We show that differences in childhood environments shape gender gaps in adulthood by documenting three facts using population tax records for children born in the 1980s. First, gender gaps in employment rates, earnings, and college attendance vary substantially across the parental income distribution. Notably, the traditional gender gap in employment rates is reversed for children growing up in poor families: boys in families in the bottom quintile of the income distribution are less likely to work than girls. Second, these gender gaps vary substantially across counties and commuting zones in which children grow up. The degree of variation in outcomes across places is largest for boys growing up in poor, single-parent families. Third, the spatial variation in gender gaps is highly correlated with proxies for neighborhood disadvantage. Low-income boys who grow up in high-poverty, high-minority areas work significantly less than girls. These areas also have higher rates of crime, suggesting that boys growing up in concentrated poverty substitute from formal employment to crime. Together, these findings demonstrate that gender gaps in adulthood have roots in childhood, perhaps because childhood disadvantage is especially harmful for boys.
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The Moving to Opportunity (MTO) experiment offered randomly selected families living in high-poverty housing projects housing vouchers to move to lower-poverty neighborhoods. We present new evidence on the impacts of MTO on children's long-term outcomes using administrative data from tax returns. We find that moving to a lower-poverty neighborhood significantly improves college attendance rates and earnings for children who were young (below age 13) when their families moved. These children also live in better neighborhoods themselves as adults and are less likely to become single parents. The treatment effects are substantial: children whose families take up an experimental voucher to move to a lower-poverty area when they are less than 13 years old have an annual income that is $3,477 (31%) higher on average relative to a mean of $11,270 in the control group in their mid-twenties. In contrast, the same moves have, if anything, negative long-term impacts on children who are more than 13 years old when their families move, perhaps because of disruption effects. The gains from moving fall with the age when children move, consistent with recent evidence that the duration of exposure to a better environment during childhood is a key determinant of an individual's long-term outcomes. The findings imply that offering families with young children living in high-poverty housing projects vouchers to move to lower-poverty neighborhoods may reduce the intergenerational persistence of poverty and ultimately generate positive returns for taxpayers.
Paper Slides
This paper illustrates how one can use causal effects of a policy change to measure its welfare impact without decomposing them into income and substitution effects. Often, a single causal effect suffices: the impact on government revenue. Because these responses vary with the policy in question, I term them policy elasticities, to distinguish them from Hicksian and Marshallian elasticities. The model also formally justifies a simple benefit-cost ratio for non-budget neutral policies. Using existing causal estimates, I apply the framework to five policy changes: top income tax rate, EITC generosity, food stamps, job training, and housing vouchers.
Paper SlidesWe present new evidence on trends in intergenerational mobility in the U.S. using administrative earnings records. We find that percentile rank-based measures of intergenerational mobility have remained extremely stable for the 1971-1993 birth cohorts. For children born between 1971 and 1986, we measure intergenerational mobility based on the correlation between parent and child income percentile ranks. For more recent cohorts, we measure mobility as the correlation between a child’s probability of attending college and her parents’ income rank. We also calculate transition probabilities, such as a child’s chances of reaching the top quintile of the income distribution starting from the bottom quintile. Based on all of these measures, we find that children entering the labor market today have the same chances of moving up in the income distribution (relative to their parents) as children born in the 1970s. However, because inequality has risen, the consequences of the “birth lottery” – the parents to whom a child is born – are larger today than in the past.
We use administrative records on the incomes of more than 40 million children and their parents to describe three features of intergenerational mobility in the United States. First, we characterize the joint distribution of parent and child income at the national level. The conditional expectation of child income given parent income is linear in percentile ranks. On average, a 10 percentile increase in parent income is associated with a 3.4 percentile increase in a child's income. Second, intergenerational mobility varies substantially across areas within the U.S. For example, the probability that a child reaches the top quintile of the national income distribution starting from a family in the bottom quintile is 4.4% in Charlotte but 12.9% in San Jose. Third, we explore the factors correlated with upward mobility. High mobility areas have (1) less residential segregation, (2) less income inequality, (3) better primary schools, (4) greater social capital, and (5) greater family stability. While our descriptive analysis does not identify the causal mechanisms that determine upward mobility, the publicly available statistics on
intergenerational mobility developed here can facilitate research on such mechanisms.
Both Akerlof (1970) and Rothschild and Stiglitz (1976) show that insurance markets may “un- ravel”. This memo clarifies the distinction between these two notions of unraveling in the context of a binary loss model of insurance. I show that the two concepts are mutually exclusive occurrences. Moreover, I provide a regularity condition under which the two concepts are exhaustive of the set of possible occurrences in the model. Akerlof unraveling characterizes when there are no gains to trade; Rothschild and Stiglitz unraveling shows that the standard notion of competition (pure strategy Nash equilibrium) is inadequate to describe the workings of insurance markets when there are gains to trade.
This paper analyzes Thailand’s 2001 healthcare reform, “30 Baht”. The program increased funding available to hospitals to care for the poor and reduced copays to 30 Baht (~$0.75). Our estimates suggest the supply-side funding of the program increased healthcare utilization, especially amongst the poor. Moreover, we find significant impacts on infant mortality: prior to 30 Baht poorer provinces had significantly higher infant mortality rates than richer provinces. After 30 Baht this correlation evaporates to zero. The results suggest that increased access to healthcare among the poor can significantly reduce their infant mortality rates.
