Restoring the theoretical foundation of John Roemer’s conceptualization of inequality of opportunity (IOp), we introduce an innovative empirical approach to measure unfair inequalities through Bayesian networks. This methodology enhances our understanding of income inequality through structural learning algorithms, generating an IOp index and, most importantly, shedding light on the underlying income formation process. We demonstrate how this proposal relates to established measurement methods through simulated data, and provide an application to five European countries to illustrate the potential of Bayesian networks in the context of measuring inequality of opportunity.

The network of injustice: A novel approach to inequality of opportunity

Lorenzo Giammei;
2025

Abstract

Restoring the theoretical foundation of John Roemer’s conceptualization of inequality of opportunity (IOp), we introduce an innovative empirical approach to measure unfair inequalities through Bayesian networks. This methodology enhances our understanding of income inequality through structural learning algorithms, generating an IOp index and, most importantly, shedding light on the underlying income formation process. We demonstrate how this proposal relates to established measurement methods through simulated data, and provide an application to five European countries to illustrate the potential of Bayesian networks in the context of measuring inequality of opportunity.
2025
Istituto di Ricerca sulla Crescita Economica Sostenibile - IRCrES - Sede Secondaria Roma
Inequality of opportunity, Bayesian networks, EU-SILC
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/536300
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