We construct a publicly available atlas of children’s outcomes in adulthood by Census tract using anonymized longitudinal data covering nearly the entire U.S. population. For each tract, we estimate children’s earnings distributions, incarceration rates, and other outcomes in adulthood by parental income, race, and gender. These estimates allow us to trace the roots of outcomes such as poverty and incarceration back to the neighborhoods in which children grew up. We find that children’s outcomes vary sharply across nearby tracts: for children of parents at the 25th percentile of the income distribution, the standard deviation of mean household income at age 35 is $5,000 across tracts within counties. We illustrate how these tract-level data can provide insight into how neighborhoods shape the development of human capital and support local economic policy using two applications. First, we show that the estimates permit precise targeting of policies to improve economic opportunity by uncovering specific neighborhoods where certain subgroups of children grow up to have poor outcomes. Neighborhoods matter at a very granular level: conditional on characteristics such as poverty rates in a child’s own Census tract, characteristics of tracts that are one mile away have little predictive power for a child’s outcomes. Our historical estimates are informative predictors of outcomes even for children growing up today because neighborhood conditions are relatively stable over time. Second, we show that the observational estimates are highly predictive of neighborhoods’ causal effects, based on a comparison to data from the Moving to Opportunity experiment and a quasi- experimental research design analyzing movers’ outcomes. We then identify high-opportunity neighborhoods that are affordable to low-income families, providing an input into the design of affordable housing policies. Our measures of children’s long-term outcomes are only weakly correlated with traditional proxies for local economic success such as rates of job growth, showing that the conditions that create greater upward mobility are not necessarily the same as those that lead to productive labor markets.
We study the sources of racial and ethnic disparities in income using de-identified longitudinal data covering nearly the entire U.S. population from 1989-2015. We document three sets of results. First, the intergenerational persistence of disparities varies substantially across racial groups. For example, Hispanic Americans are moving up significantly in the income distribution across generations because they have relatively high rates of intergenerational income mobility. In contrast, black Americans have substantially lower rates of upward mobility and higher rates of downward mobility than whites, leading to large income disparities that persist across generations. Conditional on parent income, the black-white income gap is driven entirely by large differences in wages and employment rates between black and white men; there are no such differences between black and white women. Second, differences in family characteristics such as parental marital status, education, and wealth explain very little of the black-white income gap conditional on parent income. Differences in ability also do not explain the patterns of intergenerational mobility we document. Third, the black-white gap persists even among boys who grow up in the same neighborhood. Controlling for parental income, black boys have lower incomes in adulthood than white boys in 99% of Census tracts. Both black and white boys have better outcomes in low-poverty areas, but black-white gaps are larger on average for boys who grow up in such neighborhoods. The few areas in which black-white gaps are relatively small tend to be low-poverty neighborhoods with low levels of racial bias among whites and high rates of father presence among blacks. Black males who move to such neighborhoods earlier in childhood earn more and are less likely to be incarcerated. However, fewer than 5% of black children grow up in such environments. These findings suggest that reducing the black-white income gap will require efforts whose impacts cross neighborhood and class lines and increase upward mobility specifically for black men.
The willingness to pay for insurance captures the value of insurance against only the risk that remains when choices are observed. This paper develops tools to measure the ex-ante expected utility impact of insurance subsidies and mandates when choices are observed after some insurable information is revealed. The approach retains the transparency of using reduced-form willingness to pay and cost curves. But, it requires an additional sufficient statistic: the difference in marginal utilities between insured and uninsured. I provide an estimation approach to estimate this statistics that uses only reduced-form willingness to pay and cost curves, combined with either (i) a measure of risk aversion or (ii) the reduction in variance of out of pocket expenditures generated by insurance. I apply the approach using existing willingness to pay and cost curve estimates from the low-income health insurance exchange in Massachusetts. Ex-ante optimal insurance prices are roughly 30% lower than prices that maximize market surplus. Mandates can increase expected utility despite increasing deadweight loss.
How should we measure economic efficiency? The canonical measure is an unweighted sum of willingnesses to pay. In contrast, this paper provides efficient welfare weights that implement the Kaldor-Hicks tests for efficiency but account for the distortionary cost of taxation. The shape of the income distribution yields bounds on these weights that suggest it is efficient to weight surplus to the poor more than to the rich. Point estimates suggest surplus to the poor should be weighted 1.5-2x more than surplus to the rich. I illustrate how to use these weights to evaluate the efficiency of government policy changes.
We develop a set of frameworks for welfare analysis of Medicaid and apply them to the Oregon Health Insurance Experiment, a Medicaid expansion for low-income, uninsured adults that occurred via random assignment. Across different approaches, we estimate recipient willingness to pay for Medicaid between $0.5 and $1.2 per dollar of the resource cost of providing Medicaid; estimates of the expected transfer Medicaid provides to recipients are relatively stable across approaches, but estimates of its additional value from risk protection are more variable. We also estimate that the resource cost of providing Medicaid to an additional recipient is only 40% of Medicaid's total cost; 60% of Medicaid spending is a transfer to providers of uncompensated care for the low-income uninsured.
How much are low-income individuals willing to pay for health insurance, and what are the implications for insurance markets? Using administrative data from Massachusetts' subsidized insurance exchange, we exploit discontinuities in the subsidy schedule to estimate willingness to pay and costs of insurance among low-income adults. As subsidies decline, insurance take-up falls rapidly, dropping about 25% for each $40 increase in monthly enrollee premiums. Marginal enrollees tend to be lower-cost, indicating adverse selection into insurance. But across the entire distribution we can observe – approximately the bottom 70% of the willingness to pay distribution – enrollees' willingness to pay is always less than half of their own expected costs that they impose on the insurer. As a result, we estimate that take-up will be highly incomplete even with generous subsidies: if enrollee premiums were 25% of insurers' average costs, at most half of potential enrollees would buy insurance; even premiums subsidized to 10% of average costs would still leave at least 20% uninsured. We briefly consider potential explanations for these findings and their normative implications.
We estimate the causal effect of each county in the U.S. on children's incomes in adulthood. We first estimate a fixed effects model that is identified by analyzing families who move across counties with children of different ages. We then use these fixed effect estimates to (a) quantify how much places matter for intergenerational mobility, (b) construct forecasts of the causal effect of growing up in each county that can be used to guide families seeking to move to opportunity, and (c) characterize which types of areas produce better outcomes. For children growing up in low-income families, each year of childhood exposure to a one standard deviation (SD) better county increases income in adulthood by 0.5%. Hence, growing up in a one SD better county from birth increases a child's income by approximately 10%. There is substantial local area variation in children's outcomes: for example, growing up in the western suburbs of Chicago (DuPage County) would increase a given child's income by approximately 30% relative to growing up in Cook County. Areas with less concentrated poverty, less income inequality, better schools, a larger share of two-parent families, and lower crime rates tend to produce better outcomes for children in poor families. Boys' outcomes vary more across areas than girls' outcomes, and boys have especially negative outcomes in highly segregated areas. One-fifth of the black-white income gap can be explained by differences in the counties in which black and white children grow up. Areas that generate better outcomes have higher house prices on average, but our approach uncovers many “opportunity bargains” – places that generate good outcomes but are not very expensive.
We show that the neighborhoods in which children grow up shape their earnings, college attendance rates, and fertility and marriage patterns by studying more than seven million families who move across commuting zones and counties in the U.S. Exploiting variation in the age of children when families move, we find that neighborhoods have significant childhood exposure effects: the outcomes of children whose families move to a better neighborhood – as measured by the outcomes of children already living there – improve linearly in proportion to the amount of time they spend growing up in that area, at a rate of approximately 4% per year of exposure. We distinguish the causal effects of neighborhoods from confounding factors by comparing the outcomes of siblings within families, studying moves triggered by displacement shocks, and exploiting sharp variation in predicted place effects across birth cohorts, genders, and quantiles to implement overidentification tests. The findings show that neighborhoods affect intergenerational mobility primarily through childhood exposure, helping reconcile conflicting results in the prior literature.
We estimate rates of “absolute income mobility” – the fraction of children who earn more than their parents – by combining historical data from Census and CPS cross-sections with panel data for recent birth cohorts from de-identified tax records. Our approach overcomes the key data limitation that has hampered research on trends in intergenerational mobility: the lack of large panel datasets linking parents and children. We find that rates of absolute mobility have fallen from approximately 90% for children born in 1940 to 50% for children born in the 1980s. The result that absolute mobility has fallen sharply over the past half century is robust to the choice of price deflator, the definition of income, and accounting for taxes and transfers. In counterfactual simulations, we find that increasing GDP growth rates alone cannot restore absolute mobility to the rates experienced by children born in the 1940s. In contrast, changing the distribution of growth across income groups to the more equal distribution experienced by the 1940 birth cohort would reverse more than 70% of the decline in mobility. These results imply that reviving the “American Dream” of high rates of absolute mobility would require economic growth that is spread more broadly across the income distribution.
This paper provides evidence that individuals' knowledge about their potential future job loss prevents the existence of a private market for unemployment insurance (UI). Using information contained in subjective probability elicitations, I show privately-traded UI policies would be too adversely selected to be profitable, at any price. Moreover, in response to learning about future unemployment, individuals decrease consumption and spouses are more likely to enter the labor market. From a normative perspective, this suggests existing estimates miss roughly 35% of the social value of UI because it also partially insures against the risk of learning one might lose their job.
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.
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.
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.
We 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.
Across a wide set of nongroup insurance markets, applicants are rejected based on observable, often high-risk, characteristics. This paper argues that private information, held by the potential applicant pool, explains rejections. I formulate this argument by developing and testing a model in which agents may have private information about their risk. I first derive a new no-trade result that theoretically explains how private in- formation could cause rejections. I then develop a new empirical methodology to test whether this no-trade condition can explain rejections. The methodology uses subjec- tive probability elicitations as noisy measures of agents’ beliefs. I apply this approach to three nongroup markets: long-term care, disability, and life insurance. Consistent with the predictions of the theory, in all three settings I find significant amounts of private information held by those who would be rejected; I find generally more private infor- mation for those who would be rejected relative to those who can purchase insurance, and I show it is enough private information to explain a complete absence of trade for those who would be rejected. The results suggest that private information prevents the existence of large segments of these three major insurance markets.
Nathaniel Hendren of Harvard University reviews "Insurance and Behavioral Economics: Improving Decisions in the Most Misunderstood Industry", by Howard C. Kunreuther, Mark V. Pauly, and Stacey McMorrow. The Econlit abstract of this book begins: "Explores the behavior of individuals at risk, insurance industry decision-makers, and policymakers at the local, state, and federal levels involved in the selling, buying, and regulating of insurance. Discusses an introduction to insurance in practice and theory; anomalies and rumors of anomalies; behavior consistent with benchmark models; real-world complications; why people do or do not demand insurance; demand anomalies; descriptive models of insurance supply; anomalies on the supply side; design principles for insurance; strategies for dealing with insurance-related anomalies; innovations in insurance markets through multiyear contracts; publicly provided social insurance; and a framework for prescriptive recommendations. Kunreuther is James G. Dinan Professor of Decision Sciences and Business and Public Policy in the Wharton School and Co-director of the Wharton Risk Management and Decision Processes Center at the University of Pennsylvania. Pauly is Bendheim Professor in the Department of Health Care Management in the Wharton School at the University of Pennsylvania. McMorrow is a research associate in the Health Policy Center at the Urban Institute."