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Part 1: Document Description
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Citation |
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Title: |
Replication Data for: When do voters see fraud? Evaluating the role of poll supervision on perceptions of integrity. |
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Identification Number: |
doi:10.7910/DVN/EZS44G |
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Distributor: |
Harvard Dataverse |
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Date of Distribution: |
2024-04-22 |
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Version: |
1 |
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Bibliographic Citation: |
Mbozi, Fanisi, 2024, "Replication Data for: When do voters see fraud? Evaluating the role of poll supervision on perceptions of integrity.", https://doi.org/10.7910/DVN/EZS44G, Harvard Dataverse, V1, UNF:6:1TOniBTCf7oUZHD3BB0cLQ== [fileUNF] |
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Citation |
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Title: |
Replication Data for: When do voters see fraud? Evaluating the role of poll supervision on perceptions of integrity. |
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Identification Number: |
doi:10.7910/DVN/EZS44G |
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Authoring Entity: |
Mbozi, Fanisi (Massachusetts Institute of Technology) |
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Distributor: |
Harvard Dataverse |
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Access Authority: |
Mbozi, Fanisi |
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Depositor: |
Mbozi, Fanisi |
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Date of Deposit: |
2023-12-11 |
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Holdings Information: |
https://doi.org/10.7910/DVN/EZS44G |
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Study Scope |
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Keywords: |
Social Sciences, Conjoint, Monitoring, Elections |
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Abstract: |
What shapes voter perceptions of election outcomes? Recent disputes in Malawi and Kenya highlight the vulnerability of local vote counts to accusations of malfeasance, which often generate negative public perceptions of vote reliability. Election monitoring in these countries is thought to crucially affect both the quality of the election and voters’ perceptions of the same. To date, most research on this topic has focused on the effect of non-partisan electoral observers. However, in many countries two other interest groups also monitor the vote counting process: political party agents and government election officials. Does the presence of these actors also affect voter perceptions of election integrity? To answer this question, I conducted a conjoint experiment in Malawi and Kenya in which voters evaluate the reliability of vote counts from hypothetical polling stations where the presence of party agents, non-partisan observers, and election officers is varied. I find that the presence of each of these groups does indeed shape voter perceptions: voters are more likely to view vote counts as reliable when they are co-signed by a party agent, election official, or non-partisan observer. Further, these preferences persist regardless of the voters' own party affiliation or trust in electoral institutions. |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Other Study Description Materials |
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File Description--f7666424 |
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File: Qualtrics_Dictionary_KY.tab |
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Notes: |
UNF:6:bXoV6IPkuaFrZ4itt5gh+Q== |
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File Description--f7666423 |
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File: Qualtrics_Dictionary_MW.tab |
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Notes: |
UNF:6:Nsajemvci6de0oXGKRBbdA== |
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List of Variables: |
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Variables |
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f7666424 Location: |
Variable Format: character Notes: UNF:6:wCMjqsPQ4YaGTK97Hj5ilQ== |
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f7666424 Location: |
Variable Format: character Notes: UNF:6:vtY+K16IjCQnJOCFTBsOog== |
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f7666423 Location: |
Variable Format: character Notes: UNF:6:rWNVZ9MuvaB3xc1tBiuA5w== |
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f7666423 Location: |
Variable Format: character Notes: UNF:6:7HSm9B4TYWr1oWbMwjgy3A== |
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Label: |
AP21_Kenya.csv |
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Text: |
The raw data from the Kenya Qualtrics Survey. This is used in the data cleaning Python notebook to produce part of the 'rep_full_df.csv'. |
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Notes: |
text/csv |
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AP21_Malawi.csv |
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Text: |
This is the raw data from the Malawi Qualtrics survey. It is used in the data cleaning Python notebook to build part of 'rep_full_df.csv'. |
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Notes: |
text/csv |
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Fanisi_AfroBarometer_Survey_Comparison.ipynb |
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Text: |
Qualtrics survey demographics compared to AfroBarometer. It uses the datasets AP21_Kenya and AP21_Malawi. |
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Notes: |
application/x-ipynb+json |
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Main Analyses and Appendicies__v2.R |
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The main analyses (updated replication code with better readability). All plots and outcomes seen in the paper are in this R script. It uses 'rep_full_df.csv' as its main input. |
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Notes: |
type/x-r-syntax |
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Mbozi_Data_Cleaning_Qualtrics_to_Clean_DF.ipynb |
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This is a Jupyter notebook (with discussion, notes and annotated python code) of how I cleaned and reshaped the Qualtrics datasets (AP21_Malawi and AP21_Kenya) and created the final 'rep_full_df.csv'. |
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Notes: |
application/x-ipynb+json |
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Read_Me_When do voters see fraud.docx |
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Text: |
Readme file |
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Notes: |
application/vnd.openxmlformats-officedocument.wordprocessingml.document |
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Label: |
rep_full_df.csv |
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Text: |
This is the cleaned and combined dataset of the Kenya and Malawi data. This is the core dataset of the analysis in the R-Studio replication files. ( It is the final output from the Python data cleaning notebook.) |
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Notes: |
text/csv |
