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Imputación de ingresos laborales: Una aplicación con encuestas de empleo en México
[Labor earnings imputation: An application using labor surveys in Mexico]

Author

Listed:
  • Rodriguez-Oreggia, Eduardo
  • Lopez-Videla, Bruno

Abstract

The aim of this paper is to make imputation of earnings in observations with missing values in the Encuesta Nacional de Ocupaciones y Empleo (ENOE), and also to analyze a possible bias in human capital estimations from ignoring such missings. We present imputations by two methods, and also a correction for estimations by reweighting observations with reported earnings. The results show differences in human capital estimations on wages and factors related to labor poverty when missing values of earnings are ignored. Differences are acute when measuring labor poverty.

Suggested Citation

  • Rodriguez-Oreggia, Eduardo & Lopez-Videla, Bruno, 2014. "Imputación de ingresos laborales: Una aplicación con encuestas de empleo en México [Labor earnings imputation: An application using labor surveys in Mexico]," MPRA Paper 54436, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:54436
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    References listed on IDEAS

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    1. Lillard, Lee & Smith, James P & Welch, Finis, 1986. "What Do We Really Know about Wages? The Importance of Nonreporting and Census Imputation," Journal of Political Economy, University of Chicago Press, vol. 94(3), pages 489-506, June.
    2. Jacob A. Mincer, 1974. "Introduction to "Schooling, Experience, and Earnings"," NBER Chapters, in: Schooling, Experience, and Earnings, pages 1-4, National Bureau of Economic Research, Inc.
    3. Horowitz, Joel L. & Manski, Charles F., 1998. "Censoring of outcomes and regressors due to survey nonresponse: Identification and estimation using weights and imputations," Journal of Econometrics, Elsevier, vol. 84(1), pages 37-58, May.
    4. Barry T. Hirsch & Edward J. Schumacher, 2004. "Match Bias in Wage Gap Estimates Due to Earnings Imputation," Journal of Labor Economics, University of Chicago Press, vol. 22(3), pages 689-722, July.
    5. Joachim R. Frick & Markus M. Grabka, 2003. "Missing Income Data in the German SOEP: Incidence, Imputation and its Impact on the Income Distribution," Discussion Papers of DIW Berlin 376, DIW Berlin, German Institute for Economic Research.
    6. Lopez-Acevedo, Gladys, 2001. "Evolution of earnings and rates of returns to education in Mexico," Policy Research Working Paper Series 2691, The World Bank.
    7. Christopher R. Bollinger & Barry T. Hirsch, 2006. "Match Bias from Earnings Imputation in the Current Population Survey: The Case of Imperfect Matching," Journal of Labor Economics, University of Chicago Press, vol. 24(3), pages 483-520, July.
    8. Jacob A. Mincer, 1974. "Schooling and Earnings," NBER Chapters, in: Schooling, Experience, and Earnings, pages 41-63, National Bureau of Economic Research, Inc.
    9. Jacob A. Mincer, 1974. "Schooling, Experience, and Earnings," NBER Books, National Bureau of Economic Research, Inc, number minc74-1, January.
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    2. Raymundo M. Campos-Vazquez & Gerardo Esquivel, 2023. "The Effect of the Minimum Wage on Poverty: Evidence from a Quasi-Experiment in Mexico," Journal of Development Studies, Taylor & Francis Journals, vol. 59(3), pages 360-380, March.

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    More about this item

    Keywords

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    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • D10 - Microeconomics - - Household Behavior - - - General
    • D6 - Microeconomics - - Welfare Economics
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity

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