skip to main content


Title: Declared Support and Clientelism

Recent studies of clientelism predominantly focus on how elites use rewards to influence vote choices and turnout. This article shifts attention toward citizens and their choices beyond the ballot box. Under what conditions does clientelism influence citizens’ decisions to express political preferences publicly? When voters can obtain post-election benefits by declaring support for victorious candidates, their choices to display political paraphernalia on their homes or bodies may reflect more than just political preferences. We argue that various factors—such as the size of rewards and punishments, the competitiveness of the election, and whether multiple candidates employ clientelism—affect citizens’ propensity to declare support in response to clientelist inducements. Building on insights from fieldwork, formal analyses reveal how and why such factors can distort patterns of political expression observed during electoral campaigns. We conduct an experiment in Brazil, which predominantly corroborates predictions about declared support and clientelism.

 
more » « less
NSF-PAR ID:
10373605
Author(s) / Creator(s):
 ;  
Publisher / Repository:
SAGE Publications
Date Published:
Journal Name:
Comparative Political Studies
Volume:
55
Issue:
13
ISSN:
0010-4140
Page Range / eLocation ID:
p. 2178-2216
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract

    Choices made by individuals have widespread impacts—for instance, people choose between political candidates to vote for, between social media posts to share, and between brands to purchase—moreover, data on these choices are increasingly abundant.Discrete choice modelsare a key tool for learning individual preferences from such data. Additionally, social factors like conformity and contagion influence individual choice. Traditional methods for incorporating these factors into choice models do not account for the entire social network and require hand-crafted features. To overcome these limitations, we use graph learning to study choice in networked contexts. We identify three ways in which graph learning techniques can be used for discrete choice: learning chooser representations, regularizing choice model parameters, and directly constructing predictions from a network. We design methods in each category and test them on real-world choice datasets, including county-level 2016 US election results and Android app installation and usage data. We show that incorporating social network structure can improve the predictions of the standard econometric choice model, the multinomial logit. We provide evidence that app installations are influenced by social context, but we find no such effect on app usage among the same participants, which instead is habit-driven. In the election data, we highlight the additional insights a discrete choice framework provides over classification or regression, the typical approaches. On synthetic data, we demonstrate the sample complexity benefit of using social information in choice models.

     
    more » « less
  2. Political news is often slanted toward its publisher’s ideology and seeks to influence readers by focusing on selected aspects of contentious social and political issues. We investigate political slants in news and their influence on readers by analyzing election-related news and reader reactions to the news on Twitter. To this end, we collected election-related news from six major US news publishers who covered the 2020 US presidential elections. We computed each publisher’s political slant based on the favorability of its news toward the two major parties’ presidential candidates. We found that the election-related news coverage shows signs of political slant both in news headlines and on Twitter. The difference in news coverage of the two candidates between the left-leaning (LEFT) and right-leaning (RIGHT) news publishers is statistically significant. The effect size is larger for the news on Twitter than for headlines. And, news on Twitter expresses stronger sentiments than the headlines. We identified moral foundations in reader reactions to the news on Twitter based on Moral Foundation Theory. Moral foundations in readers’ reactions to LEFT and RIGHT differ statistically significantly, though the effects are small. Further, these shifts in moral foundations differ across social and political issues. User engagement on Twitter is higher for RIGHT than for LEFT. We posit that an improved understanding of slant and influence can enable better ways to combat online political polarization. 
    more » « less
  3. Abstract

    The extensive data generated on social media platforms allow us to gain insights over trending topics and public opinions. Additionally, it offers a window into user behavior, including their content engagement and news sharing habits. In this study, we analyze the relationship between users’ political ideologies and the news they share during Argentina’s 2019 election period. Our findings reveal that users predominantly share news that aligns with their political beliefs, despite accessing media outlets with diverse political leanings. Moreover, we observe a consistent pattern of users sharing articles related to topics biased to their preferred candidates, highlighting a deeper level of political alignment in online discussions. We believe that this systematic analysis framework can be applied to similar scenarios in different countries, especially those marked by significant political polarization, akin to Argentina.

     
    more » « less
  4. Instant runoff voting (IRV) is an increasingly-popular alternative to traditional plurality voting in which voters submit rankings over the candidates rather than single votes. In practice, elections using IRV often restrict the ballot length, the number of candidates a voter is allowed to rank on their ballot. We theoretically and empirically analyze how ballot length can influence the outcome of an election, given fixed voter preferences. We show that there exist preference profiles over k candidates such that up to k-1 different candidates win at different ballot lengths. We derive exact lower bounds on the number of voters required for such profiles and provide a construction matching the lower bound for unrestricted voter preferences. Additionally, we characterize which sequences of winners are possible over ballot lengths and provide explicit profile constructions achieving any feasible winner sequence. We also examine how classic preference restrictions influence our results—for instance, single-peakedness makes k-1 different winners impossible but still allows at least Ω(√k). Finally, we analyze a collection of 168 real-world elections, where we truncate rankings to simulate shorter ballots. We find that shorter ballots could have changed the outcome in one quarter of these elections. Our results highlight ballot length as a consequential degree of freedom in the design of IRV elections. 
    more » « less
  5. Abstract

    How do voters come to support new political parties? This article contends that new types of locally organized, participant‐based societal organizations—such as neighborhood associations, informal sector unions, and indigenous movements—can play a crucial mediating role in securing electoral support for new parties. Drawing on social identity and self‐categorization theory, I argue that endorsements of new parties by such organizations sway the vote preferences of organization members and people in their larger social networks. A discrete choice experiment, presenting voters in Bolivia with campaign posters, demonstrates that organizational endorsements are highly effective in mobilizing voters, especially when voters face a new party. Endorsements can even counteract policy and ethnic differences between candidates and voters. The findings suggest an important, understudied route to partisan support in new democracies and have important implications for research on political accountability.

     
    more » « less