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Lobbying, learning and policy reinvention: an examination of the American States’ drunk driving laws

Published online by Cambridge University Press:  30 October 2018

Jinhai Yu Open the ORCID record for Jinhai Yu [Opens in a new window] ,
Edward T. Jennings Jr  and
J. S. Butler
Jinhai Yu*
Affiliation:
School of Public Economics and Administration, Shanghai University of Finance and Economics, China
Edward T. Jennings Jr
Affiliation:
Martin School of Public Policy and Administration, University of Kentucky, USA
J. S. Butler
Affiliation:
Martin School of Public Policy and Administration, University of Kentucky, USA
*
*Corresponding author. Email: yu.jinhai@mail.shufe.edu.cn
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Abstract

Scholars have consistently shown that learning of successful policies in other states leads to higher likelihood of policy adoption. This study extends this finding two ways. First, policy learning can also lead to more comprehensive adoption of successful policies. Second, the effect of policy learning on policy comprehensiveness is conditional on lobbying by interest groups, an alternative source of information about policy success. To test these hypotheses, we conduct a directed dyad-year analysis using a dataset on American state drunk driving regulations from 1983 to 2000. The results show that more comprehensive policy adoption by states is positively related to policy success in other states when lobbying by Mothers Against Drunk Driving (MADD) is relatively low. Moreover, lobbying by MADD increases policy comprehensiveness when policy success is relatively low. This study advances the literature by examining the conditional effects of lobbying on the relationship between policy learning and policy reinvention.

Keywords

drunk driving lawlobbyingpolicy diffusionpolicy learningpolicy reinvention
Type
Research Article
Information
Journal of Public Policy , Volume 40 , Issue 2 , June 2020 , pp. 259 - 279
Copyright
© Cambridge University Press 2018

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Supplementary material: File

Yu et al. supplementary material

Appendices 1-4

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Yu et al. Dataset

Dataset

https://doi.org/10.7910/DVN/Z6EZG3
Link