Global Estimates of Opportunity and Mobility: A Database

Francisco Ferreira (), Vito Peragine (), Paolo Brunori (), Pedro Salas-Rojo (), Domenico Moramarco, Luis Barajas, Teresa Barbieri, Nancy Daza-Baez, Gaurav Datt, Vito de Sandi, Fabio Farella, Arturo Martinez , John Nguyen, Albert Park (), Enza Simeone, Louis Sirugue, Pedro Torres-Lopez and Giorgia Zotti
Additional contact information
Francisco Ferreira: London School of Economics
Vito Peragine: University of Bari
Paolo Brunori: London School of Economics
Pedro Salas-Rojo: CUNEF
Domenico Moramarco: University of Bari
Luis Barajas: Secretaria de Educacion de Bogota
Teresa Barbieri: University of Bari
Nancy Daza-Baez: University College London
Gaurav Datt: Monash University
Vito de Sandi: LISER
Fabio Farella: University of Bari
Arturo Martinez : Asian Development Bank
John Nguyen: Monash University
Albert Park: Hong Kong University of Science & Technology
Enza Simeone: University of Bari
Louis Sirugue: London School of Economics
Pedro Torres-Lopez: London School of Economics
Giorgia Zotti: University of Bari

No 18367, IZA Discussion Papers from IZA Network @ LISER

Abstract: This paper describes a new public-access online database containing internationally comparable estimates of inequality of opportunity for seventy-two countries, covering two-thirds of the world’s population. The estimates were computed directly from the unit-record microdata for 196 household surveys, using a suite of machine-learning tools selected to minimize the omitted variable and overfitting biases discussed in the literature. Overall, differences in opportunities account for substantial shares of total income inequality (with the mean of our preferred estimate being 40.9%), but there is substantial variation across countries, with estimates ranging from 18.9% in Denmark (2011) to 76.7% in South Africa (2017). The latest US estimate of 41.6% places it among the most opportunity unequal high-income countries. We also find strong support for the existence of a positive association between income inequality and relative inequality of opportunity, analogous to the “Great Gatsby Curve†for mobility and inequality. Similarly, there is evidence of an inverted-U “Opportunity Kuznets curve†. The database is available at www.geom.ecineq.org.

Keywords: inequality of opportunity; mobility; machine learning (search for similar items in EconPapers)
JEL-codes: D31 D63 I39 (search for similar items in EconPapers)
Date: 2026-02
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