More than two million U.S. households have an eviction case filed against them each year. Policymakers 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 large cities. We document that prior to housing court, tenants experience declines in earnings and employment and increases in financial distress and hospital visits. These pre-trends are more pronounced for tenants who are evicted, which poses 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 reduces earnings, durable consumption, and access to credit. Effects on housing and labor market outcomes are driven by impacts for female and Black tenants.
This paper documents differences across higher education courses in the coverage of frontier knowledge. Applying natural language processing (NLP) techniques to the text of 1.7M syl- labi and 20M academic articles, we construct the “education-innovation gap,” a syllabus’s rel- ative proximity to old and new knowledge. We show that courses differ greatly in the extent to which they cover frontier knowledge. Instructors play a big role in shaping course content; instructors who are active researchers teach more frontier knowledge. More selective and bet- ter funded schools, and those enrolling socio-economically advantaged students, teach more frontier knowledge. Students from these schools are more likely to complete a doctoral degree, produce more patents, and earn more after graduation.
There is widespread consensus that US infrastructure quality has been on the decline. In response, politicians across the ideological spectrum have called for increased infrastructure spending. Although the cost of infrastructure determines how much physical output each dollar of spending yields, we know surprisingly little about these costs across time and place. We help to fill this gap by using data we digitized on the Interstate highway system—one of the nation’s most valuable infrastructure assets—to document spending per mile over the history of its construction.
We make two main contributions. First, we find that real spending per mile on Interstate construction increased more than three-fold from the 1960s to the 1980s. The increase does not appear to come from states building “easy” miles first, since the increase is roughly unchanged conditional on pre-existing observable geographic cost determinants. Second, we provide suggestive evidence of the determinants of the increase in spending per mile. Increases in per -unit labor or materials prices are inconsistent with the pattern of the increase. But increases in income and housing prices explain about half of the increase in spending per mile. We find suggestive evidence that the rise of “citizen voice” in government decision-making caused increased expenditure per mile.
Economists often point to the superiority of cash transfers over in-kind assistance as a means of redistribution because recipients can choose how to use these resources. However, among the trillions of dollars of annual U.S. transfers, redistribution is mostly in-kind. We conducted a survey experiment—using a choice between a cash transfer and a transfer that could be spent only on a bundle of “necessities”—to help explain why. We show that the general population overwhelmingly prefers in-kind redistribution, largely for paternalistic reasons. This preference was common to a majority of virtually all segments of the general population, though not to a sample of educational elites. A persuasion treatment on the value of choice, while impactful, did not change this overall preference for in-kind. For an equal-sized program, below-poverty respondents preferred receiving cash. But they appeared to prefer the larger in-kind transfer to the smaller cash transfer that the general population was willing to support. This suggests that an in-kind transfer may be preferable to both recipients and the general population.
We study the role of European Immigration on local and aggregate economic growth in the United States between 1880 and 1920. We employ a big data approach and link, at the individual-level, information from the Population Census, the universe of patents and millions of historical immigration records. We find that immigrants were more prolific innovators than natives, and document large differences in innovation potential across nationalities and regions in the United States. To measure the importance of immigrants for the creation of new ideas and economic growth, we develop a new spatial model of growth through dissemination of knowledge and workers’ mobility. The model allows us to use our micro and regional empirical findings to measure immigrants’ innovation human capital and the degree of knowledge diffusion which regulates scale effects. We quantitatively analyze the effects of imposing major immigration restrictions on American economic growth in the 19th and early 20th century. We find large, accumulating, losses from these restrictions. Both the scale effects and the exclusion of high-human capital immigrants contribute significantly to these losses.
As children reach adolescence, peer interactions become increasingly central to their development, whereas the direct influence of parents wanes. Nevertheless, parents may continue to exert leverage by shaping their children's peer groups. We study interactions of parenting style and peer effects in a model where children's skill accumulation depends on both parental inputs and peers, and where parents can affect the peer group by restricting who their children can interact with. We estimate the model and show that it can capture empirical patterns regarding the interaction of peer characteristics, parental behavior, and skill accumulation among US high school students. We use the estimated model for policy simulations. We find that interventions (e.g., busing) that move children to a more favorable neighborhood have large effects but lose impact when they are scaled up because parents' equilibrium responses push against successful integration with the new peer group.
Social movements are associated with large societal changes, but evidence of their causal effects is limited. We study the effect of the MeToo movement on reporting sex crimes to the police. We construct a new dataset of crimes reported in 31 OECD countries and employ a triple-difference strategy between crime types, across countries, and over time. The movement increased the reporting of sex crimes by 10%. Using rich US data, we find that in contrast to a common criticism of the movement, the effect is similar across socioeconomic groups, and that the movement also increased arrests for sexual assault. The increased reporting reflects a higher propensity to report sex crimes and not an increase in crime incidence. The mechanism most consistent with our findings is that victims perceive sexual misconduct to be a more serious problem following the movement. Our results demonstrate that social movements can rapidly and persistently affect high-stakes decisions.