President Biden entered office in January 2021 with the promise to end the COVID-19 pandemic and facilitate an economic transformation to “build a better America.” But what, exactly, does “better” mean? Answering that question in specific ways means establishing explicit benchmarks for progress, analyzing current trends, and identifying their impact and on whom. The Sustainable Development Goals (SDGs) can help with the answer. These 17 comprehensive, interconnected goals offer a set of metrics and evidence to better understand where the U.S. is on a set of critical economic, social, and environmental dimensions, and how far it needs to go in its quest to build a better America. The U.S. itself played a central role in shaping these benchmarks, which all countries adopted in 2015. Importantly, in a first, the goals recognized that “sustainable development” is a continuum of progress that no country has fully attained, making the goals applicable to all countries, regardless of income level. Grounded in human rights, fairness, opportunity, and justice, the goals reflect American values and anticipate the governing vision and key priorities articulated by the Biden administration. Measuring its ambitions against the targets and metrics of the SDGs provides an empirical, transparent, and accountable way to define what it means to build a better America and demonstrate progress. A commitment to the SDGs offers the administration an opportunity to reinforce and accelerate its domestic agenda while reestablishing U.S. global leadership with credibility and confidence, advancing shared global aspirations at home and abroad.

This paper documents that labor search and matching frictions endogenously generate countercyclical uncertainty. Quantitatively, this mechanism is strong enough to jointly explain uncertainty and real activity dynamics. Through this lens, uncertainty fluctuations are endogenous responses to the fluctuations in real activity, and they neither affect the severity of business cycles nor warrant policy intervention, in contrast with leading theories of the interaction between uncertainty and output. A structural VAR can recover the true shocks in our model but only if uncertainty is ordered last, the reverse of what is usually done in the literature.

This dissertation is unified by both policy opportunity and estimation method, examining the impact of Medicaid behavioral health policy in Virginia using a difference-in-differences framework. Medicaid-covered substance use disorder (SUD) treatment benefits in Virginia were significantly enhanced through a Section 1115 SUD waiver, which are required to be budget neutral. Therefore, it is critical to understand whether the objectives of the waiver are met using reliable, unbiased estimation methods. This dissertation includes two empirical research projects evaluating the impact of Medicaid policy on removing barriers in access to care and one methodological project comparing the performance of alternate approaches to difference-indifference estimation with health policies implemented at different points in time.

This dissertation examines whether place matters for Americans’ pessimism about the American Dream and if so, how place matters. First, I establish that place (conceptualized in terms of region and size of place) influences individual-level pessimism about the American Dream. Pessimism about the American Dream is measured with a General Social Survey (GSS) question on chances of improving one’s standard of living. I then use GSS data to analyze whether individual-level characteristics (race, sex, age, income, unemployment, educational attainment, marital status, and homeownership) help explain the influence of place on pessimism about the American Dream when respondents are nested in their county of residence; in other words, whether place matters because different places have different compositions of people. Next, I use GSS data merged with IPUMS USA (IPUMS) data to analyze whether county-level characteristics (race, sex, age, income, unemployment, educational attainment, marital status, and homeownership) help explain the influence of place on pessimism about the American Dream when respondents are nested in their county of residence; in other words, whether place matters because different places have different contexts. Finally, using the merged data, I analyze the interactions of the individual-level and county-level characteristics when respondents are nested in their county of residence; in other words, whether place matters differently for different people. While I am not able to quantify how much composition and/or context explain the influence of place on pessimism about the American Dream, I find that that different compositions of people in different places contributes to the influence of living in the Midwest compared to the Northeast. I also find that different age contexts in different places contribute to the influence of living in the Midwest compared to the Northeast, the influence of living in the West compared to the Northeast, and the influence of size of place (living in a suburban, exurban, micropolitan, and/or rural area compared to an urban area). County-level age is the only statistically significant county-level characteristic. My findings suggest that living in a county with a higher mean age is associated with increased odds of pessimism about the American Dream compared to living in a county with a lower mean age. I do not find any statistically significant interactions between the individual-level variables and the county-level variables. Throughout my dissertation, the influence of living in the South compared to the Northeast consistently remains statistically significant. I find that living in the South is associated with decreased odds of pessimism about the American Dream compared to living in the Northeast and that composition and context do not explain the influence of living in the South on pessimism about the America Dream. My findings suggest that the influence of living in the South on pessimism about the American Dream may be due to collective explanations, such as shared norms and values in the region.

Many Americans live paycheck to paycheck, but COVID-19 infections and related caregiving obligations are causing many workers to miss out on wages. 1 The United States is one of the few countries in the Organisation for Economic Co-operation and Development that does not have national paid sick leave or paid family and medical leave providing full or partial pay for time off from work.2 As COVID-19 reinfections become more likely,3 a lack of paid sick leave and paid family and medical leave policies increasingly contributes to economic hardship, food insufficiency, and housing insecurity among workers and their families.

Although public participation is a ubiquitous and legally required feature in urban planning, members of the public, elected officials, and planners express dissatisfaction with its process and outcomes. This study used critical incident interviews with planners who influence participation processes to investigate the factors that affect their choices in participation design and implementation. Based on analysis of 49 practice stories of “success” or “lack of success,” this study finds that public participation design (a) is a group process that (b) extends over time and (c) is shaped and constrained by institutional factors. There is wide variation in the degree of control planners have over the participation process. The study confirmed all of the factors in the literature, emphasizing the importance of the intention of the sponsor, the attitude and skills of the planner, and material resources. The study identified new factors, including the defined decision space, the type of plan, other planner assessment of the needs of the situation, in-the-moment-issues, and project team demographics. Unexpected findings included the ubiquity of race and other equity issues in planners’ stories of public participation as well as the presence and impact of participant protests that disrupted the planning process. Because participation design is a group process embedded in a particular context, improving participation is a systemic problem. Helping individual planners to develop better skills or more pro-social motivations can improve participation marginally, but it will take systemic change of the conditions supporting participation to make a significant difference in outcomes.

The U.S. Census Bureau’s plans for release of data from the 2020 Decennial Census of Population and Housing will publicly and transparently address the unavoidable trade-off between data privacy and data accuracy. Statistical analysts can and should, therefore, take into account the planned presence of well-specified, well-justified noise in data releases based on the 2020 Decennial Census.To aid researchers’ preparations, this article highlights both what is new as well as what seems new but is actually little changed. We examine strategies, trade-offs, and rationales associated with processing and releasing the decennial results. Based on this review, we offer specific conclusions to help promote appropriate and well-informed usage of the 2020 Census. Our strongest recommendation is that, in addition to publishing official tables, the Census Bureau also make either the noisy measurements file (NMF) or unbiased estimates of released table entries available for research purposes. To create official counts, the Census Bureau applies processes to restore face validity to privacy-protected counts (that is, they eliminate disturbing features such as negative and fractional counts). These processes also introduce statistical bias and intractable distortions that researchers may wish to avoid whenever possible. By contrast, the NMF entries do not suffer from the statistical ills added by restoring face validity, and can be easily interpreted by trained analysts. Our other recommendations address critical needs for input to Census Bureau decisions from researchers, for development of suitable statistical tools that work with privacy-protected data, for expanded options with regard to microdata, and for steps to improve the accuracy of decennial census data overall.

Land use integration has emerged as an important sustainability principle in the last three decades, with many American cities recently reforming their land use policies to promote mixed-use developments. This study examines policies and regulations on land use integration in the last two decades in the U.S. cities of Milwaukee, St. Louis, and Tampa. These three cities have been among the leading communities implementing New Urbanist, mixed-use developments. This study also examines land use patterns in these three cities and evaluates whether changes in land use policy and regulations can be observed in spatial patterns. The analysis of comprehensive plans and zoning ordinances reveals that these jurisdictions have established and increased efforts to promote mixeduse developments. The quantitative spatial analysis using Balance and Entropy indices shows small changes in overall city-wide land use mix patterns. Noteworthy positive changes mostly cluster in downtown areas where residential and non-residential proportions have become more even. Identified barriers to increasing land use mix include the long-standing tradition of zoning practices and the planning efforts targeting particular areas and not extending throughout these jurisdictions. This study also recognizes other potential factors affecting land use integration, including residential preferences, population growth, political leadership, and infrastructure legacies.

BACKGROUND AND OBJECTIVES: Rhetoric and policies aimed at restricting immigration to the United States, such as those proposed during the Trump administration, may lead to reduced enrollment in Medicaid for children of immigrants, even those who were legally eligible. This study assessed how children's health insurance coverage changed before versus during the Trump administration by parental immigration status. METHODS: Using American Community Survey data, we compared changes in rates of uninsurance and Medicaid enrollment for children in the United States before (2015 to 2016) versus during (2017 to 2019) the Trump administration. Children were categorized by parental immigration status: citizen children with US-born parents, citizen children with naturalized parents, children from mixed-status families, or noncitizen children. RESULTS: The study population included 2 963 787 children between 2015 and 2019, representing approximately 64 million children annually. Throughout our study period, uninsurance rates for children from mixed-status families and noncitizen children were higher than citizen children with United States-born parents. Beginning in 2017, there were significant increases in uninsurance among children from mixed-status families (0.48 percentage points [PP], 95% confidence interval [CI]: 0.06 to 0.91) that increased to 1.48 PP (95% CI: 0.98 to 1.99) by 2019 when compared with concurrent trends among citizen children with US-born parents. Changes were accompanied by significant decreases in Medicaid enrollment by 2019 (-0.89 PP, 95% CI: -1.62 to -0.16). CONCLUSIONS: There were substantial disparities in uninsurance rates by parental immigration status. Compared with citizen children with US-born parents, uninsurance rates among children from mixed-status families significantly increased between 2017 and 2019, with the magnitude of disparity widening over time.

We study how decades-long exposure to individuals of a given foreign descent shapes natives’ attitudes and behavior toward that group. Using individualized donations data from large charitable organizations, we show that long-term exposure to a given foreign ancestry leads to more generous behavior specifically toward that group’s ancestral country. To shed light on mechanisms, we focus on attitudes and behavior toward Arab-Muslims, combining several existing large-scale surveys, cross-county data on implicit prejudice, and a newly-collected national survey. We show that greater long-term exposure: (i) decreases both explicit and implicit prejudice against Arab-Muslims, (ii) reduces support for policies and political candidates hostile toward Arab-Muslims, (iii) leads to more personal contact with Arab-Muslim individuals, and (iv) increases knowledge of Arab-Muslims and Islam in general.

We investigate the impact of the COVID-19 epidemic on gender disparities in three employment outcomes: labor force participation, full-time employment, and unemployment. Using data from the monthly Current Population Survey, in this research note we test individual fixed-effects models to examine the employment status of women relative to that of men in the nine months following the onset of the epidemic in March of 2020. We also test separate models to examine differences between women and men based on the presence of young children. Because the economic effects of the epidemic coincided with the summer months, when women's employment often declines, we account for seasonality in women's employment status. After doing so, we find that women's full-time employment did not decline significantly relative to that of men during the months following the beginning of the epidemic. Gender gaps in unemployment and labor force participation did increase, however, in the early and later months of the year, respectively. Our findings regarding women's labor force participation and employment have implications for our understanding of the long-term effects of the health crisis on other demographic outcomes.

Objectives This article focuses on the older Latino undocumented population and anticipates how their current demographic characteristics and health insurance coverage might affect future population size and health insurance trends. Methods We use the 2013–2018 American Community Survey as a baseline to project growth in the Latino 55 and older undocumented population over the next 20 years. We use the cohort component method to estimate population size across different migration scenarios and distinguish between aging in place and new immigration. We also examine contemporary health insurance coverage and chronic health conditions among 55 and older undocumented Latinos from the 2003–2014 California Health Interview Survey. We then project health insurance rates in 2038 among Latino immigrants under different migration and policy scenarios. Results If current mortality, migration, and policy trends continue, projections estimate that 40% of undocumented Latino immigrants will be 55 years or older by 2038—nearly all of whom will have aged in place. Currently, 40% of older Latino undocumented immigrants do not have insurance. Without policies that increase access to insurance, projections estimate that the share who are uninsured among all older Latinos immigrants will rise from 15% to 21%, and the share who is both uninsured and living with a chronic health condition will rise from 5% to 9%. Discussion Without access to health care, older undocumented immigrants may experience delayed care and more severe morbidity. Our projections highlight the need to develop and enact policies that can address impending health access concerns for an increasingly older undocumented Latino population.

This paper examines the housing tenure choices of unauthorized immigrants following the largest immigration policy change in recent years. Our identification strategy exploits the discontinuity in eligibility criteria of the 2012 Deferred Action for Childhood Arrivals (DACA) program, which provides a renewable two-year reprieve from deportation and work authorization to eligible immigrants. We estimate a difference-in-differences model that compares eligible with ineligible individuals before and after the program's implementation. Our results indicate that DACA eligible household heads become more likely to be homeowners. Thus, DACA increases access to not only the US labor market but also the benefits of homeownership.

In this dissertation, I study the effects of state and federal programs and policies aimed to help vulnerable populations. In the second chapter, I estimate the effect of immigration enforcement on prenatal safety net programs and birth outcomes. I compare participation in prenatal WIC and Medicaid between likely undocumented mothers and US born non-Latina mothers. I find an increase in immigration enforcement lowers participation in Medicaid but has a null effect on WIC participation. Because undocumented people are ineligible for Medicaid except in special circumstances, using Medicaid to pay for the delivery of a newborn may signal a person’s immigration status. WIC eligibility does not have any restrictions regarding a person’s citizenship or legal status. This may explain the chilling effect observed in Medicaid but not in WIC. I find that undocumented mothers reduce their prenatal care. There are also improvements in infant birth weight and a decline in undocumented women’s birth rate. This suggests positive selection into birth when immigration enforcement intensifies. Chapter three examines the effect of Deferred Action for Childhood Arrivals (DACA) on living arrangements. DACA provides temporary relief from deportation for undocumented immigrants who arrived to the United States as children. DACA recipients receive a social security number, work permit, and may obtain a driver’s license in their home state. Previous studies have found that DACA improves beneficiaries’ economic wellbeing. Since housing decisions are closely linked to economic security, I compare DACA eligible and ineligible immigrants to estimate changes in living arrangements. I find DACA increases the incidence of living with a parent in a rented home by 1.9 percentage points and lowers the incidence of living with other family members by 2.4 percentage points. The economic benefits and mobility that DACA provides along with the lowered fear of deportation may change beneficiaries’ living arrangements but does not increase the likelihood of moving into a home without a family member. In chapter four, I re-examine the effect of Naloxone Access Laws on opioid mortality. In response to the opioid crisis, states adopted laws increasing the availability of Naloxone, an overdose reversal drug. The theoretical effect of these policies is ambiguous due to the potential for moral hazard. The current literature contains mixed results when using a difference-in-differences model to estimate the effect of Naloxone Access Laws on mortality. I revisit these studies and establish that the discrepancies in the findings stem from both different time periods studied and the policy definitions used. I then make a methodological correction by adjusting for the staggered policy adoption and find that Naloxone Access Laws increase opioid mortality by 39%. Finally, I discuss the validity of the results. A sharp rise in opioid mortality preceded the adoption of Naloxone Access Laws. Therefore the estimated results from a difference-in-differences model will not be causal. I propose using a contiguous county-border model to establish the causal effect of Naloxone Access Laws. The findings from this chapter emphasize the challenges of establishing causal estimates when evaluating public policies that inherently are not exogenous.

Importance Improving child and adolescent well-being is a critical public health goal, yet monitoring of this measure at the national level remains limited. Composite indices aggregate existing indicators from population data sources, but these indices currently have weaknesses that may inhibit widespread use. Objective To apply a novel, more child-centric index method to document changes in overall child and adolescent well-being in the US from 2000 to 2019, assess which states and racial and ethnic subpopulations experienced the greatest inequities in well-being, and identify the specific components associated with changes in the index over time. Design, Setting, and Participants This cross-sectional study applied the Child and Adolescent Thriving Index 1.0 to population-level data from 2000 to 2019 from several data sources. The area-based sampling frame for each of the component data sources allowed for nationally representative estimates for every year of the study period. The indices for every state and by race and ethnicity were also calculated. Due to the scope and breadth of the index components from across the life course, the Child and Adolescent Thriving Index 1.0 is intended to approximate the well-being of persons up to age 17 years. Data were analyzed from June 7, 2021, to March 17, 2022. Exposures Time in years. Main Outcomes and Measures The Child and Adolescent Thriving Index 1.0 is a weighted mean of 11 indicators intended to proxy well-being. The index comprises 11 components: non–low birth weight in neonates, preschool attendance in children aged 3 to 4 years, reading proficiency in fourth-grade students, math proficiency in eighth-grade students, food security in children younger than 18 years, general health status, nonobesity in high school students, nonsmoking in adolescents aged 12 to 17 years, non–marijuana use in adolescents aged 12 to 17 years, high school graduation in young adults aged 18 to 21 years, and nonarrest rate in children aged 10 to 17 years. The index ranges from 0 to 1, with 0 indicating minimum and 1 indicating maximum possible well-being at the population level. Results The Child and Adolescent Thriving Index 1.0 was applied to data from 12 320 national, state, and racial and ethnic population-level estimates. Over the study period, the Child and Adolescent Thriving Index 1.0 score increased from 0.780 points in 2000 to 0.843 points in 2019. Despite some convergence in geographic and racial and ethnic disparities, inequities were still present in 2019 in the South (−0.021 points) compared with the Northeast and among American Indian or Alaska Native (−0.079 points), Black (−0.053 points), and Latinx (−0.047 points) children and adolescents compared with White youths. Index components most associated with the overall increases in index scores of well-being were high school graduation rate (+0.028 units) and nonsmoking in adolescents (+0.022 units), amounting to 80.6% of the total increase. Conclusions and Relevance Results of this study suggest that child and adolescent well-being scores increased from 2000 to 2019, but substantial work remains to address persistent inequities across states and racial and ethnic populations. The newly developed Child and Adolescent Thriving Index 1.0 may be used in future work to evaluate which public policy types (economic, social, health care, housing, or education) are associated with higher levels of well-being.

In this paper I try to explain what happened to young black men's labor supply during the XX century. After 1930, there is a black/white divergence that has not been properly explained. I show that common explanations like the black migration to the North and the Great Depression are unlikely to have produced the labor supply divergence. However, the New Deal could be a far better explanation.

We examine the socioeconomic consequences of discrimination against people of Southern origins during the US Great Migration of the first half of the twentieth century. We ask whether people living in the American North and Midwest in 1940 fared worse with respect to education, occupation, and income if they were perceived to be of Southern origins. We also assess variation in these effects across racial groups and across actual region of origin groups. Using linked data from the 1920 and 1940 US censuses, we compare the life outcomes of about half a million pairs of brothers who differed with respect to the regional origin implied by their first names. For both Whites and Blacks, we find statistically significant associations between outcomes and the regional origin implied by names; regardless of where they were born, men living in the North or Midwest in 1940 did worse if their names implied Southern origins. However, these associations are entirely confounded by family-specific cultural, socioeconomic, and other factors that shaped both family naming practices and life outcomes. This finding—that regional discrimination in the early-twentieth-century United States did not happen based on names—contrasts sharply with findings from research in more recent years that uses names as proxies for people’s risk of exposure to various forms of discrimination. Whereas names are a basis for discrimination in modern times, they were not a basis for regional discrimination in an era in which people had more immediate and direct evidence about regional origins.

Each decade since the 1950s, demographers have generated high-quality net migration estimates by age, sex, and race for US counties using decennial census data as starting and ending populations. The estimates have been downloaded tens of thousands of times and widely used for planning, diverse applications, and research. Census 2020 should allow the series to extend through the 2010–2020 decade. The accuracy of new estimates, however, could be challenged by differentially private (DP) disclosure avoidance techniques in Census 2020 data products. This research brief estimates the impact of DP implementation on the accuracy of county-level net migration estimates. Using differentially private Census 2010 demonstration data, we construct a hypothetical set of DP migration estimates for 2000–2010 and compare them to published estimates, using common accuracy metrics and spatial analysis. Findings show that based on demonstration data released in 2020, net migration estimates by five-year age groups would only be accurate enough for use in about half of counties. Inaccuracies are larger in counties with populations less than 50,000, among age groups 65 and over, and among Hispanics. These problems are not fully resolved by grouping into broader age groups. Moreover, errors tend to cluster spatially in some regions of the country. Ultimately, the ability to generate accurate net migration estimates at the same level of detail as in the past will depend on the Census Bureau’s allocation of the privacy loss budget.

This special issue, Differential Privacy for the 2020 U.S. Census: Can We Make Data Both Private and Useful?, provides an entry point to help data scientists across many disciplines adjust to a big change in a key component of our national data infrastructure. The United States Census Bureau is adopting formal differential privacy protections for public products from the 2020 U.S. Decennial Census. This is the first time that a country has released most of its subpopulation counts with formal privacy protections, although certainly not the first time that other official counts have been perturbed for the purpose of disclosure avoidance. Population censuses are important. Indeed, they may be the oldest statistical products of communal societies. They are mentioned in the Bible (the book of Numbers) and required by Article I, Section 2 of the U.S. Constitution for allocating seats in Congress. After all, as Lord Kelvin noted in 1883: [W]hen you can measure what you are speaking about, and express it in numbers, you know something about it; but when you cannot measure it, when you cannot express it in numbers, your knowledge is of a meagre and unsatisfactory kind. (William Thomson, Lord Kelvin, Electrical Units of Measurement [1883]) [Lord Kelvin’s observation is often paraphrased to more zippy aphorisms such as, ‘You cannot manage what you cannot measure.’] These days, each U.S. decennial census plays a role far beyond simply determining how many seats each state holds in Congress. Statistical frames based on Census Bureau counts underlie nearly all the demographic descriptions and many decisions made by government, business, or other organizations in the United States. Massive federal expenditures are distributed according to population estimates based on census data. Furthermore, a number of active and influential research communities depend upon decennial census data products. Privacy protection for respondents is also important and getting more difficult to achieve. Such protection has long been required by law, in order to prevent harm and to encourage full and honest responses. Recently, though, growing uses of the decennial census, availability of other data sources, and increased computational firepower make protecting the privacy of census respondents more difficult. Fortunately, newly developed formal privacy protection systems can both measure the degree of privacy protection and allow adequate transparency to inform statistical inference on protected data. Previous statistical methods used to protect privacy (such as suppression and swapping observations) lack both of these desirable properties. Nevertheless, adopting a new form of privacy protection for such important data is far from easy. Some of the key challenges include implementation issues confronted by the Census Bureau, understanding analytical implications for data scientists, and managing communication so that all stakeholders can engage effectively with each other and inform the public about the implications of the change.

The development of a country relies on several factors, one of them being human capital. According to Hussain et al. (2000), human capital development is essential for ensuring sustained economic growth and fighting poverty. One of the crucial components of human capital is education. In fact, according to a new OECD measure of human capital, human capital builds on two components: years of schooling and rates of return to schooling. Some economists assume that countries with a more educated population should have higher productivity since those with more education and experience tend to earn higher salaries. There is also evidence from many countries that points to strong causal links between the average education level of women and increased levels of national economic development. For example, many developing countries reveal a large and generalized educational gender gap and a significantly low literacy rate for women. There are many barriers to girls’ education in less developed countries like poverty, child marriage, and gender-based violence, which may vary among countries and communities. For that purpose, UNICEF1 Works with communities, governments, and partners to remove barriers to girls’ education and promote gender equality in education – even in the most challenging settings. As part of the many international efforts to resolve the disparities, the second goal of the Millennium Development Goals (MDG)2 was set as “Achieve universal primary education” in 2000, to which many countries, including Benin, agreed to work towards and reach by 2015.