Paula Cesana

This paper studies intergenerational mobility in the context of Chile, an economy with a significant informal labor market. Using longitudinal data, I document two key empirical facts. First, labor informality is associated with higher income uncertainty. Second, the share of time individuals spend in informal employment is correlated with parental background, suggesting a positive association in labor informality across generations. Moreover, I show most of this association can be explained by substantial intergenerational persistence in education and occupations. I then propose and estimate a model of human capital investment and occupation choice under uncertainty to quantitatively assess the role of the income uncertainty channel in intergenerational mobility, namely, persistence in education and occupations, and in labor informality. In the model, higher income uncertainty reduces parental investment in children’s human capital, limiting access to higher-skilled occupations,which are also less affected by informality. I show that reducing parental income risk in informal employment increases the share of educated individuals and enhances upward mobility. These findings underscore how labor informality can shape future labor market outcomes and perpetuate barriers to intergenerational mobility.

This paper explores how differences in the adoption of task-biased technologies contribute to GDP gaps across countries. We introduce a country-specific measure of task intensity to quantify the relative importance of tasks within occupations, which can be readily applied in quantitative analysis. Using this measure, we show that as GDP increases, the share of routine work declines while cognitive work increases. Moreover, differences in task content within specific occupations explain more than half of the cross-country differences in routine work. We then develop a production framework where technology is task-specific, and occupations are aggregates of tasks, with which we rationalize both optimal task and occupational demands. We use this model to quantitatively assess the differences in task-biased technology adoption across countries and its implications for GDP gaps. Our main counterfactual exercise shows that closing the dispersion in task productivity adoption reduces the average GDP gap relative to the United States by around 25%.