JOURNAL ARTICLE
Turnout Decline, Mobilization, and Third-Party Surge: The 2020 Puerto Rico General Election.
Published In: Centro Journal, 2024, v. 36, n. 3. P. 37 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: CUEVAS-MOLINA, IVELISSE; CÁMARA-FUERTES, LUIS RAÚL; NEGRÓN-TORRES, GABRIEL A.; MARTÍNEZ-AMADOR, MANUEL B. 3 of 3
Abstract
The article analyzes the 2020 Puerto Rico General Election, focusing on the decline in voter turnout, the surge of third-party candidates, and the weakening dominance of the two main parties—the Partido Nuevo Progresista (New Progressive Party, PNP) and the Partido Popular Democrático (Popular Democratic Party, PPD). Using aggregate municipal-level data from the US Census and the Puerto Rico State Electoral Commission, the authors find that an enthusiasm gap existed: supporters of third parties (Partido Independentista Puertorriqueño, Puerto Rican Independence Party, PIP; Movimiento Victoria Ciudadana, Citizen's Victory Movement, MVC; and Proyecto Dignidad, Dignity Project, PD) were more motivated to vote than those of the PNP and PPD. Party mobilization efforts and self-mobilization driven by a concurrent status plebiscite on statehood were key factors influencing turnout and, consequently, the gubernatorial vote distribution. The study also highlights the limited but notable influence of demographic variables, municipal-level corruption reports, and religious factors on voting patterns, while emphasizing that turnout variations largely explained the electoral success of third-party candidates in municipalities with lower participation.
Additional Information
- Source:Centro Journal. 2024/10, Vol. 36, Issue 3, p37
- Document Type:Article
- Subject Area:Politics and Government
- Publication Date:2024
- ISSN:1538-6279
- Accession Number:183140035
- Copyright Statement:Copyright of Centro Journal is the property of Centro de Estudios Puertorriquenos (Center for Puerto Rican Studies) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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