Insper, Brazil, and IZA, Germany
IZA World of Labor role
Instituto Unibanco Professor of Economics at Insper Institute of Education and Research, Brazil
Econometrics, program evaluation, labor economics, empirical political economy, education economics
Positions/functions as a policy advisor
Consultant to: Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP, Brazil); Tribunal de Contas da União (TCU, Brazil); Confederação Nacional das Indústrias (CNI, Brazil); United Nations Development Program (UNDP); World Bank; Fundação Itaú Social (Brazil); OI Futuro (Brazil); SPTrans (Brazil); Hospital Samaritano; Inter-American Development Bank (IADB)
Associate Professor, São Paulo School of Economics FGV, 2008-2015; Assistant Professor, Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio), 2004-2008; Assistant Professor, University of British Columbia, 2003-2006
PhD Economics, University of California Berkeley, 2003
“Decomposition methods in economics.” In: Card, D., and O. Ashenfelter (eds). Handbook of Labor Economics, Vol. 4. Amsterdam: Elsevier, 2011; pp. 1–102.
“Identification and estimation of distributional impacts of interventions using changes in inequality measures.” Journal of Applied Econometrics 31:3 (2016) 457–486 (with C. Pinto).
“Unconditional quantile regressions.” Econometrica 77 (2009): 953–973 (with N. Fortin and T. Lemieux).
“Efficient semiparametric estimation of quantile treatment effects.” Econometrica 75 (2007): 259–276.
“Measurement errors in quantile regression models.” Journal of Econometrics (Forthcoming 2017) (with A. Galvao and S. Song).
Using decomposition methods helps measure both the amount and source of economic discrimination between groupsSergio Pinheiro Firpo, March 2017Differences in wages between men and women, white and black workers, or any two distinct groups are a controversial feature of the labor market, raising concern about discrimination by employers. Decomposition methods shed light on those differences by separating them into: (i) composition effects, which are explained by differences in the distribution of observable variables, e.g. education level; and (ii) structural effects, which are explained by differences in the returns to observable and unobservable variables. Often, a significant structural effect, such as different returns to education, can be indicative of discrimination.MoreLess