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Connecting to congress: improving deliberation in the information age
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ACM International Conference Proceeding Series; Vol. 228 archive
Proceedings of the 8th annual international conference on Digital government research: bridging disciplines & domains table of contents
Philadelphia, Pennsylvania
SESSION: System demonstrations and posters table of contents
Pages: 262 - 263  
Year of Publication: 2007
ISBN:1-59593-599-1
Authors
Kevin Esterling  University of California-Riverside
Michael Neblo  Ohio State University
David Lazer  Harvard University
Sponsors
: Center for Technology in Government
: CISCO
: Center for Statistical Ecology and Environmental Statistics
: CIMIC
Publisher
Bibliometrics
Downloads (6 Weeks): 1,   Downloads (12 Months): 14,   Citation Count: 0
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ABSTRACT

What are the best methods for Members of the U.S. Congress to use their Web sites to engage constituents in the public policy process? How can Congress use the Internet to foster deliberation in an emerging digital democracy? To shed light on these questions, in the summer and fall of 2006 we conducted online deliberation field experiments that involve current members of Congress interacting with their constituents. We randomly assign constituents to different experimental designs as well as to control groups. To date 12 members have participated in 20 deliberation sessions. The sessions have produced a wealth of data on the effects of this sort of interaction on constituents' attitudes toward public policy, toward government, and toward the online sessions themselves. To analyze these data, we employ an innovative statistical method that we have developed to estimate the problem of endogenous treatment in multi-site field experiments.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
Cameron, A. Colin, and Pravin K. Trivedi. 2005. Microeconometrics: Methods and Applications. New York: Cambridge University Press.
 
2
Miranda, Alfonso, and Sophia Rabe-Hesketh. n.d. "Maximum Likelihood Estimation of Endogenous Switching and Sample Selection Models for Binary, Count, and Ordinal Variables." Typescript, Department of Education, UC. Berkeley.

Collaborative Colleagues:
Kevin Esterling: colleagues
Michael Neblo: colleagues
David Lazer: colleagues