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The Women of the National Supported Work Demonstration

Author

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  • Sebastian Calónico
  • Jeffrey Smith

Abstract

This paper re-creates three of the samples from LaLonde’s famous 1986 paper that began the literature on “within-study designs” that uses experiments as benchmarks against which to assess the performance of nonexperimental identification strategies. In particular, we recreate the experimental data for the target group of women on welfare from the National Supported Work (NSW) Demonstration and two of the corresponding comparison groups drawn from the Panel Study of Income Dynamics (PSID). The loss of these data resulted in the (sizable) subsequent literature devoting its attention solely to the NSW men. In addition to repeating LaLonde’s analyses on our recreations of his files for the AFDC women, we apply (many of) the estimators from later papers by Dehejia and Wahba and by Smith and Todd to these data. Our findings support the general view in the literature that women on welfare pose a less difficult selection problem when evaluating employment and training programs. They also call into question the generalizability of some of the broad conclusions that Dehejia and Wahba and Smith and Todd draw from their analyses of the NSW men.

Suggested Citation

  • Sebastian Calónico & Jeffrey Smith, 2017. "The Women of the National Supported Work Demonstration," Journal of Labor Economics, University of Chicago Press, vol. 35(S1), pages 65-97.
    • Handle: RePEc:ucp:jlabec:doi:10.1086/692397
    DOI: 10.1086/692397
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    Citations

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    Cited by:

    1. Negi Akanksha, 2024. "Doubly weighted M-estimation for nonrandom assignment and missing outcomes," Journal of Causal Inference, De Gruyter, vol. 12(1), pages 1-25.
    2. Tarek Azzam & Michael Bates & David Fairris, 2019. "Do Learning Communities Increase First Year College Retention? Testing Sample Selection and External Validity of Randomized Control Trials," Working Papers 202002, University of California at Riverside, Department of Economics.
    3. Goller, Daniel & Lechner, Michael & Moczall, Andreas & Wolff, Joachim, 2020. "Does the estimation of the propensity score by machine learning improve matching estimation? The case of Germany's programmes for long term unemployed," Labour Economics, Elsevier, vol. 65(C).
    4. Arun Advani & Toru Kitagawa & Tymon Słoczyński, 2019. "Mostly harmless simulations? Using Monte Carlo studies for estimator selection," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(6), pages 893-910, September.
    5. Gayani Rathnayake & Akanksha Negi & Otavio Bartalotti & Xueyan Zhao, 2024. "Difference-in-Differences with Sample Selection," Papers 2411.09221, arXiv.org, revised Dec 2024.
    6. Dan A. Black & Lars Skipper & Jeffrey A. Smith & Jeffrey Andrew Smith, 2023. "Firm Training," CESifo Working Paper Series 10268, CESifo.
    7. Akanksha Negi, 2020. "Doubly weighted M-estimation for nonrandom assignment and missing outcomes," Papers 2011.11485, arXiv.org.
    8. Daniel Litwok, 2023. "Estimating the Impact of Emergency Assistance on Educational Progress for Low-Income Adults: Experimental and Nonexperimental Evidence," Evaluation Review, , vol. 47(2), pages 231-263, April.
    9. Dalla-Zuanna, Antonio & Liu, Kai, 2019. "Understanding Program Complementarities: Estimating the Dynamic Effects of a Training Program with Multiple Alternatives," IZA Discussion Papers 12839, Institute of Labor Economics (IZA).

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