thetwo samples may be the post displacement out-comes. TAA beneficiaries were less likely tohave found new employment when comparedto their CPS counterparts: 78.9% of TAA par-ticipants were employed at the time of surveycompared to 85.0% of CPS workers. Perhaps ofmore concern, TAA workers earned on average30% less than they made at their previous job.Displaced workers from the CPS also sufferedfrom reduced wages, but they earned only 18%less in their new place of employment.

These summary statistics do not indicate thatthe TAA program caused the wage loss ormade it more difficult for displaced workersto find employment. In fact, the differences inthe two samples suggest that the TAA programsuccessfully targets those workers who have amore difficult time finding new employmentfollowing displacement. As GAO (2001) notes,because TAA beneficiaries tend to be older andless educated than other workers, they are lessmobile and have a harder time reentering aworkforce that increasingly requires more skillsand training. The propensity score matchingtechnique tries to control for the selection biasthat could result from the unique characteristicsof TAA beneficiaries to allow us to determinethe impact of the TAA program.

V. PROPENSITY SCORE MATCHING

As hinted to in Section IV, the ability ofthe propensity score matching technique to suc-cessfully estimate the ATE in the presence ofpotential selection bias relies on two fairlystrong assumptions. First, the conditional inde-pendence assumption (CIA) must hold: condi-tional on a set of observable characteristics, out-comes are independent of whether the individ-ual participates in the program under investiga-tion. In other words, all variables that influencewhether the individual participates in the pro-gram and the outcome variable are observed bythe researcher.17 Second, the common supportassumption must hold: individuals with the samecovariates must have a positive probability ofbeing both TAA participant and nonparticipant.

As discussed in Caliendo and Kopeinig(2008), researchers must make a number of

17. Heckman, Ichimura, and Todd (1998) demonstratethat this assumption is overly strong for identification ofaverage treatment effects; instead, all that is needed is forthe mean outcome to be independent of whether individualsparticipate in the program.


decisions when implementing propensity scorematching to help ensure that these two assump-tions hold. First, the researcher must choosewhich observable characteristics to include inthe propensity score estimation. Next, the re-searcher must choose which matching algorithmto use. This section discusses each decisionin turn, as well as the specification tests andsensitivity analyses we conducted during ouranalysis.

A. Estimation of the Propensity Score

We estimate the propensity score using a pro-bit regression in which the dependent variableequals one for TAA participants.18 In order forCIA to hold, the researcher must include in thepropensity score estimation all variables thatjointly influence the decision to participate inthe TAA program and the employment outcomevariables. Heckman, Ichimura, and Todd (1997)show that omitting variables can significantlyincrease the bias in the estimated average treat-ment effect. However, Caliendo and Kopeinig(2008) also note that including too many vari-ables in the propensity score estimation canincrease the variance of the average treatmenteffect. Thus, they suggest that economic theoryand a clear understanding of institutional set-tings should be used to build the model.

As Heckman and Navarro-Lozano (2004)note, the propensity score matching literatureprovides no clear guidance as to how to choosewhich variables should be included in thepropensity score model. Specifically, they statethat “[t]here is no support for the commonlyused rules of selecting matching variables bychoosing the set of variables that maximizesthe probability of successful prediction intotreatment or by including variables in condi-tioning sets that are statistically significant inchoice equations.” We choose to present resultsfrom two specifications of the propensity score.The first specification includes a wide vari-ety of industry, geographic, and individual-levelcharacteristics that potentially impact both theindividual’s participation in the TAA programand his or her employment outcome. Vari-ables included in this specification are discussedbelow.

The leading determinants of whether a workeris eligible for the TAA program are associated

18. The results from estimations using a logit modelwere not qualitatively different from those presented here.

with the characteristics of their industry. First,the more import sensitive the worker’s indus-try, the more likely it is that their displace-ment is due to a surge in imports and, thus,the more likely that the worker is eligible forthe TAA program. Using trade data from theU.S. International Trade Commission and pro-duction data from the Bureau of Economic Anal-ysis (BEA), we construct the industry’s importpenetration ratio (Import Sensitivity) by divid-ing industry imports by domestic consumption,or the value of the industry’s production lessnet exports.19 We also construct the industry’s1-year growth in imports and a measure of theindustry’s intraindustry trade.20 The propensityscore estimation includes squares of both theimport sensitivity and intraindustry trade vari-ables in order to account for potential nonlin-earities in these measures.

Given the role that unions play in filing forTAA benefits, we include the unionization rateof the industry (Unionization Rate).21 We alsoinclude the average hourly wage in the industry(Average Industry Wage), which we calculateusing data from the full January 2006 CPS.

It is important to control for individual-levelcharacteristics to capture other determinants ofthe decision to participate in the TAA programand the employment outcome. The TAPR andCPS datasets include a number of demographicvariables that have traditionally been used toexplain employment outcomes, including theage of the individual, gender, the level ofeducation, and the length of time the worker hadbeen employed with the firm from which he orshe was displaced (Tenure).22

To control for both geographic differencesand differences in the macroeconomic condi-tions facing workers in the TAA and CPS sam-ples, we include the state unemployment rateand a dummy variable for those workers whowere displaced from their jobs in 2003, the

19. We use 2003 import and production data. BEA andCPS concordances were used to match BEA production datato six-digit North American Industry Classification (NAICS)import and export data and the NAICS-level data to CensusIndustry Classification Codes.

20. Intraindustry trade is calculated by dividing theminimum of the industry’s imports or exports over one-halfof the sum of imports and exports in the industry.

21. Between 2003 and 2005, 14% of TAA petitions werefiled by a union.

22. Although the employment literature also suggeststhat race plays an important role in employment outcomes,we exclude race variables from our estimation. Race vari-ables included in the TAPR were too unreliable to beincluded.


TABLE 3Estimates from Probit Estimation of Propensity Scorea

TAA Beneficiary Training Participant

Coefficient Standard Error Coefficient Standard Error

Male 0.033 0.057 0.137∗ 0.045Ln (age) 0.384∗ 0.113 −0.730∗ 0.094Ln (years education) −0.731∗ 0.234 0.656∗ 0.070Education

High school 0.324∗ 0.101 −0.159∗ 0.067Some college −0.220 0.137 −0.041 0.092BA+ −0.138 0.171 −0.397∗ 0.116

Tenure −0.022 0.089 0.266∗ 0.062Tenure2 0.000 0.000 −0.001∗ 0.000Trade

Import sensitivity 6.332∗ 0.622 1.034∗∗ 0.556Import sensitivity2 −7.087∗ 0.818 0.008 0.705Intraindustry trade −3.027∗ 0.623 0.412 0.537Intraindustry trade2 2.771∗ 0.481 0.135 0.407Year import growth 0.166 0.642 3.870∗ 0.624

Unionization rate −0.302 0.803 1.190∗ 0.494Unionization rate2 4.999∗ 2.215 −4.111∗ 1.094Average industry wage −0.057∗ 0.014 −0.068∗ 0.011Industry layoffs — — 16.721∗ 3.560State unemployment rate 17.039∗ 3.161 27.352∗ 2.741Year 1993 0.600∗ 0.060 — —Number of observations 5,472 — 5,018 —Pseudo r2 0.1609 — 0.125 —F -test 503.47 — 655.53 —

aResults from constant not reported.∗,∗∗indicate those parameters significant at the 5% and 10% levels, respectively.

first year of the sample. Recall that our sampleincludes CPS workers interviewed in January2006 and TAA participants who exited the TAAprogram and appeared in the TAPR dataset inthe fourth quarter of 2005. Workers from bothsamples were displaced between 2003 and 2005.However, a higher proportion of our TAA sam-ple was displaced in 2003 when compared tothe CPS comparison group because workers areincluded in the TAPR dataset only after theyexit the TAA program; the longer the time sincedisplacement, the more likely TAA workers areto appear in the dataset. The longer the timesince displacement, the more likely workers areto have found new employment, thus the yearof displacement will have a significant impacton employment outcomes measured in January2006.

Estimates from two probit regressions arepresented in Table 3. Columns 2 and 3 includethe estimates from the regression explaining par-ticipation in the TAA program, while columns

4 and 5 include the estimates from a regres-sion explaining participation in the training com-ponent of the TAA program; the latter resultsare discussed on page 25. Most of the coeffi-cient estimates from the regression explainingparticipation in the TAA program are statisti-cally significant and of the expected sign. Forexample, coefficient estimates confirm that indi-viduals are more likely to participate in the TAAprogram the higher the import penetration ratioof their industry, while the degree of intraindus-try trade in the industry reduces the likelihoodof participation. Individuals are more likely toparticipate in the TAA program the higher theunionization rate and the lower the average wagein their industry. Parameter estimates confirmthe results of the summary statistics that youngerand more educated workers are less likely to beTAA beneficiaries than other displaced workers.Finally, individuals are more likely to be TAAparticipants if they are from states with higherunemployment rates or were displaced in 2003.


The second specification uses leave-one-outcross-validation to select the set of condition-ing variables, a methodology suggested in Blackand Smith (2004). In this method, we startwith a minimal model containing only twoconditioning variables. We then add blocks ofadditional variables and compare the resultingmean-squared errors of the models, choosing themodel that best predicts the effect of the TAAprogram.

The problem with computing the model’smean-squared error is that we do not observethe missing counterfactual for TAA participants,their employment outcome if they had chosennot to participate in the TAA program. Leave-one-out cross-validation solves this problem byusing the observations in the comparison group(the CPS workers) to compute the mean-squarederror.23

The leave-one-out cross-validation methodsuggests that we should use a specification withsignificantly fewer variables. Specifically, speci-fication 2 includes only the age, import penetra-tion ratio, union and tenure variables, along withsquares of the later two variables. Although wedo not present the results from this specificationin the paper, coefficient estimates were qualita-tively similar to those from specification 1.

B. Matching

There are a number of potential matchingestimators that researchers can use. Each esti-mator constructs an estimate of the expectedcounterfactual (i.e., the employment outcome ofthe treated individual if they had chosen not toparticipate in the TAA program) by taking aweighted average of the employment outcomes

23. To compute each specification’s mean square errorusing the leave-one-out cross-validation method, we firstestimate propensity scores for each observation in the com-parison group (in this case, displaced workers from the CPS)using the probit coefficients from the proposed specification.We then drop the ith observation from the comparison groupand match this observation with one or more (depending onthe matching technique selected by the researcher) of theremaining N − 1 observations in the comparison group toestimate the ith observation’s employment outcome. In otherwords, the estimated employment outcome for the ith obser-vation is a weighted average of the employment outcomesof all of the comparison observations matched with the ithobservation, where the weights are a function of the differ-ence between the propensity score of the ith observation andthat of the matched observation. The error is the differencebetween the outcome of the ith observation and this esti-mated employment outcome, while the mean square error isthe average of these errors over all N observations in thecomparison group. The result essentially represents an outof sample forecast error.

of the comparison group. The estimators dif-fer in the weights assigned to each comparisongroup observation matched to the treated indi-vidual. Although asymptotically each methodshould produce the same results, in small sam-ples the results can be quite different.

To ensure that our results are not being drivenby the matching algorithm, we present resultsfrom two alternative matching algorithms. First,we match each TAA participant in the samplewith a CPS counterpart using the nearest neigh-bor matching method. In this method, each TAAparticipant is matched with the displaced workerfrom the CPS sample that has the closest propen-sity score. We use nearest neighbor matchingwith replacement of the control observations;in other words the same CPS observations canserve as the match to more than one TAA par-ticipant.24

Next, we present results from a local lin-ear matching estimator with an Epanechnikovkernel. Like kernel matching, local linear match-ing is a nonparametric matching estimator thatcalculates the missing counterfactual outcomeby taking a weighted average of employmentoutcomes from virtually all of the comparisonobservations. The weights are a function of thedifference in the propensity score of the com-parison observation and the treated individual.25

We use the leave-one-out cross-validationmethod described above to choose the optimalbandwidth. As will be seen in the next section,both matching estimators result in qualitativelysimilar results, although a leave-one-out cross-validation comparison of the two models sug-gests that the local linear matching performs bet-ter (results in a lower mean square error) whencompared to the nearest neighbor algorithm.

The final decision the researcher must makeis how to ensure that the common support

24. In specifications not presented here, we tried match-ing each TAA beneficiary with more than one CPS counter-part. We also used a variant of the nearest neighbor match-ing, caliper matching, in which we exclude those matches inwhich the difference in propensity scores exceeds a thresh-old set by the researcher. Results from specifications withoversampling and caliper matching were not significantlydifferent from those presented here.

25. Local linear matching tends to perform better thankernel matching when comparison observations are dis-tributed asymmetrically around the treated observation, likeat boundary points or where there are gaps in the propen-sity score. While kernel matching can be thought of as aweighted regression of the employment outcomes of thecomparison observations on an intercept term, local linearmatching can be thought of as a weighted regression of theseoutcomes on an intercept term and the propensity score.


TABLE 4Covariate Balancing after Matching, TAA Participants versus Nonparticipantsa

Means after Matching % Bias % Bias

TAA Participants CPS Controls Before Matching After Matching

Male 0.555 0.586 −14.2 −6.2Ln (age) 3.819 3.811 19.8 3.1Ln (years education) 2.498 2.482 −43.8 −2.4Education (highest level)

High school graduate 0.640 0.559 45.5 16.8Some college 0.134 0.199 −47.9 −15.3BA+ 0.050 0.071 −29.1 −7.0

Tenure (in years) 9.470 9.633 11.5 −1.7Import sensitivity 0.328 0.333 18.2 −3.0Year growth imports 0.089 0.092 2.7 −3.9Intraindustry trade 0.669 0.657 3.7 4.3Industry unionization 0.113 0.119 1.0 −4.8Average industry wage 14.213 14.235 −6.9 −0.8State unemployment rate 0.060 0.059 41.6 12.6Year 1993 0.834 0.772 56.8 13.7Mean standardized biasb 6.825 — — —Pseudo r2 0.027 — — —F -test 332.64 — — —

aThis table reports covariate balancing statistics using the probit specification reported in column 1 of Table 3 and locallinear matching with an Epanechnikov kernel and a bandwidth of 0.9. A common support is imposed by dropping 10% ofthe treated observations at which the density of the propensity score density of the control observations is the lowest.

bMean standardized bias has been calculated as an unweighted average of all covariates. The standardized bias is calculatedas 100 × (xtapr − xcps)/

√Vartapr(x) + Varcps (x)/2.

assumption holds. As explained in Caliendoand Kopeinig (2008), researchers typically useone of the two methods. The first, the minimaand maxima comparison, deletes all observa-tions whose propensity score is smaller thanthe minimum and larger than the maximum inthe opposite group. In our case, this amountsto eliminating the 124 TAA participants whosepropensity scores were higher than the high-est propensity score observed in the CPS com-parison group. The second method, known astrimming, eliminates a specific percentage oftreated observations whose propensity scores liein regions where the density of the propensityscores of the comparison observations is thelowest. We choose to present results from bothmethods, choosing in the later case to trim 10%(or 501 observations) from the TAA sample.

Before estimating the impact of the TAA pro-gram, we assess how well the matching proce-dure has been able to balance the distribution ofcovariates in the TAA treatment and CPS controlgroups. As suggested by Rosenbaum and Rubin(1985), we assess the matching quality using thestandardized bias of the covariates. The stan-dardized bias for each covariate is the difference

between the sample means in the TAA treat-ment group and the matched comparison CPScontrol group as a percentage of the square rootof the average of the sample variances in bothgroups. Another method of assessing the match-ing quality is the pseudo r2. Ideally, the pseudor2 should be high prior to matching but lowafter matching; in other words, after matchingthere should be no systematic difference in thecovariates in the TAA and CPS samples, and thecovariates should have little explanatory power.

Table 4 includes the weighted mean valuesof the observable characteristics from the TAAand matched CPS comparison samples, as wellas the standardized bias in these variables beforeand after matching.26 Although there is nostandard measure of how to determine whetherthe two samples are balanced, Rosenbaum andRubin (1985) suggest that a standardized biasgreater than 20 should be considered “large.” Ascan be seen from Table 4, prior to matching sixof the covariates had a standardized bias greater

26. Table 4 shows covariate balancing statistics follow-ing the local linear matching procedure. Statistics from othermatching procedures are available from the authors uponrequest.


FIGURE 1Distribution of Propensity Scores

Notes: Propensity scores following estimation of theprobit regression of the likelihood of participating in theTAA program as reported in columns 2 and 3 of Table 3.Observations not on the support include the 10% of theTAA participants whose propensity scores lie in the regionwhere the density of the propensity scores of the comparisonobservations is the lowest.

than 20, including all of the education variablesand the state unemployment rate. Matchingreduces the bias for all covariates to below 20;the mean standardized bias across the covariatesis reduced from 24% to just 6.8%. The pseudor2, which was 0.1609 in the original probitspecification, is just 0.027 after matching.

Figure 1 shows the distribution of propensityscores for both the TAA and CPS comparisonsamples. Both the covariate balancing statisticsand the distribution of propensity scores in thetwo samples suggest that the matching proce-dure does a good job of balancing the covariatesin the two samples, making the observations assimilar as possible in observable characteristics.

VI. IMPACT OF THE TAA PROGRAM

Using the matching specification discussedin Section V, we estimate the effect of theTAA treatment on two postdisplacement joboutcomes: (1) ability to find reemployment and(2) the ability to replace lost wages. We usethe TAPR data to create an indicator variable(Work ) that measures whether the TAA partici-pant was employed in any of the three quartersfollowing their exit from the program. The dis-placed worker survey from the CPS includesslightly different postdisplacement employmentinformation. CPS workers were asked in January2006 whether or not they had worked for pay

since they were displaced, as well as how longthey were unemployed after displacement andhow many jobs they had held between displace-ment and the time of the interview. We definethe indicator variable (Work ) to equal one ifthe CPS worker indicated that they had workedsince displacement in any of these questions.

Columns 2 and 3 of Table 5 present our esti-mates of the average effect of the TAA pro-gram on the ability of participants to find newemployment. A naı̈ve comparison of the proba-bility of reemployment would suggest that work-ers who participated in the TAA program were6.1 percentage points less likely to obtain reem-ployment, as reported in the first row. As dis-cussed earlier, this difference is likely drivenby differences in the employment prospects ofthe two samples rather than the impact of theTAA program itself. In comparison, none of thepropensity score matching specifications usingthe full probit specification find any statisti-cally significant impact of the TAA program onthe likelihood that beneficiaries will find newemployment.

The second specification suggested by theleave-one-out cross-validation tests, which con-trols for fewer observable characteristics, findsthat on average the TAA program reduces thelikelihood that the participant will find newemployment by anywhere from 3% to 4%.Despite the fact that the minimal probit spec-ification results in a lower mean-squared errorwhen compared to the full specification, likeBlack and Smith (2004) we would be hesitant tosay that these propensity score matching resultsare more accurate than those from the full spec-ification. The minimal specification was basi-cally chosen to maximize the model’s goodnessof fit, without taking into consideration whichvariables impact both TAA participation andemployment outcomes. As a result, it is harderto believe that the CIA holds. Thus, these resultslikely suffer from more selection bias than thosepresented in the first column.

Although our results are not directly com-parable to earlier studies of the TAA program,Marcal (2001) estimated a logit regression inwhich the dependent variable was the propor-tion of months since layoff (36 months) that theworker was employed. She found that workerswho participated in TAA training programs wereemployed 9% more than non-TAA participants,while TAA beneficiaries who chose not to par-ticipate in a training program were employed


TABLE 5Propensity Score Estimates of the Effects of the TAA Programa

Probability of Reemployment Wage Change

Specification 1 Specification 2 Specification 1 Specification 2

Not matched −0.061∗(0.019)

— −0.120∗(0.045)

—

Nearest neighbor matching withreplacement (trimming)

−0.021(0.033)

−0.042∗∗(0.024)

−0.061(0.067)

−0.101∗(0.039)

Local linear matching, Epanechnikovkernel (trimming)

−0.027(0.022)

−0.032∗∗(0.018)

−0.048(0.050)

0.102∗(0.033)

Local linear matching, Epanechnikovkernel (common support)

−0.027(0.021)

−0.039∗(0.018)

−0.049(0.046)

−0.094∗(0.033)

Number of observations TAA (5,348)CPS (454)

TAA (3,821)CPS (283)

aStandard errors in parentheses.∗,∗∗indicate those average treatment effects significant at the 5% and 10% levels, respectively. Propensity scores are

estimated using the probit model reported in column 1 of Table 3. Bandwidths, chosen by minimizing the mean square errorof the model using leave-one-out cross-validation, are 0.9 in columns 1 and 2, 0.3 in column 3, and 0.6 in column 4.

3% less than those in the comparison sample;the latter result was statistically insignificant.

It is possible that our results are being drivenby remaining differences between the TAA andCPS samples that we are unable to control for.First, the results may still suffer from selectionbias due to unobservable differences betweenthe two samples. For example, because we donot observe the local labor market of CPS work-ers we cannot control for labor market condi-tions more specific than the state unemploymentrate. It is possible that workers who participatein the TAA program are more likely to be ingeographic areas that are experiencing a surgein unemployment, making it more difficult forworkers to find new jobs.27

In addition, Heckman, Ichimura, and Todd(1997) note that it is important for data on thetreated and comparison observations to comefrom the same questionnaire. Unfortunately, theonly source of information on TAA participantsis the TAPR, which does not include compari-son observations. Differences in the definitionof the Work variable in the TAA and com-parison samples could bias our results of theaverage treatment effect. Consider two workers

27. We observe the local area of employment foronly a small subsample of metropolitan workers in theCPS comparison group. In specifications not reported here,we control for the local area unemployment rate in thesubsample of workers displaced from metropolitan areas.The results from these specifications also indicate that theTAA program has no statistically discernible impact on theemployment outcomes of participants.

displaced in 2004. The TAA worker is clas-sified as reemployed if he or she worked forpay at any time between April and December2005. The CPS worker is classified as reem-ployed if he or she worked for pay at any pointbetween the time of displacement in 2004 andJanuary 2006. It seems more likely that the CPSworker will report having worked for pay atsome point over the two or more years sincedisplacement than the TAA worker will reporthaving worked for pay in the 9 months sincethey exited from the TAA program. In this case,the propensity score matching procedure wouldunderestimate the positive impact of the TAAprogram on the ability of beneficiaries to findnew employment.

We next turn to the average effect ofthe TAA program on wage replacement. TheTAPR dataset includes the quarterly earningsof participants in each of the three quartersprior to their displacement and the three quar-ters following their exit from the TAA pro-gram. We calculate the wage change of eachworker as the percentage change in their calcu-lated weekly wage from the third quarter priorto displacement to the third quarter followingexit from the TAA program. The CPS displacedworker survey includes information on weeklywages prior to displacement, and current weeklywages in January 2006. Like the TAPR work-ers, we calculate the wage change of each CPSworker using the percentage change in the twovalues.


Estimates of the effect of the treatment onthe change in weekly earnings are providedin the last two columns of Table 5. Note thatthere are fewer observations when compared tothe employment analysis because we observeweekly earnings in only a subset of observations.The results should be interpreted as the changein weekly earnings due to the TAA programfor those workers who were reemployed. Notethat we reestimated the propensity score for thesubsample of workers who were reemployed toensure that the TAA and CPS comparison sam-ples continued to have observable characteristicsas similar as possible.28

As illustrated in the first row of the table, thenaı̈ve estimate of the impact of the TAA pro-gram would suggest that those who participatein the TAA program experience an extremelylarge loss of weekly earnings; the wage loss ofTAA participants is 12 percentage points greaterthan that of nonparticipants. After controllingfor covariates using propensity score matchingtechniques, we find no statistically significantdifference in the wage loss of the TAA benefi-ciaries when compared to their matched coun-terparts. In other words, the TAA program hasno statistically significant impact on the wagesof beneficiaries. This result mirrors the result inMarcal (2001), who also found an insignificantimpact of the TAA program on wages after shecontrolled for selection bias.

Once again, the second, more minimal spec-ification suggested by the leave-one-out cross-validation tests finds that the TAA program actu-ally has a negative impact on the ability of par-ticipants to replace their predisplacement wages.Estimates suggest that on average the wageloss of TAA participants is about 10 percent-age points greater than that of nonparticipants.However, we believe that because the minimalspecification fails to control for key observablecharacteristics that impact both TAA participa-tion and wages, these estimates continue to suf-fer from selection bias.

In summary, we find no evidence that theTAA program has a positive impact on theemployment outcomes of the average partici-pant. Although our results could still suffer fromselection bias, they are strikingly similar to thefindings of Marcal (2001), which evaluated theeffectiveness of the TAA program between 1988and 1989.

28. Although not reported here, coefficients from thepropensity score estimation and balancing statistics areavailable from the authors upon request.

VII. DOES TRAINING MAKE A DIFFERENCE?

A natural hypothesis is that TAA participantsare more likely to find new employment ifthey take advantage of program-funded trainingopportunities. In the two studies of the impact oftraining on the employment outcomes of TAAbeneficiaries, both Marcal (2001) and Deckerand Corson (1995) found that workers whoparticipated in the training component of theTAA program had better employment outcomesthan those who chose not to participate.

More generally, there is a large literaturedevoted to the evaluation of various training pro-grams, although the conclusions of these studieshave by no means been identical or overwhelm-ing in support of more funding for such pro-grams. For example, Heckman, LaLonde, andSmith (1999) conclude in their review of theliterature that government-funded training pro-grams may increase the probability of reemploy-ment, but have only a modest positive impact onearnings. They also find that while formal class-room training appears to help women, men gainlittle from such training. A more recent meta-analysis of 97 studies conducted between 1995and 2007 by Card, Kluve, and Weber (2009)found that classroom and on-the-job trainingprograms are more likely to yield favorablemedium-term than short-term results.

We estimate the ATE of training on TAAbeneficiaries using the propensity score match-ing technique described in Section IV; the TAAbeneficiaries who do not participate in trainingserve as the control sample. As we did withthe evaluation of the TAA program as a whole,we estimate two specifications of the propensityscore—the first a full specification motivated bywhat observable characteristics should impacta worker’s decision to participate in a TAA-funded training program and a second specifica-tion with covariates suggested by leave-one-outcross-validation. We also present results fromtwo matching techniques, nearest neighbor andlocal linear matching, and two methods of ensur-ing that the common support assumption holds.

Columns 4 and 5 of Table 3 present thecoefficient estimates associated with the probitregression explaining the likelihood of a TAAparticipant also participating in a TAA-fundedtraining program. We include most of the samevariables as discussed in Section V. In addition,we include the number of claimants in the masslayoffs that occurred in worker’s industry duringtheir year of displacement (Industry Layoffs),


TABLE 6Covariate Balancing after Matching, Training Participants versus Nonparticipantsa

Means after Matching % Bias % Bias

TrainingParticipants No Training

BeforeMatching

AfterMatching

Male 0.565 0.574 13.1 −1.7Ln (age) 3.826 3.822 −33.6 1.6Ln (years of education) 2.475 2.512 34.4 −7.6Education (highest level)

High school graduate 0.620 0.638 −0.8 −3.6Some college 0.142 0.141 19.7 0.4BA+ 0.062 0.072 0.5 −4.3

Tenure (in years) 9.433 8.932 −11.1 4.8Import sensitivity 0.334 0.328 16.2 3.7Year growth imports 0.090 0.090 42.6 −0.4Intraindustry trade 0.668 0.645 −11.7 8.1Industry unionization 0.114 0.120 −14.7 −4.3Average industry wage 14.052 13.871 −31.6 6.3Industry layoffs 0.107 0.107 25.2 4.6State unemployment rate 0.060 0.059 44.4 11.6Mean standardized biasb 4.478 — — —Pseudo r2 0.009 — — —

aThis table reports covariate balancing statistics using the probit specification reported in column 3 of Table 3 and locallinear matching with an Epanechnikov kernel and a bandwidth of 0.8. A common support is imposed by dropping 10% ofthe treated observations at which the density of the propensity score density of the control observations is the lowest.

bMean standardized bias has been calculated as an unweighted average of all covariates. The standardized bias is calculatedas 100 × (xtrain − xnotrain)/

√Vartrain(x) + Varnotrain (x)/2.

which we collected from the Bureau of LaborStatistics’ Mass Layoff Statistics database. Webelieve that workers displaced from jobs inindustries with a high number of layoffs maybe more likely to engage in training to obtainemployment in a new industry.

Many of the estimated coefficients are sig-nificant and of the expected sign. For example,we find that workers who already have a highschool diploma are less likely to participate inTAA-funded training programs when comparedto high school dropouts, and those with a col-lege degree are even less likely to participate intraining. Younger individuals with more tenurewith their employer are more likely to partici-pate in TAA training programs, as are men whencompared to women TAA beneficiaries. Work-ers from states with higher unemployment rates,or that were displaced from industries with ahigher number of mass layoffs, are more likelyto participate in training.

The second specification, chosen by leave-one-out cross-validation and not reported here,includes a subset of these variables: gender,age, and years of education (but not the dummy

variables for high school graduate, some college,and college graduate).

Table 6 presents covariate balancing statis-tics following the local linear matching pro-cedure. As described in Section IV, we usean Epanechnikov kernel and use leave-one-outcross-validation to choose the optimal band-width. The matching technique significantlyincreases the degree of similarity of the twosamples. Prior to matching, a number of theobserved characteristics had standardized biasesgreater than 20, including the participants’ ageand education level, as well as the state unem-ployment rate and average industry wage of theaverage participant. Matching reduces the biasof all observable characteristics; after matching,the maximum standardized bias is slightly under12, while the mean standardized bias falls to4.5. Results suggest that the matching proce-dure does a good job of creating a comparisonsample of individuals who have similar charac-teristics as those TAA beneficiaries who chooseto participate in a TAA-funded training program.

Columns 2 and 3 of Table 7 present thepropensity score matching estimates of the aver-age effect of TAA-funded training programs on


TABLE 7Propensity Score Estimates of the Effects of Traininga

Probability of Reemployment Wage Change

Specification 1 Specification 2 Specification 1 Specification 2

Not matched 0.210∗(0.014)

— 0.084∗(0.032)

—

Nearest neighbor matching withreplacement (trimming)

0.103∗(0.025)

0.048(0.039)

0.093∗(0.047)

−0.005(0.050)

Local linear matching, Epanechnikovkernel (trimming)

0.123∗(0.019)

0.157∗(0.017)

0.089∗(0.034)

0.084∗(0.031)

Local linear matching, Epanechnikovkernel (common support)

0.125∗(0.019)

0.151∗(0.017)

0.106∗(0.034)

0.093∗(0.030)

Number of observations Training participants: 3,944Nontrained: 1,074

Training participants: 3,195Nontrained: 626

aStandard errors in parentheses.∗indicates those average treatment effects significant at the 5% level. Propensity scores are estimated using the probit model

reported in column 3 of Table 3. Bandwidths, chosen by minimizing the mean square error of the model using leave-one-outcross-validation, are 0.8 in column 1 and 0.7 in columns 2, 3, and 4.

program beneficiaries. As noted in row 1, thenaı̈ve (unmatched) estimate suggests that par-ticipating in a training program increases thelikelihood that a TAA beneficiary will find newemployment by 21% when compared to thosewho do not participate in training. The propen-sity score matching procedure also finds a pos-itive impact of training on the reemploymentprospects of beneficiaries. Estimates suggest thaton average participating in a TAA-funded train-ing program increases the likelihood that ben-eficiaries will find new employment by 10–12percentage points.

The results from the second specificationof the propensity score equation suggest aslightly higher average effect of 15 percent-age points; as discussed earlier, we believe thefirst specification gives more accurate results.Our results, particularly from the first specifi-cation, are similar to those found in other stud-ies. Marcal (2001) found that TAA beneficia-ries who participated in training were employed6% more than those who did not engage intraining.

We next investigate whether participating inTAA-funded training opportunities increases thewages of TAA beneficiaries. When we controlfor worker characteristics using the propensityscore matching technique, we find that train-ing reduces the average earnings loss by TAAbeneficiaries by somewhere between 8 and 10percentage points when compared to those ben-eficiaries who receive a training waiver. Again,this is fairly similar to other studies of the TAA

program; Marcal (2001) found that training par-ticipants earned 9% more than those that did notparticipate in training.

It is possible that these results are beingdriven by differences between the training andnontraining participant samples that we areunable to control for. Recall that although TAAbeneficiaries must participate in training in orderto receive extended unemployment benefits,nearly 20% of TAA participants receive a waiverfrom the training requirement. Program admin-istrators are allowed to grant waivers for a widevariety of reasons, including the health, age,and skill level of the worker. Waivers are alsogranted to workers who can prove that trainingis unavailable in their area. Although we controlfor such characteristics as the age and educa-tion level of the participant, we do not haveinformation on other characteristics such as thehealth status or the local labor market conditionsof the participant. It is likely that workers in poorhealth would be both more likely to receive awaiver and more likely to remain unemployed.Moreover, workers in small rural areas may belimited in both the number of training and thenumber of new employment opportunities. Nev-ertheless, our results seem to indicate that TAAparticipants who qualify for a waiver would sig-nificantly benefit from training.29

29. Although it would be informative to analyze whetherour results are sensitive to the reason for the trainingwaiver, we are unable to conduct such analysis becauseofficials failed to record a reason for the waiver in 62%of observations.


VIII. CONCLUSIONS AND POLICY IMPLICATIONS

This paper uses data collected by the Depart-ment of Labor in their Trade Act ParticipantReport (TAPR) database to provide a statisti-cal evaluation of the effectiveness of the TAAprogram. Summary statistics indicate that TAAbeneficiaries have a much harder time findingnew, well-paying jobs when compared to otherdisplaced workers. As GAO (2001) notes, TAAbeneficiaries tend to be older and less educatedthan other workers, thus they have a harder timereentering a workforce that increasingly requiresmore skills and training. These facts suggest thatthere may be an important role for the TAA pro-gram to play in helping those workers most atrisk following displacement.

Unfortunately, we find no statistical evidencethat the TAA program improves the averageemployment outcome of beneficiaries over acomparison group of nonbeneficiary displacedworkers with characteristics similar to thoseworkers in the TAA program. Our results implythat while the TAA program may provide anincome safety net, it does not help the averagedisplaced worker who is enrolled in the programfind new, well-paying employment opportuni-ties. To answer the question posed in the titleof this paper, our initial results indicate that theTAA program does not make a difference.

Upon further examination, however, we findstrong evidence that those workers who par-ticipate in a TAA-funded training opportunityare more likely to obtain reemployment, andat higher wages, when compared to the TAAbeneficiaries who do not participate in training.Specifically, participating in the training com-ponent of the TAA program increases the likeli-hood that the average TAA beneficiary will findnew employment by 10–12 percentage points,and reduces the earnings losses of the aver-age worker by 8–10 percentage points, whencompared to a group of similar TAA benefi-ciaries who do not participate in the trainingcomponent. Although the income support, joband relocation payments, and other TAA bene-fits may not help workers find new, well-payingemployment, training seems to improve employ-ment outcomes for these workers.

Policy makers should take these results intoconsideration when making future decisionsregarding the TAA program. Although programrules require beneficiaries to participate in atraining program in order to receive increasedunemployment benefits, almost one-quarter of

TAA beneficiaries are able to opt out of trainingafter receiving a waiver from program adminis-trators. Administrators should consider reducingthe number of waivers from training require-ments given to participants in order to improveemployment outcomes. Moreover, at least 10%of the workers who received a training waiverdid so because training was unavailable in theirarea. As reported in GAO (2001), governmentofficials in areas with large numbers of TAAbeneficiaries have admitted that they need to“improve local educational systems, which oftenhad high school dropout rates much higherthan the national average.” Investing more ineducational or vocational training programs inthe disadvantaged areas that currently have fewtraining opportunities may improve the efficacyof the TAA program.

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. “Trade Adjustment Assistance: Improvements Nec-essary but Programs Cannot Solve Communities’ LongTerm Problems.” Report No. GAO-01-998, 2001.

. “Trade Adjustment Assistance: Labor Should TakeAction to Ensure Performance Data Are Complete,Accurate, and Accessible.” Report No. GAO-06-496,2006a.

. “Trade Adjustment Assistance: Most Workers inFive Layoffs Received Services, but Better OutreachNeeded on New Benefits.” Report No. GAO-06-43,2006b.

. “Trade Adjustment Assistance: Changes to Fund-ing Allocation and Eligibility Requirements CouldEnhance States’ Ability to Provide Benefits and Ser-vices.” Report No. GAO-07-702, 2007.

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