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Estimating the Intergenerational Correlation of Incomes : An Errors in Variables Framework

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  • Ramses H. ABUL NAGA

Abstract

Because the permanent incomes of parents and children are typically unobservable, the estimation of the intergenerational correlation of incomes is usually carried out via averaging methods or instrumentation. In this paper we take the permanent income of the parent family to be unobserved, but we assume that a model for its determinants is known to the researcher. In turn, this leads us to propose two related estimators for the intergenerational correlation: a two-stage least squares procedure and a more efficient MIMIC estimator. The MIMIC framework also provides estimates for the determinants of permanent income and the variance parameters required to evaluate the bias of the OLS estimator. Using a US sample of parents and children we provide estimates for the intergenerational correlation ranging between 0.30 and 0.78. The bias of the OLS estimator is estimated to be in the order of 40%.

Suggested Citation

  • Ramses H. ABUL NAGA, 1998. "Estimating the Intergenerational Correlation of Incomes : An Errors in Variables Framework," Cahiers de Recherches Economiques du Département d'Econométrie et d'Economie politique (DEEP) 9812, Université de Lausanne, Faculté des HEC, DEEP.
  • Handle: RePEc:lau:crdeep:9812
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    Citations

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    Cited by:

    1. Ramses H. ABUL NAGA, 2001. "Biases of the Ordinary Least Squares and Instrumental Variables Estimators of the Intergenerational Earnings Correlation : Revisited in the Light of Panel Data," Cahiers de Recherches Economiques du Département d'Econométrie et d'Economie politique (DEEP) 01.05, Université de Lausanne, Faculté des HEC, DEEP.
    2. Ramses ABUL NAGA & Jaya KRISHNAKUMAR, 1999. "Panel Data Estimation of the Intergenerational Correlation of Incomes," Cahiers de Recherches Economiques du Département d'Econométrie et d'Economie politique (DEEP) 9910, Université de Lausanne, Faculté des HEC, DEEP.
    3. Ramses Abul Naga, 2008. "Biases of the ordinary least squares and instrumental variables estimators of the intergenerational earnings elasticity: Revisited in the light of panel data," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 6(4), pages 323-350, December.
    4. Jantti, Markus & Jenkins, Stephen P., 2013. "Income mobility," ISER Working Paper Series 2013-23, Institute for Social and Economic Research.
    5. Lindquist, Matthew J. & Böhlmark, Anders, 2005. "Life-Cycle Variations in the Association between Current and Lifetime Income: Country, Cohort and Gender Comparisons," Working Paper Series 4/2005, Stockholm University, Swedish Institute for Social Research.
    6. Sari Pekkala & Robert E. B. Lucas, 2004. "On the Importance of Finnishing School: Half a Century of Inter-Generational Economic Mobility in Finland," Boston University - Department of Economics - The Institute for Economic Development Working Papers Series dp-141, Boston University - Department of Economics.
    7. Nilsson, William, 2005. "Opportunities, Preferences and Incomes," Umeå Economic Studies 649, Umeå University, Department of Economics.
    8. Donal O’Neill & Olive Sweetman & Dirk Van de gaer, 2007. "The effects of measurement error and omitted variables when using transition matrices to measure intergenerational mobility," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 5(2), pages 159-178, August.

    More about this item

    Keywords

    intergenerational mobility; errors in variables;

    JEL classification:

    • I3 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty
    • J62 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Job, Occupational and Intergenerational Mobility; Promotion

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