Noncarceral conviction is a common outcome of criminal court cases: for every individual incarcerated, there are approximately three who were recently convicted but not sentenced to prison or jail. We extend the binary-treatment judge IV framework to settings with multiple treatments and use it to study the consequences of noncarceral conviction. We outline assumptions under which widely-used 2SLS regressions recover margin-specific treatment effects, relate these assumptions to models of judge decision-making, and derive an expression that provides intuition about the direction and magnitude of asymptotic bias when a key assumption on judge decision-making is not met. We find that noncarceral conviction (relative to dismissal) leads to a large and long-lasting increase in recidivism for felony defendants in Virginia. In contrast, incarceration (relative to noncarceral conviction) leads to a short-run reduction in recidivism, consistent with incapacitation. Our empirical results suggest that noncarceral felony conviction is an important and overlooked driver of recidivism.

"Right-to-counsel" programs provide free legal assistance to tenants in eviction court. Legal assistance can delay or prevent eviction. However, large-scale legal assistance programs can also generate costs for tenants due to equilibrium rental market responses. In this paper, we study how right to counsel impacts rental markets when implemented at scale, and quantify the policy's impact on tenant welfare. Leveraging the geographic rollout of New York City's program, we find listed rent prices rose by $22-$38/month within two years of policy implementation, with larger increases in areas with higher baseline eviction rates. We do not find evidence that landlords adjusted on other margins, such as tenant screening or improvements to habitability. Guided by these results, we develop a framework to evaluate the policy's welfare implications for tenants, incorporating the trade-off between protection from eviction and higher rent prices. We quantify the parameters of our framework using linked data on eviction court cases, rental housing listings, and tenant earnings trajectories. Despite the direct benefits and insurance value of stronger eviction protections, the estimated price increases are large enough to generate a small net reduction in ex-ante tenant welfare.

Eviction may be an important channel for the intergenerational transmission of poverty, and concerns about its effects on children are often raised as a rationale for tenant protection policies. We study how eviction impacts children's home environment, school engagement, educational achievement, and high school completion by assembling new data sets linking eviction court records in Chicago and New York to administrative public school records and restricted Census records. To disentangle the consequences of eviction from the effects of correlated sources of economic distress, we use a research design based on the random assignment of court cases to judges who vary in their leniency. We find that eviction increases children's residential mobility, homelessness, and likelihood of doubling up with grandparents or other adults. Eviction also disrupts school engagement, causing increased absences and school changes. While we find little impact on elementary and middle school test scores, eviction substantially reduces high school course credits. Lastly, we find that eviction reduces high school graduation and use a novel bounding method to show that this finding is not driven by differential attrition. The disruptive effects of eviction appear worse for older children and boys. Our evidence suggests that the impact of eviction on children runs through the disruption to the home environment or school engagement rather than deterioration in school or neighborhood quality, and may be moderated by access to family support networks.

This paper asks whether universal pre-kindergarten (UPK) programs can increase parental earnings and, if so, how much these gains affect the economic returns to UPK. Using admissions lotteries for an extended-day UPK program in New Haven, Connecticut, we find that UPK enrollment increases childcare coverage to span the workday and raises parents' earnings by 21.7% during pre-kindergarten. Gains persist for at least six years. We find little evidence of effects on children's academic and behavioral outcomes during elementary and middle school. Combining these results, we demonstrate that tax revenues from parents' earnings gains reduce the net government costs of UPK by 90% relative to estimates that ignore gains for parents. Overall, we estimate that each dollar spent yields $10 in benefits. Our findings demonstrate the potential of UPK programs that combine quality education with full-day childcare and underscore the importance of thinking about parents when designing and evaluating early-childhood policies.

This paper examines how high school specialization shapes college investment decisions and their subsequent returns through dynamic complementarities. Using Swedish administrative data, we estimate a dynamic Roy model that accounts for selection on multidimensional skills, family background, prior investments, and unobserved heterogeneity. We identify the model using rich skill measures and quasi-experimental variation in program popularity. For marginal students, STEM specialization in high school increases wages by 9%, with more than half this return attributed to dynamic complementarities that enhance the productivity of subsequent college investments. Consequently, we find that counterfactual policies encouraging high school STEM specialization generate twice the returns of equivalent college-level interventions. These findings demonstrate how the timing of specialized human capital investments matters during adolescence, with important implications for education policies that encourage or restrict specialization.

Short-term rental assistance programs expanded to unprecedented scale during the COVID-19 pandemic. We evaluate these programs using applications to five assistance programs that disbursed more than $200 million via lottery. Drawing on administrative and original survey data, we estimate effects on rent payment, housing stability, financial distress, and health. Assistance led to increases in rent payment and reduced tenants' concerns about eviction. We also find suggestive evidence of improvements in self-reported mental and physical health. However, we find little effect on housing stability or financial distress. Relative to Economic Impact Payments, we find that ERA was well-targeted to high-poverty neighborhoods.

Recent research has documented the prevalence and consequences of evictions, but our understanding of underlying drivers of the eviction rate and the scope for policy to affect it remains limited. In this paper, we study landlords' decisions to evict tenants and how these decisions may be influenced by policy. We combine novel lease-level ledger data from low-income rental markets with a model of the landlord's eviction decision to characterize the persistence of shocks to tenant default risk, landlords' information about these shocks, and landlords' cost of eviction. Our data show that nonpayment is common, is frequently tolerated by landlords, and is often followed by recovery, suggesting that landlords face a trade-off between initiating a costly eviction or waiting to learn whether a tenant can continue paying. Our dynamic discrete choice model of the eviction decision captures this tradeoff. Estimates indicate that filing an eviction costs landlords the equivalent of 2-3 months of rent, and that the majority of evictions involve tenants who are unlikely to pay going forward. This implies that uniformly applied policies can generate additional forbearance for tenants, but they do not prevent most evictions. We find that 15% of those evicted would have resumed paying rent, suggesting a role for more targeted interventions. Among the policy instruments we consider, direct financial incentives for landlords—such as taxes and subsidies—are more likely to durably prevent evictions than procedural delays.

Using newly-linked administrative and commercial data from Virginia spanning 25 years, we study the consequences of incarceration. While previous research has examined labor market outcomes and recidivism, we focus on two of the primary channels through which low-income households build wealth: asset ownership (homes and cars) and human capital formation. To identify causal effects, we use a matched differencein-differences design. In line with much of the literature on the impact of incarceration in the U.S., we find no evidence of scarring effects on labor market outcomes or changes in recidivism beyond the incapacitation period. However, we find that incarceration leads to a persistent reduction in asset accumulation: seven years after sentencing, homeownership has declined by 1.1 percentage points (12.1%) and car ownership by 2.7 percentage points (18.1%). Incarceration also lowers human capital formation, reducing college enrollment by 1.4 percentage points (15.1%).

This paper uses population panel data from Sweden to investigate the causes and consequences of self-employment over the life cycle, and to evaluate how self-employment decisions can be influenced by policy. In the first part of the paper, I use machine learning methods to summarize the patterns of self-employment behavior observed in the data. I find that careers involving self-employment fit into a small number of economically distinct groups. Some self-employment spells are short, with minimal capital investment and rapid return to paid employment, while others persist and have substantial capital devoted to the business from the outset. Guided by these descriptive results, I develop and estimate a dynamic Roy model in which self-employment decisions depend on factors such as cognitive and non-cognitive skills, prior work experience, the cost of capital, and other labor market opportunities. The model integrates traditional models of dynamic career choice, which feature human capital investment, with models of business start-up, which feature physical capital investment. I estimate the model and use it to evaluate policies designed to promote self-employment. Cognitive and non-cognitive skills, education, and prior work experience are important determinants of the types of businesses individuals start, how much capital they employ, and how long they remain in self-employment. Subsidies that incentivize self-employment are generally ineffective in promoting long-lasting firms and in improving the welfare and earnings of those induced to enter self-employment.

We study labor market attachment among homeless families using baseline survey data from a large-scale study in Illinois. Of homeless parents, 40 percent worked in the past month. Among those not working, most report wanting and actively searching for a job. When asked about barriers to employment, parents overwhelmingly cite childcare constraints, lack of housing, lack of transportation, and family responsibilities. Health problems or skills deficits are mentioned far less frequently. The nature of these barriers—each associated with upfront investments—suggests that homeless families lack the initial financial resources needed to obtain stable employment.

More than two million U.S. households have an eviction case filed against them each year. Policy makers at the federal, state, and local levels are increasingly pursuing policies to reduce the number of evictions, citing harm to tenants and high public expenditures related to homelessness. We study the consequences of eviction for tenants using newly linked administrative data from two major urban areas: Cook County (which includes Chicago) and New York City. We document that before housing court, tenants experience declines in earnings and employment and increases in financial distress and hospital visits. These pre trends pose a challenge for disentangling correlation and causation. To address this problem, we use an instrumental variables approach based on cases randomly assigned to judges of varying leniency. We find that an eviction order increases homelessness and hospital visits and reduces earnings, durable goods consumption, and access to credit in the first two years. Effects on housing and labor market outcomes are driven by effects for female and Black tenants. In the longer run, eviction increases indebtedness and reduces credit scores.

There is a large gender wage gap among college graduates. This gender gap could be partially driven by differences in college major and prior skills. We use Swedish register data to study how much of the gender gap can be explained by differences in majors, skills, and skill prices. College majors explain 60 percent of the gender wage gap, but large gaps remain within majors. We find that within-major wage gaps are driven by neither differences in multidimensional skills nor returns to these skills. In fact, women are positively selected in terms of college preparation and skills in almost every major.

This paper uses a college-by-graduate degree fixed effects estimator to evaluate the returns to 19 different graduate degrees for men and women. We find substantial variation across degrees, and evidence that OLS overestimates the returns to degrees with high average earnings and underestimates the returns to degrees with low average earnings. Second, we decompose the impacts on earnings into effects on wage rates and effects on hours. For most degrees, the earnings gains come from increased wage rates, though hours play an important role in some degrees, such as medicine, especially for women. Third, we estimate the net present value and internal rate of return for each degree, which account for the time and monetary costs of degrees. We show annual earnings and hours worked while enrolled in graduate school vary a lot by gender and degree. Finally, we provide descriptive evidence that gains in overall job satisfaction and satisfaction with contribution to society vary substantially across degrees.

Leveraging data from Sweden and Chicago, we study the educational pipeline for science, technology, engineering, and mathematics (STEM) and economics majors to better understand the determinants of the gender gap and when these determinants arise. We present three findings. First, females are less likely to select STEM courses in high school despite equal or better preparation. Second, there are important gender differences in preferences and beliefs, even conditional on ability. Third, early differences in preferences and beliefs explain more of the gaps in high school sorting than other candidate variables. High school sorting then explains a large portion of the gender difference in college majors.

This paper studies the effects of the COVID-19 pandemic on small businesses between March and November 2020 using new survey data on 35,000 small businesses in eight Latin American countries. We document that the pandemic had large negative impacts on employment and beliefs regarding the future, which in turn predict meaningful economic outcomes in the medium-term. Despite the unprecedented amount of aid, policies had limited impact for small and informal firms. These firms were less aware of programs, applied less, and received less assistance. This may have lasting consequences, as businesses that received aid reported better outcomes and expectations about the future.

The Paycheck Protection Program (PPP) extended 669 billion dollars of forgivable loans in an unprecedented effort to support small businesses affected by the COVID-19 crisis. This paper provides evidence that information frictions and the "first-come, first-served" design of the PPP program skewed its resources towards larger firms and may have permanently reduced its effectiveness. Using new daily survey data on small businesses in the U.S., we show that the smallest businesses were less aware of the PPP and less likely to apply. If they did apply, the smallest businesses applied later, faced longer processing times, and were less likely to have their application approved. These frictions may have mattered, as businesses that received aid report fewer layoffs, higher employment, and improved expectations about the future.

This paper estimates returns to education using a dynamic model of educational choice that synthesizes approaches in the structural dynamic discrete choice literature with approaches used in the reduced form treatment effect literature. It is an empirically robust middle ground between the two approaches which estimates economically interpretable and policy-relevant dynamic treatment effects that account for heterogeneity in cognitive and non-cognitive skills and the continuation values of educational choices. Graduating college is not a wise choice for all. Ability bias is a major component of observed educational differentials. For some, there are substantial causal effects of education at all stages of schooling.

This paper analyzes the non-market benefits of education and ability. Using a dynamic model of educational choice we estimate returns to education that account for selection bias and sorting on gains. We investigate a range of non-market outcomes including incarceration, mental health, voter participation, trust, and participation in welfare. Unlike previous evidence on the monetary benefits of education, the benefits to education for many non-market outcomes appear to be larger for low-ability individuals. College graduation decreases welfare use, lowers depression, and raises self-esteem more for less-able individuals. Accounting for the non-market benefits of education is an important component of any analysis of educational policy.

Across academic sub-fields such as labor, education, and behavioral economics, the measurement and interpretation of non-cognitive skills varies widely. As a result, it is difficult to compare results on the importance of non-cognitive skills across literatures. Drawing from these literatures, this paper systematically relates various prototypical non-cognitive measures within one data set. Specifically, we estimate and compare several different strategies for measuring non-cognitive skills. For each, we compare their relative effectiveness at predicting educational success and decompose what is being measured into underlying personality traits and economic preferences. We demonstrate that the construction of the non-cognitive factor greatly influences what is actually measured and what conclusions are reached about the role of non-cognitive skills in life outcomes such as educational attainment. Furthermore, we demonstrate that, while sometimes difficult to interpret, factors extracted from self-reported behaviors can have predictive power similar to well established taxonomies, such as the Big Five.

This paper develops robust models for estimating and interpreting treatment effects arising from both ordered and unordered multi-stage decision problems. Identification is secured through instrumental variables and/or conditional independence (matching) assumptions. We decompose treatment effects into direct effects and continuation values associated with moving to the next stage of a decision problem. Using our framework, we decompose the IV estimator, showing that IV generally does not estimate economically interpretable or policy-relevant parameters in prototypical dynamic discrete choice models, unless policy variables are instruments. Continuation values are an empirically important component of estimated total treatment effects of education. We use our analysis to estimate the components of what LATE estimates in a dynamic discrete choice model.

Intelligence quotient (IQ), grades, and scores on achievement tests are widely used as measures of cognition, but the correlations among them are far from perfect. This paper uses a variety of datasets to show that personality and IQ predict grades and scores on achievement tests. Personality is relatively more important in predicting grades than scores on achievement tests. IQ is relatively more important in predicting scores on achievement tests. Personality is generally more predictive than IQ on a variety of important life outcomes. Both grades and achievement tests are substantially better predictors of important life outcomes than IQ. The reason is that both capture personality traits that have independent predictive power beyond that of IQ.

The option to obtain a General Educational Development (GED) certificate changes the incentives facing high school students. This article evaluates the effect of three different GED policy innovations on high school graduation rates. A 6-point decrease in the GED pass rate produced a 1.3-point decline in high school dropout rates. The introduction of a GED certification program in high schools in Oregon produced a 4% decrease in high school graduation rates. Introduction of GED certificates for civilians in California increased the dropout rate by 3 points. The GED program induces students to drop out of high school.

This paper discusses and illustrates identification problems in personality psychology. The measures used by psychologists to infer traits are based on behaviors, broadly defined. These behaviors are produced from multiple traits interacting with incentives in situations. In general, measures are determined by these multiple traits and do not identify any particular trait unless incentives and other traits are controlled for. Using two data sets, we show, as an example, that substantial portions of the variance in achievement test scores and grades, which are often used as measures of cognition, are explained by personality variables.

Achievement tests play an important role in modern societies. They are used to evaluate schools, to assign students to tracks within schools, and to identify weaknesses in student knowledge. The GED is an achievement test used to grant the status of high school graduate to anyone who passes it. GED recipients currently account for 12 percent of all high school credentials issued each year in the United States. But do achievement tests predict success in life? The Myth of Achievement Tests shows that achievement tests like the GED fail to measure important life skills. James J. Heckman, John Eric Humphries, Tim Kautz, and a group of scholars offer an in-depth exploration of how the GED came to be used throughout the United States and why our reliance on it is dangerous. Drawing on decades of research, the authors show that, while GED recipients score as well on achievement tests as high school graduates who do not enroll in college, high school graduates vastly outperform GED recipients in terms of their earnings, employment opportunities, educational attainment, and health. The authors show that the differences in success between GED recipients and high school graduates are driven by character skills. Achievement tests like the GED do not adequately capture character skills like conscientiousness, perseverance, sociability, and curiosity. These skills are important in predicting a variety of life outcomes. They can be measured, and they can be taught. Using the GED as a case study, the authors explore what achievement tests miss and show the dangers of an educational system based on them. They call for a return to an emphasis on character in our schools, our systems of accountability, and our national dialogue.

The General Educational Development (GED) credential is issued on the basis of an eight hour subject-based test. The test claims to establish equivalence between dropouts and traditional high school graduates, opening the door to college and positions in the labor market. In 2008 alone, almost 500,000 dropouts passed the test, amounting to 12% of all high school credentials issued in that year. This chapter reviews the academic literature on the GED, which finds minimal value of the certificate in terms of labor market outcomes and that only a few individuals successfully use it as a path to obtain post-secondary credentials. Although the GED establishes cognitive equivalence on one measure of scholastic aptitude, recipients still face limited opportunity due to deficits in noncognitive skills such as persistence, motivation and reliability. The literature finds that the GED testing program distorts social statistics on high school completion rates, minority graduation gaps, and sources of wage growth. Recent work demonstrates that, through its availability and low cost, the GED also induces some students to drop out of school. The GED program is unique to the United States and Canada, but provides policy insight relevant to any nation's educational context.

This chapter examines the characteristics and demographics of individuals who obtain General Educational Development (GED) certificates as an alternative pathway to high school completion.

This chapter analyzes the economic and social outcomes for GED recipients compared to traditional high school graduates, examining earnings, employment, and educational attainment patterns.

This chapter documents the historical growth and expansion of GED testing programs across the United States and analyzes the factors driving increased adoption.

This chapter examines whether the availability of GED testing creates perverse incentives that encourage students to drop out of traditional high school programs.

This chapter presents policy recommendations and alternatives to current achievement testing practices, emphasizing the importance of character skill development in education.

IMPORTANCE: It is not known how effective child masking is in childcare settings in preventing the transmission of SARS-CoV-2. This question is critical to inform health policy and safe childcare practices. OBJECTIVE: To assess the association between masking children 2 years and older and subsequent childcare closure because of COVID-19. DESIGN, SETTING, AND PARTICIPANTS: A prospective, 1-year, longitudinal electronic survey study of 6654 childcare professionals at home- and center-based childcare programs in all 50 states was conducted at baseline (May 22 to June 8, 2020) and follow-up (May 26 to June 23, 2021). Using a generalized linear model (log-binomial model) with robust SEs, this study evaluated the association between childcare program closure because of a confirmed or suspected COVID-19 case in either children or staff during the study period and child masking in both early adoption (endorsed at baseline) and continued masking (endorsed at baseline and follow-up), while controlling for physical distancing, other risk mitigation strategies, and program and community characteristics. EXPOSURES: Child masking in childcare programs as reported by childcare professionals at baseline and both baseline and follow-up. MAIN OUTCOMES AND MEASURES: Childcare program closure because of a suspected or confirmed COVID-19 case in either children or staff as reported in the May 26 to June 23, 2021, end survey. RESULTS: This survey study of 6654 childcare professionals (mean [SD] age, 46.9 [11.3] years; 750 [11.3%] were African American, 57 [0.9%] American Indian/Alaska Native, 158 [2.4%] Asian, 860 [12.9%] Hispanic, 135 [2.0%] multiracial [anyone who selected >1 race on the survey], 18 [0.3%] Native Hawaiian/Pacific Islander, and 5020 [75.4%] White) found that early adoption (baseline) of child masking was associated with a 13% lower risk of childcare program closure because of a COVID-19 case (adjusted relative risk, 0.87; 95% CI, 0.77-0.99), and continued masking for 1 year was associated with a 14% lower risk (adjusted relative risk, 0.86; 95% CI, 0.74-1.00). Conclusions and Relevance This survey study of childcare professionals suggests that masking young children is associated with fewer childcare program closures, enabling in-person education. This finding has important public health policy implications for families that rely on childcare to sustain employment.

OBJECTIVES Ensuring high coronavirus disease-2019 (COVID-19) vaccine uptake among US child care providers is crucial to mitigating the public health implications of child-staff and staff-child transmission of severe acute respiratory syndrome coronavirus 2; however, the vaccination rate among this group was previously unknown. METHODS To characterize vaccine uptake among US child care providers, we conducted a multistate cross-sectional survey of the child care workforce. Providers were identified through various national databases and state registries. A link to the survey was sent via e-mail between May 26 and June 23, 2021. A 37.8% response yielded 21,663 respondents, with 20,013 satisfying inclusion criteria. RESULTS Overall COVID-19 vaccine uptake among US child care providers (78.2%, 90% confidence interval: 77.5% to 78.9%) was higher than the US general adult population (65%). Vaccination rates varied between states from 53.5% to 89.4%. Vaccine uptake among respondents differed significantly (P < .01) based on respondent age (70.0% for ages 25-34, 91.6% for ages 75-84), race (70.0% for Black or African Americans, 92.5% for Asian Americans), annual household income (70.8% for <$35,000, 85.1% for >$75,000), and child care setting (73.0% for home-based, 79.7% for center-based). CONCLUSIONS COVID-19 vaccine uptake among US child care providers was higher than the general US adult population. Those who were younger, lower income, Black or African American, resided in states either in the Mountain West or the South, and/or worked in home-based child care programs reported the lowest rates of vaccination. State public health leaders and lawmakers should prioritize these subgroups to realize the largest gains in vaccine uptake among providers.

OBJECTIVES: Central to the debate over school and child care reopening is whether children are efficient coronavirus disease 2019 (COVID-19) transmitters and are likely to increase community spread when programs reopen. We compared COVID-19 outcomes in child care providers who continued to provide direct in-person child care during the first 3 months of the US COVID-19 pandemic with outcomes in those who did not. METHODS: Data were obtained from US child care providers (N = 57,335) reporting whether they had ever tested positive or been hospitalized for COVID-19 (n = 427 cases) along with their degree of exposure to child care. Background transmission rates were controlled statistically, and other demographic, programmatic, and community variables were explored as potential confounders. Logistic regression analysis was used in both unmatched and propensity score matched case-control analyses. RESULTS: No association was found between exposure to child care and COVID-19 in both unmatched (odds ratio [OR], 1.06; 95% confidence interval [CI], 0.82-1.38) and matched (OR, 0.94; 95% CI, 0.73-1.21) analyses. In matched analysis, being a home-based provider (as opposed to a center-based provider) was associated with COVID-19 (OR, 1.59; 95% CI, 1.14-2.23) but revealed no interaction with exposure. CONCLUSIONS: Within the context of considerable infection mitigation efforts in US child care programs, exposure to child care during the early months of the US pandemic was not associated with an elevated risk for COVID-19 transmission to providers. These findings must be interpreted only within the context of background transmission rates and the considerable infection mitigation efforts implemented in child care programs.

OBJECTIVE: To design and implement a tool that creates a secure, privacy preserving linkage of electronic health record (EHR) data across multiple sites in a large metropolitan area in the United States (Chicago, IL), for use in clinical research. METHODS: The authors developed and distributed a software application that performs standardized data cleaning, preprocessing, and hashing of patient identifiers to remove all protected health information. The application creates seeded hash code combinations of patient identifiers using a Health Insurance Portability and Accountability Act compliant SHA-512 algorithm that minimizes re-identification risk. The authors subsequently linked individual records using a central honest broker with an algorithm that assigns weights to hash combinations in order to generate high specificity matches. RESULTS: The software application successfully linked and de-duplicated 7 million records across 6 institutions, resulting in a cohort of 5 million unique records. Using a manually reconciled set of 11,292 patients as a gold standard, the software achieved a sensitivity of 96% and a specificity of 100%, with a majority of the missed matches accounted for by patients with both a missing social security number and last name change. Using 3 disease examples, it is demonstrated that the software can reduce duplication of patient records across sites by as much as 28%. CONCLUSIONS: Software that standardizes the assignment of a unique seeded hash identifier merged through an agreed upon third-party honest broker can enable large-scale secure linkage of EHR data for epidemiologic and public health research. The software algorithm can improve future epidemiologic research by providing more comprehensive data given that patients may make use of multiple healthcare systems.