Candidate Quota and Incumbent Deselection

This project entails a survey experiment to be carried out with party elites from major parties in the three countries of Austria, Germany and Switzerland. Following a conjoint-ish experimental design, respondents are given a hypothetical scenario of a new quota set in place in their (regional) party and will be ask to de-select incumbents and replace them with other candidates. This shall allow me to examine how the introduction of quotas might affect the nomination calculus for party leaders and whether the introduction of quotas entails a trade-off in descriptive representation for certain socio-demographic categories including gender and age. Importantly, in both scenarios all incumbents and potential replacements are shown at the same time to allow the respondents to properly compare whom to replace with whom. Two hypothetical scenarios are presented randomly to the respondents. One scenario talks about the introduction/change of a gender quota stipulating to nominate at least one women among the top 3 list positions currently held by three male incumbents who all wish to run again. Respondents are asked to de-nominate one male incumbent and nominate one out of 3 potential women candidates on the vacant spot. All incumbents and potential candidates are hypothetical persona as is common in conjoint experiments. They differ along a line of dimensions including age, tenure, political capital, urban/ruralness of their party branch and field of policy expertise. Attribute restrictions denies younger incumbents and newcomers (among the replacements) higher levels of political capital (e.g., committee chair positions). In the second scenario respondents are confronted with the introduction of a newcomer quota with a similar set-up. Three incumbents are considered set for the three top positions, but one has to be de-nominated and be replaced by a person (man or woman) from a set of 3 newcomers (all below the age of 35). These hypothetical persons too differ along a line of dimensions including age, tenure, political capital, urban/ruralness of their party branch, field of policy expertise. Here, restrictions deny women incumbents to chair a committee in line with descriptive statistics on actual committee chairs in the three German speaking countries. Target population are the members of the regional (Bundesland, Kantons) party branches of the major parties in the countries of Austria, Germany and Switzerland. These people are involved in the actual candidate nomination in their countries. They will be contacted through personalized survey links (programmed in Qualtrics) send to their official emails collected from the party websites. The total population may be around 3000 persons. Hypotheses: I expect the following hypotheses: 1) Candidates/incumbents with lower levels of political capital are more likely to be de-selection/non-selected 2) Incumbents who have a more unique profile among the 3 incumbents, are less likely to be de-selected. 3) Incumbents that resemble strongly a newcomer/replacement candidate are more likely to be de-selected. 4) Incumbents with higher tenure are more likely to be deselected 5) Incumbents are more likely to be replaced by candidates from the same local party branch (rural/urban) 6) Incumbents are more likely to be replaced by candidates with the same policy expertise. Variables In the gender quota scenarios, the following dimensions and attribute values for the candidates are used: For incumbents: 1) "Age" : ["in his/her 20s","in his/her 40s","in his/her 60s"], 2) "Tenure" : ["1st term","2nd term","3rd term"], 3) "Policy field" : ["environment","tax","health","foreign","domestic","agricultural"], 4) "Political capital" : ["committee chair","spokesperson","none"], 5) Party Branch : ["urban chapter","rural chapter"]}; Restrictions are: 1) [["Age","in his/her 20s"],["Political capital","committee chair"]], 2) [["Age","in his/her 20s"],["Tenure","3rd term"]] Potential replacement in the same scenario: 1) "Age" : ["in his/her 20s","in his/her 40s","in his/her 60s"] 2) "Tenure" : ["1st term","2nd term","none"] 3) "Policy field" : ["environment","tax","health","foreign","domestic","agricultural"] 4) "Political capital" : ["committee chair","spokesperson","none"] 5) Party Branch : ["urban chapter","rural chapter"] Restrictions are: 1) [["Age","in his/her 20s"],["Political capital","committee chair"]] 2) [["Tenure","none"],["Political capital","committee chair"]] 3) [["Tenure","none"],["Political capital","spokesperson"]] In the newcomer quota scenario, the incumbents can have the following values: "Age" : ["in his/her 50s"," in his/her 40s ","in his/her 60s "] "Tenure" : ["4th term","2nd term","3rd term"] "Policy field" : ["environment","tax","health","foreign","domestic","agricultural"] "Political capital" : ["committee chair","spokesperson","none"] "Gender" : ["Man","Woman"] "Party Branch" : ["urban chapter","rural chapter"] Restrictions are: [ ["Political capital","Committee Chair"], ["Gender","Woman"] ] this is based on contemporary data on the share of female committee chairs in all three countries, ranging from a high of 42% to a low of 24% Replacement can have the following values: "Gender" : ["Man","Woman"] "Policy field" : ["environment","tax","health","foreign","domestic","agricultural"] "Political capital" : ["no office","youth party leadership","member in regional party leadership"] "Age" : ["in his/her 20s","early 30s"] "Party Branch" : ["urban chapter","rural chapter"] No restrictions here.