Replication Data for: Selective Reciprocity in Bipartisan Collaboration: How Majority Security Shapes Legislative Success

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Authors: Mackenzie R. Dobson
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Abstract

How does majority party security shape reciprocal bipartisan collaboration and influence legislative success? U.S. state legislatures vary widely in the stability of majority control, offering a valuable opportunity for examining how party security conditions the incentives for cross-party collaboration. Insecure majorities may foster reciprocity as both a behavioral norm and a strategic path to legislative advancement, while long-term one-party control can diminish the returns to bipartisan engagement. I develop a theory of selective reciprocity, arguing that majority security fundamentally restructures how legislators engage in and benefit from bipartisan collaboration. Drawing on data from 401,720 bills introduced across 43 state legislatures between 2009 and 2018, I construct novel measures of bipartisan collaboration to evaluate reciprocity. I find that minority party legislators build reputational capital by consistently cosponsoring majority party bills---but their efforts yield few legislative gains in secure majority chambers. Instead, majority legislators selectively reciprocate only on minority party initiatives unlikely to pass, preserving the appearance of cooperation while protecting their policy agenda. By contrast, in insecure chambers, bipartisan cooperation is more likely to produce substantive outcomes. Reciprocity endures but is constrained---selective in form, asymmetric in effect, and structured by the institutional advantages of majority control. These findings raise broader concerns about the marginalization of minority party legislators and the limits of representation under conditions of majority security.

Fields of Study & Subjects

Electoral Systems and Political Participation Political Influence and Corporate Strategies Gender Politics and Representation

Publication details

Journal / source
Harvard Dataverse
Publication date
2 February 2026
Language
EN
Document type
Dataset