Genetic information, obesity, and labor market outcomes
dc.contributor.author | Norton, Edward C. | en_US |
dc.contributor.author | Han, Euna | en_US |
dc.date.accessioned | 2008-08-27T20:05:23Z | |
dc.date.available | 2009-11-06T18:12:57Z | en_US |
dc.date.issued | 2008-09 | en_US |
dc.identifier.citation | Norton, Edward C.; Han, Euna (2008). "Genetic information, obesity, and labor market outcomes." Health Economics 17(9): 1089-1104. <http://hdl.handle.net/2027.42/60913> | en_US |
dc.identifier.issn | 1057-9230 | en_US |
dc.identifier.issn | 1099-1050 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/60913 | |
dc.identifier.uri | http://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=18615836&dopt=citation | en_US |
dc.description.abstract | Economists have argued that obesity may lead to worse labor market outcomes, especially for women. Empirical methods to test this hypothesis have not thus far adequately controlled for the endogeneity of obesity. We use variation in genotype to predict variation in phenotype (obesity). Genetic information from specific genes linked to obesity in the biomedical literature provides strong exogenous variation in the body mass index and thus can be used as instrumental variables. These genes predict swings in weight of between 5 and 20 pounds for persons between five and six feet tall. We use additional genetic information to control for omitted variables correlated with both obesity and labor market outcomes. We analyzed data from the third wave of the Add Health data set, when respondents are in their mid-twenties. Results from our preferred models show no effect of lagged obesity on the probability of employment or on wages, for either men or women. This paper shows the potential of using genetic information in social sciences. Copyright © 2008 John Wiley & Sons, Ltd. | en_US |
dc.format.extent | 154459 bytes | |
dc.format.extent | 3118 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.publisher | John Wiley & Sons, Ltd. | en_US |
dc.subject.other | Life and Medical Sciences | en_US |
dc.subject.other | Epidemiology, Biostatistics and Public Health | en_US |
dc.title | Genetic information, obesity, and labor market outcomes | en_US |
dc.type | Article | en_US |
dc.rights.robots | IndexNoFollow | en_US |
dc.subject.hlbsecondlevel | Economics | en_US |
dc.subject.hlbsecondlevel | Medicine (General) | en_US |
dc.subject.hlbsecondlevel | Statistics and Numeric Data | en_US |
dc.subject.hlbsecondlevel | Public Health | en_US |
dc.subject.hlbtoplevel | Business | en_US |
dc.subject.hlbtoplevel | Health Sciences | en_US |
dc.subject.hlbtoplevel | Science | en_US |
dc.subject.hlbtoplevel | Social Sciences | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Department of Health Management and Policy, University of Michigan, Ann Arbor, MI, USA ; Department of Economics, University of Michigan, Ann Arbor, MI, USA ; Department of Health Management and Policy, 109 S. Observatory St., University of Michigan, Ann Arbor, MI 48109-2029, USA | en_US |
dc.contributor.affiliationother | Institute for Health Research and Policy, University of Illinois at Chicago, Chicago, IL, USA | en_US |
dc.identifier.pmid | 18615836 | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/60913/1/1383_ftp.pdf | |
dc.identifier.doi | http://dx.doi.org/10.1002/hec.1383 | en_US |
dc.identifier.source | Health Economics | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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