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 7 million families who move across commuting zones and counties in the United States. 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 use anonymized admissions data from several colleges linked to income tax records and SAT and ACT test scores to study the determinants and causal effects of attending Ivy-Plus colleges (Ivy League, Stanford, MIT, Duke, and Chicago). Children from families in the top 1% are more than twice as likely to attend an Ivy-Plus college as those from middle-class families with comparable SAT/ACT scores. Two-thirds of this gap is due to higher admission rates for students with comparable test scores from high-income families; the remaining third is due to differences in rates of application and matriculation. In contrast, children from high-income families have no admissions advantage at flagship public colleges. The high-income admissions advantage at Ivy-Plus colleges is driven by three factors: (i) preferences for children of alumni, (ii) weight placed on nonacademic credentials, and (iii) athletic recruitment. Using a new research design that isolates idiosyncratic variation in admissions decisions for waitlisted applicants, we show that attending an Ivy-Plus college instead of the average flagship public college increases students’ chances of reaching the top 1% of the earnings distribution by 50%, nearly doubles their chances of attending an elite graduate school, and almost triples their chances of working at a prestigious firm. The three factors that give children from high-income families an admissions advantage are uncorrelated or negatively correlated with postcollege outcomes, whereas academic credentials such as SAT/ACT scores are highly predictive of postcollege success.

We show that intergenerational mobility changed rapidly by race and class in recent decades in the United States and study the causal mechanisms underlying those changes. Between the 1978 and 1992 birth cohorts, earnings increased for white children from high-income families relative to white children from low-income families, increasing earnings gaps by parental income (class) by 30%. Earnings increased for Black children at all parental income levels, reducing white-Black earnings gaps for children from low-income families by 30%. Class gaps grew and race gaps shrank similarly for nonmonetary outcomes such as educational attainment, standardized test scores, and mortality rates. Using a quasi-experimental design, we show that the divergent trends in economic mobility were caused by differential changes in childhood environments, as proxied by parental employment rates, in local communities defined by race, class, and childhood county. Outcomes improve across birth cohorts for children who grow up in communities with increasing parental employment rates, with larger effects for children who move to such communities at younger ages. Children’s outcomes are most strongly related to the parental employment rates of peers they are more likely to interact with, such as those in their own birth cohort, suggesting that the relationship between children’s outcomes and parental employment rates is mediated by social interaction.

Value-added (VA) models measure agents' productivity based on the outcomes they produce. The utility of VA models for performance evaluation depends on the extent to which VA estimates are biased by selection. One common method of evaluating bias in VA is to test for balance in lagged values of the outcome. We show that such balance tests do not yield robust information about bias in value-added models using Monte Carlo simulations. Even unbiased VA estimates can be correlated with lagged outcomes. More generally, tests using lagged outcomes are uninformative about the degree of bias in misspecified VA models. The source of these results is that VA is itself estimated using historical data, leading to non-transparent correlations between VA and lagged outcomes.

We estimate the impacts of the Earned Income Tax Credit on labor supply using local variation in knowledge about the EITC schedule. We proxy for EITC knowledge in a Zip code with the fraction of individuals who manipulate reported self-employment income to maximize their EITC refund. This measure varies significantly across areas. We exploit changes in EITC eligibility at the birth of a child to estimate labor supply effects. Individuals in high-knowledge areas change wage earnings sharply to obtain larger EITC refunds relative to those in low-knowledge areas. These responses come primarily from intensive-margin earnings increases in the phase-in region. (JEL H23, H24, H31, J22, J23, J31)

Rothstein (2017) successfully replicates Chetty, Friedman, and Rockoff's (2014a, b)—henceforth, CFR's—results using data from North Carolina, but raises concerns about CFR's methods. We show that Rothstein's methodological critiques are invalid by presenting simulations and supplementary empirical evidence which show that (i) his preferred imputation of missing data generates bias; (ii) his “placebo test” rejects valid research designs; and (iii) his method of controlling for covariates yields inconsistent estimates of teachers' long-term effects. Consistent with the conclusions of Bacher-Hicks, Kane, and Staiger (2016) using data from Los Angeles, we conclude that Rothstein's replication study ultimately reinforces CFR's methods and results. (JEL H75, I21, J45)

Are teachers' impacts on students' test scores (value-added) a good measure of their quality? One reason this question has sparked debate is disagreement about whether value-added (VA) measures provide unbiased estimates of teachers' causal impacts on student achievement. We test for bias in VA using previously unobserved parent characteristics and a quasi-experimental design based on changes in teaching staff. Using school district and tax records for more than one million children, we find that VA models which control for a student's prior test scores provide unbiased forecasts of teachers' impacts on student achievement. (JEL H75, I21, J24, J45)

Are teachers' impacts on students' test scores (value-added) a good measure of their quality? This question has sparked debate partly because of a lack of evidence on whether high value-added (VA) teachers improve students' long-term outcomes. Using school district and tax records for more than one million children, we find that students assigned to high-VA teachers are more likely to attend college, earn higher salaries, and are less likely to have children as teenagers. Replacing a teacher whose VA is in the bottom 5 percent with an average teacher would increase the present value of students' lifetime income by approximately $250,000 per classroom. (JEL H75, I21, J24, J45)

We construct publicly available statistics on parents’ incomes and students’ earnings outcomes for each college in the United States using deidentified data from tax records. These statistics reveal that the degree of parental income segregation across colleges is very high, similar to that across neighborhoods. Differences in postcollege earnings between children from low- and high-income families are much smaller among students who attend the same college than across colleges. Colleges with the best earnings outcomes predominantly enroll students from high-income families, although a few mid-tier public colleges have both low parent income levels and high student earnings. Linking these income data to SAT and ACT scores, we simulate how changes in the allocation of students to colleges affect segregation and intergenerational mobility. Equalizing application, admission, and matriculation rates across parental income groups conditional on test scores would reduce segregation substantially, primarily by increasing the representation of middle-class students at more selective colleges. However, it would have little effect on the fraction of low-income students at elite private colleges because there are relatively few students from low-income families with sufficiently high SAT/ACT scores. Differences in parental income distributions across colleges could be eliminated by giving low- and middle-income students a sliding-scale preference in the application and admissions process similar to that implicitly given to legacy students at elite private colleges. Assuming that 80% of observational differences in students’ earnings conditional on test scores, race, and parental income are due to colleges’ causal effects—a strong assumption, but one consistent with prior work—such changes could reduce intergenerational income persistence among college students by about 25%. We conclude that changing how students are allocated to colleges could substantially reduce segregation and increase intergenerational mobility, even without changing colleges’ educational programs.