Local preferences in candidate selection
Evidence from a conjoint experiment among party leaders in Germany
Bibliographic Data
| ID | 6316889 |
|---|---|
| Authors | Jan Berz (0000-0003-2846-6307, Trinity College Dublin, Dublin, Ireland, corresponding author), Michael Jankowski (0000-0002-7765-9132, University of Oldenburg, Oldenburg, Germany) |
| Year | 2022 |
| Volume | 28 |
| Issue | 6 |
| Pages | 1136-1149 |
| Publication date | 2022-11-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Party Politics (JOURNAL) |
| Journal identifiers | ISSN: 1354-0688 • E-ISSN: 1460-3683 |
| Publisher | SAGE Publications Inc (PUBLISHER) |
| DOI | 10.1177/13540688211041770 |
| OpenAlex | W4205253304 |
| Language | EN |
| Citations received | 30 |
| References cited | 53 |
Candidate selection is one of the most relevant tasks of parties and has important consequences for various aspects of political representation. While previous research has addressed many important aspects of the candidate selection process, we know little about the question of which candidate characteristics are preferred by party members. We address this research gap by conducting a conjoint experiment among more than 300 local party leaders in Germany. In the experiment, potential candidates differed on various important dimensions regarding their socio-demographic background, prior political experience, local roots, and work within the political party. We find that prior political experience and engagement within the party are the most important features. However, socio-demographic characteristics and deviation from the party line also matter. These findings have implications for theories of descriptive representation as well as the impact of decentralization on party cohesiveness
Decentralization · Political science · Politics · Representation (politics · Selection (genetic algorithm · Sociology · Computer Science · Electoral Systems and Political Participation · Gender Politics and Representation · Law · Political Influence and Corporate Strategies · Psychology · Social Psychology · Artificial Intelligence
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| Unique citing works | 30 |
|---|---|
| Citations per year | 7,5 |
| Citation span | 2022 - 2026 (5) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 30 |