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Title Strategic Discrimination
Post date 03/04/2019
C1 Background and Explanation of Rationale When women and people of color run for office, they face two distinct types of discrimination. Donors, party delegates, or voters may be biased against these candidates because of their identities. This is what political scientists typically study when investigating discrimination in politics. But during a primary, candidates must also convince members of their own party that they have the best possible chance of winning the general election. In these conversations, candidates sometimes encounter a second type of discrimination. Even if gatekeepers and primary voters are not themselves be biased against a candidate, they may be concerned that other voters will harbor biases against the candidate, and therefore the candidate will have a hard time winning in the general election. I call this strategic discrimination. Even when party gatekeepers and primary voters value diversity and are not themselves biased, concerns about electability may drive them to strategically prefer white and/or male candidates over women and/or people of color.
C2 What are the hypotheses to be tested?

H1a: When prompted to think strategically about gender and racial discrimination by swing states voters, subjects will increase their support for white, male candidates to be the 2020 Democratic nominee.
H1b: When prompted to think strategically about gender and racial discrimination by swing states voters, subjects will see white, male candidates as more capable of beating Trump in 2020.
H2a: When told that winning over white, rural voters is key to beating Donald Trump in 2020, subjects will increase their support for white candidates to be the 2020 Democratic nominee.
H2b: When told that winning over white, rural voters is key to beating Donald Trump in 2020, subjects will see white candidates as most capable of beating Trump in 2020.
H3a: When told that winning over male voters is key to beating Donald Trump in 2020, subjects will increase their support for male candidates to be the 2020 Democratic nominee.
H3b: When told that winning over male voters is key to beating Donald Trump in 2020, subjects will see male candidates as most capable of beating Trump in 2020.
H4a: When told that high turnout among women voters is key to beating Donald Trump in 2020, subjects will increase their support for female candidates to be the 2020 Democratic nominee.
H4b: When told that high turnout among women voters is key to beating Donald Trump in 2020, subjects will see female candidates as most capable of beating Trump in 2020.
H5a: When told that high turnout among black voters is key to beating Donald Trump in 2020, subjects will increase their support for black candidates to be the 2020 Democratic nominee.
H5b: When told that high turnout among black voters is key to beating Donald Trump in 2020, subjects will see black candidates as most capable of beating Trump in 2020.

C3 How will these hypotheses be tested? *

Please see attached document for further explanation.

H1a Operationalization: Compare dependent variables 2a, 2b, 2d, and 2e across Groups 1 (control) and 2 (treatment).
H1b Operationalization: Compare dependent variables 2a, 2b, 2d, and 2e across Group 1 (control) and Group 2 (treatment).
H2a Operationalization: Compare dependent variables 2b and 2e across Group 1 (control) and Group 3 (treatment).
H2b Operationalization: Compare dependent variables 1b and 1e across Group 1 (control) and Group 3 (treatment).
H3a Operationalization: Compare dependent variables 2a and 2d across Group 1 (control) and Group 6 (treatment).
H3b Operationalization: Compare dependent variables 1a and 1d across Group 1 (control) and Group 6 (treatment).
H4a Operationalization: Compare dependent variables 2a and 2d across Group 1 (control) and Group 5 (treatment).
H4b Operationalization: Compare dependent variables 1a and 1d across Group 1 (control) and Group 5 (treatment).
H5a Operationalization: Compare dependent variable 2c and 2f across Group 1 (control) and Group 4 (treatment).
H5b Operationalization: Compare dependent variables 1c and 1f across Group 1 (control) and Group 4 (treatment).

C4 Country United States
C5 Scale (# of Units) 1,000
C6 Was a power analysis conducted prior to data collection? Yes
C7 Has this research received Insitutional Review Board (IRB) or ethics committee approval? Yes
C8 IRB Number deemed exempt through self-administered worksheet
C9 Date of IRB Approval Jan. 22, 2019
C10 Will the intervention be implemented by the researcher or a third party? Researchers
C11 Did any of the research team receive remuneration from the implementing agency for taking part in this research? No
C12 If relevant, is there an advance agreement with the implementation group that all results can be published? not provided by authors
C13 JEL Classification(s) Z0