Issues related to professional sport transcend far beyond the world of sport itself. Previous research on the topic of sport and suicide has found that a significant association exists between the success of professional sport teams and the well-being of people in general. Data on suicide rates in United States’ metropolitan areas were analyzed from 2006 to 2007 and from 2009 to 2010 to measure the association between suicide rates and the success of professional sport teams in these same metropolitan areas. Results from statistical analyses reveal that the successes of professional sport teams are significantly related to lower suicide rates, while the only game in town hypothesis was not supported.

For decades, Americans have generally been moving away from the Northeast and the Midwest toward the South and West. Mountainous and desert areas in the West and Southwest have been especially popular internal migration destinations. However, these same areas are particularly susceptible to the increasing impacts of climate change on extreme heat and wildfire. This paper investigates relationships between disaster-level fire events and extreme heat on county-level migration in the USA using annual panel data for the years 1990–2015. Are wildfire and extreme heat associated with changes in migration patterns? And, are relationships between such “disamenities” and migration stronger in counties with closer ties to environmental amenities? Fixed effects and random effects regression analysis show that both experiencing a disaster-level wildfire and extreme heat in the prior year were associated with reduced migration. Relationships are stronger in counties rich in natural amenities and outdoor recreation, suggesting that in places known for their environmental amenities, shifts toward environmental disamenities may be particularly impactful.

Objectives. To estimate the effect of Medicaid expansion on noncitizens' and citizens' participation in the Supplemental Security Income (SSI) program. The Affordable Care Act (ACA) expanded Medicaid eligibility to cover low-income nonelderly adults without children, thus delinking their Medicaid participation from participation in the SSI program. Methods. Using data from the Social Security Administration for 2009 through 2018 (n = 1020 state-year observations) and the Current Population Survey for 2009 through 2019 (n = 78 776 respondents), we employed a difference-in-differences approach comparing SSI participation rates in US states that adopted Medicaid expansion with participation rates in nonexpansion states before and after ACA implementation. Results. Medicaid expansion reduced the SSI (disability) participation of nonelderly noncitizens by 12% and of nonelderly citizens by 2%. Estimates remained robust with administrative and survey data. Conclusions. Medicaid expansion caused a substantially larger decline in the SSI participation of noncitizens, who face more restrictive SSI eligibility criteria, than of citizens. Our estimates suggest an annual savings of $619 million in the federal SSI cost because of the decline in SSI participation among noncitizens and citizens.

I examine the college attendance patterns of second-generation Russian-Jewish immigrants in Maine in the early 20 th century relative to other ethnic groups using individual-level Census records. I employ the Abramitzky, Boustan, and Eriksson (ABE) algorithm to track second-generation Jewish, Italian, French Canadian, English Canadian and European immigrants from the 1910 Census to the 1940 Census. My logistic regression analysis indicates that second-generation Jewish immigrants in Maine attended college at significantly higher rates than their peers of similar background in every other ethnic group. While I cannot evaluate them, I also discuss potential explanations for the disparity in college attendance between Jews and other groups. Nash 1 Acknowledgements

PURPOSE: Since Affordable Care Act (ACA) implementation in 2014, studies have demonstrated gains in insurance coverage for cancer survivors < 65 years. We assessed the impact of ACA implementation on financial barriers to care by stratifying survivors at age 65 years, when individuals typically become Medicare-eligible. METHODS: We used data from respondents with cancer in the 2009-2018 National Health Interview Survey. We identified 21,954 respondents representing approximately 7.4 million survivors, who were then age-stratified at age 65 years. Survey responses regarding financial barriers to medical care and medications were analyzed, and age-stratified multivariable logistic regression modeling was performed, which evaluated the impact of ACA implementation on these measures, adjusted for demographic and socioeconomic variables. RESULTS: After multivariable logistic regression, ACA implementation was associated with higher adjusted odds of Medicaid insurance (odds ratio [95% CI] 2.02 [1.72 to 2.36]; P < .0001) and lower adjusted odds of no insurance (0.57 [0.48 to 0.68]; P < .0001). Regarding financial barriers, ACA implementation was associated with lower adjusted odds of inability to afford medications (0.68 [0.59 to 0.79]; P < .0001), inability to afford dental care (0.83 [0.73 to 0.94]; P = .004), and delaying care (0.78 [0.69 to 0.89]; P = .002) in the 18-64 years group. Similarly, ACA implementation was associated with lower adjusted odds of secondary outcomes such as delaying refills, skipping doses, and anxiety over medical bills. Similar associations were not seen in the > 65 years group. CONCLUSION: Survivor-reported measures of financial barriers in cancer survivors age 18-64 years significantly improved following ACA implementation. Similar changes were not seen in the Medicare-eligible cohort, likely because of high Medicare enrollment and few uninsured.

Using rich data, we establish a national profile of Hispanic families in assisted housing and compare this profile to that of non-Hispanic Black and White families. Through multivariate regression and decomposition analysis, we then estimate the effect of being Hispanic on the odds of receiving assistance and whether being Hispanic per se could explain Hispanic families’ significantly lower chances of assistance receipt than their Black and White counterparts. The additional analysis estimates whether Hispanic families are receiving their “fair share” of housing assistance. We find significant disparities in the size of assisted housing units among Hispanics compared to Blacks and Whites. Being Hispanic lowers the odds of receiving housing assistance by about one-third relative to Blacks and Whites. Neither this disparity nor that in housing unit size is explained by measured characteristics of the three race and ethnic groups. Hispanic families represent one-third of income-eligibles in the three race and ethnic groups but 20 percent of assisted housing recipients. Across program types, Hispanics are overrepresented in public housing and under-represented in the multifamily and voucher programs.

In the 1960s, landmark legislation targeted the long-standing practice of labor market discrimination against US women. The Equal Pay Act of 1963, an amendment to the Fair Labor Standards Act (FLSA), became the first piece of federal legislation mandating equal pay for equal work. Title VII of the Civil Rights Act followed in 1964 with a provision that more broadly prohibited any sex-based discrimination in employment. Complementing this legislation, the 1961 and 1966 FLSA amendments increased the real minimum wage by 24 percent by 1970 and almost doubled the number of workers it covered, extending the FLSA’s provisions to an additional 22.6 million individuals (US Department of Labor 1961, 1970). These changes benefited many workers in some of the economy’s lowest-earning industries, such as services, retail trade, and government (that is, schools and hospitals)—industries where many women worked. Yet the gender gap in pay was unchanged over the 1960s. Figure 1 shows that the ratio of women’s to men’s median annual and weekly wages for full-time, full-year (FTFY) workers hovered around 60 percent of men’s until the early 1980s (Blau and Kahn 2017). This paper examines changes in the distribution of wages for clues about the persistence of the gender gap in the 1960s. Our key finding is that the 1960s witnessed large increases in women’s relative pay in the lower part of the wage distribution, where the Equal Pay Act, Title VII, and the FLSA would have tended to increase wages. These gains are not explained by improvements in working women’s observed characteristics and run contrary to simple models of negative selection, which predict that women’s increased entry into the labor market would tend to decrease wages in the lower part of the skill distribution (Beller 1977, Heckman 1979).

The last decade has seen a significant increase in usage of cloud services. This trend is not only related to the low cost and high availability of cloud providers, but also to the ease of use of the service and the reliability over time. Digital devices become rapidly obsolete and are subject to failures, hence outsourcing data permits to reduce the risks linked to data loss. Albeit there are advantages in uploading data to the cloud, there are also several security and privacy challenges. The experience gained by the Research and Industry communities attest that it is not enough to just change the visibility of data by applying a cryptographic transformation, to ensure an adequate level of protection. A cloud-oriented architecture has a wide attack surface, hence it is necessary to pay attention to the whole data lifecycle, from data collection and sanitization, to storage and processing, and finally the release. This doctoral thesis analyzes each of these stages, proposing solutions that push forward the current state of the art. The first part of the thesis deals with the collection of data, in particular in the mobile scenario. The mobile environment is especially relevant as smartphones are devices with limited storage, that are connected to the network, and with the ability to sense and log confidential data and Personal Identifiable Information. To access this information, an application must be granted the proper permission. Yet, all the components running inside the application (either trusted or included from third-parties) share the same execution environment, thus have the same visibility and access constraints. This is a limitation of the current mobile Operating Systems. Focusing on the Android, which is open source and available to researchers, we propose a set of modifications to achieve internal application compartmentalization leveraging the Mandatory Access Control (MAC) layer. With this approach, the developer can add a policy module to the application to confine each component, effectively restricting access to the application internal storage, to services, and to isolate vulnerability prone components. After the data are collected, a user or a company may apply to it sanitization before being uploaded to the cloud or being released to a consumer. Data sanitization (or anonymization) is a process by which data are irreversibly altered so that a subject (referenced within the data) cannot be identified, given a certain security parameter, while the data remain practically useful. The second part of the thesis presents an approach based on k-anonymity and `-diversity to apply data sanitization over large collections of sensors data. The approach described can be applied in parallel in a distributed environment and is characterized by a limited information loss. The third part of the thesis investigates the storage and processing stages. In this scenario, the cloud provider is typically considered honest-but-curious, which assumes that it will always comply with the requests issued by the user, but may abuse the access to the information provided. Hence, the goal is to support the execution of queries over outsourced data with a guarantee that the cloud provider does not have access to the data content. Unfortunately, the simple use of deterministic encryption does not offer a real protection against a curious provider, as the encrypted data maintain the same distribution of the original data. The approach presented in this thesis is applicable to relational data, and enables the execution of queries involving evaluation of equality and range conditions over attributes. The data is saved encrypted to the server into equally large blocks containing a fixed number of tuples. The blocks are managed by the server as single atomic units, and accessed through an encrypted multidimensional index also stored by the server. By doing this, the cloud provider is unable to identify the single items stored within each block. Local maps are saved by the client to search the index efficiently. The approach proposed provides perfect indistinguishability to an attacker with access to the stored data. This is achieved applying probabilistic encryption to the blocks storing the data, and by destroying (i.e., flattening) the frequencies of the encrypted index. The index is built as an evolution of the partitioning technique presented in the second part of the thesis to sanitize the dataset. The last part of the thesis addresses the data release stage. The goal is to provide a solution that can be used to schedule the release of chunks or partitions of data at a future point in time. Due to the confidential nature of data, we cannot rely on any honesty assumption. Hence, we move to a decentralized environment in which the parties (i.e., the network nodes) are mutually distrusting. In this setting, we model the parties as rational (or rather driven by pure economic interest), and propose a solution that is only based on economic incentives and penalties. All the technologies detailed in this thesis have been released under open source licenses and can be readily integrated with real systems.

We investigate the racial differences in positional and directional earning mobility for blacks and whites using seven 6-year longitudinal samples drawn from the Panel Study of Income Dynamics, extending from 1973 through 2015. Positional mobility comparisons are mixed for the proportion of each sample moving to a higher earnings category but reveal a higher percentage of blacks than whites trapped in the bottom 25% of the distribution of earnings. Directional mobility comparisons show that the mean increase from the initial to final earnings distribution was significantly greater for whites than for blacks throughout 1973–85 and 1997–2009. A breakdown of these findings by gender reveals that they arise primarily from the labor market experience of black men, who face more severe racial disparities in positional and directional earning mobility than black women. Overall, these findings on intragenerational earning mobility are in line with recent research on intergenerational mobility in the US.

This study presents the design and results of a rapid-fire survey that collects labor market data for individuals in the United States. The purpose is to test online panels for their application to social, economic, and demographic information as well as to apply this approach to the U.S. labor market. The Yale Labor Survey (YLS) used an online panel from YouGov to replicate statistics from the Current Population Survey (CPS), the government’s official source of household labor market statistics. The YLS’s advantages included its timeliness, low cost, and ability to develop new questions quickly to study unusual labor market patterns during the COVID-19 pandemic. Results from the YLS track employment data closely from the CPS during the pandemic. Although YLS estimates of unemployment and participation rates mirrored the broad trends in CPS data, YLS estimates of those two rates were less accurate than for employment. The study demonstrates the power of carefully crafted online surveys to replicate expensive traditional methods quickly and inexpensively.

In this article, we explore how 50 Maine municipalities communicated their response to COVID-19 in the earliest stages of the pandemic. Our study answers two questions: (1) What information and resources did Maine municipalities communicate about COVID-19? and (2) What characterizes a more robust communication response? Analyzing digital communications from March through July 2020, we found almost all municipalities in our sample communicated basic information about altered town operations. Some towns provided more robust responses that evolved over time and included nuanced messages about COVID-19, a sense of community, and collaborations with partners. While smaller, more rural municipalities may have fewer residents and resources, many showed a larger-than-expected capacity to pivot quickly and rally together to respond to COVID-19 and communicate about that response.

This article addresses whether and how unions help to dismantle workplace inequality experienced by people with different types of disabilities. Using pooled 2009–2018 CPS MORG data of 630,799 respondents covering almost a decade, we find that union membership benefits workers with disabilities more than other groups and workers with the severest disabilities benefit the most from being in unionized work. Because union membership increases disabled workers’ weekly earnings by more than double the increase experienced by people without disabilities, it brings unionized disabled workers closer to overall average earnings with important implications for inequality. Unionized work reduces earnings inequality between disabled and non-disabled workers, but earnings boosts associated with union membership generate more pronounced inequality within groups of workers with disabilities depending on whether individuals have access to unionized employment. We find that gaps among employed unionized and nonunionized disabled workers are significantly larger than those experienced by unionized and nonunionized female, Black, and Hispanic workers.

We study the geographic incidence and efficiency of an income tax by estimating a spatial equilibrium model with heterogeneous workers. The US income tax shifts households out of high-productivity cities, leading to locational inefficiency of 0.25 percent of output. Removing spatial tax distortions increases inequality because more educated households are more mobile and own larger shares of land. Flattening the tax schedule, or introducing cost-of-living adjustments or local wage adjustments leads to efficiency gains but causes substantial increases in inequality. Differences in mobility and land ownership across skill groups create an equity-efficiency trade-off that is unique to spatial settings.

In a first-ever nation-wide census tract evaluation, we assess the accuracy of the Hamilton–Perry population projection method for 65,221 census tracts. We started with 73,607 census tracts but eliminated those for which zeros appeared in age/sex groups. The test uses 1990 and 2000 census tract data by age and gender to construct cohort change ratios, which are then applied to 2000 census tract data to generate 2010 Hamilton–Perry projections that are evaluated in an ex post facto test against the reported 2010 census tract data by age and gender. The projections include: (1) uncontrolled age and gender projections; and (2) age and gender projections controlled to a projection of the population total by census tract. Mean Absolute Percent Error (MAPE) is used to evaluate precision and Mean Algebraic Percent Error (MALPE) is used to evaluate bias. We find that controlling the Hamilton–Perry projections by age for each tract to the linearly projected total population of each tract reduces both MAPE and MALPE within age groups by gender and for total females and total males. As this result suggests, simple linear extrapolation provides more accurate projections of the total population than does the Hamilton–Perry Method. However, even with controlling we find the Hamilton–Perry projections by age to be biased upward. Finally, we use MAPE-R (MAPE- Revised) to evaluate the effect of extreme outliers and find that high MAPEs in the uncontrolled projections are largely driven by extreme errors (outliers) found in less than 1 percent of the 65,221 census tracts used in the study.

This research examines the effect that low skill job opportunities have on the probability of enrollment in postsecondary institutions between men and women, namely the construction and manufacturing industries. The research is based on the human capital investment theory, which states that individuals will enroll in postsecondary institutions when the perceived benefits outweigh the costs. More job opportunity heightens the opportunity cost of enrollment, hence lowering the probability of enrollment. After running a probit model, there is evidence that enrollment is countercyclical and that enrollment decisions do not vary significantly between men and women. I find that a 1 percent increase in the state unemployment rate increases the probability of enrollment by 0.312 and 0.331 percent for men and women, respectively. It is found that an increase in the employment in the construction and manufacturing industries leads to an increased probability of enrollment in postsecondary institutions, which rejects the hypothesis in this research. However, there is a limitation to this since not all low skilled industries are accounted for.

Having a racially and ethnically diverse workforce is an important goal for any industry interested in improving efficiency and supporting equity. This paper examines diversity within the travel/hospitality industry in California. We find significant differences in earnings across races and ethnicities, with Blacks and Hispanics earning less than their white counterparts. These patterns have only been exacerbated by the COVID-19 pandemic, which has resulted in significant losses across the board, particularly for the noncollege educated workers. However, despite existing inequities within the industry, travel/hospitality still exhibits lower earnings disparities for nonwhites/non-Asians than other sectors in California. It also remains a steppingstone to a higher earning-job, particularly for those outside of the labor market. Nonetheless, the industry should continue to investigate ways to improve workforce racial and ethnic diversity, especially among higher paying and managerial jobs.

Joint distribution estimation of a dataset under differential privacy is a fundamental problem for many privacy-focused applications, such as query answering, machine learning tasks and synthetic data generation. In this work, we examine the joint distribution estimation problem given two data points: 1) differentially private answers of a workload computed over private data and 2) a prior empirical distribution from a public dataset. Our goal is to find a new distribution such that estimating the workload using this distribution is as accurate as the differentially private answer, and the relative entropy, or KL divergence, of this distribution is minimized with respect to the prior distribution. We propose an approach based on iterative optimization for solving this problem. An application of our solution won second place in the NIST 2020 Differential Privacy Temporal Map Challenge, Sprint 2.

I am very pleased to welcome this year’s United States Sustainable Development Report 2021. This report provides an up-to-date benchmarking of the progress of the 50 states towards the Sustainable Development Goals. The message is sobering. Most of the states are far off the mark in achieving the SDGs. Parts of the US are especially off track, notably the states of the Southern and Appalachian regions. The overall shortcoming of the United States in progress towards the SDGs is easily grasped by the overall US ranking among nations. In the global Sustainable Development Report 2021, the US ranks 32nd in the world, far behind most of the high-income countries. The US is especially lagging because of high obesity (SDG 2), low share of renewable energy (SDG 7), high inequality of income (SDG 10), high flows of wastes and emissions embodied in imports (SDG 12), high CO2 emissions (SDG 13), low protection of marine (SDG 14) and terrestrial (SDG 15) ecosystems, high rates of homicide and incarceration (SDG 16), low levels of development assistance (SDG 17), and excessive tax secrecy (SDG 17). The state level data not only replicate this overall lack of progress, but signal enormous variations across the US states. The highest performing region is New England, with Vermont ranking 1st, followed by Massachusetts (2nd), Maine (5th), New Hampshire (7th), and Connecticut (12th). The lowest ranking states are in the South and Appalachia, with Arkansas (47th), Louisiana (48th), West Virginia (49th), and Mississippi (50th). The poor performance of the US states reflects, among other factors, the enormous and rising inequality in American society. America is starkly divided by class, race, gender, and geography. This year’s report, and SDSN’s companion report In the Red: The US Failure to Deliver on the Promise of Racial Equity, put a special focus on the conditions of the poor and excluded in American society, especially regarding disparities by race and ethnicity. The evidence suggests that the large gaps in social and economic conditions in America are actually worsening on several crucial dimensions, such as wages, residential segregation, and child poverty. Unfortunately, the SDGs have not yet achieved a prominent position in US politics and public policy. While many universities, businesses, NGOs, and cities are aligning with the SDGs, the engagement of the federal and state governments on the SDGs remains far weaker, especially in comparison with the prominence of the SDGs in peer nations, notably in the European Union. The Trump Administration not only ignored the SDGs in policy making but avoided reference to them in US diplomacy as well. We hope that this year’s report will not only shine a light on the US shortcomings in progress towards the SDGs, but also spur a new national commitment to the goals, which after all have been adopted by all 193 member states of the United Nations. As we go to print, President Joe Biden’s proposals for Building Back Better (BBB) are still in legislative limbo. I would like to note that many of the proposals — for example on expanded healthcare, access to education, and sustainable infrastructure — are strongly aligned with the SDGs, and would help to advance America’s progress towards the goals. Ironically, the lead political opposition to the BBB proposals often arise in Congressional delegations of the states in the South and Appalachian Region that are farthest behind in achieving prosperous, inclusive, and sustainable economies. I hope that the detailed data and analysis in this report will help to align federal actions with rapid progress towards the SDGs.

Rationale & Objective: The burden of financial hardship among individuals with chronic kidney disease (CKD) has not been extensively studied. Therefore, we describe the scope and determinants of financial hardship among a nationally representative sample of adults with CKD. Study Design: Cross-sectional. Setting & Participants: Non-elderly adults with CKD from the 2014-2018 National Health Interview Survey. Exposure: Sociodemographic and clinical characteristics. Outcome: Financial hardship based on medical bills and consequences of financial hardship (high financial distress, food insecurity, cost-related medication non-adherence, delayed/forgone care due to cost). Financial hardship was categorized into 3 levels: no financial hardship, financial hardship but able to pay bills, and unable to pay bills at all. Financial hardship was then modeled in 2 different ways: (1) any financial hardship (regardless of ability to pay) versus no financial hardship and (2) inability to pay bills versus no financial hardship and financial hardship but able to pay bills. Analytical Approach: Nationally representative estimates of financial hardship from medical bills were computed. Multivariable logistic regression models were used to examine the associations of sociodemographic and clinical factors with the outcomes of financial hardship based on medical bills. Results: A total 1,425 individuals, representing approximately 2.1 million Americans, reported a diagnosis of CKD within the past year, of whom 46.9% (95% CI, 43.7%-50.2%) reported experiencing financial hardship from medical bills; 20.9% (95% CI, 18.5%-23.6%) reported inability to pay medical bills at all. Lack of insurance was the strongest determinant of financial hardship in this population (odds ratio, 4.06 [95% CI, 2.18-7.56]). Limitations: Self-reported nature of CKD diagnosis. Conclusions: Approximately half the non-elderly US population with CKD experiences financial hardship from medical bills that is associated strongly with lack of insurance. Evidence-based clinical and policy interventions are needed to address these hardships.

In an effort to promote comparative research on pensions, the Gateway to Global Aging Data is developing harmonized cross-national panel data on pension benefits and retirement incentives. Past research has varied in how it predicts pension benefits for individuals who have not yet claimed their benefits when administrative data on earnings histories is unavailable. We use the Gateway data to evaluate several alternative approaches to computing prospective pension benefits using common survey questions and validate them against matched administrative data. We find that in some settings naïve measures of pension benefit growth from continued work and delayed benefit claiming can perform as well as measures based on administrative data. We also find that prospective benefit levels are sensitive to the heterogeneity of lifecycle earnings dynamics, resulting in substantial measurement error even after accounting for work history.