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Global Estimates of Opportunity and Mobility: A Database

  • Francisco H. G. Ferreira
  • , Vito Peragine
  • , Paolo Brunori
  • , Pedro Salas-Rojo
  • , Domenico Moramarco
  • , Luis Barajas
  • , Teresa Barbieri
  • , Vito De Sandi
  • , Nancy Daza-Baez
  • , Gaurav Datt
  • , Vito De Sandi
  • , Fabio Farella
  • , Arturo Martinez Jr
  • , John Nguyen
  • , Albert Park
  • , Enza Simeone
  • , Luis Sirugue
  • , Pedro Torres-Lopez
  • , Giorgia Zotti

Research output: Working paper

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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.
Original languageEnglish
Number of pages51
Publication statusPublished - Feb 2026

Publication series

NameIZA@LISER Discussion Paper Series
PublisherIZA@LISER Network
No.18367
ISSN (Electronic)2365-9793

Keywords

  • inequality of opportunity
  • mobility
  • machine learning

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