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Young in-Old out: a new evaluation based on Generalized Propensity Score

Author

Listed:
  • Michela Bia
  • Roberto Leombruni
  • Pierre-Jean Messe

Abstract

This paper aims at evaluating the effect of the amount of older workers exits (aged 50 or more) on the entries of youngsters at a local labour market level, during years 1985 - 2002. If we can observe some effect of the exits on the entries, it will shed light on the substitution between older workers and youngsters. Moreover, since in our model the causal agent cannot be specified a – priori, we don’t know what causes what. Hence, we are actually looking for a correlation between these two quantities. Systematic differences in background characteristics, between local markets with different levels of the older workers exits, can bias the effect estimation on the entries of youngsters. In order to adjust for this, we apply propensity score methods as extended and generalized in a setting with a continuous treatment by Hirano and Imbens (2004). Our results show a positive and significant correlation between exits of older workers and entries of youngsters.

Suggested Citation

  • Michela Bia & Roberto Leombruni & Pierre-Jean Messe, 2009. "Young in-Old out: a new evaluation based on Generalized Propensity Score," LABORatorio R. Revelli Working Papers Series 93, LABORatorio R. Revelli, Centre for Employment Studies.
    • Handle: RePEc:cca:wplabo:93
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    References listed on IDEAS

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    1. Kewei Ming & Paul R. Rosenbaum, 2000. "Substantial Gains in Bias Reduction from Matching with a Variable Number of Controls," Biometrics, The International Biometric Society, vol. 56(1), pages 118-124, March.
    2. Kosuke Imai & David A. van Dyk, 2004. "Causal Inference With General Treatment Regimes: Generalizing the Propensity Score," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 854-866, January.
    3. Raffaello Bronzini & Guido de Blasio & Guido Pellegrini & Alessandro Scognamiglio, 2008. "The effect of investment tax credit: Evidence from an atypical programme in Italy," Temi di discussione (Economic working papers) 661, Bank of Italy, Economic Research and International Relations Area.
    4. Ahn, Hyungtaik & Powell, James L., 1993. "Semiparametric estimation of censored selection models with a nonparametric selection mechanism," Journal of Econometrics, Elsevier, vol. 58(1-2), pages 3-29, July.
    5. Heckman, J.J. & Hotz, V.J., 1988. "Choosing Among Alternative Nonexperimental Methods For Estimating The Impact Of Social Programs: The Case Of Manpower Training," University of Chicago - Economics Research Center 88-12, Chicago - Economics Research Center.
    6. James Heckman, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    7. Bryson, Alex & Dorsett, Richard & Purdon, Susan, 2002. "The use of propensity score matching in the evaluation of active labour market policies," LSE Research Online Documents on Economics 4993, London School of Economics and Political Science, LSE Library.
    8. Alberto Abadie & Guido W. Imbens, 2011. "Bias-Corrected Matching Estimators for Average Treatment Effects," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(1), pages 1-11, January.
    9. Hausman, Jerry A & Newey, Whitney K, 1995. "Nonparametric Estimation of Exact Consumers Surplus and Deadweight Loss," Econometrica, Econometric Society, vol. 63(6), pages 1445-1476, November.
    10. Valentina Adorno & Cristina Bernini & Guido Pellegrini, 2007. "The Impact of Capital Subsidies: New Estimations under Continuous Treatment," Giornale degli Economisti, GDE (Giornale degli Economisti e Annali di Economia), Bocconi University, vol. 66(1), pages 67-92, March.
    11. Susan Athey & Guido W. Imbens, 2006. "Identification and Inference in Nonlinear Difference-in-Differences Models," Econometrica, Econometric Society, vol. 74(2), pages 431-497, March.
    12. DiPrete, Thomas A. & Gangl, Markus, 2004. "Assessing bias in the estimation of causal effects: Rosenbaum bounds on matching estimators and instrumental variables estimation with imperfect instruments," Discussion Papers, Research Unit: Labor Market Policy and Employment SP I 2004-101, WZB Berlin Social Science Center.
    13. Contini, Bruno & Revelli, Riccardo, 1997. "Gross flows vs. net flows in the labor market: What is there to be learned?," Labour Economics, Elsevier, vol. 4(3), pages 245-263, September.
    14. Martins, Pedro S. & Novo, Alvaro A. & Portugal, Pedro, 2009. "Increasing the Legal Retirement Age: The Impact on Wages, Worker Flows and Firm Performance," IZA Discussion Papers 4187, Institute of Labor Economics (IZA).
    15. Guido Pellegrini, 2001. "La struttura produttiva delle piccole e medie imprese italiane: il modello dei distretti," Banca Impresa Società, Società editrice il Mulino, issue 2, pages 237-248.
    16. Guido Pellegrini & Carla Carlucci, 2003. "Gli effetti della legge 488/92: una valutazione dell'impatto occupazionale sulle imprese agevolate," Rivista italiana degli economisti, Società editrice il Mulino, issue 2, pages 267-286.
    17. James J. Heckman & Hidehiko Ichimura & Petra Todd, 1998. "Matching As An Econometric Evaluation Estimator," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 65(2), pages 261-294.
    18. Bruno Contini & Fabio Rapiti, 1999. "'Young In, Old Out' Revisited: New Patterns of Employment Replacement in the Italian Economy," International Review of Applied Economics, Taylor & Francis Journals, vol. 13(3), pages 395-415.
    19. Bondonio, Daniele, 2002. "Evaluating the Employment Impact of Business Incentive Programs in EU Disadvantaged Areas. A case from Northern Italy," POLIS Working Papers 27, Institute of Public Policy and Public Choice - POLIS.
    20. Gruber, Jonathan & Wise, David, 1998. "Social Security and Retirement: An International Comparison," American Economic Review, American Economic Association, vol. 88(2), pages 158-163, May.
    21. Zhong Zhao, 2004. "Using Matching to Estimate Treatment Effects: Data Requirements, Matching Metrics, and Monte Carlo Evidence," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 91-107, February.
    22. Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2003. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometrica, Econometric Society, vol. 71(4), pages 1161-1189, July.
    23. Meyer, Bruce D, 1995. "Natural and Quasi-experiments in Economics," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(2), pages 151-161, April.
    24. Michela Bia & Alessandra Mattei, 2008. "A Stata package for the estimation of the dose–response function through adjustment for the generalized propensity score," Stata Journal, StataCorp LLC, vol. 8(3), pages 354-373, September.
    25. Alberto Abadie & Guido W. Imbens, 2006. "Large Sample Properties of Matching Estimators for Average Treatment Effects," Econometrica, Econometric Society, vol. 74(1), pages 235-267, January.
    26. Powell, James L., 1987. "Semiparametric Estimation Of Bivariate Latent Variable Models," SSRI Workshop Series 292689, University of Wisconsin-Madison, Social Systems Research Institute.
    27. Sascha O. Becker & Andrea Ichino, 2002. "Estimation of average treatment effects based on propensity scores," Stata Journal, StataCorp LLC, vol. 2(4), pages 358-377, November.
    28. Alberto Abadie & Guido W. Imbens, 2002. "Simple and Bias-Corrected Matching Estimators for Average Treatment Effects," NBER Technical Working Papers 0283, National Bureau of Economic Research, Inc.
    29. Lu B. & Zanutto E. & Hornik R. & Rosenbaum P.R., 2001. "Matching With Doses in an Observational Study of a Media Campaign Against Drug Abuse," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1245-1253, December.
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    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other
    • J68 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Public Policy

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