Abstract
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.
Keywords
Ecological inference; measurement error; omitted variable bias; temporal dynamics.
