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Some Black households live in neighborhoods with lower incomes, as well as higher unemployment rates and lower educational attainment, than their own incomes might suggest, and this may impede their economic mobility. We investigate reasons for the neighborhood sorting patterns we observe and find that differences in financial factors such as income, wealth, or housing costs between Black and white households do not explain racial distributions across neighborhoods. Our findings suggest other factors are at work, including discrimination in the housing market, ongoing racial hostility, or preferences by Black households for the strength of social networks or other neighborhood amenities that some lower-socioeconomic locations provide.

This paper estimates neighborhood effects on adult labor market outcomes using the Moving to Opportunity (MTO) housing mobility experiment. We propose and implement a new strategy for identifying transition-specific effects that exploits identification of the unobserved component of a neighborhood choice model. Estimated local average treatment effects (LATEs) are large, result from moves between the first and second deciles of the national distribution of neighborhood quality, and pertain to a subpopulation of nine percent of program participants.

Medical data are new to the analyses and deliberations of Federal Reserve monetary policymakers, but such data are now of primary importance to policymakers who need to understand the virus’s trajectory to assess economic conditions and address the virus’s impacts on the economy. The number of deaths caused by COVID-19 is one key metric that is often referred to, but as with other COVID metrics, it is a challenge to measure accurately. We discuss the issues involved in measuring COVID-19 deaths and argue that the change in the number of directly observed COVID-19 deaths is the most reliable and timely approach when using deaths to judge the trajectory of the pandemic in the United States, which is critical given the current inconsistencies in testing and limitations of hospitalization data.

Moving to Opportunity (MTO) was a housing mobility program designed to investigate neighborhood effects, the influences of the social and physical environment on human development and well-being. Some of the results from MTO have been interpreted as evidence that neighborhood effects are not as strong as earlier evidence had indicated. This Commentary discusses new research suggesting that neighborhood effects are, on the contrary, as strong and policy relevant as suspected before the experiment. This Commentary also discusses why the interpretation of the MTO data is important: If neighborhood effects drive outcomes, then addressing racial inequality requires concerted efforts beyond ending racial discrimination.

Most studies of the persistent gap in wealth between whites and blacks have investigated the large gap in income earned by the two groups. Those studies generally concluded that the wealth gap was “too big” to be explained by differences in income. We study the issue using a different approach, capturing the dynamics of wealth accumulation over time. We find that the income gap is the primary driver behind the wealth gap and that it is large enough to explain the persistent difference in wealth accumulation. The key policy implication of our work is that policies designed to speed the closing of the racial wealth gap would do well to focus on closing the racial income gap.

The Opportunity Atlas (OA) is an innovative data set that ranks neighborhoods according to children’s adult outcomes in several domains, including income. Conceptually, outcomes offer new evidence about neighborhood effects when measured in isolation from neighborhood sorting. This paper shows that neighborhood sorting contributes to OA estimates. We document cases in which small sample sizes and changes over time can explain disagreements between OA rankings and those based on contemporaneous variables. Our results suggest caution for interpretations of the OA data set at a granular level, particularly for predictions about the outcomes of black children in high-income neighborhoods.

This paper studies how design features influence the success of Housing Mobility Programs (HMPs) in reducing racial segregation. Targeting neighborhoods based on previous residents' outcomes does not allow for targeting race-specific outcomes, generates uncertainty when targeting income-specific outcomes, and generates bias in ranking neighborhoods' effects. Moreover, targeting opportunity bargains based on previous residents' outcomes selects tracts with large disagreements in current and previous residents' outcomes, with such disagreements predicted by sorting since 1990. HMP success is aided by the ability to port vouchers across jurisdictions, access to cars, and relaxing supply constraints, perhaps by targeting lower-ranked neighborhoods. This paper incorporates content from two previous working papers, one under the same title (WP 20-36) and the other circulated as "Neighborhood Sorting Obscures Neighborhood Effects in the Opportunity Atlas" (WP 20-37).

Drug overdoses now account for more deaths in the United States than traffic deaths or suicides, and most of the increase in overdose deaths since 2010 can be attributed to opioids--a class of drugs that includes both prescription pain relievers and illegal narcotics. We look at trends in drug use and overdose deaths to document how the opioid epidemic has evolved over time and to determine whether it could be large enough to impact the labor force.

Why do high-income black households live in neighborhoods with characteristics similar to those of low-income white households? We find that neighborhood sorting by income and race cannot be explained by financial constraints: High-income, high-wealth black households live in similar-quality neighborhoods as low-income, low-wealth white households. We provide evidence that black households sort across neighborhoods according to some non-pecuniary factor(s) correlated with the racial composition of neighborhoods. Black households sorting into black neighborhoods can explain the racial gap in neighborhood quality at all income levels. The supply of high-quality black neighborhoods drives the neighborhood quality of black households.

What drives the dynamics of the racial wealth gap? We answer this question using a dynamic stochastic general equilibrium heterogeneous-agents model. Our calibrated model endogenously produces a racial wealth gap matching that observed in recent decades along with key features of the current cross-sectional distribution of wealth, earnings, intergenerational transfers, and race. Our model predicts that equalizing earnings is by far the most important mechanism for permanently closing the racial wealth gap. One-time wealth transfers have only transitory effects unless they address the racial earnings gap, and return gaps only matter when earnings inequality is reduced.

Despite being eligible for use in any neighborhood, housing choice vouchers tend to be redeemed in low-opportunity neighborhoods. This paper investigates how landlords contribute to this outcome and how they respond to efforts to change it. We leverage a policy change in Washington, DC, that increased voucher rental payments only in high-rent neighborhoods. Using two waves of a correspondence experiment that bracket the policy change, we show that most opportunity landlords screen out prospective voucher tenants, and we detect no change in average screening behavior after a $450 per month increase in voucher payments. In lease-up data, however, enough landlords do respond to increased payments to equalize the flow of voucher tenants into high- vs. low-rent neighborhoods. Using tax data and listings from a website specializing in subsidized housing, we characterize a group of marginal opportunity landlords who respond to higher payments. Marginal opportunity landlords are relatively rare, list their units near market rates, operate on a small scale, and negatively select into the voucher program based on hard-to-observe differences in unit quality.

This paper studies the relationship between local opioid prescription rates and labor market outcomes. We improve the joint measurement of labor market outcomes and prescription rates in the rural areas where nearly 30 percent of the US population lives. We find that increasing the local prescription rate by 10 percent decreases the prime-age employment rate by 0.50 percentage points for men and 0.17 percentage points for women. This effect is larger for white men with less than a BA (0.70 percentage points) and largest for minority men with less than a BA (1.01 percentage points). Geography is an obstacle to giving a causal interpretation to these results, especially since they were estimated in the midst of a large recession and recovery that generated considerable cross-sectional variation in local economic performance. We show that our results are not sensitive to most approaches to controlling for places experiencing either contemporaneous labor market shocks or persistently weak labor market conditions. We also present evidence on reverse causality, finding that a short-term unemployment shock did not increase the share of people abusing prescription opioids. Our estimates imply that prescription opioids can account for 44 percent of the realized national decrease in men’s labor force participation between 2001 and 2015.

Despite being eligible for use in any neighborhood, housing choice vouchers tend to be redeemed in low-opportunity neighborhoods. This paper investigates whether landlord behavior contributes to this outcome by studying the recent expansion of neighborhood-based voucher limits in Washington, DC. We conduct two waves of a correspondence experiment: one before and one after the expansion. Landlords heavily penalize tenants who indicate a desire to pay by voucher. The voucher penalty is larger in high-rent neighborhoods, pushing voucher tenants to low-rent neighborhoods. We find no evidence that indexing rents to small areas affects landlord acceptance of voucher tenants. The data can reject the claim that increasing rent limits by less than $3,000 per month can eliminate the voucher penalty. Neighborhood rent limits do shift lease-up locations toward high-rent neighborhoods in the year after the policy change, an effect that is large relative to the number of voucher households that move but small relative to all voucher tenants.

We investigate the impact that exposure to violence in childhood has on an individual's propensity to engage in risky behaviors later in life and their probability of dying young. We document that black young males in the United States are exposed to much more violence in early childhood than their white counterparts. We also show that exposure to violence has a strong relationship with a host of undesirable later outcomes, and that relationship tends to be the same regardless of race, household income, mother's educational attainment, or family structure.

Why do high-income blacks live in neighborhoods with characteristics similar to those of low-income whites? One plausible explanation is wealth, since homeownership requires some wealth, and black households hold less wealth than white households at all levels of income. We present evidence against this hypothesis by showing that wealth does not predict sorting into neighborhood quality once race and income are taken into account. An alternative explanation is that the scarcity of high-quality black neighborhoods increases the cost of living in a high-quality neighborhood for black households with even weak race preferences. We present evidence in favor of this hypothesis by showing that sorting into neighborhood racial composition is similar across wealth levels conditional on race and income.

This paper quantitatively compares water infrastructure interventions that prioritize equity with those that prioritize efficiency. The community-based model developed by Haiti Outreach (HO) trains communities to operate and maintain wells, and has clear effi ciency gains over the status quo aid model in Haiti that gives communities wells: HO’s wells were 8.7 percentage points more likely to be functioning after one year than similarly-constructed wells managed under the status quo model. Because HO’s model includes user fees, which raise concerns about equity, I quantify the equity-efficiency tradeoff posed by community-based and aid interventions by determining the preferences of a social planner indifferent between these types of water infrastructure interventions. Since HO’s user fees are only 0.6 percent of median income in rural Haiti, under most specifications the efficiency gains of the community-based model outweigh the equity concerns addressed by the aid model.

This paper finds evidence that opioid availability decreases labor force participation while a large labor market shock does not influence the share of opioid abusers. We first identify the effect of availability on participation using the geographic variation in opioid prescription rates. We use a combination of the American Community Survey (ACS) and Centers for Disease Control and Prevention (CDC) county-level prescription data to examine labor market patterns across both rural and metropolitan areas of the United States from 2007 to 2016. Individuals in areas with higher prescription rates are less likely to participate after accounting for standard demographic factors and regional controls. This relationship remains significant for important demographic groups when increasingly strong panel data controls, including a full set of geographic fixed effects and measures of local labor market conditions in 2000, are introduced to the regressions. We also investigate the possibility of reverse causality, using the Great Recession as an instrument to identify the effect of weak labor demand on opioid abuse. The share abusing opioids did not increase after the onset of the Great Recession. The evidence on the frequency of abuse is more ambiguous since the identified increases could be the continuation of a pre-trend.

This paper studies the relationship between local opioid prescription rates and labor market outcomes for prime-age men and women between 2006 and 2016. We estimate the relationship at the most disaggregated level feasible in the American Community Survey in order to provide estimates that include rural areas that have, in some cases, seen particularly high prescription rates. Given the limited time period, it is particularly important to account for geographic variation in both short-term and long-term economic conditions. We estimate three panel models to control for evolving local economic conditions: a difference-in-differences specification, a specification with specific controls for economic conditions, and a model that focuses on a comparison group of place with similar performance in 2000. These modelling approaches find a range of statistically significant and economically substantial results for both prime-age men and women. For example, we find that a 10 percent higher local prescription rate is associated with a decrease in the prime-age labor force participation rate of between 0.15 and 0.47 percentage points for men and between 0.15 and 0.19 percentage points for women, depending on the control strategy. We also estimate effects for narrower demographic groups and find substantially larger estimates for some groups, notably for white and minority men with less than a BA. We also present evidence on reverse causality. We show that a short-term unemployment shock did not increase the share of people misusing prescription opioids and that prescription levels vary substantially within quintiles of longer-term labor market performance. Our estimates are generally robust to estimation within those quintiles of 2000 labor market performance. These results argue against theories of reverse causation that rely on prescriptions rates being higher in labor markets that were already weaker.

This paper estimates the effect that the closure and demolition of roughly 20,000 units of geographically concentrated high-rise public housing had on crime in Chicago. We estimate local effects of closures on crime in the neighborhoods where high-rises stood and in proximate neighborhoods. We also estimate the impact that households displaced from high-rises had on crime in the neighborhoods to which they moved and neighborhoods close to those. Overall, reductions in violent crime in and near the areas where high-rises were demolished greatly outweighed increases in violent crime associated with the arrival of displaced residents in new neighborhoods.

Why has average income grown in some poor neighborhoods over the past 30 years and not in others? We explore that question and find that low-income neighborhoods that experienced large improvements in income over the past three decades tended to be located in large, densely populated metro areas that grew in income and population. Residential sorting—changes in population and demographics within neighborhoods—could help to explain this relationship.

Not only has poverty recently increased in the United States, it has also become more concentrated. This Commentary documents changes in the concentration of poverty in metropolitan areas over the last decade. The analysis shows that the concentration of poverty tends to be highest in northern cities, and that wherever overall poverty or unemployment rates went up the most over the course of the decade, the concentration of poverty tended to increase there as well.

Although the U.S. poverty rate was the same in 2000 as it was in 1970, the geographic distribution of the poor has become more concentrated. A higher concentration of poor in poor neighborhoods is a concern because it may mean the poor are exposed to fewer opportunities that affect their outcomes in life, like employment and income. We show where and how poverty has become more concentrated in the United States, and who is most likely to be affected.

Workers with more education typically earn more than those with less education, and the difference has been growing in recent decades. Not surprisingly, the percentage of the population going after and getting a college degree has been rising as well. Since the late 1970s, though, the increase in college attainment has stalled for men and gathered steam for women. Among college-age individuals, more women now graduate than men. Changes in labor market incentives appear to explain the increased investment in education made by women. But men’s investments in education have been much less responsive to the same incentives.

Researchers suspect that some of the disparities that exist in such outcomes as health, employment, and education might be attributable to inequality of opportunity as determined by neighborhood environments. We study census data to identify neighborhood characteristics in addition to poverty that might help to explain these disparities. We focus on the Moving to Opportunity housing-relocation experiment and show that because program participants typically moved from one predominately black neighborhood to another, their new low-poverty neighborhoods may have provided little to no change in neighborhood quality. These circumstances are helpful in understanding how results from the Moving to Opportunity program should inform views of neighborhood effects.

This paper shows that treatment effects of the Moving to Opportunity (MTO) housing mobility program should not be interpreted as evidence on neighborhood effects. In a standard joint model of potential outcomes and selection into treatment, defining treatment as moving with an MTO voucher generates a model of program effects, while defining treatment as moving to a high-quality neighborhood generates a model of neighborhood effects. I state the assumptions necessary for using the random assignment of vouchers in a housing mobility program as an instrument to identify neighborhood effects. I then show that the literature using program effects to learn about neighborhood effects implicitly imposes dubious versions of these assumptions.

This paper quantitatively characterizes the "code of the street" from the sociology literature, using the nationally-representative National Longitudinal Survey of Youth 1997 data set to investigate how black young males alter their behavior when living in violent neighborhoods. An astounding 26 percent of black males in the United States report seeing someone shot before turning 12. Conditional on reported exposure to violence, black and white young males are equally likely to engage in violent behavior. Black males’ education and labor market outcomes are much worse when reporting exposure to violence; these gaps persist in estimated models controlling for many patterns of selection.

The literature contrasting structural and experimental econometrics highlights that stronger assumptions are required to identify direct effects than total effects. This paper examines the additional assumptions required to predict future experience using either type of causal effect identified in past data.

Black males in the United States are exposed to tremendous violence at young ages: In the NLSY97 26 percent report seeing someone shot by age 12, and 43 percent by age 18. This paper studies how this exposure to violence and its associated social isolation affect education and labor market outcomes. I use Elijah Anderson’s ethnographic research on the “code of the street” to guide the specification of a model of human capital accumulation that includes street capital, the skills and knowledge useful for providing personal security in neighborhoods where it is not provided by state institutions. The model is estimated assuming either selection on observables or dynamic selection with permanent unobserved heterogeneity. Counterfactuals from these estimated models indicate that exposure to violence has large effects, decreasing the high school graduation rate between 6.1 and 10.5 percentage points (20 and 35 percent of the high school dropout rate) and hours worked between 3.0 and 4.0 hours per week (0.15 and 0.19 s).

Kindergarten-entrance-age effects are difficult to identify due to the nonrandom allocation of entrance-age and simultaneous relative-age effects. This paper presents evidence that instrumental variable frameworks do not identify age effects for the youngest children of a cohort using the results of statistical tests for essential heterogeneity in initial enrollment decisions. Restricting attention to the oldest children in a cohort yields a sample with quasirandom variation in entrance and relative ages. This variation is used to identify the parameters of education production functions in which both entrance and relative ages are inputs for achievement. Estimates of entrance-age parameters from the ECLS-K data set are positive, large, and persist until the spring of third grade. Relative-age parameters are smaller, tend to be negative, and fade-out for math achievement by third grade. For the average child in our sample these estimates imply that both an earlier entrance cutoff date and an earlier birthdate will increase achievement if the child remains eligible. There is extreme heterogeneity in effects by gender and home environment, and these results are likely to be the most relevant for policy.