Legislative Voting Behavior, Seen and Unseen
A Theory of Roll‐Call Vote Selection
Bibliographic Data
| ID | 6300748 |
|---|---|
| Authors | Clifford J Carrubba (0000-0001-6397-7343, Emory University), Clifford Carrubba, Matthew Gabel, Matthew J Gabel (0000-0001-6886-2828, Washington University in St. Louis), Simon Hug (0000-0002-7741-2201, University of Geneva) |
| Year | 2008 |
| Volume | 33 |
| Issue | 4 |
| Pages | 543-572 |
| Publication date | 2008-11-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Legislative Studies Quarterly (JOURNAL) |
| Journal identifiers | ISSN: 0362-9805 • E-ISSN: 1939-9162 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.3162/036298008786403079 |
| OpenAlex | W2022312374 |
| Language | EN |
| Citations received | 96 |
| References cited | 13 |
The empirical study of legislative behavior largely relies on roll‐call vote analysis, but roll‐call votes in many legislatures represent only a sample of legislative votes. We have good reasons to believe this sample is particularly poor for inferring party effects on legislative behavior. The selection of votes for roll call may be endogenous to exactly the characteristics of voting behavior (for instance, party cohesion) that we want to study. We must understand the roll‐call vote institution and account for its selection effects before we can draw inferences about legislative behavior from roll‐call results. This article develops a game‐theoretic model of roll‐call vote requests predicated on party leaders requesting votes to enforce party discipline. The model offers general and testable predictions about the selection process and how it affects observed and unobserved legislative voting behavior, particularly party cohesion
Cohesion (chemistry · Empirical research · Legislature · Political science · Politics · Roll call · Sample (material · Selection (genetic algorithm · Voting · Voting behavior · Computer Science · Electoral Systems and Political Participation · Labor Movements and Unions · Law · Race, History, and American Society · Artificial Intelligence
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| Unique citing works | 96 |
|---|---|
| Citations per year | 5,33 |
| Citation span | 2008 - 2026 (19) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 96 |