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Health and Wages: Panel Evidence on Men and Women using IV Quantile Regression

Author

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  • Abbi M. Kedir

Abstract

Using panel data from a developing country on individuals aged 16 to 59 who reported their monthly wages, we estimated a relationship between health (nutrition) measures (i.e. height and BMI) and wages (which proxies productivity/growth). We controlled for endogeneity of BMI and found heterogeneous returns to different human capital indicators. Our findings indicate that productivity is positively and significantly affected by education, height and BMI. The return to BMI is important both at the lower and upper end of the wage distribution for men while women at the upper end of the distribution suffer a wage penalty due to BMI. Height has been a significant factor affecting men’s productivity but not women. The results in general support the high-nutrition and high- productivity equilibrium story. Returns to schooling showed a declining trend as we move from lower to higher quantiles for both sub-samples. This might suggest that schooling is more beneficial for the less able. In addition, the returns to schooling of women are higher than men. The results have important implications for policy making in the form of nutrition interventions and targeted education on women.

Suggested Citation

  • Abbi M. Kedir, 2008. "Health and Wages: Panel Evidence on Men and Women using IV Quantile Regression," Discussion Papers in Economics 08/37, Division of Economics, School of Business, University of Leicester.
    • Handle: RePEc:lec:leecon:08/37
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    File URL: https://www.le.ac.uk/economics/research/RePEc/lec/leecon/dp08-37.pdf
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    References listed on IDEAS

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    1. Omar Arias & Walter Sosa-Escudero & Kevin F. Hallock, 2001. "Individual heterogeneity in the returns to schooling: instrumental variables quantile regression using twins data," Empirical Economics, Springer, vol. 26(1), pages 7-40.
    2. Charles L. Baum & William F. Ford, 2004. "The wage effects of obesity: a longitudinal study," Health Economics, John Wiley & Sons, Ltd., vol. 13(9), pages 885-899, September.
    3. Roger Koenker & Kevin F. Hallock, 2001. "Quantile Regression," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 143-156, Fall.
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    Citations

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    Cited by:

    1. Pierre LEVASSEUR, 2016. "The effects of bodyweight on wages in urban Mexico," Cahiers du GREThA (2007-2019) 2016-18, Groupe de Recherche en Economie Théorique et Appliquée (GREThA).
    2. Levasseur, Pierre, 2017. "The ambiguous causal relationship between body-mass and labour income in emerging economies: The case of Mexico," MPRA Paper 81933, University Library of Munich, Germany.
    3. Ahsan, Henna & Idrees, Dr Muhammad, 2014. "Impact of Health on Earnings: Individual and District Level Analysis for Pakistan," MPRA Paper 56769, University Library of Munich, Germany, revised 20 Jun 2014.

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    More about this item

    Keywords

    height; BMI; schooling; heterogeneity; endogeneity; quantile; IV;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • O12 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Microeconomic Analyses of Economic Development

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