Estimating the Effect of Training Programs on Earnings
Abstract
GOVERNMENTAL post-schooling training programs have become a permanent fixture of the U.S. economy in the last decade. These programs are typically advocated for diverse reasons: (1) to reduce inflation by the provision of more skilled workers to alleviate shortages, (2) to reduce unemployment of certain groups, and (3) to reduce poverty by increasing the skills of certain groups. All of these objectives require that training programs increase the earnings of trainees above what they otherwise would be. For example, alleviating shortages by training more highly skilled workers should increase the earnings of these workers. Likewise, the concern for unemployed workers is derived from a concern for the decreased earnings of these workers; and if trainees subsequently suffer less unemployment, their earnings should be higher. Finally, training programs are intended to reduce poverty by increasing the earnings of low income workers. Evaluating the success of training programs is thus inherently a quantitative assessment of the effect of training on trainee earnings.' It is an important process both because it helps to inform discussions of public policy by shedding light on the past value of these programs as investments and because it can provide a means of testing our ability to augment the human capital of certain workers. Although there have been many studies of the effect of post-school classroom training on earnings it is by now rather widely agreed that very little is reliably known about the actual effects of these programs.2 Three main problems account for this state of affairs: (1) the large sample sizes required to detect relatively small anticipated program effects in a variable with such high variance as earnings, (2) the considerable expense required to keep track of trainees over a long enough period of time to measure the full inter-temporal impact of training, and (3) the extreme difficulty of implementing an adequate experimental design so as to obtain a group against which to reliably compare trainees.3 The purpose of this paper is to report on efforts to cope with this third problem using a data collection system that comes some way towards resolving the first two. The basic idea of this data system is to match the program record on each trainee with the trainee's Social Security earnings history. The Social Security Administration maintains a summary year-by-year earnings history for each Social Security account over the period since 1950 that may be used, under the appropriate confidentiality restrictions, for this purpose.4 In this paper I have concentrated on an analysis of all classroom trainees who started training under the Manpower Development and Training Act (MDTA) in the first 3 months of 1964 so as to ensure their having completed training in that year. In choosing to analyze trainees from so early a cohort something is clearly lost. On the one hand, the nature of the participants in these early years was considerably different than in the later years. In particular, programs geared Received for publication February 9, 1977. Revision accepted for publication August 1, 1977. * Princeton University. This research was supported by ASPER, U.S. Department of Labor, but does not represent an official position of the Department of Labor, its agencies, or staff. I would like to thank Gregory Chow, Ronald Ehrenberg, Roger Gordon, Zvi Griliches, George E. Johnson, Nicholas Kiefer, Richard Quandt, and Sherwin Rosen for helpful comments. I also owe a heavy debt to D. Alton Smith for computational and other assistance. 'See Reid (1976), for example, for a clear analysis of how knowledge of these effects is required in order to establish the impact of government training on the black/white wage differential. 2 Surveys of many of these studies may be found in Stromsdorfer (1972) and O'Neill (1973). 3For further discussion of these points see Ashenfelter (1975). 4The idea for using these data to analyze the effectiveness of government training programs is apparently quite an old one, having been suggested by the National Manpower Advisory Committee (U.S. Department of Labor, 1972) to the Secretary of Labor at its first meeting in a letter dated October 10, 1962, the year of passage of the Manpower Development and Training Act. Actual efforts along these lines were ultimately reported by Borus (1967), Commins (1970), Farber (1970), and Prescott and Cooley (1972).
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
As far as the authors are aware, the very first paper to use a difference-in-differences approach is Ashenfelter (1978), although that paper did not use the difference-in-differences language.
“As far as we are aware, the very first paper to use a difference-in-differences approach is Ashenfelter (1978), although that paper did not use the difference-in-differences language.”
From Technology and Big Data Are Changing Economics: Mining Text to Track Methods (2020) · cites this paper as Ashenfelter (1978)
Ashenfelter's models were not presented in the now-traditional event study graphical format, but his table's results have an event study framing, including showing a pretraining drop in earnings.
“Ashenfelter's models were not presented in the nowtraditional event study graphical format, but his table's results have an event study framing, including showing a pretraining drop in earnings.”
From An Introductory Guide to Event Study Models (2023) · cites this paper as Ashenfelter (1978)
Cited by
Sorted by citations
Estimating dynamic treatment effects in event studies with heterogeneous treatment effects
Sun, L., & Abraham, S. (2021) · Journal of Econometrics · doi:10.1016/j.jeconom.2020.09.006 · Cited by 5,518 · see citing works
Matching as an econometric evaluation estimator: Evidence from evaluating a job training programme
Heckman, J. J., Ichimura, H., & Todd, P. E. (1997) · The Review of Economic Studies · doi:10.2307/2971733 · Cited by 5,486 · see citing works
Two-Way Fixed Effects Estimators with Heterogeneous Treatment Effects
de Chaisemartin, C., & D’Haultfœuille, X. (2020) · American Economic Review · doi:10.1257/aer.20181169 · Cited by 5,031 · see citing works
“This rules out the possibility that a group gets treated because it experiences negative shocks, the so-called Ashenfelter’s dip ( see Ashenfelter 1978 .”
Recent Developments in the Econometrics of Program Evaluation
Imbens, G. W., & Wooldridge, J. M. (2009) · Journal of Economic Literature · doi:10.1257/jel.47.1.5 · Cited by 4,926 · see citing works
Does matching overcome lalonde’s critique of nonexperimental estimators?
A. Smith, J., & E. Todd, P. (2005) · Journal of Econometrics · doi:10.1016/j.jeconom.2004.04.011 · Cited by 3,176 · see citing works
Causal effects in nonexperimental studies: Reevaluating the evaluation of training programs
Dehejia, R. H., & Wahba, S. (1999) · Journal of the American Statistical Association · doi:10.1080/01621459.1999.10473858 · Cited by 2,395 · see citing works
Semiparametric Difference-in-Differences Estimators
Abadie, A. (2005) · The Review of Economic Studies · doi:10.1111/0034-6527.00321 · Cited by 2,286 · see citing works
A More Credible Approach to Parallel Trends
Rambachan, A., & Roth, J. (2023) · Review of Economic Studies · doi:10.1093/restud/rdad018 · Cited by 1,559 · see citing works
Active Labour Market Policy Evaluations: A Meta‐Analysis
Card, D., Kluve, J., & Weber, A. (2010) · The Economic Journal · doi:10.1111/j.1468-0297.2010.02387.x · Cited by 1,298 · see citing works
Natural and Quasi-Experiments in Economics
Meyer, B. D. (1995) · Journal of Business & Economic Statistics · doi:10.1080/07350015.1995.10524589 · Cited by 1,100 · see citing works
The Estimation of Causal Effects by Difference-in-Difference Methods
Lechner, M. (2011) · Foundations and Trends® in Econometrics · doi:10.1561/0800000014 · Cited by 935 · see citing works
“As another example, it has been observed by Ashenfelter (1978) that trainees from public training programmes suffer a larger drop in earnings prior to training than nontrainees. Suppose these drops are due to negative “idiosyncratic temporary shocks.” The temporary nature of these shocks implies that individuals who r…”
InfluentialTHE ESTIMATION OF CAUSAL EFFECTS FROM OBSERVATIONAL DATA
Winship, C., & Morgan, S. L. (1999) · Annual Review of Sociology · doi:10.1146/annurev.soc.25.1.659 · Cited by 890 · see citing works
Assessing the impact of planned social change
Campbell, D. T. (1979) · Evaluation and Program Planning · doi:10.1016/0149-7189(79)90048-x · Cited by 867 · see citing works
Models for Sample Selection Bias
Winship, C., & Mare, R. D. (1992) · Annual Review of Sociology · doi:10.1146/annurev.so.18.080192.001551 · Cited by 812 · see citing works
Pretest with Caution: Event-Study Estimates after Testing for Parallel Trends
Roth, J. (2022) · American Economic Review: Insights · doi:10.1257/aeri.20210236 · Cited by 769 · see citing works
Jacob, B. A. (2005) · Journal of Public Economics · doi:10.1016/j.jpubeco.2004.08.004 · Cited by 751 · see citing works
Shadish, W. R., Clark, M. H., & Steiner, P. M. (2008) · Journal of the American Statistical Association · doi:10.1198/016214508000000733 · Cited by 479 · see citing works
Pre-Event Trends in the Panel Event-Study Design
Freyaldenhoven, S., Hansen, C., & Shapiro, J. M. (2019) · American Economic Review · doi:10.1257/aer.20180609 · Cited by 427 · see citing works
An Introductory Guide to Event Study Models
Miller, D. L. (2023) · Journal of Economic Perspectives · doi:10.1257/jep.37.2.203 · Cited by 360 · see citing works
Yang, X., Zhang, J., Ren, S., & Ran, Q. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2020.125015 · Cited by 264 · see citing works
The Labor Market Impacts of Youth Training in the Dominican Republic
Card, D., Ibarrarán, P., Regalia, F., Rosas-Shady, D., & Soares, Y. (2011) · Journal of Labor Economics · doi:10.1086/658090 · Cited by 225 · see citing works
Interpreting pre-trends as anticipation: Impact on estimated treatment effects from tort reform
Malani, A., & Reif, J. (2015) · Journal of Public Economics · doi:10.1016/j.jpubeco.2015.01.001 · Cited by 178 · see citing works
What Works? A Meta Analysis of Recent Active Labor Market Program Evaluations
Card, D., Kluve, J., & Weber, A. (2015) · doi:10.3386/w21431 · Cited by 136 · see citing works
Estimating Dynamic Treatment Effects in Event Studies With Heterogeneous Treatment Effects
Sun, L., & Abraham, S. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3158747 · Cited by 118 · see citing works
Achievement and behavior in charter schools: drawing a more complete picture
Imberman, S. A. (2011) · Review of Economics and Statistics · doi:10.1162/rest_a_00077 · Cited by 98 · see citing works
Technology and Big Data Are Changing Economics: Mining Text to Track Methods
Currie, J., Kleven, H., & Zwiers, E. (2020) · AEA Papers and Proceedings · doi:10.1257/pandp.20201058 · Cited by 86 · see citing works
The anticipatory effects of Medicare Part D on drug utilization
Alpert, A. (2016) · Journal of Health Economics · doi:10.1016/j.jhealeco.2016.06.004 · Cited by 69 · see citing works
Evidence for and against banning mobile phones in schools: a scoping review.
Campbell, M., Edwards, E. J., Pennell, D., Poed, S., Lister, V., Gillett-Swan, J., Kelly, A., Zec, D., & Nguyen, T.-A. (2024) · Journal of Psychologists and Counsellors in Schools · doi:10.1177/20556365241270394 · Cited by 66 · see citing works
The Effectiveness of Private Voucher Education: Evidence From Structural School Switches
Lara, B., Mizala, A., & Repetto, A. (2011) · Educational Evaluation and Policy Analysis · doi:10.3102/0162373711402990 · Cited by 72 · see citing works
Does incarceration length affect labor market outcomes?
Landersø, R. (2015) · The Journal of Law and Economics · doi:10.1086/682911 · Cited by 53 · see citing works
Causal models for longitudinal and panel data: a survey
Arkhangelsky, D., & Imbens, G. (2024) · The Econometrics Journal · doi:10.1093/ectj/utae014 · Cited by 50 · see citing works
Carbon Abatement and Leakage in China's Regional Carbon Emission Trading
Jiang, J., Ye, B., Zeng, Z., Yang, X., Sun, Z., Shao, S., Feng, K., & Tan, X. (2024) · Environmental Science & Technology · doi:10.1021/acs.est.4c04738 · Cited by 23 · see citing works
Borgen, N. T., Raaum, O., Kirkebøen, L. J., Sørlie, M.-A., Ogden, T., & Frønes, I. (2021) · Journal of Research on Educational Effectiveness · doi:10.1080/19345747.2020.1862375 · Cited by 7 · see citing works
- Authors
- Ashenfelter, O.
- Corresponding author
- Ashenfelter, Orley
- Venue
- The Review of Economics and Statistics
- Publisher
- JSTOR
- Published
- 1978-02
- Volume
- 60
- Issue
- 1
- Pages
- 47
- ISSN
- 0034-6535 (ISSN-L)
- OpenAlex
- W2151367131
- Cited by
- 1,515
- References
- 0
- Citations by year
- 2026: 35 · 2025: 58 · 2024: 51 · 2023: 64 · 2022: 78 · 2021: 60 · 2020: 54 · 2019: 48 · 2018: 59 · 2017: 58 · 2016: 67 · 2015: 61 · 2014: 63 · 2013: 57 · 2012: 51
- FWCI
- 21.14
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Review of Economics and Statistics (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Labor market dynamics and wage inequality
- Social Sciences › Education › Education Systems and Policy
- Social Sciences › Political Science and International Relations › Education and Labor Relations
- Keywords
- training program, earnings effects, program evaluation, human capital, poverty reduction, low-income workers, returns to training, public policy evaluation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review