IDEAS home Printed from https://ideas.repec.org/p/bri/cmpowp/11-274.html
   My bibliography  Save this paper

Genetic Markers as Instrumental Variables

Author

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

    ()

Abstract

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.

Suggested Citation

  • Stephanie von Hinke Kessler Scholder & George Davey Smith & Debbie A. Lawlor & Carol Propper & Frank Windmeijer, 2011. "Genetic Markers as Instrumental Variables," The Centre for Market and Public Organisation 11/274, Department of Economics, University of Bristol, UK.
  • Handle: RePEc:bri:cmpowp:11/274
    as

    Download full text from publisher

    File URL: http://www.bristol.ac.uk/cmpo/publications/papers/2011/wp274.pdf
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    2. Constantine E. Frangakis & Donald B. Rubin, 2002. "Principal Stratification in Causal Inference," Biometrics, The International Biometric Society, vol. 58(1), pages 21-29, March.
    3. 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.
    4. 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, August.
    5. von Hinke Kessler Scholder, Stephanie & Davey Smith, George & Lawlor, Debbie A. & Propper, Carol & Windmeijer, Frank, 2013. "Child height, health and human capital: Evidence using genetic markers," European Economic Review, Elsevier, vol. 57(C), pages 1-22.
    6. 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, August.
    7. Stephanie Hinke Kessler Scholder & George L. Wehby & Sarah Lewis & Luisa Zuccolo, 2014. "Alcohol Exposure In Utero and Child Academic Achievement," Economic Journal, Royal Economic Society, vol. 0(576), pages 634-667, May.
    8. Fletcher, Jason M. & Lehrer, Steven F., 2011. "Genetic lotteries within families," Journal of Health Economics, Elsevier, vol. 30(4), pages 647-659, July.
    9. Joshua D. Angrist & Kathryn Graddy & Guido W. Imbens, 2000. "The Interpretation of Instrumental Variables Estimators in Simultaneous Equations Models with an Application to the Demand for Fish," Review of Economic Studies, Oxford University Press, vol. 67(3), pages 499-527.
    10. Taylor, Amy E. & Davies, Neil M. & Ware, Jennifer J. & VanderWeele, Tyler & Smith, George Davey & Munafò, Marcus R., 2014. "Mendelian randomization in health research: Using appropriate genetic variants and avoiding biased estimates," Economics & Human Biology, Elsevier, vol. 13(C), pages 99-106.
    11. von Hinke Kessler Scholder, Stephanie & Davey Smith, George & Lawlor, Debbie A. & Propper, Carol & Windmeijer, Frank, 2012. "The effect of fat mass on educational attainment: Examining the sensitivity to different identification strategies," Economics & Human Biology, Elsevier, vol. 10(4), pages 405-418.
    12. Frolich, Markus, 2007. "Nonparametric IV estimation of local average treatment effects with covariates," Journal of Econometrics, Elsevier, vol. 139(1), pages 35-75, July.
    13. Joshua D. Angrist & Jörn-Steffen Pischke, 2009. "Mostly Harmless Econometrics: An Empiricist's Companion," Economics Books, Princeton University Press, edition 1, number 8769.
    14. Arvid Sjölander & Keith Humphreys & Stijn Vansteelandt & Rino Bellocco & Juni Palmgren, 2009. "Sensitivity Analysis for Principal Stratum Direct Effects, with an Application to a Study of Physical Activity and Coronary Heart Disease," Biometrics, The International Biometric Society, vol. 65(2), pages 514-520, June.
    15. Douglas Staiger & James H. Stock, 1997. "Instrumental Variables Regression with Weak Instruments," Econometrica, Econometric Society, vol. 65(3), pages 557-586, May.
    16. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-475, March.
    17. Stock, James H & Wright, Jonathan H & Yogo, Motohiro, 2002. "A Survey of Weak Instruments and Weak Identification in Generalized Method of Moments," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(4), pages 518-529, October.
    18. Jin, Hui & Rubin, Donald B., 2008. "Principal Stratification for Causal Inference With Extended Partial Compliance," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 101-111, March.
    19. Donald B. Rubin, 2005. "Causal Inference Using Potential Outcomes: Design, Modeling, Decisions," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 322-331, March.
    20. John Cawley, 2004. "The Impact of Obesity on Wages," Journal of Human Resources, University of Wisconsin Press, vol. 39(2).
    21. Bartolucci, Francesco & Grilli, Leonardo, 2011. "Modeling Partial Compliance Through Copulas in a Principal Stratification Framework," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 469-479.
    22. 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.
    23. Hastie, Nicholas D. & van der Loos, Matthijs J. H. M. & Vitart, Veronique & Völzke, Henry & Wellmann, Jürgen & Yu, Lei & Zhao, Wei & Allik, Jüri & Attia, John R. & Bandinelli, Stefania & Bastardot,, 2013. "GWAS of 126,559 Individuals Identifies Genetic Variants Associated with Educational Attainment," Scholarly Articles 13383543, Harvard University Department of Economics.
    24. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. Genes as Instrumental Variables
      by David Stern in Stochastic Trend on 2013-01-12 14:26:00

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Frank Windmeijer & Helmut Farbmacher & Neil Davies & George Davey Smith, 2016. "On the Use of the Lasso for Instrumental Variables Estimation with Some Invalid Instruments," Bristol Economics Discussion Papers 16/674, Department of Economics, University of Bristol, UK, revised 08 Aug 2017.
    2. Jan-Emmanuel De Neve & James H. Fowler & Bruno S. Frey, 2010. "Genes, economics, and happiness," IEW - Working Papers 475, Institute for Empirical Research in Economics - University of Zurich.
    3. Stephanie Hinke Kessler Scholder & George L. Wehby & Sarah Lewis & Luisa Zuccolo, 2014. "Alcohol Exposure In Utero and Child Academic Achievement," Economic Journal, Royal Economic Society, vol. 0(576), pages 634-667, May.
    4. Padraig Dixon & George Davey Smith & Stephanie von Hinke & Neil M. Davies & William Hollingworth, 2016. "Estimating Marginal Healthcare Costs Using Genetic Variants as Instrumental Variables: Mendelian Randomization in Economic Evaluation," PharmacoEconomics, Springer, vol. 34(11), pages 1075-1086, November.
    5. von Hinke Kessler Scholder, Stephanie & Davey Smith, George & Lawlor, Debbie A. & Propper, Carol & Windmeijer, Frank, 2013. "Child height, health and human capital: Evidence using genetic markers," European Economic Review, Elsevier, vol. 57(C), pages 1-22.
    6. Hafner, Lucas & Tauchmann, Harald & Wübker, Ansgar, 2017. "Does moderate weight loss affect subjective health perception in obese individuals? Evidence from field experimental data," FAU Discussion Papers in Economics 26/2017, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    7. Böckerman, Petri & Viinikainen, Jutta & Vainiomäki, Jari & Hintsanen, Mirka & Pitkänen, Niina & Lehtimäki, Terho & Pehkonen, Jaakko & Rovio, Suvi & Raitakari, Olli, 2017. "Stature and long-term labor market outcomes: Evidence using Mendelian randomization," Economics & Human Biology, Elsevier, vol. 24(C), pages 18-29.
    8. Michael W. L. Elsby & Ryan Michaels & David Ratner, 2015. "The Beveridge Curve: A Survey," Journal of Economic Literature, American Economic Association, vol. 53(3), pages 571-630, September.
    9. Hafner, Lucas & Tauchmann, Harald & Wübker, Ansgar, 2017. "Does moderate weight loss affect subjective health perception in obese individuals? Evidence from field experimental data," Ruhr Economic Papers 730, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    10. Biørn, Erik, 2017. "Identification, Instruments, Omitted Variables, and Rudimentary Models: Fallacies in the ‘Experimental Approach’ to Econometrics," Memorandum 13/2017, Oslo University, Department of Economics.

    More about this item

    Keywords

    ALSPAC; Fat mass; Genetic Variants; Instrumental Variables; Mendelian Randomization; Potential Outcomes;

    JEL classification:

    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation
    • I1 - Health, Education, and Welfare - - Health
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bri:cmpowp:11/274. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (). General contact details of provider: http://edirc.repec.org/data/cmbriuk.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.