Research

Published Articles

This paper provides the first estimates of intergenerational earnings mobility in Chile using administrative data linking parents’ and children’s earnings from the formal private sector. We calculate mobility measures across the earnings distribution, revealing high mobility in the bottom 80% and 65% of the parents’ and children’s distribution, respectively. However, we observe significant persistence in the upper tail of the earnings distribution. Additionally, we identify notable gender heterogeneities in these mobility patterns. Specifically, the intergenerational mobility gender gap shows a nonlinear relationship with respect to parental earnings. Furthermore, we find that differences in mobility between the upper tail of the earnings distribution and the rest of the population are more pronounced for daughters than for sons. These findings suggest that the dynamics of gender-based mobility at the upper tail of the earnings distribution differ from those observed in the rest of the population.


This paper revisits the Two-Sample Two-Stage Least Squares (TSTSLS) method, which is commonly used to estimate intergenerational mobility in the absence of parental earnings data. First, we decompose the TSTSLS intergenerational earnings elasticity (IGE) into the linked administrative data estimate, a projection bias, and a variance bias. We propose a parsimonious imputation procedure to eliminate the variance bias in the IGE and show that, under plausible conditions, the corrected TSTSLS IGE estimate provides a lower bound for the linked administrative IGE. Furthermore, we demonstrate that the uncorrected rank-rank correlation estimated through TSTSLS only exhibits projection bias, thus providing a lower bound to the linked administrative rank-rank correlation. Second, we use administrative data from a developing country to test our lower bound methodology through an Empirical Monte Carlo approach, confirming its validity. These estimates suggest that the following practices should be implemented when the TSTSLS method is used to estimate intergenerational mobility: i) report the variance bias corrected-IGE; and ii) report the results of the rank-rank correlation estimated through TSTSLS. Our empirical results shows that both estimates provide a lower bound for the linked administrative IGE and rank-rank correlation, respectively.

This paper studies the relationship between intergenerational economic persistence and preferences for the provision of public goods. Specifically, we develop a simple theoretical model in which a public good is financed through proportional taxation, and that predicts a lower provision of public goods given an increase in the intergenerational earnings elasticity (IGE), which is widely recognized as a measure of the degree of economic persistence from one generation to the next in society. We test this model empirically using the results of the 2020 Chilean national plebiscite, which asked for the replacement of the standing constitution by a new one that would potentially expand the role of the state in the provision of public goods. Our estimates suggest the existence of a positive association between the IGE and the share of the vote against a new constitution, even after controlling for median income and income inequality.  These findings are consistent with our model and suggest that sectors of society that exhibit higher degrees of economic persistence also show greater reluctance towards redistributive policies that increase public goods provision. 

We estimate spatially disaggregated measures of intergenerational mobility in Chile through an administrative dataset linking children's and their parents' earnings from the formal private labour sector. We report remarkable heterogeneity as we find higher and lower upward mobility in mining and agricultural regions, respectively, corroborating Connolly et al., (2019) with the distinction that Chile is a unitary state, implying that factors other than institutional differences shape mobility. 

This study uses climate data 155 countries with a period spanning 46 years (1970-2016). It adopts a statistical and econometric approach, instead of climate models to assess the factors that have contributed to the increase in the frequency of itense flood and storm events. The findings show that in addition to socioeconomic factors, the continuos increase in the atmospheric CO2 concentration during the pas four decades is significantly correlated with the increase in the number of extreme flood and storm events. Moreover, the results show that global climate conditions significantly affect the frequency of these disasters.


I study the impact of a pension reform that changes the requirements for government transfers. Using a unique database that links individual administrative records with a representative longitudinal survey, I estimate the causal impacts using a difference-in-differences approach. I find that some people do not behave as predicted by a full-information standard model because individuals underestimate pension wealth and overestimate monthly contributions and contribution rates. Thus, people lacking pension knowledge behave according to their perceived incentives, which might be incorrect. A model in the presence of sources of error about the pension rationalizes the results.


We show theoretically and empirically that, contrary to what the literature has established, the differences between the Two-Sample Two-Stage Least Squares (TSTSLS) Intergenerational Earnings Elasticity (IGE) estimate and the OLS IGE estimate are purely due to a deficient prediction of missing parental earnings and not because of endogenous instruments. Indeed, the use of a valid instrument does not guarantee equality between both methods nor improve bias. In addition, we perform an empirical Monte Carlo exercise with administrative data for Chile linking children's and parents' earnings, which shows that improving the first stage prediction of the TSTSLS procedure reduces the difference between the IGE estimated using TSTSLS and linked administrative data. 

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