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Treatment Evaluation in the Case of Interactions within Markets

Author

Listed:
  • Ferracci, Marc

    (CREST-INSEE)

  • Jolivet, Grégory

    (University of Bristol)

  • van den Berg, Gerard J.

    (University of Groningen)

Abstract

We extend the standard evaluation framework to allow for interactions between individuals within segmented markets. An individual's outcome depends not only on the assigned treatment status but also on (features of) the distribution of the assigned treatments in his market. To evaluate how the distribution of treatments within a market causally affects the average effect within the market, averaged over the full population, we develop an identification and estimation method in two steps. The first one focuses on the distribution of the treatment within markets and between individuals and the second step addresses the distribution of the treatment between markets. We apply our method to data on training programs for unemployed workers in France. We use a rich administrative register of unemployment and training spells as well as the information on local labor demand that is used by unemployment agencies to allocate training programs. The results show that the average treatment effect on the employment rate causally decreases with respect to the proportion treated in the market. Our analysis accounts for unobserved heterogeneity between markets (using the longitudinal dimension of the data) and, in a robustness check, between individuals.

Suggested Citation

  • Ferracci, Marc & Jolivet, Grégory & van den Berg, Gerard J., 2010. "Treatment Evaluation in the Case of Interactions within Markets," IZA Discussion Papers 4700, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp4700
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    Cited by:

    1. Huber, Martin & Steinmayr, Andreas, 2017. "A Framework for Separating Individual Treatment Effects From Spillover, Interaction, and General Equilibrium Effects," Rationality and Competition Discussion Paper Series 21, CRC TRR 190 Rationality and Competition.
    2. Ronald Wolthoff, 2014. "It'S About Time: Implications Of The Period Length In An Equilibrium Search Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 55(3), pages 839-867, August.
    3. Jesse Rothstein & Till von Wachter, 2016. "Social Experiments in the Labor Market," NBER Working Papers 22585, National Bureau of Economic Research, Inc.
    4. Bruno Crépon & Esther Duflo & Marc Gurgand & Roland Rathelot & Philippe Zamora, 2013. "Do Labor Market Policies have Displacement Effects? Evidence from a Clustered Randomized Experiment," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 128(2), pages 531-580.
    5. Rafael Lalive & Camille Landais & Josef Zweimüller, 2015. "Market Externalities of Large Unemployment Insurance Extension Programs," American Economic Review, American Economic Association, vol. 105(12), pages 3564-3596, December.
    6. Goulas, Eleftherios & Zervoyianni, Athina, 2018. "Active labour-market policies and output growth: Is there a causal relationship?," Economic Modelling, Elsevier, vol. 73(C), pages 1-14.
    7. Pieter Gautier & Paul Muller & Bas van der Klaauw & Michael Rosholm & Michael Svarer, 2018. "Estimating Equilibrium Effects of Job Search Assistance," Journal of Labor Economics, University of Chicago Press, vol. 36(4), pages 1073-1125.
    8. Bruno Crepon & Marc Ferracci & Grégory Jolivet & Gerard Van Den Berg, 2010. "Analyzing the Anticipation of Treatments with Data on Notification Dates," Working Papers 2010-41, Center for Research in Economics and Statistics.
    9. Marco Caliendo & Steffen Künn, 2015. "Getting back into the labor market: the effects of start-up subsidies for unemployed females," Journal of Population Economics, Springer;European Society for Population Economics, vol. 28(4), pages 1005-1043, October.
    10. Marios Michaelides & Peter Mueser, 2018. "Are Reemployment Services Effective? Experimental Evidence from the Great Recession," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 37(3), pages 546-570, June.
    11. Blasco, Sylvie & Pertold-Gebicka, Barbara, 2013. "Employment policies, hiring practices and firm performance," Labour Economics, Elsevier, vol. 25(C), pages 12-24.
    12. Dayanand S. Manoli & Marios Michaelides & Ankur Patel, 2018. "Long-Term and Heterogeneous Effects of Job-Search Assistance," NBER Working Papers 24422, National Bureau of Economic Research, Inc.
    13. Saez, Emmanuel & Landais, Camille & Michaillat, Pascal, 2010. "Optimal Unemployment Insurance over the Business Cycle," CEPR Discussion Papers 8132, C.E.P.R. Discussion Papers.
    14. Andrew C. Johnston & Alexandre Mas, 2018. "Potential Unemployment Insurance Duration and Labor Supply: The Individual and Market-Level Response to a Benefit Cut," Journal of Political Economy, University of Chicago Press, vol. 126(6), pages 2480-2522.

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    Keywords

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    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • J64 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Unemployment: Models, Duration, Incidence, and Job Search

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