Replication Data for: Running Towards Rankings: Ranked Choice Voting's Impact on Candidate Entry and Descriptive Representation (doi:10.7910/DVN/HQQJUK)

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Part 1: Document Description
Part 2: Study Description
Part 5: Other Study-Related Materials
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Replication Data for: Running Towards Rankings: Ranked Choice Voting's Impact on Candidate Entry and Descriptive Representation

doi:10.7910/DVN/HQQJUK

Harvard Dataverse

2024-08-09

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Colner, Jonathan, 2024, "Replication Data for: Running Towards Rankings: Ranked Choice Voting's Impact on Candidate Entry and Descriptive Representation", https://doi.org/10.7910/DVN/HQQJUK, Harvard Dataverse, V1

Replication Data for: Running Towards Rankings: Ranked Choice Voting's Impact on Candidate Entry and Descriptive Representation

doi:10.7910/DVN/HQQJUK

Colner, Jonathan (University of California, Davis)

Jonathan Colner

Harvard Dataverse

Colner, Jonathan

Colner, Jonathan

2024-04-18

https://doi.org/10.7910/DVN/HQQJUK

Social Sciences, Ranked Choice Voting, Candidate Entry, Difference-in-Differences

Does the implementation of a Ranked Choice Voting System increase the number, diversity, and quality of candidates competing in local elections? Using original data from 273 cities across three decades and employing a pre-registered difference-in-differences design with matching, I find that the size of the candidate pool increases following implementation. However, this effect dissipates in later election cycles, indicating that RCV has no long-term effect on candidate entry. Indeed, the short-term increase in the candidate pool mostly reflects increased entry by low-quality candidates with little chance of winning. Additionally, I find that RCV has no effect on the proportion of female and non-white candidates running for office. These results call into question several purported benefits of RCV, and suggest that RCV, by itself, might not be sufficient to influence candidate entry at the local level.

This dataset underwent an independent verification process, complying with the AJPS Verification Policy updated June 2023, which replicated the tables and figures in the primary article. For the supplementary materials, verification was performed solely for the successful execution of the code. The verification process was carried out by the Cornell Center for Social Sciences at Cornell University. <br></br> The associated article has been awarded the Open Materials Badge. Learn more about the Open Practice Badges from the <a href="https://www.cos.io/">Center for Open Science</a>. <br></br> <img src="https://socialsciences.cornell.edu/sites/default/files/2024-04/materials_large_color.png" alt="Open Materials Badge " width="60" height="60"> <br></br> Open Materials Badge

U.S. Census Bureau’s 2010 – 2020 American Community Survey 5-year estimates. https://www.census.gov/data/developers/data-sets/acs-5year.html <br></br> International City/County Management Association. Municipal Form of Government 2018: Full Dataset. Washington, DC: ICMA, 2019. (Accessed June, 2021). http://icma.org.

The <i>American Journal of Political Science</i> and the Cornell Center for Social Sciences are not responsible for the accuracy or quality of data uploaded within the <i>AJPS</i> Dataverse, for the use of those data, or for interpretations or conclusions based on their use.

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24 Replication Code.R

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Includes all code needed to replicate analyses and figures from the manuscript and the appendix.

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cc_data4.csv

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Contains data needed to run analyses and generate figures for the city council data.

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Codebook for cc_data4 and mayordata4.pdf

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List of variables contained in cc_data4.csv and mayordata4.csv with descriptions.

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Codebook for matches.pdf

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List of variables contained in matches.csv with descriptions.

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Codebook for merge_for_match2.pdf

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List of variables contained in merge_for_match2.csv with descriptions.

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Codebook for PV1.pdf

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List of variables contained in PV1.csv with descriptions.

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Codebook for RCVinfo.pdf

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List of variables contained in RCVinfo.csv with descriptions.

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Codebook for SE_final.pdf

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List of variables contained in SE_final.csv with descriptions.

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matches.csv

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Contains the unique ids for each treatment city and the control cities matched to those cities using the four matching strategies.

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mayordata4.csv

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Contains data needed to run analyses and generate figures for the mayoral data.

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merge_for_match2.csv

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Contains data needed to carry out the balance tests in Table 1 and Appendix Tables 2 and 3. This data was collected using the Census API to access the ACS 5-year estimates.

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PV1.csv

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Contains data needed to generate Figure 1.

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RCVinfo.csv

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Contains hand collected information on RCV cities in my Dataset.

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readme.txt

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Contains a short description of the sources for the analysis datasets, a list of the files contained in this data repository, and a short description of what each of those files contains.

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SE_final.csv

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Contained data needed for Appendix Table 10, which duplicates the main analyses using State Senate elections data as the control group.

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