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Lose Weight for Money Only if Over-Weight: Marginal Integration for Semi-Linear Panel Models

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  • Kan K
  • Lee M

Abstract

Body mass index (BMI), weight(kg)/ height(m)2, is a widely used measure for obesity in medical science. In economics, there appeared studies (e.g., Cawley (2004) and Burkhauser and Cawley (2008)) showing that BMI has a negative (or no) effect on wage. But BMI is a tightly specified function of weight and height, and there is no priori reason to believe why the particular function is the best to combine weight and height. In this paper, we address the question of weight effect on wage, employing two-wave panel data for white females; the same panel data with more waves were used originally in Cawley (2004). We posit a semi-linear model consisting of a nonparametric function of height and weight and a linear function of the other regressors. The model is differenced to get rid of the unit specific effect, which results in a difference of two nonparametric functions with the same shape. We estimate each nonparametric function with a ‘marginal integration method’, and then combine the two estimated functions using the same shape restriction. We find that there is no weight effect on wage up to the average weight, beyond which a large negative effect kicks in. The effect magnitude is greater than that in Cawley (2004) who used a linear BMI model. The linear model gives the false impression that there would be a wage gain by becoming slimmer than the average and that the ‘obesity penalty’ is less that what it actually is.

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Bibliographic Info

Paper provided by HEDG, c/o Department of Economics, University of York in its series Health, Econometrics and Data Group (HEDG) Working Papers with number 09/19.

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Date of creation: Jul 2009
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Handle: RePEc:yor:hectdg:09/19

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Keywords: BMI; weight effect on wage; panel data; semi-linear model; marginal integration.;

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References

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  1. Burkhauser, Richard V. & Cawley, John, 2008. "Beyond BMI: The value of more accurate measures of fatness and obesity in social science research," Journal of Health Economics, Elsevier, vol. 27(2), pages 519-529, March.
  2. Myoung-jae Lee, 1999. "A Root-N Consistent Semiparametric Estimator for Related-Effect Binary Response Panel Data," Econometrica, Econometric Society, vol. 67(2), pages 427-434, March.
  3. John Cawley, 2004. "The Impact of Obesity on Wages," Journal of Human Resources, University of Wisconsin Press, vol. 39(2).
  4. Xihong Lin & Raymond J. Carroll, 2006. "Semiparametric estimation in general repeated measures problems," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(1), pages 69-88.
  5. Ekaterini Kyriazidou, 1997. "Estimation of a Panel Data Sample Selection Model," Econometrica, Econometric Society, vol. 65(6), pages 1335-1364, November.
  6. Henderson, Daniel J. & Carroll, Raymond J. & Li, Qi, 2008. "Nonparametric estimation and testing of fixed effects panel data models," Journal of Econometrics, Elsevier, vol. 144(1), pages 257-275, May.
  7. Newey, W.K., 1992. "Kernel Estimation of Partial Means and a General Variance Estimator," Working papers 93-3, Massachusetts Institute of Technology (MIT), Department of Economics.
  8. Li, Qi & Stengos, Thanasis, 1996. "Semiparametric estimation of partially linear panel data models," Journal of Econometrics, Elsevier, vol. 71(1-2), pages 389-397.
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Cited by:
  1. Guardado, José R. & Ziebarth, Nicolas R., 2013. "A Model of Worker Investment in Safety and Its Effects on Accidents and Wages," IZA Discussion Papers 7428, Institute for the Study of Labor (IZA).

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