Olivieri, Sergio

Global Practice on Poverty, The World Bank
Profile Picture
Author Name Variants
Fields of Specialization
Poverty and growth, Poverty measurement, Distributional impact of shocks, Labor informality, Inequality, Social Protection and Labor
Global Practice on Poverty, The World Bank
Externally Hosted Work
Contact Information
Last updated: July 12, 2023
Sergio Olivieri is an economist in the Poverty Reduction and Equity department of the World Bank, based in Washington, DC.  His main research areas are ex-ante analysis of the distributional impact of macroeconomic shocks, understanding the main channels through which economic growth affects poverty reduction, income distribution and multidimensional poverty. Olivieri has published articles about labor informality, polarization, mobility and inequality issues, most of them focused on Latin-American countries. He has also contributed to research reports on inequality, poverty, social cohesion and macroeconomic shocks. Before joining the Bank, Olivieri worked as a consultant for the Inter-American Development Bank, the United Nation Development Program and the European Commission. He has taught courses on micro-simulation and micro-decomposition techniques for public servants and staff in international organizations around the world. He has also worked as an assistant professor of labor economics in the Department of Economics of Universidad National de La Plata in Buenos Aires, and as a researcher in the university's Center of Distributional, Labor and Social Studies.
Citations 5 Scopus

Publication Search Results

Now showing 1 - 2 of 2
  • Publication
    A Methodology for Updating International Middle-Class Lines for the Latin American and Caribbean Region
    (World Bank, Washington, DC, 2023-05-22) Fernandez, Jaime; Olivieri, Sergio; Sanchez, Diana
    The middle class in Latin America and the Caribbean has been a central focus of policy debates in the region since the COVID-19 pandemic began. To identify and track vulnerable and middle-class populations accurately, it is necessary to update the upper and lower bounds for the middle class using 2017 purchasing power parity exchange rates. This paper contributes with a two-step methodology for updating these thresholds. The method indicates that updating the $13 lower-bound line in 2011 purchasing power parity dollars to 2017 purchasing power parity dollars results in a vulnerability line of $14. The study also finds an upper bound of $81 per person per day in 2017 purchasing power parity, compared with $70 in 2011 purchasing power parity. These thresholds are robust to a variety of assumptions and methodologies. The results of this study indicate that the proportion of the population in Latin America and the Caribbean classified as middle class increased from 36.3 percent in 2011 to 37.2 percent in 2017. However, there were no significant changes in the characteristics of this group.
  • Publication
    Considering Labor Informality in Forecasting Poverty and Inequality: A Microsimulation Model for Latin American and Caribbean Countries
    (World Bank, Washington, DC, 2023-07-12) Montoya, Kelly; Olivieri, Sergio; Braga, Cicero
    Economists have long been interested in measuring the poverty and distributional impacts of macroeconomic projections and shocks. In this sense, microsimulation models have been widely used to estimate the distributional effects since they allow accounting for several transmission channels through which macroeconomic forecasts could impact individuals and households. This paper innovates previous microsimulation methodology by introducing more flexibility in labor earnings, considering intra-sectoral variation according to the formality status, and assessing its effect on forecasting country-level poverty, inequality, and other distributive indicators. The results indicate that the proposed methodology accurately estimates the intensity of poverty in the most immediate years indistinctively of how labor income is simulated. However, allowing for more intra-sectoral variation in labor income leads to more accurate projections in poverty and across the income distribution, with gains in performance in the middle term, especially in atypical years such as 2020.