(with Annabelle Fowler and Nicolás Herrera L.)
This paper examines why a larger share of COVID-19 deaths occurs among young and middle-aged adults in developing countries than in high-income countries. Using novel data at the country, city, and patient levels, we investigate the drivers of this gap in terms of the key components of the standard Susceptible-Infected-Recovered framework. We obtain three main results. First, we show that the COVID-19 mortality age gap is not explained by younger susceptible populations in developing countries. Second, we provide indirect evidence that higher infection rates play a role, showing that variables linked to faster COVID-19 spread – such as residential crowding and labor informality – are correlated with younger mortality age profiles across cities. Third, we show that lower recovery rates in developing countries account for nearly all of the higher death shares among young adults, and for almost half of the higher death shares among middle-aged adults. Our evidence suggests that lower recovery rates in developing countries are driven by a higher prevalence of preexisting conditions that have been linked to more severe COVID-19 complications, and by more limited access to hospitals and intensive care units in some countries.
Rural-Urban Migration at High Urbanization Levels (NEW DRAFT!)
with Matias Busso and Nicolás Herrera L. Revise & Resubmit, Regional Science and Urban Economics.
This study assesses the empirical relevance of the Harris-Todaro model at high levels of urbanization — a feature that characterizes an increasing number of developing countries, which were largely rural when the model was created 50 years ago. Using data from Brazil, we compare observed and model-based predictions of the equilibrium urban employment rate of 449 cities and the rural regions that are the historic sources of their migrant populations. We find little support in the data for the most basic version of the model. However, extensions that incorporate labor informality and housing markets have much better empirical traction. Harris-Todaro equilibrium relationships are relatively stronger among workers with primary but no high-school education, and are more frequently found under certain conditions: when cities are relatively larger; and when associated rural areas are closer to the magnet city, and are populated to a greater degree by young adults, who are most likely to migrate.
on December 15, 2020
The risk of dying from Covid-19 is significantly lower for young and middle-aged adults than for the elderly. The fact that Latin America’s population is younger relative to high-income regions would suggest that age-based prioritization of vaccine delivery, targeted confinement, and other […]
on April 29, 2020
As the first wave of COVID-19 infections advances across the globe, more data becomes available that can help us better understand where we are, how we arrived here, and what may be on the horizon. This blog post explores what widely available data can tell us about how the much-cited curve of […]
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.
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.
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.