Abstract
Electoral registers provide the definitive record of who can participate in an election, but there is often thought to be considerable variations in their quality cross-nationally. This leads to concerns about eligible voters being de facto disenfranchised on election day; but also ineligible voters or fictitious names appearing on the roll which can enable electoral fraud. In either case, the legitimacy of the election can be questioned. The electoral register is also used for other purposes such as drawing electoral boundaries. This article introduces some common international terminology for electoral register quality and a conceptualisation of the different ways in which an electoral register can be compiled. It then introduces a new global dataset on registration procedures (n = 159). The article hypotheses that automatic voter registration, as well as organisational and structural factors, strongly affects accuracy and completeness. The results show that automatic voter registration increases the completeness of the electoral register and also has a positive impact on accuracy. The organisational performance of the electoral management body was also shown to have positive effects on completeness and accuracy, suggesting an additional means of improving electoral registers beyond the registration model, which also rest in the hands of policy makers.
Original language | English |
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Pages (from-to) | 279-302 |
Number of pages | 24 |
Journal | Representation |
Volume | 60 |
Issue number | 2 |
Early online date | 12 May 2023 |
DOIs | |
Publication status | Published - Aug 2024 |
Keywords
- elections
- electoral integrity
- democracy
- Voter registration
- development
Datasets
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Dataset – Electoral Legislation by Country
Garnett, H. A. (Creator), James, T. (Creator), MacGregor, M. (Creator) & Caal-Lam, S. (Creator), Electoral Integrity Project, 29 Mar 2023
DOI: 10.7910/DVN/TIH5FK, https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/TIH5FK
Dataset
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Perceptions of Electoral Integrity v8.0
Garnett, H. A. (Creator), James, T. (Creator) & MacGregor, M. (Creator), Harvard Dataverse, 1 Jun 2022
DOI: 10.7910/DVN/YSNYXD, https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi%3A10.7910%2FDVN%2FYSNYXD
Dataset