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Detecting Spillover Effects: Design and Analysis of Multilevel Experiments


  • Betsy Sinclair
  • Margaret McConnell
  • Donald P. Green


Interpersonal communication presents a methodological challenge and a research opportunity for researchers involved in field experiments. The challenge is that communication among subjects blurs the line between treatment and control conditions. When treatment effects are transmitted from subject to subject, the stable unit treatment value assumption (SUTVA) is violated, and comparison of treatment and control outcomes may provide a biased assessment of the treatment’s causal influence. Social scientists are increasingly interested in the substantive phenomena that lead to SUTVA violations, such as communication in advance of an election. Experimental designs that gauge SUTVA violations provide useful insights into the extent and influence of interpersonal communication. This article illustrates the value of one such design, a multilevel experiment in which treatments are randomly assigned to individuals and varying proportions of their neighbors. After describing the theoretical and statistical underpinnings of this design, we apply it to a large‐scale voter‐mobilization experiment conducted in Chicago during a special election in 2009 using social‐pressure mailings that highlight individual electoral participation. We find some evidence of within‐household spillovers but no evidence of spillovers across households. We conclude by discussing how multilevel designs might be employed in other substantive domains, such as the study of deterrence and policy diffusion.

Suggested Citation

  • Betsy Sinclair & Margaret McConnell & Donald P. Green, 2012. "Detecting Spillover Effects: Design and Analysis of Multilevel Experiments," American Journal of Political Science, John Wiley & Sons, vol. 56(4), pages 1055-1069, October.
  • Handle: RePEc:wly:amposc:v:56:y:2012:i:4:p:1055-1069
    DOI: 10.1111/j.1540-5907.2012.00592.x

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    Cited by:

    1. 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.
    2. Davide Viviano, 2020. "Experimental Design under Network Interference," Papers 2003.08421,, revised Jun 2020.
    3. Tiziano Arduini & Eleonora Patacchini & Edoardo Rainone, 2014. "Identification and Estimation of Outcome Response with Heterogeneous Treatment Externalities," EIEF Working Papers Series 1407, Einaudi Institute for Economics and Finance (EIEF), revised Sep 2014.
    4. Lan Liu & Michael G. Hudgens, 2014. "Large Sample Randomization Inference of Causal Effects in the Presence of Interference," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(505), pages 288-301, March.
    5. James N. Druckman & Donald P. Green, 2013. "Mobilizing Group Membership," SAGE Open, , vol. 3(2), pages 21582440134, June.
    6. Parker, A. Rani & Coleman, Eric & Manyindo, Jacob & Mukuru, Emmanuel & Schultz, Bill, 2020. "Bridging the academic-practitioner gap in RCTs," World Development, Elsevier, vol. 127(C).
    7. Sarah Baird & Aislinn Bohren & Craig McIntosh & Berk Ozler, 2017. "Optimal Design of Experiments in the Presence of Interference*, Second Version," PIER Working Paper Archive 16-025, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 30 Nov 2017.
    8. Manuel E. Lago & Ignacio Lago, 2019. "From the brady bunch to gilmore girls: The effect of household size on economic voting," Working Papers. Collection A: Public economics, governance and decentralization 1901, Universidade de Vigo, GEN - Governance and Economics research Network.
    9. Anna M. Wilke & Donald P. Green & Jasper Cooper, 2020. "A placebo design to detect spillovers from an education–entertainment experiment in Uganda," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 1075-1096, June.

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