Job Market Paper

  • Locally Robust Policy Learning: Inequality, Inequality of Opportunity and Intergenerational Mobility
    • Abstract: Policy makers need to decide whether to treat or not to treat different individuals. The optimal choice depends on the welfare function that the policy maker has in mind. I study a general setting for policy learning with general semiparametric Social Welfare Functions (SWFs), possibly defined by semiparametric U-statistics, which accomodate a wide range of distributional preferences. I use locally robust/orthogonal scores to provide strong statistical guarantees for the estimated policy rules even in observational settings where the propensity score is unkown. This work expands previous results in Athey and Wager (2021). Three main applications of the general theory motivate the paper: (i) Inequality aware SWFs, (ii) Inequality of Opportunity aware SWFs and (iii) Intergenerational Mobility SWFs. I use the Panel Study of Income Dynamics (PSID) to asses the effect of attending preschool on adult earnings and estimate optimal policy rules based on parental years of education and parental income. Cite.

Books

  • Causal Inference and Machine Learning: A Locally Robust Approach: with Juan Carlos Escanciano, forthcoming (Taylor & Francis Group)

Working Papers

  • Machine Learning Inference on Inequality of Opportunity: with Juan Carlos Escanciano, R&R at Review of Economic Studies.
    • Abstract: Equality of opportunity has emerged as an important ideal of distributive justice. Empirically, Inequality of Opportunity (IOp) is measured in two steps: first, an outcome (e.g., income) is predicted given individual circumstances; and second, an inequality index (e.g., Gini) of the predictions is computed. Machine Learning (ML) methods are tremendously useful in the first step. However, they can cause sizable biases in IOp since the bias-variance trade-off allows the bias to creep in the second step. We propose a simple debiased IOp estimator robust to such ML biases and provide the first valid inferential theory for IOp. We demonstrate improved performance in simulations and report the first unbiased measures of income IOp in Europe. Mother’s education and father’s occupation are the circumstances that explain the most. Plug-in estimators are very sensitive to the ML algorithm, while debiased IOp estimators are robust. These results are extended to a general U-statistics setting. Cite.
  • Educational Inequality of Opportunity and Mobility in Europe
    • Abstract: Educational attainment generates labor market returns, societal gains and has intrinsic value for individuals. We study Inequality of Opportunity (IOp) and intergenerational mobility in the distribution of educational attainment. We propose to use debiased IOp estimators based on the Gini coefficient and the Mean Logarithmic Deviation (MLD) which are robust to machine learning biases. We also measure the effect of each circumstance on IOp, we provide tests to compare IOp in two populations and to test joint significance of a group of circumstances. We find that circumstances explain between 38% and 74% of total educational inequality in European countries. Mother’s education is the most important circumstance in most countries. There is high intergenerational persistence and there is evidence of an educational Great Gatsby curve. We also construct IOp aware educational Great Gatsby curves and find that high income IOp countries are also high educational IOp and less mobile countries. Cite.