Hostname: page-component-78c5997874-4rdpn Total loading time: 0 Render date: 2024-11-12T21:44:23.375Z Has data issue: false hasContentIssue false
  • English
  • Français

Information Spillovers: Another Look at Experimental Estimates of Legislator Responsiveness

Published online by Cambridge University Press:  12 January 2015

Alexander Coppock
Alexander Coppock*
Affiliation:
Columbia University, New York, NY, USA; e-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

A field experiment carried out by Butler and Nickerson (Butler, D. M., and Nickerson, D. W. (2011). Can learning constituency opinion affect how legislators vote? Results from a field experiment. Quarterly Journal of Political Science 6, 55–83) shows that New Mexico legislators changed their voting decisions upon receiving reports of their constituents’ preferences. The analysis of the experiment did not account for the possibility that legislators may share information, potentially resulting in spillover effects. Working within the analytic framework proposed by Bowers et al. (2013), I find evidence of spillovers, and present estimates of direct and indirect treatment effects. The total causal effect of the experimental intervention appears to be twice as large as reported originally.

Keywords

Field experimentspillovers
Type
Research Article
Information
Journal of Experimental Political Science , Volume 1 , Issue 2 , Winter 2014 , pp. 159 - 169
Copyright
Copyright © The Experimental Research Section of the American Political Science Association 2015 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Aronow, P. M. 2013. “Comment: Reasoning About Interference Between Units”. Unpublished Mm, 1–7.Google Scholar
Aronow, P. M., and Samii, C. 2013. Estimating Average Causal Effects Under Interference Between Units. Unpublished manuscript, Yale University, New Haven, CT.Google Scholar
Berger, R. L., and Boos, D. D. 1994. P Values Maximized over a Confidence Set for the Nuisance Parameter. Journal of the American Statistical Association 89 (427): 1012–16.Google Scholar
Bowers, J., Fredrickson, M. M., and Panagopoulos, C. 2013. Reasoning About Interference Between Units: A General Framework. Political Analysis 21 (1): 97–124.Google Scholar
Butler, D. M., and Nickerson, D. W. 2011. Can Learning Constituency Opinion Affect how Legislators Vote? Results from a Field Experiment. Quarterly Journal of Political Science 6: 55–83.Google Scholar
Cox, D. R. 1958. Planning of Experiments. New York: Wiley.Google Scholar
Fisher, R. A. 1935. The Design of Experiments. Edinburgh: Oliver and Boyd.Google Scholar
Hodges, J. L. Jr., and Lehmann, E. L. 1963. Estimates of Location Based on Rank Tests. The Annals of Mathematical Statistics 34 (2): 598–611.Google Scholar
Hong, G., and Raudenbush, S. W. 2006. Evaluating Kindergarten Retention Policy. Journal of the American Statistical Association 101 (475): 901–10.Google Scholar
Hudgens, M. G., and Halloran, M. E. 2008. Toward Causal Inference with Interference. Journal of the American Statistical Association 103 (482): 832–42.CrossRefGoogle ScholarPubMed
Jacobs, L. R., Lawrence, E. D., Shapiro, R. Y., and Smith, S. S. 1998. Congressional Leadership of Public Opinion. Political Science Quarterly 113 (1): 21–41.Google Scholar
Kingdon, J. W. 1989. Congressman’s Voting Decisions. Ann Arbor, MI: University of Michigan Press.Google Scholar
Manski, C. F. 2013. Identification of Treatment Response with Social Interactions. The Econometrics Journal 16 (1): S1–23.Google Scholar
Nolen, T. L., and Hudgens, M. G. 2011. Randomization-Based Inference Within Principal Strata. Journal of the American Statistical Association 106 (494): 581–93.Google Scholar
Poole, K. 2007. Changing Minds? Not in Congress! Public Choice 131 (3/4): 435–51.CrossRefGoogle Scholar
Poole, K., Lewis, J., Lo, J., and Carroll, R. 2011. Scaling Roll Call Votes with Wnominate in R. Journal of Statistical Software 42 (14): 1–21.Google Scholar
Rosenbaum, P. R. 2002. Observational Studies (2nd ed). New York: Springer-Verlag.Google Scholar
Rosenbaum, P. R. 2007. Interference Between Units in Randomized Experiments. Journal of the American Statistical Association 102 (477): 191–200.Google Scholar
Rubin, D. B. 1980. Randomization Analysis of Experimental Data: The Fisher Randomization Test Comment. Journal of the American Statistical Association 75 (371): 591–93.Google Scholar
Sobel, M. E. 2006. What Do Randomized Studies of Housing Mobility Demonstrate? Journal of the American Statistical Association 101 (476): 1398–1407.CrossRefGoogle Scholar
Vote Smart. 2008. Vote Smart of 2008 New Mexico Key Votes. Retrieved from http://votesmart.org/bills/NM/2008/ on November 1, 2012.Google Scholar
Supplementary material: File

Coppock supplementary material

Replication files

Download Coppock supplementary material(File)
File 2.2 MB
Supplementary material: PDF

Coppock supplementary material

Appendix

Download Coppock supplementary material(PDF)
PDF 206.2 KB