We introduce the Congressional Petitions Database (CPD), an original endeavor tracking virtually every petition introduced to Congress from 1789 to 1949. Exploiting Congress’s ritual reading of petition prayers, we leverage a supervised machine learning algorithm to create a database comprising over 537,000 petitions. For each petition we code the prayer and its subject matter, geographic origin, initial disposition and other information. Initial analyses suggest that (1) per-capita petitioning peaked nationwide in the mid- and late-nineteenth century and remained at higher levels until World War I, declining appreciably thereafter; (2) the South exhibits lower petitioning from 1802 to 1870 (but not before 1800), cratering in the 1840s through 1860s and again later in the Jim Crow Era; and (3) the unenfranchised petitioned regularly and their petitions were afforded process similar to all others. The CPD will be useful for studies of legislative development, social movements, interest group advocacy, federalism and sectionalism.

Background: School closures have been enacted as a measure of mitigation during the ongoing coronavirus disease 2019 (COVID-19) pandemic. It has been shown that school closures could cause absenteeism among healthcare workers with dependent children, but there remains a need for spatially granular analyses of the relationship between school closures and healthcare worker absenteeism to inform local community preparedness. Methods: We provide national- and county-level simulations of school closures and unmet child care needs across the USA. We develop individual simulations using county-level demographic and occupational data, and model school closure effectiveness with age-structured compartmental models. We perform multivariate quasi-Poisson ecological regressions to find associations between unmet child care needs and COVID-19 vulnerability factors. Results: At the national level, we estimate the projected rate of unmet child care needs for healthcare worker households to range from 7.4 to 8.7%, and the effectiveness of school closures as a 7.6% and 8.4% reduction in fewer hospital and intensive care unit (ICU) beds, respectively, at peak demand when varying across initial reproduction number estimates by state. At the county level, we find substantial variations of projected unmet child care needs and school closure effects, 9.5% (interquartile range (IQR) 8.2-10.9%) of healthcare worker households and 5.2% (IQR 4.1-6.5%) and 6.8% (IQR 4.8-8.8%) reduction in fewer hospital and ICU beds, respectively, at peak demand. We find significant positive associations between estimated levels of unmet child care needs and diabetes prevalence, county rurality, and race (p<0.05). We estimate costs of absenteeism and child care and observe from our models that an estimated 76.3 to 96.8% 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. Conclusions: School closures are projected to reduce peak ICU and hospital demand, but could disrupt healthcare systems through absenteeism, especially in counties that are already particularly vulnerable to COVID-19. Child care subsidies could help circumvent the ostensible trade-off between school closures and healthcare worker absenteeism.

This paper examines the effects of population growth and decline on county-level income inequality in the rural United States from 1980 to 2016. Findings from previous research have shown that population growth is positively associated with income inequality. However, these studies are largely motivated by theories of urbanization and growth in metropolitan areas and do not explicitly test for differences between the impacts of population growth and decline. Examining the effects of both forms of population change on income inequality is particularly important in rural areas of the United States, the majority of which are experiencing population decline. We analyze county-level data (N = 11,320 county-decades) from the U.S. Decennial Census and American Community Survey, applying fixed effects regression models to estimate the respective effects of population growth and decline on income inequality within rural counties. We find that both forms of population change have significant effects on income inequality relative to stable growth. Population decline is associated with increases in income inequality, while population growth is marginally associated with decreases in inequality. These relationships are consistent across a variety of model specifications, including models that account for counties’ employment, sociodemographic, and ethno-racial composition. We also find that the relationship between income inequality and population change varies by counties’ geographic region, baseline level of inequality, and baseline population size, suggesting that the links between population change and income inequality are not uniform across rural America.

To better understand patterns of prekindergarten applications and lottery outcomes, ideal analyses would draw on a rich set of background data on children and families and explore variation in participation and match rates across demographic and socioeconomic groups. But the DC prekindergarten lottery collects minimal background data because of its primary mission to support a streamlined and accessible application system. Instead, we use families' listed addresses, geocoded and linked to American Community Survey five-year microdata on community characteristics at the Public Use Microdata Area (PUMA) level, to proxy for the individual characteristics of prekindergarten applicants and matched and wait-listed applicants. Although this approach has limitations, it provides the best opportunity to learn about applicants and their families before school enrollment. Additional details on methods and limitations can be found in our report, Who Wins the Preschool Lottery? (Greenberg et al. 2020). Table 1 describes the characteristics of 3-year-olds in the District of Columbia, prekindergarten applicants, and matched and wait-listed applicants. Columns 2, 3, and 4 describe the average community characteristics of DC prekindergarten applicants, those matched by the lottery, and those wait-listed, respectively. Comparisons with column 1 show striking similarity: applicants and children matched to prekindergarten look nearly identical across all characteristics examined, differing from all young children by 3 percentage points, at most. Larger differences appear in comparing wait-listed applicants and the population as a whole. Communities of children wait-listed into public prekindergarten for 3-year-olds (PK3) contain, on average, lower shares of Black families and higher shares of Hispanic 1 and white families. Their communities have lower shares of families with one parent and higher shares of families with two or no parents. Wait-listed applicants come from communities with higher shares of families with at least one immigrant parent and lower shares of families speaking only English at home. Their communities are also more socioeconomically advantaged: they have higher shares of two-parent full-time working households, families with higher incomes, and families with four-year college degrees or more, along with lower shares of families receiving food stamps. Given that these comparisons rely on data from only five PUMAs, the number and magnitude of these differences is remarkable. Patterns look similar for public prekindergarten for 4-year-olds (PK4). These findings suggest that wait-listed applicants disproportionately come from socioeconomically advantaged communities. Findings also suggest disparities in lottery outcomes for immigrant families that warrant further consideration, especially as public preschool has been shown to improve access and school readiness for children of immigrants in other contexts (Greenberg, Michie, and Adams 2018; Greenberg, Rosenboom, and Adams 2019).

We present and empirically implement an equilibrium labor market search model where risk-averse workers facing medical expenditure shocks are matched with firms making health insurance coverage decisions. We use our estimated model to evaluate the equilibrium impact of many health care reform proposals, including the 2010 Affordable Care Act (ACA). We use the estimates of the early impact of the ACA as a model validation. We find that income-based subsidies for health insurance premiums are crucial for the sustainability of the ACA, while the ACA can still substantially reduce the uninsured rate without the individual or the employer mandate.

This preprint is an exploration in how a single mathematical idea– entropy– can be applied to redistricting in a number of ways. It’s meant to be read not so much as a call to action for entropy, but as a case study illustrating one of the many ways math can inform our thinking on redistricting problems. This preprint was prepared as a chapter in the forthcoming edited volume Political Geometry, an interdisciplinary collection of essays on redistricting.

To reduce poverty and improve family well-being, the 20th century saw the creation and expansion of scores of federal programs offering assistance to low-income families with children. Major programs included Aid to Dependent Children (created in 1935, later known as AFDC and now Temporary Assistance for Needy Families or TANF), the Social Security survivor insurance program (1939), Food Stamps (1964, now known as the Supplemental Nutrition Assistance Program or SNAP), Medicaid (1965), rental assistance (1965), Supplemental Security Income (1972), the earned income tax credit (1975), the child tax credit (1997), and the Children’s Health Insurance Program (1997). These and dozens of other programs provide an array of cash, food, housing, health, and other benefits designed to assist families, including those with children, with material and other needs.1 Drawing on data from the Urban Institute, the Roadmap displays annual federal expenditures on children between 1960 and 2017, in inflation-adjusted terms.2 Figure 4-5 in the Roadmap shows that spending grew from $60.5 billion in 1960 to $516.4 billion in 2010, before moderating to $481.5 billion by 2017 “largely due to the decrease in transfers during the economic recovery that followed the Great Recession.” The Roadmap finds that “the eight-fold growth in real spending between 1960 and 2010 is striking, and it is many times larger than the 15-percent increase in the number of children in the population.”3 Table D4-1 breaks out federal expenditures on children by program for selected years between 1960 and 2017, in constant dollars. It shows how real spending almost universally grew in those programs between those years, including because most current programs didn’t exist in 1960.4 State spending adds to that federal spending on children.

Violence often induces white flight to the suburbs and traps blacks in high-crime cities, shaping black-white suburbanization inequality. This study examines the emergence of the crack epidemic in the mid-1980s and city-to-suburb mobility between and within ethno-racial groups. Using the 1980 and 1990 IPUMS data, I compare the mean and dispersion of city-to-suburban mobility before and during the period of the crack epidemic in cities with high intensity of the epidemic relative to cities with low intensity. The results suggest that the crack epidemic increased black and Hispanic flight to the suburbs, but it did not increase white flight. Furthermore, the crack epidemic increased disparity in city-to-suburb mobility among blacks. I find that the source of this heterogeneity is middle-class black migrants who fled to the suburbs. Supporting evidence is consistent with the idea that the crack epidemic changed the location of business establishments from the inner-city to the suburbs, which results in greater economic returns to city-to-suburb migration among selective demographic groups who were heavily affected by the crack epidemic but had resources to migrate. Given the historically lower rates of suburbanization among blacks and Hispanics, the results suggest that the crack epidemic decreased suburbanization inequality between minorities and whites but increased suburbanization inequality among blacks.

Measuring physical activity reliably and accurately is challenging. The gold standard is limited because it is resource intensive. Accelerometer data has been used as a more accessible option but other less explored data such as time diary data could be a good alternative. Understanding how estimates differ by measurement strategy, including time diary data and accelerometer data, is thus an important contribution to the limited research base about physical activity measurement. Using nationally representative data from the 2003-2006 National Health and Nutrition Examination Survey (NHANES) and the 2006-2008 American Time Use Survey (ATUS), we investigate the following questions. First, how do recall estimates of time spent in physical activity in the ATUS compare to NHANES estimates based on accelerometer data? And, to what extent do these estimates vary by demographic characteristics?

Housing affordability is an issue of increasing importance and interest, particularly in the United States. Much of this interest is due to skyrocketing rents in coastal cities with tight housing markets. Shrinking cities, in contrast, are often characterized as rich in low-cost housing, providing an affordable alternative to superstar cities. This paper compares income and rent dynamics in cities with growing versus shrinking populations. While costs may be lower in shrinking cities, falling incomes have likely rendered housing unaffordable for many residents. We employ multiple lines of evidence to test for different dynamics between growing and shrinking cities. Matching is used to explore changes in income and rent between 1980 and 2017 in shrinking and the most similar non-shrinking cities. After controlling for baseline conditions, shrinking cities exhibit faster falling incomes and growing cities exhibit faster rising rents, while rent burden increases at a very similar rate in both groups. We also use a fixed effects regression model to test for differences between growing and shrinking cities in sensitivity of rent burden to changes in income and rent. Rent burden has considerably increased across US cities since 1980, yet growing and shrinking cities exhibit clearly different pathways toward that end. Shrinking cities are more sensitive to identical changes in income and rent, likely because a greater share of their residents live near the edge of affordability.

Over the last two decades, the percentage of households that rent has risen as has the percentage of renter households who are burdened by housing costs. Today, most low-income renters and low-income homeowners spend more than half of their income on housing. Housing insecurity has increased since the beginning of the pandemic, particularly among Black and Hispanic households. Absent massive increases in the supply of affordable housing, including private and social housing, and direct financial assistance to struggling renters and homeowners, millions of strapped renters and homeowners will go deeper into debt, face more hardship and insecurity, and ultimately lose or be evicted from their homes.

Researchers make hundreds of decisions about data collection, preparation, and analysis in their research. We use a many-analysts approach to measure the extent and impact of these decisions. Two published causal empirical results are replicated by seven replicators each. We find large differences in data preparation and analysis decisions, many of which would not likely be reported in a publication. No two replicators reported the same sample size. Statistical significance varied across replications, and for one of the studies the effect’s sign varied as well. The standard deviation of estimates across replications was 3-4 times the typical reported standard error.

Scholars have declared that underserved communities will face the greatest marginalization due to disruptions in the 21st century. In our previous research engagements, we developed papers and presentations on 'The Future of Work and Entrepreneurship for the Underserved' and we shared data and our concerns for global income disparities and the need for a global perspective in this discourse. Purpose of Research-In this paper, we present a global snapshot regarding the future of work and entrepreneurship for the underserved and various perspectives from different authors on what these new changes predict for the underserved in the world. Design/ Methodology: After reviewing literature and determining key themes imperative to this topic, we put up a call for chapters that attracted diverse authors in academia and industry across the world representing different geographical regions including the USA, South America, Asia, and Africa. The abstracts were peer-reviewed and analyzed to identify commonalities and key areas of focus among the underserved communities worldwide. Results/Findings: The submitted abstracts found aligned with themes in education, technology and innovation, small business development, and diverse labor markets to the future of work and entrepreneurship for the underserved. They also explored other areas such as increasing utilization of labor in the unscaled economy through creativity and the use of emerging innovations and technologies. Additionally, the COVID 19 pandemic phenomenon was highlighted as a challenge that has exacerbated the need to address the future of work and entrepreneurship post-COVID. Practical Implications and Conclusions: We propose that unity in community and capacity building is vital to create shared prosperity. In this paper, we will share a summary of the chapters which will be included in the forthcoming handbook and perspectives on what the future of work and entrepreneurship will evolve into the new normal. We hope that this analysis will create further dialogue in academia, industry, and policy on how to ensure that the underserved are included in the future of work and entrepreneurship.

Objectives. To estimate the effects of same-sex marriage recognition on health insurance coverage. Methods. We used 2008–2017 data from the American Community Survey that represent 18 416 674 adult respondents in the United States. We estimated changes to health insurance outcomes using state–year variation in marriage equality recognition in a difference-in-differences framework. Results. Marriage equality led to a 0.61 percentage point (P = .03) increase in employer-sponsored health insurance coverage, with similar results for men and women. Conclusions. US adults gained employer-sponsored coverage as a result of marriage equality recognition over the study period, likely because of an increase in dependent coverage for newly recognized same-sex married partners.

This study investigates the observed positive relationship between educational attainment and likelihood of black-white interracial marriages. Different from the previous studies that focus only on the role of individual education levels in interracial marriages, this study contributes to the literature by examining the impact of the spatial variations in relative black/white educational distributions in marriage markets. The first contribution of this study is to provide an answer to the low black-white intermarriage rate puzzle by suggesting that as black and white educational differences in general between lessen and as individual educational attainment increase black-white interracial marriages may not become more common. The relative importance of three mechanisms through which education may affect intermarriage probability is examined: (1) racial adaptability effect, (2) enclave effect, and (3) educational dissimilarity effect. Using the U.S. Census Data, this study’s second contribution is the finding that the enclave and the educational dissimilarity effects are more important than the racial adaptability effect in explaining intermarriage probability of black males. Our results suggest that rising black individual educational attainments may not always result in an increased intermarriage likelihood. Differences in the black and white education distributions have a significant impact on the black/white interracial marriage probability

This paper addresses the question of why many public school teachers work substantially more hours than required by contract given that the elasticity of earnings with respect to hours is close to zero in this occupation. I introduce a theoretical framework in which some public sector employees are intrinsically motivated to supply effort above the level stipulated by their contract, while others have low productivity and require high effort to maintain the minimally required level of output. In this setting, high levels of effort can be indicative of either altruism or low productivity. Because intrinsically motivated employees derive higher utility from working in the public sector, they are less likely to exit it. Over time, selection makes high levels of effort more strongly predictive of altruism than of low ability. I show empirical evidence consistent with this model from the market for public school teachers, where I define effort as working hours. At very low levels of experience, there is little or no relationship between weekly hours and the probability of remaining in teaching or a subjective measure of intrinsic motivation. These correlations become more positive as teaching experience increases. Similarly, hours are positively related to self-reported burnout at low levels of experience, but the relationship is reversed for teachers who have been in the profession longer.

Climatic variability affects many underlying determinants of child malnutrition, including food availability, access, and utilization. Evidence of the effects of changing temperatures and precipitation on children’s nutritional status nonetheless remains limited. Research addressing this knowledge gap is merited given the short- and long-run consequences of malnutrition. We address this issue by estimating the effects of temperature and precipitation anomalies on the weight and wasting status of children ages 0–59 months across 18 countries in sub-Saharan Africa. Linear regression models show that high temperatures and low precipitation are associated with reductions in child weight, and that high temperatures also lead to increased risk of wasting. We find little evidence of substantively meaningful differences in these effects across sub-populations of interest. Our results underscore the vulnerability of young children to climatic variability and its second-order economic and epidemiological effects. The study also highlights the corresponding need to design and assess interventions to effectively mitigate these impacts.

A growing number of cities organize common application systems for families seeking free public preschool for their children. These cities, including Atlanta, Boston, New Orleans, New York City, and Washington, DC, differ substantially in the parameters of their systems, their preschool eligibility criteria, and the quality and availability of their preschool programs. What they share is the use of an innovative assignment mechanism—the deferred acceptance (DA) algorithm—to assign students to schools that receive more applications than they have seats (Abdulkadiroğlu et al. 2017; Gale and Shapley 1962; and Pathak 2011). The DA algorithm, in turn, provides the basis for a naturally occurring randomized experiment with the potential to revolutionize preschool impact evaluation.

Background and objective Simulation models can project effects of tobacco use and cessation and inform tobacco control policies. Most existing tobacco models do not explicitly include relapse, a key component of the natural history of tobacco use. Our objective was to develop, calibrate and validate a novel individual-level microsimulation model that would explicitly include smoking relapse and project cigarette smoking behaviours and associated mortality risks. Methods We developed the Simulation of Tobacco and Nicotine Outcomes and Policy (STOP) model, in which individuals transition monthly between tobacco use states (current/former/never) depending on rates of initiation, cessation and relapse. Simulated individuals face tobacco use-stratified mortality risks. For US women and men, we conducted cross-validation with a Cancer Intervention and Surveillance Modeling Network (CISNET) model. We then incorporated smoking relapse and calibrated cessation rates to reflect the difference between a transient quit attempt and sustained abstinence. We performed external validation with the National Health Interview Survey (NHIS) and the linked National Death Index. Comparisons were based on root-mean-square error (RMSE). Results In cross-validation, STOP-generated projections of current/former/never smoking prevalence fit CISNET-projected data well (coefficient of variation (CV)-RMSE≤15%). After incorporating smoking relapse, multiplying the CISNET-reported cessation rates for women/men by 7.75/7.25, to reflect the ratio of quit attempts to sustained abstinence, resulted in the best approximation to CISNET-reported smoking prevalence (CV-RMSE 2%/3%). In external validation using these new multipliers, STOP-generated cumulative mortality curves for 20-year-old current smokers and never smokers each had CV-RMSE ≤1% compared with NHIS. In simulating those surveyed by NHIS in 1997, the STOP-projected prevalence of current/former/never smokers annually (1998–2009) was similar to that reported by NHIS (CV-RMSE 12%). Conclusions The STOP model, with relapse included, performed well when validated to US smoking prevalence and mortality. STOP provides a flexible framework for policy-relevant analysis of tobacco and nicotine product use.

The Current Population Survey (CPS) classifies the jobs of respondents into hundreds of detailed industry and occupation categories. The classification systems change periodically, creating breaks in time series. Standard concordances bridge the periods, but often leave empty cells or inaccurate sharp changes in time series. They also usually build in the assumption that categories from a certain period of time can be representative, on more aggregate levels, and of longer historical periods. For each employed CPS respondent from before the year 2000 we impute post-2000 Census industry and occupation classifications and related variables. The imputations use micro data about each individual and training data sets that were classified by specialists into two industry and occupation category systems-that is, they are dual-coded. We train a random forests classifier to handle the changes in classification between the 1990s and 2000s largely on the dual-coded data set and apply it to the full CPS and IPUMS-CPS to impute several variables, including industry and occupation. For changes in classification when an industry or occupation splits, we train the algorithms on the observations with the newly classified industry or occupation split to predict how the historical observations would have been classified. We generate an augmented CPS, with additional columns of standardized industry and occupation. This data can serve research on many topics.