Genetic Lotteries within Families

46 Pages Posted: 5 Oct 2009 Last revised: 15 Dec 2009

See all articles by Jason M. Fletcher

Jason M. Fletcher

University of Wisconsin - Madison - Robert M. La Follette School of Public Affairs; Yale University - School of Public Health

Steven F. Lehrer

Queen's University - School of Policy Studies; National Bureau of Economic Research (NBER)

Date Written: August 1, 2009

Abstract

Drawing on findings from the biomedical literature, this paper introduces the idea that specific exogenously inherited differences in the genetic code between full biological siblings can be used to test within-family estimators and potentially improve our understanding of economic relationships. These points are illustrated with an application to identify the causal impact of several poor health conditions on academic outcomes. We present evidence of large impacts of poor mental health on academic achievement and demonstrate that our results are robust to reasonable violations of the exclusion restriction assumption. Further, our estimates suggest that family fixed effects estimators by themselves cannot fully account for the endogeneity of poor health.

Suggested Citation

Fletcher, Jason M. and Lehrer, Steven F., Genetic Lotteries within Families (August 1, 2009). Netspar Discussion Paper No. 08/2009-026. Available at SSRN: https://ssrn.com/abstract=1481309 or http://dx.doi.org/10.2139/ssrn.1481309

Jason M. Fletcher (Contact Author)

University of Wisconsin - Madison - Robert M. La Follette School of Public Affairs ( email )

1180 Observatory Drive
Madison, WI 53706-1393
United States

Yale University - School of Public Health ( email )

PO Box 208034
60 College Street
New Haven, CT 06520-8034
United States

Steven F. Lehrer

Queen's University - School of Policy Studies ( email )

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Register to save articles to
your library

Register

Paper statistics

Downloads
44
Abstract Views
573
PlumX Metrics