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Testing Theories about Advocacy and Public Policy

Published online by Cambridge University Press:  23 January 2020

Paul Burstein Open the ORCID record for Paul Burstein [Opens in a new window]
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Abstract

One critical way that social scientists contribute to our understanding of policy change is developing and testing theories to explain the impact of advocacy efforts by nonparty organizations and activists to influence policy. What does the theory testing discover? To find out, this article analyzes all tests of such theories published in 25 major journals in political science and sociology between 2000 and 2018. Nineteen theories were tested and are generally quite similar, proposing that advocacy will affect policy and seeing electoral concerns as the basis of that influence. But they differ in terms of whose impact they seek to explain: there are different theories for interest groups, social movement organizations, and nongovernmental organizations. Predictions made by the theories are consistent with the data just over half the time. The theory-testing articles fail to show what their findings add to the weight of evidence for or against their theories, rarely test competing theories against each other, and seldom generalize or make specific suggestions for future work. This article highlights the most constructive suggestions for future work and argues for breaking down barriers between subdisciplines and systematically spelling out the value added by each new test of theory.

Type
Reflection
Information
Perspectives on Politics , Volume 19 , Issue 1 , March 2021 , pp. 148 - 159
Copyright
© American Political Science Association 2020

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Footnotes

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Data replication sets are available in Harvard Dataverse at: https://doi.org/10.7910/DVN/7VW5HS

A previous version of this article was presented at the 2018 annual meeting of the American Political Science Association in Boston. I would like to thank Christopher Witko and Patrick Bernhagen for helpful advice and comments, and Kathleen Collins for bibliographic assistance.

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Supplementary material: Link

Burstein Dataset

Dataset

https://doi.org/10.7910/DVN/7VW5HS
Link