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

Abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. The Institute for the Study of Labor (IZA) in Bonn is a local and virtual international research center and a place of communication between science, politics and business. IZA is an independent nonprofit organization supported by Deutsche Post Foundation. The center is associated with the University of Bonn and offers a stimulating research environment through its international network, workshops and conferences, data service, project support, research visits and doctoral program. IZA engages in (i) original and internationally competitive research in all fields of labor economics, (ii) development of policy concepts, and (iii) dissemination of research results and concepts to the interested public. Terms of use: Documents in D I S C U S S I O N P A P E R S E R I E SIZA Discussion Papers often represent preliminary work and are circulated to encourage discussion. Citation of such a paper should account for its provisional character. A revised version may be available directly from the author. The increase in the prevalence of obesity worldwide has led to great interest in the economic consequences of obesity, but valid and powerful instruments for obesity, which are needed to estimate its causal effects, are rare. This paper contributes to the literature by using a novel instrument: genetic risk score, which reflects the predisposition to higher body mass index across many genetic loci. We estimate IV models of the effect of BMI on labor market outcomes using Finnish data that have many strengths: genetic information, measured body mass index, and administrative earnings records that are free of the problems associated with nonresponse, self-reporting error or top-coding. The first stage of the IV models indicate that genetic risk score is a powerful instrument, and the available evidence from the genetics literature is consistent with instrument validity. The results of the IV models indicate weight reduces earnings and employment and increases social income transfers, although we caution that the results are based on small samples, and are sensitive to specification and subsample.JEL Classification: I10, J23, J31

Within-family studies for Mendelian randomization: avoiding dynastic, assortative mating, and population stratification biases

“…47,48 These findings raise questions about whether height and BMI have causal effects on socioeconomic outcomes later in life. [49][50][51] However, larger studies of related individuals with information on socioeconomic outcomes, such as income and occupation later in life are required to provide definitive evidence about the consequences of height and BMI later in life.…”

Association of Obesity with Health Care Costs: A Mendelian Randomization study

“…We found that some SNPs are related to obesity-related illnesses like type 2 diabetes and high blood pressure. As in Böckerman et al (2019) we assume that the associations with obesity-related conditions occur because of the SNPs' association with high BMI and WHR, but we cannot definitely rule out other pathways. In a second check, we used the MR-Egger method (Bowden et al, 2015) to test for pleiotropy.…”

“…However, a potential concern with this approach is that unobserved characteristics may be correlated with both a person's own BMI and their relative's BMI, for example when they live in the same household (Böckerman et al, 2019).…”

“…Recent literature suggested that MR can be a powerful tool for economic evaluation (Dixon et al, 2016), however few studies have yet used it (Ding et al, 2009;Fletcher and Lehrer, 2011;Norton and Han, 2008;Cawley et al, 2011;Willage, 2017;von Hinke et al, 2016;Böckerman et al, 2019).…”

Assessing the robustness of sisVIVE in a Mendelian randomization study to estimate the causal effect of body mass index on income using multiple SNPs from understanding society

“…MR has been used recently to understand the mechanism of relationship of high BMI and disadvantage of social-economic status, such as low income: Tyrrell et al 28 used highly self-selected UK Biobank data for their analysis, where income is reported at the household level, and Böckerman et al 29 used a small sample for their analysis, which severely limits the power of that study; we refer the reader to these articles for a justification for the existence of a linear effect of BMI on income. We apply the methods introduced above to the larger and more representative UKHLS sample data to estimate the causal effect of BMI on personal income.…”