The Relationship between Ideology and COVID-19 Deaths: What We Know and What We Still Need to Know

Barberia, Lorena; Moreira, Natália de Paula; Carvalho, Rebeca de Jesus; Oliveira, Maria Leticia Claro; Rosa, Isabel Seelaender Costa; Zamudio, Marcela

doi:10.1590/1981-3821202200030001

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Brazilian Political Science Review

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ARTICLE • Braz. political sci. rev. 16 (3) • 2022 • https://doi.org/10.1590/1981-3821202200030001 copy

The Relationship between Ideology and COVID-19 Deaths: What We Know and What We Still Need to Know ^* * The authors would like to thank researchers from the Rede de Pesquisa Solidária em Políticas Públicas e Sociedade and the Observatório COVID-19 Br with special acknowledgement to Luciana Santana, Rafael Lopes, and Paulo Inácio de Knegt López de Prado. We also thank participants of the XI Seminário Discente of the Programa de Pós-Graduação em Ciência Política at the Universidade de São Paulo, as well as four anonymous reviewers. The data and replication instructions for all figures and analyses are available on the Harvard Dataverse except for the data for Figure 09 . The mobility data provided by InLoco is proprietary and the authors are not authorized to distribute it.

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Several recent studies have investigated if support for Jair Bolsonaro in the presidential election of 2018 is positively associated with COVID-19 infections and deaths in Brazil. In these studies, COVID-19 outcomes in 2020 and 2021 are the dependent variables, and votes for Jair Bolsonaro in the 2018 presidential election (as a proxy for ideology) are the key explanatory variable. This article discusses why ecological research designs are difficult to test empirically. We discuss why correlations between vote shares and COVID-19 outcomes using aggregate data can produce biased inferences, and we specifically focus on measurement error, aggregation bias, and spatial and temporal dynamics.

Ecological inference; measurement error; omitted variable bias; temporal dynamics

	Strengths	Limitations
Key explanatory variable: 2018 Bolsonaro’s vote share	Reliable aggregate data.	By employing vote shares for Bolsonaro as a proxy for ideology, authors rely on the fragile notion that voter behavior is not only coherent but also ideologically oriented. The use of vote shares as a proxy for ideology implies that those who voted for Bolsonaro in 2018 were consistently loyal and supportive of his policies during his mandate. The underscoring assumption that support for Bolsonaro remains static amongst voters is not necessarily validated in research. Individuals who voted for Bolsonaro in the 2018 presidential election do not necessarily support the president (with the same intensity), regardless of events and actions. Aggregation assumes that everyone in a municipality supports the president equally, which leads to potential misclassification, especially for the largest municipalities. The link between ideological positioning and levels of compliance with social distancing measures, however, remains unclear and needs to be assessed by researchers. Even when effectively captured, ideology is hardly the only aspect affecting COVID-19 outcomes in terms of cases, hospitalizations, and deaths. Multifaced dynamics involving local infrastructure, public policies, sociodemographic characteristics, etc. should be considered. Considering the multiplicity of relevant factors that might affect and define COVID-19 outcomes, identifying a reliable measure for ideology is an important challenge.
Dependent variable: COVID-19 Outcome	Frequently updated, publicly available data.	Low and heterogeneous testing and notification rates may lead to underestimating the number of COVID-19 cases and deaths over time and space, which could bias results. Data on cases and deaths could be similarly affected by local-level variables, such as available infrastructure and RT-PCR test processing laboratories, among others.
Study design: ecological regression	Feasible, low cost, and convenient.	Difficulty in employing a variable capable of capturing the direct relation between ideology (or support) and COVID-19 outcomes.
	Allows inference at the municipal, state, and country levels.	Cannot be used to make inferences about individual-level associations. Aggregation bias could occur if trends observed at aggregate-level data are assumed to apply to individual data points. Cannot adjust for variation in individual-level relevant factors such as age, gender, race, or behavior.

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[1] Correspondence: Lorena Barberia. E-mail: lorenabarberia@usp.br