IDEAS home Printed from https://ideas.repec.org/p/iza/izadps/dp10648.html
   My bibliography  Save this paper

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 Munich)

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 for the Study of Labor (IZA).
  • Handle: RePEc:iza:izadps:dp10648
    as

    Download full text from publisher

    File URL: http://ftp.iza.org/dp10648.pdf
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    4. 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 for the Study of Labor (IZA).
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. Abadie, Alberto, 2003. "Semiparametric instrumental variable estimation of treatment response models," Journal of Econometrics, Elsevier, vol. 113(2), pages 231-263, April.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

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

    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

    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:iza:izadps:dp10648. 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: (Mark Fallak). General contact details of provider: http://www.iza.org .

    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.