Best of both worlds? Independent lists and voter turnout in local elections

Tavares, António, Raudla, Ringa and Silva, Tiago, (2020). Best of both worlds? Independent lists and voter turnout in local elections. Journal of Urban Affairs, 42(7), 955-974

Document type:

  • Sub-type Journal article
    Author Tavares, António
    Raudla, Ringa
    Silva, Tiago
    Title Best of both worlds? Independent lists and voter turnout in local elections
    Appearing in Journal of Urban Affairs
    Volume 42
    Issue No. 7
    Publication Date 2020
    Place of Publication Milton Park
    Publisher Taylor & Francis
    Start page 955
    End page 974
    Language eng
    Abstract How does the presence of independent lists influence voter turnout in municipal and sub-municipal elections? Despite the persistence of independent lists in local elections of European countries, this question has remained underexplored. Our paper examines the influence of independent lists on voter turnout both theoretically and empirically. In the theoretical discussion, we outline two competing hypotheses. On one hand, the best of both worlds hypothesis predicts that owing to increased choice for the voters, the presence of nonpartisan lists would increase voter turnout. On the other hand, the competing hypothesis suggests the opposite due to higher information costs associated with independent lists. We test our hypotheses using data from four election cycles of Portuguese municipal and sub-municipal levels of government. Since 2001, Portugal’s electoral law allows the participation of nonpartisan lists of candidates in local elections. The empirical analysis employs fractional probit and beta regression models and finds strong support for the best of both worlds hypothesis, both at the municipal and the sub-municipal levels.
    Keyword research line people
    research line governance
    Copyright Holder Taylor & Francis
    Copyright Year 2020
    Copyright type All rights reserved
    DOI 10.1080/07352166.2019.1623682
  • Versions
    Version Filter Type
  • Citation counts
    Google Scholar Search Google Scholar
    Access Statistics: 598 Abstract Views  -  Detailed Statistics
    Created: Thu, 11 Feb 2021, 03:05:28 JST by Mario Peixoto on behalf of UNU EGOV