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DC Field | Value | Language |
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dc.contributor.advisor | Imai, Kosuke | - |
dc.contributor.author | Simko, Tyler | - |
dc.date.accessioned | 2018-07-30T18:57:38Z | - |
dc.date.available | 2018-07-30T18:57:38Z | - |
dc.date.created | 2018-04-03 | - |
dc.date.issued | 2018-7-30 | - |
dc.identifier.uri | http://arks.princeton.edu/ark:/88435/dsp01kp78gk09f | - |
dc.description.abstract | The vast majority of American elections take place at the local level in low-turnout, low-salience environments yet we know little about how voters select which local candidates to support. The literature is split on whether or not local voters choose candidates for ideological reasons. Previous work has suggested that both ideological and non-ideological messages can influence local voting behavior, but it is not yet established whether voters prioritize certain types of information or if, instead, hearing non-negative information of any kind about a local candidate is enough to generate support in a low-salience election. This study presents the results from a field experiment (N = 5484 voters in 2957 households) which, in partnership with a campaign for local office, enlisted canvassers to present voters with either a message containing non-ideological personal information about the candidate or a policy-rich message outlining the candidate's ideological proposals. I find compelling evidence that voters are persuaded by personal contact with a local campaign but care little about campaign messaging in low-salience environments - that is, in the absence of information about other candidates or the election, voters are equally persuaded by ideological and non-ideological information. This suggests that uncertainty plays a significant role in low-salience elections and that voters may select candidates in these environments on the basis of familiarity and not necessarily ideology. | en_US |
dc.format.mimetype | application/pdf | - |
dc.language.iso | en | en_US |
dc.title | Campaign Messaging and Optimization in Local Elections: Evidence from a Field Experiment | en_US |
dc.type | Princeton University Senior Theses | - |
pu.date.classyear | 2018 | en_US |
pu.department | Politics | en_US |
pu.pdf.coverpage | SeniorThesisCoverPage | - |
pu.contributor.authorid | 960764911 | - |
pu.certificate | Center for Statistics and Machine Learning | en_US |
Appears in Collections: | Politics, 1927-2020 |
Files in This Item:
File | Description | Size | Format | |
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SIMKO-TYLER-THESIS.pdf | 1.03 MB | Adobe PDF | Request a copy |
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