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Genetic Markers as Instrumental Variables

  • Stephanie von Hinke Kessler Scholder
  • George Davey Smith
  • Debbie A. Lawlor
  • Carol Propper
  • Frank Windmeijer

    ()

The use of genetic markers as instrumental variables (IV) is receiving increasing attention from epidemiologists, economists, statisticians and social scientists. This paper examines the conditions that need to be met for genetic variants to be used as instruments. Although these have been discussed in the epidemiological, medical and statistical literature, they have not been well-defined in the economics and social science literature. The increasing availability of biomedical data however, makes understanding of these conditions crucial to the successful use of genotypes as instruments for modifiable risk factors. We combine the econometric IV literature with that from genetic epidemiology using a potential outcomes framework and review the IV conditions in the context of a social science application, examining the effect of child fat mass on academic performance.

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File URL: http://www.bristol.ac.uk/cmpo/publications/papers/2011/wp274.pdf
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Paper provided by Department of Economics, University of Bristol, UK in its series The Centre for Market and Public Organisation with number 11/274.

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Length: 21 pages
Date of creation: Oct 2011
Date of revision:
Handle: RePEc:bri:cmpowp:11/274
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  1. von Hinke Kessler Scholder, S & Davey Smith, G & Lawlor, DA & Propper, C & Windmeijer, F, 2010. "Child height, health and human capital: evidence using genetic markers," Working Papers 5947, Imperial College, London, Imperial College Business School.
  2. Barnard J. & Frangakis C.E. & Hill J.L. & Rubin D.B., 2003. "Principal Stratification Approach to Broken Randomized Experiments: A Case Study of School Choice Vouchers in New York City," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 299-323, January.
  3. Stephanie von Hinke Kessler Scholder & George Davey Smith & Debbie A. Lawlor & Carol Propper & Frank Windmeijer, 2011. "Mendelian randomization: the use of genes in instrumental variable analyses," Health Economics, John Wiley & Sons, Ltd., vol. 20(8), pages 893-896, 08.
  4. Frölich, Markus, 2002. "Nonparametric IV Estimation of Local Average Treatment Effects with Covariates," IZA Discussion Papers 588, Institute for the Study of Labor (IZA).
  5. Angrist, Joshua D & Graddy, Kathryn & Imbens, Guido W, 2000. "The Interpretation of Instrumental Variables Estimators in Simultaneous Equations Models with an Application to the Demand for Fish," Review of Economic Studies, Wiley Blackwell, vol. 67(3), pages 499-527, July.
  6. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-75, March.
  7. Edward C. Norton & Euna Han, 2008. "Genetic information, obesity, and labor market outcomes," Health Economics, John Wiley & Sons, Ltd., vol. 17(9), pages 1089-1104.
  8. John Cawley & Euna Han & Edward C. Norton, 2011. "The validity of genes related to neurotransmitters as instrumental variables," Health Economics, John Wiley & Sons, Ltd., vol. 20(8), pages 884-888, 08.
  9. Ding, Weili & Lehrer, Steven F. & Rosenquist, J.Niels & Audrain-McGovern, Janet, 2009. "The impact of poor health on academic performance: New evidence using genetic markers," Journal of Health Economics, Elsevier, vol. 28(3), pages 578-597, May.
  10. Fletcher, Jason M. & Lehrer, Steven F., 2011. "Genetic lotteries within families," Journal of Health Economics, Elsevier, vol. 30(4), pages 647-659, July.
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