Gender, electoral incentives, and crisis response: Evidence from Brazilian mayors
with Clemence Tricaud
While there is evidence of gender differences in policy preferences and electoral strategic behaviors, less is known about how these differences play out during crises. We use a close election RD design to compare the performance of female- and male-led Brazilian municipalities during the COVID-19 pandemic. We find that having a female mayor led to more deaths per capita early in the first wave of the pandemic — a period characterized by great uncertainty about the severity of the disease and the effectiveness of containment policies. In contrast, having a female mayor led to fewer deaths per capita early in the second wave — a period where this uncertainty was reduced, and when the 2020 mayoral election took place. Consistent with the evolution of deaths, we find that female mayors were less likely to implement commerce restrictions at the beginning of the period, while they became more likely to do so at the end. We also show that the second-wave effect coincides with a lower tendency of the population in male-led municipalities to stay at home around election day. Both the first and second wave effects are driven by municipalities whose mayors were not term limited, and thus allowed to run for re-election. These findings suggest that the gender differences we observe stem from female and male mayors reacting differently to electoral incentives. While electorally motivated female mayors were more likely to delay restrictive policies at the beginning, electorally motivated male mayors were more likely to open-up the municipality closer to the election.
Why does COVID-19 affect some cities more than others? Evidence from Brazil
This paper investigates what explains the variation in impacts of COVID-19 across Brazilian cities. I assemble data from over 2,500 cities on COVID-19 cases and deaths, population mobility, and local policy responses. I study how these outcomes correlate with pre-pandemic local characteristics, drawing comparisons with existing US estimates when possible. As in the United States, the connections between city characteristics and outcomes in Brazil can evolve over time, with some early correlations fading as the pandemic entered a second wave. Population density is associated with greater local impact of the disease in both countries. However, in contrast to the US, the pandemic in Brazil took a greater toll in cities with higher income levels — consistent with the fact that higher incomes correlate with greater mobility in Brazil. Socioeconomic vulnerabilities, such as the presence of slums and high residential crowding, correlate with higher death rates per capita. Cities with such vulnerabilities in Brazil suffered higher COVID-19 death rates despite their residents’ greater propensity to stay home. Policy responses do not appear to drive these connections.
Blog Posts
What Can City Governments in Latin America Do to Improve Public Health?
on August 25, 2022
The place where somebody lives matters for their physical well-being. Even within the same country, residents of different cities can have on average better or worse health, partly due to policies their city governments have adopted […]
Why has COVID-19 Affected Some Cities More Than Others?
on December 8, 2021
As we approach the two-years mark since the onset of COVID-19, countries around the world continue to struggle with the health and economic effects of the pandemic, many facing their third, and even their fourth wave of infections. However, within each country, not all areas have been affected with the same intensity […]
Book chapters
Regional Disparities and Urban Segregation
with Julian Messina. Chapter 4 in Busso and Messina (eds.), “The Inequality Crisis: Latin America and the Caribbean at the Crossroads”, IADB, 2020.
Book available in English and Spanish
All countries, developed or underdeveloped, have rich and poor regions. And both types of regions have cities and rural settlements that are themselves characterized by stark differences in income and access to services. Within cities one can observe substantial variations in income, wages, access, and quality of services across neighborhoods and households. This chapter provides a snapshot of the geography of inequality, highlighting subnational differences in Latin American countries. The chapter first characterizes income and wage gaps across major regions of eleven Latin American countries. Average earnings in the country’s richest region can be up to three times higher than in the poorest. A decomposition analysis shows, however, that regional disparities account for only 4 percent of the overall wage inequality in this group of countries, compared with almost 10 percent stemming from cross-country disparities. Most of the wage inequality is explained by intraregional differences. The chapter then looks at spatial inequality at smaller geographic scales, focusing on the region’s largest country. In Brazil, less than 1 percent of total wage inequality is explained by differences among large regions and states, and an additional 2 percent by differences across cities. By way of contrast, differences across neighborhoods account for 9 percent. To shed light on these results, the latter part of this chapter explores recent academic research on possible causes, consequences, and alternative policy responses to spatial inequality within cities.
The Spatial Dimension of Inequality
with Julian Messina. Chapter 4 in Nuguer and Powell (eds.), “Inclusion in Times of Covid-19”, IADB, 2020.
Book available in English and Spanish
The COVID-19 pandemic is having devastating consequences for the livelihoods of Latin Americans, in particular among the poor and vulnerable. The focus of this report is on how to boost inclusive growth—growth that at the same time reduces inequality. While this is always important, the current crisis has brought this agenda to the forefront. But inequality comes in many dimensions: in incomes, in wealth, in access to education and to other services. But less is known about inequality across regions within countries. And yet this is critical to be able to craft effective policies to boost inclusive growth. If inequality across regions is unimportant, then policies to further equality likely should be nationally planned and administrated. If inequality has a regional dimension, then specific policies to assist poorer areas should be part of the policy mix and subregional authorities should likely develop specific policies for their own territories. This chapter discusses the measurement of regional inequality, whether regional inequality in Latin America and the Caribbean is exceptional, whether poorer regions are converging, and how regional inequality contributes to overall inequality.
