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Health Econ. 2019 Jan;28(1):65-77. doi: 10.1002/hec.3828. Epub 2018 Sep 21.

The effect of weight on labor market outcomes: An application of genetic instrumental variables.

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Labour Institute for Economic Research and IZA, Jyväskylä University School of Business and Economics, University of Jyväskylä, Jyväskylä, Finland.
Department of Policy Analysis and Management, and Department of Economics, Cornell University, Ithaca, New York.
Jyväskylä University School of Business and Economics, University of Jyväskylä, Jyväskylä, Finland.
Department of Clinical Chemistry, Fimlab Laboratories and Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland.
Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland.
Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland.


This paper contributes to the literature on the labor market consequences of obesity by using a novel instrument: genetic risk score, which reflects the predisposition to higher body mass index (BMI) across many genetic loci. We estimate instrumental variable models of the effect of BMI on labor market outcomes using Finnish data that have many strengths, for example, BMI that is measured rather than self-reported, and data on earnings and social income transfers that are from administrative tax records and are thus free of the problems associated with nonresponse, reporting error or top coding. The empirical results are sensitive to whether we use a narrower or broader genetic risk score, and to model specification. For example, models using the narrower genetic risk score as an instrument imply that a one-unit increase in BMI is associated with 6.9% lower wages, 1.8% fewer years employed, and a 3 percentage point higher probability of receiving any social income transfers. However, when we use a newer, broader genetic risk score, we cannot reject the null hypothesis of no effect. Future research using genetic risk scores should examine the sensitivity of their results to the risk score used.


earnings; employment; genetics; obesity; overweight; social income transfers

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