IDEAS home Printed from https://ideas.repec.org/p/zbw/rwirep/306835.html
   My bibliography  Save this paper

Gene-environment interactions with essential heterogeneity

Author

Listed:
  • Hollenbach, Johannes
  • Schmitz, Hendrik
  • Westphal, Matthias

Abstract

We show that two-stage least squares (2SLS) estimates of interactions can be misleading in settings with essential heterogeneity (e.g., selection into gains) and where complier status to the instrument depends on the interaction variable. The 2SLS estimator cannot disentangle interaction effects from shifts in complier groups. Estimating marginal treatment effects addresses this problem by fixing the underlying population and unobserved heterogeneity. We illustrate this using the example of gene-environment studies, where the central parameter is the interaction effect between an endogenous, instrumented measure of environment or behavior and a predetermined measure of genetic endowment. Our application examines the effect of education on cognitive performance in old age. The results show complementarities between education and genetic predisposition in determining cognitive abilities. The marginal treatment effect estimates reveal a substantially larger gene-environment interaction, exceeding the 2SLS estimate by a factor of at least 2.5.

Suggested Citation

  • Hollenbach, Johannes & Schmitz, Hendrik & Westphal, Matthias, 2024. "Gene-environment interactions with essential heterogeneity," Ruhr Economic Papers 1105, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
  • Handle: RePEc:zbw:rwirep:306835
    DOI: 10.4419/96973283
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/306835/1/1909368237.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.4419/96973283?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Charles F. Manski, 1997. "Monotone Treatment Response," Econometrica, Econometric Society, vol. 65(6), pages 1311-1334, November.
    2. Amanda E. Kowalski, 2023. "Reconciling Seemingly Contradictory Results from the Oregon Health Insurance Experiment and the Massachusetts Health Reform," The Review of Economics and Statistics, MIT Press, vol. 105(3), pages 646-664, May.
    3. James J. Heckman & Edward Vytlacil, 2005. "Structural Equations, Treatment Effects, and Econometric Policy Evaluation," Econometrica, Econometric Society, vol. 73(3), pages 669-738, May.
    4. Ding, Xuejie & Barban, Nicola & Tropf, Felix C. & Mills, Melinda C., 2019. "The relationship between cognitive decline and a genetic predictor of educational attainment," Social Science & Medicine, Elsevier, vol. 239(C).
    5. Pedro Carneiro & James J. Heckman & Edward J. Vytlacil, 2011. "Estimating Marginal Returns to Education," American Economic Review, American Economic Association, vol. 101(6), pages 2754-2781, October.
    6. Damon Clark & Heather Royer, 2013. "The Effect of Education on Adult Mortality and Health: Evidence from Britain," American Economic Review, American Economic Association, vol. 103(6), pages 2087-2120, October.
    7. Daniel A Kamhöfer & Hendrik Schmitz & Matthias Westphal, 2019. "Heterogeneity in Marginal Non-Monetary Returns to Higher Education," Journal of the European Economic Association, European Economic Association, vol. 17(1), pages 205-244.
    8. Schiele, Valentin & Schmitz, Hendrik, 2023. "Understanding cognitive decline in older ages: The role of health shocks," European Economic Review, Elsevier, vol. 151(C).
    9. 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.
    10. James J. Heckman & Sergio Urzua & Edward Vytlacil, 2006. "Understanding Instrumental Variables in Models with Essential Heterogeneity," The Review of Economics and Statistics, MIT Press, vol. 88(3), pages 389-432, August.
    11. Martin Nybom, 2017. "The Distribution of Lifetime Earnings Returns to College," Journal of Labor Economics, University of Chicago Press, vol. 35(4), pages 903-952.
    12. Carneiro, Pedro & Lee, Sokbae, 2009. "Estimating distributions of potential outcomes using local instrumental variables with an application to changes in college enrollment and wage inequality," Journal of Econometrics, Elsevier, vol. 149(2), pages 191-208, April.
    13. Daniel Barth & Nicholas W. Papageorge & Kevin Thom, 2020. "Genetic Endowments and Wealth Inequality," Journal of Political Economy, University of Chicago Press, vol. 128(4), pages 1474-1522.
    14. Evan K. Rose & Yotam Shem-Tov, 2021. "How Does Incarceration Affect Reoffending? Estimating the Dose-Response Function," Journal of Political Economy, University of Chicago Press, vol. 129(12), pages 3302-3356.
    15. Nicholas W Papageorge & Kevin Thom, 2020. "Genes, Education, and Labor Market Outcomes: Evidence from the Health and Retirement Study," Journal of the European Economic Association, European Economic Association, vol. 18(3), pages 1351-1399.
    16. Guido W. Imbens & Charles F. Manski, 2004. "Confidence Intervals for Partially Identified Parameters," Econometrica, Econometric Society, vol. 72(6), pages 1845-1857, November.
    17. James Banks & Fabrizio Mazzonna, 2012. "The Effect of Education on Old Age Cognitive Abilities: Evidence from a Regression Discontinuity Design," Economic Journal, Royal Economic Society, vol. 122(560), pages 418-448, May.
    18. A. D. Roy, 1951. "Some Thoughts On The Distribution Of Earnings," Oxford Economic Papers, Oxford University Press, vol. 3(2), pages 135-146.
    19. Erik Plug & Wim Vijverberg, 2003. "Schooling, Family Background, and Adoption: Is It Nature or Is It Nurture?," Journal of Political Economy, University of Chicago Press, vol. 111(3), pages 611-641, June.
    20. Christian N. Brinch & Magne Mogstad & Matthew Wiswall, 2017. "Beyond LATE with a Discrete Instrument," Journal of Political Economy, University of Chicago Press, vol. 125(4), pages 985-1039.
    21. Magne Mogstad & Andres Santos & Alexander Torgovitsky, 2018. "Using Instrumental Variables for Inference About Policy Relevant Treatment Parameters," Econometrica, Econometric Society, vol. 86(5), pages 1589-1619, September.
    22. Schmitz, Lauren L. & Conley, Dalton, 2017. "The effect of Vietnam-era conscription and genetic potential for educational attainment on schooling outcomes," Economics of Education Review, Elsevier, vol. 61(C), pages 85-97.
    23. Yeongmi Jeong & Nicholas W. Papageorge & Meghan Skira & Kevin Thom, 2024. "Genetic Risk for Alzheimer’s Disease and Related Dementias: Cognition, Economic Behavior, and Actionable Information," NBER Working Papers 32181, National Bureau of Economic Research, Inc.
    24. Guido W. Imbens & Donald B. Rubin, 1997. "Estimating Outcome Distributions for Compliers in Instrumental Variables Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 64(4), pages 555-574.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Matthias Westphal & Daniel A Kamhöfer & Hendrik Schmitz, 2022. "Marginal College Wage Premiums Under Selection Into Employment," The Economic Journal, Royal Economic Society, vol. 132(646), pages 2231-2272.
    2. Amanda E Kowalski, 2023. "Behaviour within a Clinical Trial and Implications for Mammography Guidelines," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 90(1), pages 432-462.
    3. Mogstad, Magne & Torgovitsky, Alexander & Walters, Christopher R., 2024. "Policy evaluation with multiple instrumental variables," Journal of Econometrics, Elsevier, vol. 243(1).
    4. Bartalotti, Otávio & Kédagni, Désiré & Possebom, Vitor, 2023. "Identifying marginal treatment effects in the presence of sample selection," Journal of Econometrics, Elsevier, vol. 234(2), pages 565-584.
    5. Thomas Carr & Toru Kitagawa, 2021. "Testing Instrument Validity with Covariates," Papers 2112.08092, arXiv.org, revised Sep 2023.
    6. Huber, Martin & Wüthrich, Kaspar, 2017. "Evaluating local average and quantile treatment effects under endogeneity based on instruments: a review," FSES Working Papers 479, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    7. Pereda-Fernández, Santiago, 2023. "Identification and estimation of triangular models with a binary treatment," Journal of Econometrics, Elsevier, vol. 234(2), pages 585-623.
    8. Cornelissen, Thomas & Dustmann, Christian & Raute, Anna & Schönberg, Uta, 2016. "From LATE to MTE: Alternative methods for the evaluation of policy interventions," Labour Economics, Elsevier, vol. 41(C), pages 47-60.
    9. Yu-Chang Chen & Haitian Xie, 2022. "Personalized Subsidy Rules," Papers 2202.13545, arXiv.org, revised Mar 2022.
    10. Sung Jae Jun & Sokbae Lee, 2023. "Identifying the Effect of Persuasion," Journal of Political Economy, University of Chicago Press, vol. 131(8), pages 2032-2058.
    11. Kédagni, Désiré, 2023. "Identifying treatment effects in the presence of confounded types," Journal of Econometrics, Elsevier, vol. 234(2), pages 479-511.
    12. Black, Dan A. & Joo, Joonhwi & LaLonde, Robert & Smith, Jeffrey A. & Taylor, Evan J., 2022. "Simple Tests for Selection: Learning More from Instrumental Variables," Labour Economics, Elsevier, vol. 79(C).
    13. Patrick Kline & Christopher R. Walters, 2016. "Evaluating Public Programs with Close Substitutes: The Case of HeadStart," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1795-1848.
    14. Gathmann, Christina & Vonnahme, Christina & Kim, Jongoh & Busse, Anna, 2021. "Marginal Returns to Citizenship and Educational Performance," CEPR Discussion Papers 16636, C.E.P.R. Discussion Papers.
    15. Domenico Depalo, 2020. "Explaining the causal effect of adherence to medication on cholesterol through the marginal patient," Health Economics, John Wiley & Sons, Ltd., vol. 29(S1), pages 110-126, October.
    16. Robert A. Moffitt & Matthew V. Zahn, 2019. "The Marginal Labor Supply Disincentives of Welfare: Evidence from Administrative Barriers to Participation," NBER Working Papers 26028, National Bureau of Economic Research, Inc.
    17. Tafti, Elena Ashtari, 2023. "Technology, Skills, and Performance: The Case of Robots in Surgery," CINCH Working Paper Series (since 2020) 78746, Duisburg-Essen University Library, DuEPublico.
    18. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
    19. Antill, Samuel, 2022. "Do the right firms survive bankruptcy?," Journal of Financial Economics, Elsevier, vol. 144(2), pages 523-546.
    20. Laura Schmitz, 2022. "Heterogeneous Effects of After-School Care on Child Development," Discussion Papers of DIW Berlin 2006, DIW Berlin, German Institute for Economic Research.

    More about this item

    Keywords

    Two-stage least squares estimation; marginal treatment effects; gene-environment interactions; cognitive decline;
    All these keywords.

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • J14 - Labor and Demographic Economics - - Demographic Economics - - - Economics of the Elderly; Economics of the Handicapped; Non-Labor Market Discrimination
    • 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:zbw:rwirep:306835. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/rwiesde.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.