CEA - Comissão de Pesquisa do Instituto de Economia

CEA

desigualdade genero e raca

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

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.

lousa

Capacidade de coordenação em tempos de complexidade: o ativo invisível das organizações

Antônio Márcio Buainain | Pedro Abel Vieira | Maria Alice de Medeiros

Cadeias de valor em bioeconomia

He economic and environmental impacts of Brazilian National Biofuel Polic

The Brazilian National Policy on Biofuels (Renovabio) sets targets to reduce greenhouse gas emissions (GGE) in the transportation mix. Through the policy, a financial asset known as CBIO (a Decarbonization Credit) was created. CBIOs represent the equivalent CO2 emissions prevented when substituting fossil fuels for renewable fuels and are traded on the stock market, which sets the price. Fossil fuel distributors are obliged to buy these assets and biofuel producers can issue the CBIOs on the stock market. A dynamic computable general equilibrium model was applied to estimate the CBIO prices and the annual economic and environmental impacts of RenovaBio from 2021 to 2029. The impacts on fuel and energy consumption, fuel prices, inflation, gross domestic product (GDP) and agricultural productivity considering ethanol, gasoline, biodiesel and diesel were calculated. To achieve the required decarbonization level by 2029, ethanol production must increase by 87.7%, led by a drop in its price relative to gasoline. The total fuel energy consumed would drop by 7.10% and inflation would increase by 0.35 percentage points, while GDP would increase by only 0.08% in the scenario of the last year, when the CBIO price would reach R\$223.35 (similar to US\$74.45). RenovaBio can be an effective policy to promote carbon intensity reduction and works as a hedge against unfavorable scenarios for biofuel production. Therefore, the policy can help Brazil reach its intended nationally determined contribution by boosting the use of biofuels in the country's transportation sector. (C) 2021 Society of Chemical Industry and John Wiley \& Sons, Ltd (AU)

O uso de big data para avaliação da efetividade de intervenções não farmacológicas no enfrentamento de epidemias e seus impactos econômicos

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)