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      Local preferences in candidate selection: Evidence from a conjoint experiment among party leaders in Germany

      1 , 2
      Party Politics
      SAGE Publications

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          Abstract

          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.

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          Most cited references61

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          Should Blacks Represent Blacks and Women Represent Women? A Contingent "Yes"

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            The Concept of Representation

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              Causal Inference in Conjoint Analysis: Understanding Multidimensional Choices via Stated Preference Experiments

              Survey experiments are a core tool for causal inference. Yet, the design of classical survey experiments prevents them from identifying which components of a multidimensional treatment are influential. Here, we show howconjoint analysis, an experimental design yet to be widely applied in political science, enables researchers to estimate the causal effects of multiple treatment components and assess several causal hypotheses simultaneously. In conjoint analysis, respondents score a set of alternatives, where each has randomly varied attributes. Here, we undertake a formal identification analysis to integrate conjoint analysis with the potential outcomes framework for causal inference. We propose a new causal estimand and show that it can be nonparametrically identified and easily estimated from conjoint data using a fully randomized design. The analysis enables us to propose diagnostic checks for the identification assumptions. We then demonstrate the value of these techniques through empirical applications to voter decision making and attitudes toward immigrants.
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                Author and article information

                Contributors
                Journal
                Party Politics
                Party Politics
                SAGE Publications
                1354-0688
                1460-3683
                November 2022
                January 19 2022
                November 2022
                : 28
                : 6
                : 1136-1149
                Affiliations
                [1 ]Trinity College Dublin, Dublin, Ireland
                [2 ]University of Oldenburg, Oldenburg, Germany
                Article
                10.1177/13540688211041770
                1557bb11-340a-4685-8c9b-e97e17d08250
                © 2022

                https://creativecommons.org/licenses/by-nc/4.0/

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