Using Genetic Lotteries within Families to Examine the Causal Impact of Poor Health on Academic Achievement
While there is a well-established, large positive correlation between mental and physical health and education outcomes, establishing a causal link remains a substantial challenge. Building on findings from the biomedical literature, we exploit specific differences in the genetic code between siblings within the same family to estimate the causal impact of several poor health conditions on academic outcomes. We present evidence of large impacts of poor mental health on academic achievement. Further, our estimates suggest that family fixed effects estimators by themselves cannot fully account for the endogeneity of poor health. Finally, our sensitivity analysis suggests that these differences in specific portions of the genetic code have good statistical properties and that our results are robust to reasonable violations of the exclusion restriction assumption.
We are grateful to Ken Chay, Dalton Conley, Weili Ding, Ted Joyce, Robert McMillan, John Mullahy, Matthew Neidell, Jody Sindelar and participants at the 2007 NBER Summer Institute, Northwestern University, Brown University, CUNY, McGill University, University of Calgary, Tinbergen Institute, Institute for Fiscal Studies, Warwick University, University of Calgary, 2008 AHEC Conference at the University of Chicago, 2008 SOLE meetings, Yale Health Policy Colloquium, University of British Columbia, University of Connecticut, University of Saskatchewan, University of Tennessee, University of Toronto and Simon Fraser University for comments and suggestions that have improved this paper. We are both grateful to the CLSRN for research support. Lehrer also wishes to thank SSHRC for additional research support. We are responsible for all errors. This research uses data from Add Health, a program project designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris, and funded by a grant P01-HD31921 from the National Institute of Child Health and Human Development, with cooperative funding from 17 other agencies. Special acknowledgment is due Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. Persons interested in obtaining data files from Add Health should contact Add Health, Carolina Population Center, 123 W. Franklin Street, Chapel Hill, NC 27516-2524 (firstname.lastname@example.org). The views expressed herein are those of the author(s) and do not necessarily reflect the views of the National Bureau of Economic Research.