The Black Lives Matter (BLM) movement was a prominent social movement largely focused on raising awareness of and reducing police use-of-force and fatal interactions with police. However, opponents of the movement have feared it could lead to decreased proactive policing and increased crime. Using a state-by-month fixed effects model, I find evidence that an additional protest in the preceding month leads to a decrease of.225 fatal interactions between Blacks and police per 10 million Black population. In addition, I find no evidence supporting increased crime or arrests as a result of the BLM movement. (JEL J15, D91, Z13).

School closures have been enacted as a measure of mitigation during the ongoing COVID-19 pandemic due to their ability to reduce transmission. It has been shown that school closures could cause absenteeism amongst healthcare workers with dependent children, but there is a need for detailed high-resolution analysis of the relationship between school closures and healthcare worker absenteeism to inform local community preparedness. We provide national-and county-level simulations of school closures and absenteeism across the United States. At the national level, we estimate the projected absenteeism rate to range from 7.5% to 8.6%, and the effectiveness of school closures to range from 172 to 218 fewer hospital beds used per 100,000 people at peak demand. At the county-level, we find substantial variations of projected absenteeism and school closure effects, ranging from 2.0% to 18.6% absenteeism and 88 to 280 fewer hospital beds used per 100,000 people at peak demand. We also find significant associations between levels of absenteeism and COVID-19 complication factors. We observe from our models that an estimated 98.8% of counties would find it less expensive to provide child care to all healthcare workers with children than to bear the costs of healthcare worker absenteeism during school closures, identifying child care subsidization as a potential solution to help maintain healthcare systems during a pandemic.

This study uses the 2013–2017 American Community Survey to explore differences in the returns to obtaining US citizenship for immigrants from the four largest source countries relative to all other immigrants. We find that Chinese, Mexican, and Filipino immigrants face a wage penalty prior to naturalization, while Indian immigrants experience higher wages than other immigrants. Naturalization more than offsets the wage penalty for Chinese and Filipino immigrants and partially offsets the wage penalty for Mexican immigrants. However, naturalized Indian immigrants earn less than non-naturalized Indian immigrants. We find only limited evidence of a naturalization premium for immigrants from other countries.

These are unprecedented times, as the COVID-19 pandemic disrupts public health, social interaction, and employment attachments. Evidence to date has been about broad shifts in unemployment rates as a percent of the labor force. We draw on monthly Current Population Survey data to examine subpopulation changes in employment states across the life course, from January through April 2020. COVID-19 downturns produced disparate life-course impacts. There are increases in unemployment and being out of the workforce at all ages, but especially among young adults, with young women most at risk. Intersectional analyses document conjoint life-course vulnerabilities by gender, educational attainment, and race/ethnicity. For example, Black men aged 20–29 with a college degree experienced a 12.4 percentage point increase in being not in the labor force for other reasons (NILF-other). Individuals with less than a college degree in their 50s and 60s were more likely to become unemployed, regardless of race. And more non-college-educated Asian men in their 60s and 70s reported being retired (6.6 and 8.9 percentage point increases, respectively). Repercussions from the pandemic may well challenge assumptions and possibilities for older adults’ working longer.

The race for innovation has turned into a race for data. Rapid developments of new technologies, especially in the field of artificial intelligence, are accompanied by new ways of accessing, integrating, and analyzing sensitive personal data. Examples include financial transactions, social network activities, location traces, and medical records. As a consequence, adequate and careful privacy management has become a significant challenge. New data protection regulations, for example in the EU and China, are direct responses to these developments. Data anonymization is an important building block of data protection concepts, as it allows to reduce privacy risks by altering data. The development of anonymization tools involves significant challenges, however. For instance, the effectiveness of different anonymization techniques depends on context, and thus tools need to support a large set of methods to ensure that the usefulness of data is not overly affected by risk-reducing transformations. In spite of these requirements, existing solutions typically only support a small set of methods. In this work, we describe how we have extended an open source data anonymization tool to support almost arbitrary combinations of a wide range of techniques in a scalable manner. We then review the spectrum of methods supported and discuss their compatibility within the novel framework. The results of an extensive experimental comparison show that our approach outperforms related solutions in terms of scalability and output data quality—while supporting a much broader range of techniques. Finally, we discuss practical experiences with ARX and present remaining issues and challenges ahead.

This dissertation presents three studies exploring the impact of immigration and diversity and policies governing the two on a set of productive and behavioral outcomes. Migration is a fundamental aspect of modern economic activity, and as geographic mobility increases and the world continues to become more integrated, challenges associated with the movement of people are likely to arise. These studies contribute to our understanding of the complex consequences of immigration and related policies, focusing particularly on the United States. As high-skilled immigration has accelerated in recent decades, the workforces of developed nations has become more diverse. The first chapter, “Cross-Ethnic Diversity and Innovative Output”, explores the implications of this diversity in the context of scientific and innovative productivity. Beyond the admissions of skilled scientists, immigration policy can of course affect a much wider range of populations. In the second chapter, “Immigration Relief and Insurance Coverage: Evidence from Deferred Action for Childhood Arrivals” (DACA), I turn to studying a subset of undocumented immigrants. This chapter examines the effect of a U.S. executive order protecting undocumented immigrants brought to the U.S. as children on health insurance coverage. Another topical concern in public discourse on immigration is the idea that more immigration is associated with elevated crime. The third chapter, “Does Refugee Inflow Affect Urban Crime? Evidence from Miami and Houston”, seeks to shed light on this concern by studying two large-scale refugee settlement episodes in U.S. cities: the 1978 Vietnamese refugee settlement in Houston, and the 1980 Mariel Boatlift in Miami.

The traditional role of physical therapists is to prescribe exercises and provide hands-on care to help patients develop, maintain, and restore functional ability that may be limited by injuries, aging, and chronic or progressive diseases. Physical therapists may provide preventive care, rehabilitation, education, and treatment for those with chronic conditions including scoliosis, arthritis, obesity, amputations, and cerebral palsy.

As Millennials reach a new stage of life – the oldest among them will turn 39 this year – a clearer picture of how members of this generation are establishing their own families is coming into view. Previous research highlights not only the sheer size of the Millennial generation, which now surpasses Baby Boomers as the largest, but also its racial and ethnic diversity and high rates of educational attainment. This research also notes that Millennials have been slower than previous generations to establish their own households. A new analysis of government data by Pew Research Center shows that Millennials are taking a different path in forming – or not forming – families. Millennials trail previous generations at the same age across three typical measures of family life: living in a family unit, marriage rates and birth rates.

In Los Angeles, the fraction of all-cash home purchases quintupled during the last decade, with the growth being more pronounced among experienced buyers, flippers, and out-of-state buyers. Exploiting these variations, we quantify a new source of housing market frictions: financing risk, referring to delays and uncertainties that a seller faces in closing a transaction with a mortgage offer. Using the transaction level data, we find that an all-cash purchase is associated with a 33% shorter time to close and a 5% discount in the sales price. The estimate is robust to controlling for unobserved house and buyer attributes through a rich interaction of fixed effects as well as an instrumental variable strategy. We further find that the estimated cash discount is larger among more experienced buyers and smaller among out-of-state buyers, suggesting the importance of an informational advantage when a buyer bargains over a cash offer. Finally, our market level analysis shows that the rising all-cash purchases help mitigate the impact of economic shocks on house prices.

This Rapid Response Brief provides information about the respiratory therapist workforce in the U.S. It includes descriptions of the supply, distribution, education pathways, as well as COVID-19 emergency concerns about the respiratory therapist workforce capacity and examples of state approaches to address workforce gaps.

We propose a novel framework, called INCOMPFUSE, that significantly improves the accuracy of existing methods for reconstructing aggregated historical data from inaccurate historical reports. INCOMPFUSE supports efficient data reliability assessment using the incompatibility probability of historical reports. We provide a systematic approach to define this probability based on properties of the data and relationships between the reports. Our experimental study demonstrates high utility of the proposed framework. In particular, we were able to detect noisy historical reports with very high detection accuracy.

The odds of experiencing a public mass shooting in first person are low. Approximately 411 people have died in such tragic events during the past decade in the United States. However, the high saliency of mass shooting events might induce changes in behavior that may pose an external cost on society. In this paper, I estimate the impact of indirect exposure to high profile acts of violence on behavior by using information on 45 mass shootings from 2003 to 2016. Using individual-level data, I find that being within 250 miles shortly after a mass shooting, individuals overall activity levels decrease by 1.7 percent. This decline is equivalent to a daily 16 minute walk. For an average weight person, this decline would mean 50 less calories burned per day. The decline in activity is mainly explained by a 10 percent decrease in minutes of moderate to vigorous activities and is driven mostly by individuals under 30. In addition to a decrease in activity levels, I find an increase in the probability of having more days where perceived mental health is poor. And interestingly, I find an increase in the probability of binge drinking. Finally, I also find a statistically significant decrease of 10.6 minutes worked per week shortly after a mass shooting has occurred. These results show that aside from direct victims, mass shootings also impact the short-term behavior of a broader portion of the population.

In the wake of the 1994 genocide, Rwanda’s government created the Gacaca courts to hold suspected perpetrators accountable. Although much has been written about these courts, researchers know comparatively less about the 250,000 individuals who served as Gacaca court judges (inyangamugayo). We draw upon 135 interviews to explore how the inyangamugayo entered and adapted to their new public roles as moral arbiters, how these judges understood Gacaca’s missions, and how their social identities evolved over the course of multiple status transitions. Building on Erving Goffman’s sequential approach to moral careers, we trace the process of becoming a judge. In doing so, we highlight the two overarching missions that surfaced during the interviews – justice and reconciliation – and how the judges continued to view themselves as inyangamugayo even after the courts closed.

COVID-19 and associated economic shutdowns have led to unprecedented job losses, with up to 20 million households and 24 million individuals experiencing an unemployment spell between March 2020 and August 2020.1 The scale of these losses, their disproportionate impact on lower-income workers, and the uncertain timeline of economic recovery have raised concerns about the ability of households to maintain rent payments while out of work. Helping households stay in their homes is important for public health reasons and because eviction is associated with many negative outcomes, particularly for disadvantaged households (Desmond 2012, Desmond and Bell 2015, Desmond and Kimbro 2015), and in particular causes lost earnings, financial strain, homelessness, and health emergencies (Collinson and Reed 2019, Humphries et al. 2019). Beyond the effects on renters, the inability to repay rental debt could create cascading financial challenges for smaller landlords and significantly disrupt local housing markets (Choi and Young 2020, Brennan et al. 2020).

Water is critical to human development and well-being. Its importance is not only captured explicitly in the Sustainable Development Goal 6 (SDG6) regarding universal access to safe, affordable, and adequate water and sanitation, but also is embedded within the goals on energy, food security, poverty, energy, health, disaster risk, and cities. In 2016-2018, the United Nations and World Bank Group convened a High Level Panel on Water to accelerate progress towards SDG6, which identified a number of ways in which such progress was off-track, particularly in low-and middle-income countries (LMICs). This panel recommended an agenda to enable stakeholders to make decisions and take action. Prior to decision-making, it is necessary for stakeholders "to understand the quantity, quality, distribution, use, and risks of the water they have." This understanding in turn depends on investments in institutional and technical infrastructure for "water-related data as well as the systems to share, analyze, and take decisions with this data" (United Nations and World Bank 2018). This makes sense-decisions require information based on data. But what data investments need to be made? What should water data infrastructure do? "Water-related" data is collected on many topics by many different actors, each in a different format and quality fit for a different, often highly localized primary use, even if secondary use of such data by other parties may be beneficial. Well-informed decisions may require data to be integrated from many of these different sources. Currently, the typical data-to-decisions cycle can be long, winding, difficult, and full of uncertainty. For example, a regional water infrastructure planner may need to answer many interrelated questions in order to even begin costing out alternative scenarios of water supply improvements in an application to a government agency or donor organization: • How many people live in this region, and how are they spatially distributed among settlements of different sizes? • Where do existing water points serve each of these settlements? What types of water points are they, and what levels of service in terms of water quantity, quality, travel times, and wait times do they provide? What are people paying for water from these points? Where do people retrieve water if not from one of these points? • How much water is being used, including for domestic, agricultural, livestock, and commercial purposes? • What are prevailing rates of waterborne disease in these settlements, and in their vulnerable subpopulations? • What is the potential to increase safely managed water supply quantity? What are the surface and groundwater availability conditions in the

One of the fundamental questions in the social sciences is whether modern welfare states can be sustained as countries welcome more immigrants. On theoretical grounds, the relationship between immigration and support for redistribution is ambiguous. Immigration may increase ethnic diversity, which may reduce the support for redistribution. On the other hand, natives may demand more redistribution as an insurance against labour market risks brought by immigration. In this chapter, we review the theoretical and empirical literature on immigration and redistribution from across the social sciences. We focus on two themes, namely the effect of immigration on natives’ support for redistribution, and the effect on the actual setting of tax and spending policies. Recent empirical evidence suggests that immigration lowers the support for redistribution and leads to lower taxation and spending. However, the magnitude of these effects appears to be highly context-dependent.

I use Current Population Survey Data from February and April 2020 to examine how individual workers have transitioned between labor-market states and which workers have been hurt most by the COVID-19 pandemic. I find not only large effects on workers becoming unemployed but also a decline in labor-force participation, an increase in absence from one’s job, and a decrease in hours worked. Generally, more vulnerable populations—racial and ethnic minorities, those born outside the U.S., women with children, the least educated, and workers with a disability—have experienced the largest declines in the likelihood of (full-time) work and work hours.

This map shows the location of historic Black communities in Minneapolis based on the enumeration districts used in the 1940 federal census. Contemporary freeways and interstates are shown in red.

This study evaluates employment patterns of domestic migrants and immigrants and also explores the role of ethnic niching for Asian and African American recent arrivals in the context of local structural transformation (e.g., the shift from manufacturing to services). Employment patterns are examined for industry‐occupation groups using the 5% Public Use Micro Sample of the 2000 U.S. Decennial Census and the 2006–2010 of the American Community Survey. The study area is a Rustbelt city that has suffered from population loss: the Buffalo‐Niagara Falls MSA. Location quotients and shift‐share analysis are used to classify employment shifts into four industry‐occupation groups: growing, transforming, declining, and emerging. Logistic regressions are estimated to identify the role of various demographic factors and ethnic niches on the employment of recent arrivals in the above four industry‐occupation groups. The results show that the employment of recent arrivals vary by migration status and ethnicity. The role of ethnic niches is especially important in declining industry‐occupation groups. The analytical framework can be used to understand where and how migrants, both domestic and immigrants, find work. Future studies can focus on the pros and cons of ethnic niching (e.g., barriers to suitable jobs, ethnic entrepreneurship through niche experience) as structural transformation continues across U.S. regions, especially with new technologies.

Young people are frequently told that higher education is important for obtaining a rewarding career. The income gap between workers with and without a college education is large, growing, and expected to continue widening. Yet higher education is also increasingly expensive, with rising tuition and fees burdening students with ever-heavier debts. Even if higher education is still a good investment on average, that investment yields returns that vary widely by institution, field of study, and geography, even within states. Specific income and employment opportunities depend on many factors, not just college attendance and degree acquisition. This report focuses on the state of Idaho.2 Mean earned income is computed for workers with different levels of education for the state as a whole and for the Boise Metropolitan Statistical Area (MSA).3 The analysis then compares earnings across these groups of workers to gauge how much difference in earnings is related to different levels of education. Specifically, the report investigates four main questions: 1. How do earnings vary by education level in Idaho? 2. How do earnings vary by education level in Boise? 3. Why might earnings in Boise differ from those in the rest of Idaho? 4. How do earnings in Idaho differ from those in neighboring states?