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Direct and indirect effects based on difference-in-differences with an application to political preferences following the Vietnam draft lottery

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  • Deuchert, Eva
  • Huber, Martin
  • Schelker, Mark

Abstract

This paper proposes a difference-in-differences approach for disentangling a total treatment effect on some outcome into a direct effect as well as an indirect effect operating through a binary intermediate variable – or mediator – within strata defined upon how the mediator reacts to the treatment. Imposing random treatment assignment along with specific common trend (and further) assumptions identifies the direct effects on the always and never takers, whose mediator is not affected by the treatment, as well as the direct and indirect effects on the compliers, whose mediator reacts to the treatment. We provide an empirical application based on the Vietnam draft lottery, where we analyse the impact of the random draft lottery number on political preferences. The results suggest that a high draft risk due to the lottery leads to a relative increase in mild preferences for the Republican Party, but has no effect on strong preferences for either party or on policy contents. Moreover, the increase in Republican support is mostly driven by the direct effect.

Suggested Citation

  • Deuchert, Eva & Huber, Martin & Schelker, Mark, 2017. "Direct and indirect effects based on difference-in-differences with an application to political preferences following the Vietnam draft lottery," FSES Working Papers 473, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    • Handle: RePEc:fri:fribow:fribow00473
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    References listed on IDEAS

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    1. Erikson, Robert S. & Stoker, Laura, 2011. "Caught in the Draft: The Effects of Vietnam Draft Lottery Status on Political Attitudes," American Political Science Review, Cambridge University Press, vol. 105(2), pages 221-237, May.
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    14. VanderWeele, Tyler J., 2008. "Simple relations between principal stratification and direct and indirect effects," Statistics & Probability Letters, Elsevier, vol. 78(17), pages 2957-2962, December.
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    Cited by:

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    2. Wunsch, Conny & Strobl, Renate, 2018. "Identification of causal mechanisms based on between-subject double randomization designs," CEPR Discussion Papers 13028, C.E.P.R. Discussion Papers.
    3. Viviana Celli, 2019. "Causal Mediation Analysis in Economics: objectives, assumptions, models," Working Papers 12/19, Sapienza University of Rome, DISS.
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    5. Doerr Annabelle & Strittmatter Anthony, 2021. "Identifying Causal Channels of Policy Reforms with Multiple Treatments and Different Types of Selection," Journal of Econometric Methods, De Gruyter, vol. 10(1), pages 67-88, January.
    6. Brewer, Mike & Dang, Thang & Tominey, Emma, 2024. "Universal Credit: Welfare reform and mental health," Journal of Health Economics, Elsevier, vol. 98(C).
    7. Hersche Markus & Moor Elias, 2020. "Identification and Estimation of Intensive Margin Effects by Difference-in-Difference Methods," Journal of Causal Inference, De Gruyter, vol. 8(1), pages 272-285, January.
    8. Masayuki Sawada, 2019. "Noncompliance in randomized control trials without exclusion restrictions," Papers 1910.03204, arXiv.org, revised Jun 2021.
    9. Sonia Bhalotra & Martin Karlsson & Therese Nilsson & Nina Schwarz, 2022. "Infant Health, Cognitive Performance, and Earnings: Evidence from Inception of the Welfare State in Sweden," The Review of Economics and Statistics, MIT Press, vol. 104(6), pages 1138-1156, November.
    10. Viviana Celli, 2022. "Causal mediation analysis in economics: Objectives, assumptions, models," Journal of Economic Surveys, Wiley Blackwell, vol. 36(1), pages 214-234, February.
    11. Rajkumar, Vidya Bharathi, 2021. "Male Migration and the Emergence of Female Farm Management in India," 2021 Conference, August 17-31, 2021, Virtual 315329, International Association of Agricultural Economists.
    12. Holm, Anders & Breen, Richard, 2023. "Causal Mediation in Panel Data – Estimation Based on Difference in Differences," SocArXiv kwscz, Center for Open Science.
    13. Giovanni Mellace & Alessandra Pasquini, 2019. "Identify More, Observe Less: Mediation Analysis Synthetic Control," CEIS Research Paper 474, Tor Vergata University, CEIS, revised 20 Nov 2019.
    14. Mellace, Giovanni & Pasquini, Alessandra, 2019. "Identify More, Observe Less: Mediation Analysis: Mediation Analysis Synthetic Control," Discussion Papers on Economics 12/2019, University of Southern Denmark, Department of Economics.

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    Keywords

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    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • D70 - Microeconomics - - Analysis of Collective Decision-Making - - - General
    • D72 - Microeconomics - - Analysis of Collective Decision-Making - - - Political Processes: Rent-seeking, Lobbying, Elections, Legislatures, and Voting Behavior

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