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A Framework for Separating Individual Treatment Effects from Spillover, Interaction, and General Equilibrium Effects

Author

Listed:
  • Huber, Martin

    (University of Fribourg)

  • Steinmayr, Andreas

    (University of Innsbruck)

Abstract

This paper suggests a causal framework for disentangling individual level treatment effects and interference effects, i.e., general equilibrium, spillover, or interaction effects related to treatment distribution. Thus, the framework allows for a relaxation of the Stable Unit Treatment Value Assumption (SUTVA), which assumes away any form of treatment-dependent interference between study participants. Instead, we permit interference effects within aggregate units, for example, regions or local labor markets, but need to rule out interference effects between these aggregate units. Borrowing notation from the causal mediation literature, we define a range of policy-relevant effects and formally discuss identification based on randomization, selection on observables, and difference-in-differences. We also present an application to a policy intervention extending unemployment benefit durations in selected regions of Austria that arguably affected ineligibles in treated regions through general equilibrium effects in local labor markets.

Suggested Citation

  • Huber, Martin & Steinmayr, Andreas, 2017. "A Framework for Separating Individual Treatment Effects from Spillover, Interaction, and General Equilibrium Effects," IZA Discussion Papers 10648, Institute of Labor Economics (IZA).
  • Handle: RePEc:iza:izadps:dp10648
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    References listed on IDEAS

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    1. Bruno Crépon & Esther Duflo & Marc Gurgand & Roland Rathelot & Philippe Zamora, 2013. "Do Labor Market Policies have Displacement Effects? Evidence from a Clustered Randomized Experiment," The Quarterly Journal of Economics, Oxford University Press, vol. 128(2), pages 531-580.
    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.
    3. 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).
    4. Sarah Baird & Aislinn Bohren & Berk Ozler & Craig McIntosh, 2014. "Designing Experiments to Measure Spillover Effects," Working Papers 2014-11, The George Washington University, Institute for International Economic Policy.
    5. Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2003. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometrica, Econometric Society, vol. 71(4), pages 1161-1189, July.
    6. Marc FERRACCI & Grégory JOLIVET & Gerard J van den Berg, 2009. "Treatment Evaluation in the Case of Interaction Within Markets," Working Papers 2009-22, Center for Research in Economics and Statistics.
    7. Gustavo J. Bobonis & Frederico Finan, 2009. "Neighborhood Peer Effects in Secondary School Enrollment Decisions," The Review of Economics and Statistics, MIT Press, vol. 91(4), pages 695-716, November.
    8. Abadie, Alberto, 2003. "Semiparametric instrumental variable estimation of treatment response models," Journal of Econometrics, Elsevier, vol. 113(2), pages 231-263, April.
    9. 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.
    10. Edward Miguel & Michael Kremer, 2004. "Worms: Identifying Impacts on Education and Health in the Presence of Treatment Externalities," Econometrica, Econometric Society, vol. 72(1), pages 159-217, January.
    11. 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.
    12. Bryan S. Graham & Guido W. Imbens & Geert Ridder, 2010. "Measuring the Effects of Segregation in the Presence of Social Spillovers: A Nonparametric Approach," NBER Working Papers 16499, National Bureau of Economic Research, Inc.
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    Cited by:

    1. Lafférs, Lukáš & Mellace, Giovanni, 2020. "Identification of the average treatment effect when SUTVA is violated," Discussion Papers on Economics 3/2020, University of Southern Denmark, Department of Economics.
    2. Stefano, Roberta di & Mellace, Giovanni, 2020. "The inclusive synthetic control method," Discussion Papers on Economics 14/2020, University of Southern Denmark, Department of Economics.

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    More about this item

    Keywords

    mediation analysis; propensity score; inverse probability weighting; interference effects; interaction effects; spillover effects; general equilibrium effects; treatment effect; difference-in-differences;
    All these keywords.

    JEL classification:

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

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