Conteúdo principal Menu principal Rodapé
CEA
Artigos CEA

Gender and Racial Disparities in the Earnings Effects of Remote Work Before and During the COVID-19 Pandemic: The Case of Brazil

desigualdade genero e raca

This research partnership between Alexandre Gori Maia (CEA/IE) and Yao Lu (Columbia University), funded by the Inter-American Development Bank (IDB), investigated how the earnings penalty associated with working from home (WFH) varied by gender and race before and during the COVID-19 pandemic in Brazil. Using a longitudinal dataset representative of the Brazilian population, the study estimated changes in earnings among workers who shifted from office-based work to WFH, and vice versa. The findings show that, during the pandemic, the earnings penalty associated with WFH declined for White and Black men but remained high for White and Black women. The study identifies three mechanisms that help explain these changes. First, more women than men transitioned to WFH during the pandemic, changing the balance between supply and demand for remote work. Second, WFH affected labor productivity differently across groups, especially by reducing the effective working hours of Black women. Third, remote work may have reduced workplace visibility and promotion opportunities, as White women WFH became less likely than White men to be promoted during the pandemic. The main policy implication is that measures to encourage remote work may unintentionally worsen gender and racial pay inequalities if they are not accompanied by broader changes in the social and labor-market norms that generate the earnings penalty associated with WFH.

Pesquisa financiada pelo Banco Interamericano de Desenvolvimento mostra como o trabalho remoto pode ampliar desigualdades no mercado de trabalho

25 out 24

Can the Content of Social Networks Explain Epidemic Outbreaks?

People share and seek information online that reflects a variety of social phenomena, including concerns about health conditions. We analyze how the contents of social networks provide real-time information to monitor and anticipate policies aimed at controlling or mitigating public health outbreaks. In November 2020, we collected tweets on the COVID-19 pandemic with content ranging from safety measures, vaccination, health, to politics. We then tested different specifications of spatial econometrics models to relate the frequency of selected keywords with administrative data on COVID-19 cases and deaths. Our results highlight how mentions of selected keywords can significantly explain future COVID-19 cases and deaths in one locality. We discuss two main mechanisms potentially explaining the links we find between Twitter contents and COVID-19 diffusion: risk perception and health behavior. (AU)
Ir para o topo