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Direct and Indirect Effects based on Changes-in-Changes

Author

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  • Martin Huber
  • Mark Schelker
  • Anthony Strittmatter

Abstract

We propose a novel approach for causal mediation analysis based on changes-in-changes assumptions restricting unobserved heterogeneity over time. This allows disentangling the causal effect of a binary treatment on a continuous outcome into an indirect effect operating through a binary intermediate variable (called mediator) and a direct effect running via other causal mechanisms. We identify average and quantile direct and indirect effects for various subgroups under the condition that the outcome is monotonic in the unobserved heterogeneity and that the distribution of the latter does not change over time conditional on the treatment and the mediator. We also provide a simulation study and two empirical applications regarding a training program evaluation and maternity leave reform.

Suggested Citation

  • Martin Huber & Mark Schelker & Anthony Strittmatter, 2022. "Direct and Indirect Effects based on Changes-in-Changes," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(1), pages 432-443, January.
    • Handle: RePEc:taf:jnlbes:v:40:y:2022:i:1:p:432-443
    DOI: 10.1080/07350015.2020.1831929
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    1. Martin Huber & Michael Lechner & Anthony Strittmatter, 2018. "Direct and indirect effects of training vouchers for the unemployed," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(2), pages 441-463, February.
    2. Martin Huber, 2014. "Identifying Causal Mechanisms (Primarily) Based On Inverse Probability Weighting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(6), pages 920-943, September.
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    4. Nattavudh Powdthavee & Warn N. Lekfuangfu & Mark Wooden, 2013. "The Marginal Income Effect of Education on Happiness: Estimating the Direct and Indirect Effects of Compulsory Schooling on Well-Being in Australia," Melbourne Institute Working Paper Series wp2013n16, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    5. Giorgio Brunello & Margherita Fort & Nicole Schneeweis & Rudolf Winter‐Ebmer, 2016. "The Causal Effect of Education on Health: What is the Role of Health Behaviors?," Health Economics, John Wiley & Sons, Ltd., vol. 25(3), pages 314-336, March.
    6. Stacey H. Chen & Yen-Chien Chen & Jin-Tan Liu, 2019. "The Impact of Family Composition on Educational Achievement," Journal of Human Resources, University of Wisconsin Press, vol. 54(1), pages 122-170.
    7. 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.
    8. Martin Huber, 2015. "Causal Pitfalls in the Decomposition of Wage Gaps," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(2), pages 179-191, April.
    9. Strittmatter, Anthony, 2019. "Heterogeneous earnings effects of the job corps by gender: A translated quantile approach," Labour Economics, Elsevier, vol. 61(C).
    10. James Heckman & Rodrigo Pinto & Peter Savelyev, 2013. "Understanding the Mechanisms through Which an Influential Early Childhood Program Boosted Adult Outcomes," American Economic Review, American Economic Association, vol. 103(6), pages 2052-2086, October.
    11. Markus Frölich & Martin Huber, 2017. "Direct and indirect treatment effects–causal chains and mediation analysis with instrumental variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(5), pages 1645-1666, November.
    12. Eva Deuchert & Martin Huber & Mark Schelker, 2019. "Direct and Indirect Effects Based on Difference-in-Differences With an Application to Political Preferences Following the Vietnam Draft Lottery," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(4), pages 710-720, October.
    13. Luna Bellani & Michela Bia, 2019. "The long‐run effect of childhood poverty and the mediating role of education," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(1), pages 37-68, January.
    14. Anthony Strittmatter, 2019. "Heterogeneous Earnings Effects of the Job Corps by Gender Earnings: A Translated Quantile Approach," Papers 1908.08721, arXiv.org.
    15. Constantine E. Frangakis & Donald B. Rubin, 2002. "Principal Stratification in Causal Inference," Biometrics, The International Biometric Society, vol. 58(1), pages 21-29, March.
    16. Keele, Luke & Tingley, Dustin & Teppei Yamamoto, "undated". "Identifying Mechanisms behind Policy Interventions via Causal Mediation Analysis," Working Paper 135661, Harvard University OpenScholar.
    17. Flores, Carlos A. & Flores-Lagunes, Alfonso, 2009. "Identification and Estimation of Causal Mechanisms and Net Effects of a Treatment under Unconfoundedness," IZA Discussion Papers 4237, Institute of Labor Economics (IZA).
    18. Luke Keele & Dustin Tingley & Teppei Yamamoto, 2015. "Identifying Mechanisms Behind Policy Interventions Via Causal Mediation Analysis," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 34(4), pages 937-963, September.
    19. Martin Huber & Michael Lechner & Giovanni Mellace, 2017. "Why Do Tougher Caseworkers Increase Employment? The Role of Program Assignment as a Causal Mechanism," The Review of Economics and Statistics, MIT Press, vol. 99(1), pages 180-183, March.
    20. Jeffrey M. Albert & Suchitra Nelson, 2011. "Generalized Causal Mediation Analysis," Biometrics, The International Biometric Society, vol. 67(3), pages 1028-1038, September.
    21. Imai, Kosuke & Yamamoto, Teppei, 2013. "Identification and Sensitivity Analysis for Multiple Causal Mechanisms: Revisiting Evidence from Framing Experiments," Political Analysis, Cambridge University Press, vol. 21(2), pages 141-171, April.
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    23. Lars Skipper & Marianne Simonsen, 2006. "The costs of motherhood: an analysis using matching estimators," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(7), pages 919-934.
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    Cited by:

    1. Wallimann, Hannes & Imhof, David & Huber, Martin, 2020. "A Machine Learning Approach for Flagging Incomplete Bid-rigging Cartels," FSES Working Papers 513, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    2. 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.
    3. Masayuki Sawada, 2019. "Noncompliance in randomized control trials without exclusion restrictions," Papers 1910.03204, arXiv.org, revised Jun 2021.

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    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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