Kory Kroft, Ismael Mourifié, Atom Vayalinkal
arXiv 6 Sep 2024 · Econometrics · 1 citations (OpenAlex)
arXiv:2409.04589 · PDF · DOI · OpenAlex · Extracted main text
This paper investigates the causal effect of job training on wage rates in the presence of firm heterogeneity. When training affects the sorting of workers to firms, sample selection is no longer binary but is “multilayered". This paper extends the canonical Heckman (1979) sample selection model -- which assumes selection is binary -- to a setting where it is multilayered. In this setting Lee bounds set identifies a total effect that combines a weighted-average of the causal effect of job training on wage rates across firms with a weighted-average of the contrast in wages between different firms for a fixed level of training. Thus, Lee bounds set identifies a policy-relevant estimand only when firms pay homogeneous wages and/or when job training does not affect worker sorting across firms. We derive analytic expressions for sharp bounds for the causal effect of job training on wage rates at each firm that leverage information on firm-specific wages. We illustrate our partial identification approach with two empirical applications to job training experiments. Our estimates demonstrate that even when conventional Lee bounds are strictly positive, our within-firm bounds can be tight around 0, showing that the canonical Lee bounds may capture only a pure sorting effect of job training.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Semenova, Vira (2020) Generalized Lee Bounds, Publisher: [object Object] Version Number: 3 | 0.928 | 4 | 3 | 100% |
| 2 | Lee, David S (2009) Training, Wages, and Sample Selection: Estimating Sharp Bounds on Treatment Effects | 0.901 | 41 | 9 | 73% |
| 3 | Horowitz, Joel L. and Charles F. Manski (1995) Identification and Robustness with Contaminated and Corrupted Data | 0.899 | 11 | 5 | 73% |
| 4 | Heckman, James J (1979) Sample Selection Bias as a Specification Error | 0.874 | 9 | 2 | 100% |
| 5 | Katz, Lawrence F., Jonathan Roth, Richard Hendra, and Kelsey Schaberg (2022) Why Do Sectoral Employment Programs Work? Lessons from WorkAdvance | 0.811 | 4 | 2 | 100% |
| 6 | Schochet, Peter Z, John Burghardt, and Sheena McConnell (2008) Does Job Corps Work? Impact Findings from the National Job Corps Study | 0.737 | 3 | 3 | 67% |
| 7 | Olma, Tomasz (2021) Nonparametric Estimation of Truncated Conditional Expectation Functions, Version Number: 1 | 0.737 | 3 | 2 | 100% |
| 8 | Card, David, Jochen Kluve, and Andrea Weber (2010) Active Labour Market Policy Evaluations: A Meta‐Analysis | 0.644 | 4 | 1 | 100% |
| 9 | United States Equal Employment Opportunity Commission (2009) Federal Laws Prohibiting Job Discrimination Questions and Answers, Tech | 0.644 | 4 | 1 | 100% |
| 10 | Honoré, Bo E. and Luojia Hu (2020) Selection Without Exclusion | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 61 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Lee bounds for random objects | 0.405 | 1 | 1 |
| 2 | Causal Effects in Matching Mechanisms with Strategically Reported Preferences | 0.000 | 1 | 1 |