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Endogenous Attrition in Panels

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  • Laurent Davezies

    ()
    (CREST)

  • Xavier d'Haultfoeuille

    ()
    (CREST)

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    Abstract

    We consider endogenous attrition in panels where the probability of attrition may depend on current and past outcomes. We show that this probability is nonparametrically identified provided that instruments affecting the outcomes but not directly attrition, and whose distribution is identified, are available. We thus complement Hirano et al. (2001)’s framework, which does not rely on such instruments. Contrary to their approach, neither a refreshment sample nor an additive decomposition on the probability of attrition are needed. We also show that the exclusion restriction has testable implications. We propose an efficient estimation and a test of the exclusion restriction when the outcome and instruments are discrete. The continuous case, which shares some similar features with nonparametric instrumental variable additive models, is also investigated. Finally, we apply our results to the French labor force survey, and provide evidence that attrition is related to transitions on the employment status

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    Bibliographic Info

    Paper provided by Centre de Recherche en Economie et Statistique in its series Working Papers with number 2013-17.

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    Length: 38
    Date of creation: Oct 2013
    Date of revision:
    Handle: RePEc:crs:wpaper:2013-17

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    Keywords: Panel data; Endogenous attrition; Instrumental variables;

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    1. James J. Heckman, 2001. "Micro Data, Heterogeneity, and the Evaluation of Public Policy: Nobel Lecture," Journal of Political Economy, University of Chicago Press, vol. 109(4), pages 673-748, August.
    2. Emmanuelle Nauze-Fichet & Christine Gonzalez-Demichel, 2003. "Les contours de la population active : aux frontières de l'emploi, du chômage et de l'inactivité," Économie et Statistique, Programme National Persée, vol. 362(1), pages 85-103.
    3. Keisuke Hirano & Guido W. Imbens & Geert Ridder & Donald B. Rubin, 2001. "Combining Panel Data Sets with Attrition and Refreshment Samples," Econometrica, Econometric Society, vol. 69(6), pages 1645-1659, November.
    4. Donald W. K. Andrews, 2000. "Inconsistency of the Bootstrap when a Parameter Is on the Boundary of the Parameter Space," Econometrica, Econometric Society, vol. 68(2), pages 399-406, March.
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    8. Hall, Robert E & Mishkin, Frederic S, 1982. "The Sensitivity of Consumption to Transitory Income: Estimates from Panel Data on Households," Econometrica, Econometric Society, vol. 50(2), pages 461-81, March.
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    10. Thomas A. Severini & Gautam Tripathi, 2005. "Some Identification Issues in Nonparametric Linear Models with Endogenous Regressors," Working papers 2005-12, University of Connecticut, Department of Economics.
    11. Donald W. K. Andrews, 1999. "Estimation When a Parameter Is on a Boundary," Econometrica, Econometric Society, vol. 67(6), pages 1341-1384, November.
    12. Hausman, Jerry A & Wise, David A, 1979. "Attrition Bias in Experimental and Panel Data: The Gary Income Maintenance Experiment," Econometrica, Econometric Society, vol. 47(2), pages 455-73, March.
    13. Michael Carter, 2001. "Foundations of Mathematical Economics," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262032899, December.
    14. Michael Carter, 2001. "Foundations of Mathematical Economics," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262531925, December.
    15. Whitney K. Newey & James L. Powell, 2003. "Instrumental Variable Estimation of Nonparametric Models," Econometrica, Econometric Society, vol. 71(5), pages 1565-1578, 09.
    16. Heckman, James J, 1974. "Shadow Prices, Market Wages, and Labor Supply," Econometrica, Econometric Society, vol. 42(4), pages 679-94, July.
    17. Bhattacharya, Debopam, 2008. "Inference in panel data models under attrition caused by unobservables," Journal of Econometrics, Elsevier, vol. 144(2), pages 430-446, June.
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