Quarterly Journal of Political Science > Vol 8 > Issue 2
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Safety in Numbers: Mainstream-Seeking Diffusion in Response to Executive Compensation Regulations

  • David Glick 1

[1]David Glick, Department of Political Science, Boston University, USA, dmglick@bu.edu

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Table of contents

1 Background and Hypotheses
2 CD&A Regulations and Empirical Opportunities
3 Measuring Social Learning
4 Substantive Compliance: Annual Performance Bonuses
5 Additional Evidence: Stylistic Dimensions
6 Tables, How to Start, and Disclosing Peers
7 Conditional Variation within Reports
8 Conclusion
References

Quarterly Journal of Political Science

(Vol 8, Issue 2, 2013, pp 95-125)

DOI: 10.1561/100.00012032

Abstract

Research across subfields has explored questions of how and why one political actor's decisions are affected by others'. I investigate recent executive compensation disclosure regulations to make theoretical, substantive, and methodological contributions to the diffusion literature. I emphasize mainstream-seeking in the face of monitoring from regulators, interest groups, or voters. When there are high costs to sticking out, actors may look at others to identify the safe mainstream. I investigate the diffusion of practices amongst the regulated to extend the literature beyond policy creation to equally important implementation questions. I show that diffusion is an important factor affecting legal and policy impact. I do so by measuring social learning in new ways taking advantage of quasi-random assignment to social learning opportunities and independent decision making treatments. I show that social learning opportunities had a substantial negative effect on the quality of disclosures and led to less varied reports.

Data Files

Replication Data | 100.00012032_supp.zip (ZIP),

This file contains the data that is required to replicate the data on your own system.

DOI: 10.1561/100.00012032_supp

Appendices

Online Appendix | 100.00012032_app.zip (ZIP),

This is the article's accompanying appendix.

DOI: 10.1561/100.00012032_app