I am interested in combining novel machine learning methods and precise experimental approaches to study inequality from both computational and behavioral perspectives.

On the one hand, I analyze inequality from the perspective of inequality of opportunity, distinguishing between factors that generate inequality but are-to some extent-attributable to individual responsibility (“fair” inequality) and those that arise from circumstances beyond the individual’s control (“unfair” inequality). On the other hand, I study how people consider it fair to redistribute outcomes in order to correct such inequalities, once they are aware of the contribution of both types of factors (fair and unfair) to overall inequality.

For the first objective, I use novel machine learning techniques to achieve a more accurate measurement of inequality of opportunity, as well as the relative weight of each contributing factor. For the second objective, I employ experimental methods to elicit distributive preferences in a clean and isolated manner, capturing how individuals perceive fairness in redistribution.

This dual perspective provides valuable insights into the relative importance of different sources of inequality and how fair or unfair they are perceived to be, offering an empirical foundation and support for policies aimed at reducing inequality and promoting social mobility.