Childhood poverty and life expectancy: Evidence from Brazilian human development units

This paper analyzes data from urban Brazil using supervised machine-learning techniques to shed greater light on the role that childhood poverty plays in lifelong health and longevity. By examining a unique dataset collected over a 10-year period from thousands of very small, sub-neighborhood-level geographic areas, I document that child poverty measures have higher predictive power than household income, and other major socioeconomic variables, in forecasting child and adult health outcomes and lifespans. In addition, using a rich dictionary of hundreds of variables and different data-driven specification selections, the machine-learning models reveal that experiencing more severe deprivation in childhood is associated with a decrease of 4 percentage points in the probability of survival to ages 40 and 60. These predictions offer further economic insights on the importance of early life circumstances for human development.