In 2016, Montana expanded its Medicaid program under the Health and Economic Livelihood Partnership (HELP) Act. Legislators are considering a bill, the Medicaid Reform and Integrity Act (MeRIA), to impose work requirements (“community engagement”) and terminate Medicaid insurance coverage if beneficiaries work less than 20 hours a week for three or more months. This analysis is based on a draft of the bill dated February 5, 2019. Work requirements could cause between 26,000 and 36,000 low-income adults to lose Medicaid coverage (30% to 41% of the 87,000 beneficiaries aged 19 to 59 years old). Analyses of Census data show that among those most likely to be terminated: • One-quarter (26%) are parents of minor children. • One-quarter (23%) have a dependent with a disability. • One-quarter (26%) are in school. • More than a third (37%) have seasonal employment and work six or more months of the year, but not enough to meet the requirements all year. • One-sixth (17%) lack internet access, reducing their ability to report their work hours or exemptions. • More than a third (37%) live in more rural areas of Montana. Because there may be fewer job opportunities in rural areas, rural Montanans may experience greater losses. • One-ninth (11%), or more than 3,000 adults, are Native Americans. These changes are especially problematic since Montana has already pioneered HELP-Link, its voluntary work promotion system for those on Medicaid, that has provided training and helped increase employment. HELP-Link has been viewed as a national leader. Those who lose insurance coverage will have worse access to . . .

Agriculture is essential for feeding the large and growing world population, but it can also generate pollution that harms ecosystems and human health. Here, we explore the human health effects of air pollution caused by the production of maize—a key agricultural crop that is used for animal feed, ethanol biofuel and human consumption. We use county-level data on agricultural practices and productivity to develop a spatially explicit life-cycle-emissions inventory for maize. From this inventory, we estimate health damages, accounting for atmospheric pollution transport and chemistry, and human exposure to pollution at high spatial resolution. We show that reduced air quality resulting from maize production is associated with 4,300 premature deaths annually in the United States, with estimated damages in monetary terms of US$39 billion (range: US$14–64 billion). Increased concentrations of fine particulate matter (PM2.5) are driven by emissions of ammonia—a PM2.5 precursor—that result from nitrogen fertilizer use. Average health damages from reduced air quality are equivalent to US$121 t−1 of harvested maize grain, which is 62% of the US$195 t−1 decadal average maize grain market price. We also estimate life-cycle greenhouse gas emissions of maize production, finding total climate change damages of US$4.9 billion (range: US$1.5–7.5 billion), or US$15 t−1 of maize. Our results suggest potential benefits from strategic interventions in maize production, including changing the fertilizer type and application method, improving nitrogen use efficiency, switching to crops requiring less fertilizer, and geographically recating production.

Traditional classification methods cannot well capture the characteristics of complex problems, thus leading to poor performance. In this paper, we propose a new framework named Partition based LAzy Classification (PLAC) tobetter characterize complex problems by dividing the training data space into smaller and easier-to-learn partitions. In PLAC, only the nearest partition of a new instance is used to train a local classifier that is finally used to classify the new instance. As the partitioning is performed based on information gain before receiving a new instance, the resulting partitions are groups of similar instances and the chance of the nearest instances of the new instance coming from different regions by accident isreduced. Moreover, our method uses only one partition to conducta prediction and employs the caching mechanism to avoid work replication during classification, thus efficiency is improved. An extensive experimental evaluation on 40 real world data sets shows that PLAC effectively improves the performance of base classifiers and outperforms existing mainstream ensemble methods.

Tennessee is seeking federal permission to impose a work reporting requirement on low-income parents and caregivers receiving health coverage through Medicaid. Under the proposal, these beneficiaries ages 19 to 64 would have to document that they are working at least 20 hours a week or participating in job-training, education, or volunteer activities in order to maintain their TennCare II coverage. One parent in a household with children under age 6 would be exempt. Because Tennessee has not expanded Medicaid under the Affordable Care Act, the only adults targeted are parents whose incomes are at or below 98 percent of the federal poverty level. The impact of the proposal could mean some of the state’s poorest parents would lose health coverage altogether. And that loss of coverage will affect their children, who may lose access to care, as well, even though they are technically exempt. Tennessee’s proposal does not provide any estimate of how the new reporting rules would affect enrollment in TennCare if the Centers for Medicare and Medicaid Services (CMS) approve the request to amend the state’s section 1115 “TennCare II” demonstration waiver. Nor does the state even mention the real possibility that many of these parents (and some of their children) would become uninsured.

There is a growing body of literature on the impacts of labor-replacing technologies. Most of this literature is focused on developed economies, since they are at the frontier of adoption of new technologies, and concerns are rising about the negative impacts on workers. The findings of this empirical literature show that while these technologies can bring about significant efficiency gains (Graetz & Michaels 2017), they can also have negative impacts on employment outcomes (Acemoglu & Restrepo 2017). However, evidence for developing countries is scarce. This is driven not only by data constraints, but also because poorer countries in general have not been as successful as their richer counterparts at adopting new technologies at such a large scale. This article argues that developing countries can still experience some disruptive effects by being exposed to automation in developed economies. For example, automation in the US may reduce the demand for Mexican goods if it allows to reduce marginal . . .

One major criticism of Universal Basic Income (UBI) is that unconditional cash transfers discourage recipients from working. We estimate the causal effects of a universal cash transfer on short-run labor market activity by exploiting the timing and variation of a long-running unconditional and universal transfer: Alaska's Permanent Fund Dividend. We find evidence of both a positive labor demand and negative labor supply response to the transfers. Altogether, a $1,000 increase in the per-person disbursement leads to a 0.2% labor-market contraction on an annual basis, suggesting that concerns over adverse labor market impacts from modest universal transfers may be overstated.

I analyze the importance of the local environment on the direction and subsequent diffusion of agricultural innovations. By comparing counties that are near and far from land grant colleges using a variety of distance measures, I show that proximity is more important for agricultural productivity and output than for other measures of innovation in other sectors. To shed light on how widely innovations from land grant colleges diffuse, I exploit data on the histories of new wheat varieties introduced in the U.S. before 1920 and find that only 10-17% of wheat acreage planted in varieties developed since the establishment of land grant colleges is planted in varieties developed at land grant colleges. To present direct evidence that the local environment affects the direction of innovation, I use data on publications by researchers affiliated with land grant colleges to show that, even more than a century after the land grant colleges were established, land grant research is biased towards crops that were initially most prevalent in land grant college counties, rather than those that were most prevalent in the rest of the state. Finally, I show that alumni of land grant colleges with agricultural degrees were more likely to live near their alma maters than were alumni with other majors, which I interpret as evidence that agricultural human capital is more location-specific than other forms of human capital.

Coming out of the 1960s, unearned income has expanded to comprise over one-third of total personal income. Transfer payments, commonly social safety net benefits, comprise the largest component. In Appalachia, where transfer payments have historically been relied upon at an elevated level, their importance has decreased as the region integrates into national economic processes and economic structures change. This integration has been uneven, with multiple theories and processes offered as explanation. As such, no complete understanding of the factors leading to a change in transfer payment income in Appalachia has yet been made. To inform this deficiency, this paper utilizes spatial regression techniques to examine the role of Appalachian disadvantage and economic integration in county level transfer payment income change in Appalachia, 1990–2010. Results indicate that trajectory stemming from local structures of Appalachian disadvantage drive transfer payment change more so than specific industries related to neo-classical growth. Further, results identify a spatial structure where factors of disadvantage (e.g. unemployment) produce stronger spillover effects than factors of advantage (e.g. manufacturing growth) in changing transfer payment reliance.

Since 1985, the Demographic and Health Surveys (DHS) Program has conducted more than 400 surveys in over 90 countries. These surveys provide decision markers with key measures of population demographics, health and nutrition, which allow informed policy evaluation to be made. Though standard health indicators are routinely published in survey final reports, much of the value of DHS is derived from the ability to download and analyse standardised microdata datasets for subgroup analysis, pooled multi-country analysis, and extended research studies. We have developed an open-source freely available R package ‘rdhs’ to facilitate management and processing of DHS survey data. The package provides a suite of tools to (1) access standard survey indicators through the DHS Program API, (2) identify all survey datasets that include a particular topic or indicator relevant to a particular analysis, (3) directly download survey datasets from the DHS website, (4) load datasets and data dictionaries into R, and (5) extract variables and pool harmonised datasets for multi-survey analysis. We detail the core functionality of ‘rdhs’ by demonstrating how the package can be used to firstly compare trends in the prevalence of anaemia among women between countries before conducting secondary analysis to assess for the relationship between education and anemia.

The past several decades have witnessed growing geographic disparities in life expectancy within the United States, yet the mortality experience of U.S. cities has received little attention. We examine changes in men’s life expectancy at birth for the 25 largest U.S. cities from 1990 to 2015, using mortality data with city of residence identifiers. We reveal remarkable increases in life expectancy for several U.S. cities. Men’s life expectancy increased by 13.7 years in San Francisco and Washington, DC, and by 11.8 years in New York between 1990 and 2015, during which overall U.S. life expectancy increased by just 4.8 years. A significant fraction of gains in the top-performing cities relative to the U.S. average is explained by reductions in HIV/AIDS and homicide during the 1990s and 2000s. Although black men tended to see larger life expectancy gains than white men in most cities, changes in socioeconomic and racial population composition also contributed to these trends.

We analyze the disparate effects of a recent California sentencing reform on the arrest, booking, and incarceration rates experienced by California residents from different racial and ethnic groups. In November 2014 California voters passed state proposition 47 that redefined a series of felony and “wobbler” offenses (offenses that can be charged as either a felony or misdemeanor) as straight misdemeanors, causing an immediate 15 percent decline in total drug arrests, an approximate 20 percent decline in total property crime arrests, and shifts in the composition of arrests away from felonies towards misdemeanors. Using microdata on the universe of arrests in the state in conjunction with demographic data from the American Community Survey, we document a substantial narrowing in inter-racial differences in overall arrest rates and arrest rates by offense type, with very large declines in the inter-racial arrest rate gaps for felony drug offenses. Conditional on being arrested, we see declines in bookings rates for all groups, though we find a larger decrease for white arrestees. This relatively larger decline for white arrests is largely explained by difference in the distribution of arrests across recorded offenses. Despite the widening of racial gaps in the conditional booking rate, we observe substantial declines in overall booked arrests that are larger for African Americans and Hispanics relative to whites. For some offenses (felony drug offenses), inter-racial disparities in jail booking rates narrow by nearly half. Finally, we use data from the American Community Survey to analyze change in the proportion incarcerated on any given day and how these changes vary by race and ethnicity. For these results, we present trends for the time period spanning the larger set of policy reforms that have been implemented in the state since 2011. We observe sizable declines . . .

Using data from the American Community Survey, this article assesses the effects of the 2014 Federal Housing Administration (FHA) loan limit reductions on homeownership decisions. Employing a difference-in-differences identification strategy, we find little evidence that the loan limit reductions caused an overall decline in homeownership rates. However, we do find that overall homeownership rates (as well as African American homeownership rates more specifically) increased in low-price parts of metropolitan statistical areas that experienced a loan limit reduction relative to high-price areas, suggesting that the lack of an overall effect may be because of changing decisions on where to own a home, not whether to own a home. This thesis is further supported by evidence of an increase in commuting times for residents in areas that experienced a limit reduction. Our findings contribute to the debate over how individuals respond and adapt their homeownership decisions to policy changes and credit constraints.

Objective: This study examined how characteristics of households and communities are implicated in the intergenerational transmission of gender inequality and particularly female genital cutting (FGC). Background: Human capital perspectives suggest that socioeconomic inequality predicts FGC continuation. This study contributes to discussions of institutional change by examining the association of decisions to forego FGC with household decision making patterns and community gender norms. Method: Multilevel logistic regression was deployed to analyze a pooled sample (N = 12,144) of six demographic and health surveys from Burkina Faso, Egypt, Guinea, Kenya, Mali, and Nigeria. A series of models examined how decision making styles, both at the household and community levels (2,524 demographic and health survey cluster aggregations), and community levels of FGC correspond with the risk of having a daughter cut. Results: The results show that daughters are less likely to be cut when parents make key household decisions jointly. Autonomous decision making by women at the community level was associated with lower odds of daughters being cut. However, at the community level, the impacts of women's household decision making were attenuated when FGC was more prevalent. Conclusion: The findings suggest that women's decision making status is an important factor in FGC abandonment, although that association is less robust when FGC is highly institutionalized. This study provides new insights into how women, families, and communities can disrupt the intergenerational transmission of behaviors associated with institutionalized gender inequality.

The study of homicide by police (HbP) has been hampered by measurement error in existing data. This study draws from three separate flawed data systems and uses confirmatory factor analysis (CFA) to quantify and correct for these errors. Results show that the CFA models presented can be used to construct more valid and reliable measures of sub-national HbP rates. The improved measures are useful for conducting analyses of causes and correlates of HbP. The implications of these findings for future research are discussed.

This research investigates the interstate migration of workers in the United States who have earned an undergraduate STEM (science, technology, engineering, and mathematics) degree compared with those who have not. We build on previous studies that (a) classified “skilled” workers as having earned an undergraduate degree (b) used net migration gain or loss as a yardstick of relative destination attraction, and (c) advanced the idea that physical amenities play an outsized role in labour market preferences for skilled workers. We calibrate the attractivity of states for three levels of human capital and then evaluate these assessments of relative attractivity to show that workers with different types of human capital respond to different labour market signals in contradictory ways. Amenity, measured by heating degree days, has little to do with the state‐to‐state migration of workers who have a STEM degree, yet helps explain the migration patterns of workers with no undergraduate degree. Employment growth in a state influences migration for degreed workers in the recessionary years but not in the period of recovery. The opposite holds for workers without a degree. States with high percentages of any type of degreed workers attract both STEM and non‐STEM degreed migrants but not those without a degree. States with a large share of STEM degreed workers in their degreed workforce are especially attractive for STEM degreed migrants. The conclusions discuss what the findings imply about diverging access to labour market opportunity by human capital and state higher education policy.

Community- associated methicillin resistant Staphylococcus aureus (CA-MRSA) cause serious infections and rates continue to rise worldwide. Use of geocoded electronic health record (EHR) data to prevent spread of disease is limited in health service research. We demonstrate how geocoded EHR and spatial analyses can be used to identify risks for CA-MRSA in children, which are tied to place-based determinants and would not be uncovered using traditional EHR data analyses. An epidemiology study was conducted on children from January 1, 2002 through December 31, 2010 who were treated for Staphylococcus aureus infections. A generalized estimated equations (GEE) model was developed and crude and adjusted odds ratios were based on S. aureus risks. We measured the risk of S. aureus as standardized incidence ratios (SIR) calculated within aggregated US 2010 Census tracts called spatially adaptive filters, and then created maps that differentiate the geographic patterns of antibiotic resistant and non-resistant forms of S. aureus. CA-MRSA rates increased at higher rates compared to non-resistant forms, p = 0.01. Children with no or public health insurance had higher odds of CA-MRSA infection. Black children were almost 1.5 times as likely as white children to have CA-MRSA infections (aOR 95% CI 1.44,1.75, p < 0.0001); this finding persisted at the block group level (p < 0.001) along with household crowding (p < 0.001). The youngest category of age (< 4 years) also had increased risk for CA-MRSA (aOR 1.65, 95%CI 1.48, 1.83, p < 0.0001). CA-MRSA encompasses larger areas with higher SIRs compared to non-resistant forms and were found in block groups with higher proportion of blacks (r = 0.517, p < 0.001), younger age (r = 0.137, p < 0.001), and crowding (r = 0.320, p < 0.001). In the Atlanta MSA, the risk for CA-MRSA is associated with neighborhood-level measures of racial composition, household crowding, and age of children. Neighborhoods which have higher proportion of blacks, household crowding, and children < 4 years of age are at greatest risk. Understanding spatial relationship at a community level and how it relates to risks for antibiotic resistant infections is important to combat the growing numbers and spread of such infections like CA-MRSA.

Disruptions in health coverage are associated with adverse health consequences.1 This is especially true for women in their childbearing years, when a pregnancy means having health coverage is even more important. The stakes are high as the care a woman receives during pregnancy is critical to her own health, as well as to the health of her newborn. In the United States, maternal and infant mortality is higher than most other industrialized nations,2 lending urgency to strategies to address the overall health of women.3 In this paper we review the substantial new research showing the significant . . .

Since the 1970s, the U.S. has experienced dramatic increases in income inequality. Although this macro-level trend is well-established in research literature, less is known about subnational patterns of income inequality in the U.S., particularly as they vary between and within rural and urban localities. Using Census and ACS data, this study produces Gini estimates of within-county income inequality and examines these trends across a six-strata urban-rural typology from 1970 to 2016. This study finds the following. Income inequality has remained consistently higher in nonmetropolitan counties than metropolitan counties throughout the study period. However, levels of inequality have converged by 2016, a convergence that has been driven by increases in metropolitan counties. There are notable exceptions to the secular trend of increasing inequality. The central Plains region has experienced decreasing levels of inequality, and inequality in large, peripheral metropolitan counties lags noticeably behind other types of counties. Almost all lowinequality counties in 1970 have shifted to moderate- or high-inequality, such that almost no one lives in low-inequality places by 2016. This increase in exposure to inequality has been particularly dramatic among residents of large, central metropolitan counties. As the only county-level analysis to track income inequality across the rural-urban continuum from 1970 to 2016, this study lays the foundation for more sophisticated analyses that explain spatial variation in income inequality and that account for the demographic and economic diversity of the rural and urban United States.

This dissertation consists of three essays investigating the historical roots of economic development and inequality in the US South. The first essay examines the impact of slavery on long-run development. Using county-level data from the US South, I show that slavery has impeded long-run development through the human capital channel. The mechanism involves labor market institutions and their impact on demand for human capital. I find that the history of slavery hindered integration of black workers into the labor market. Moreover, border-county analyses show that selective application of laws and regulations was a primary tool for impeding labor market integration. Through estimating the relative return to education for each county, I further argue that blacks in a region with a greater legacy of slavery had fewer incentives to invest in human capital. The second essay studies the long run effects of cotton agriculture focusing on a novel aspect of structural change. I show that cotton specialization in the late 19th century had long-run negative impact on local development, and the negative relationship became only evident in the second half of the 20th century. I argue that the change was caused by the mechanization of cotton production. After cotton mechanization, cotton labor with low human capital was relocated to local manufacturing. In response to the inflow of cotton labor, there was a decline in labor productivity in manufacturing which persisted through directed technical change. Using census data, I show that initial cotton specialization reduces demand for skills in manufacturing even to this day. The third essay addresses the legacy of cotton agriculture on economic inequality. Using the Gini index of household income, I show that initial cotton specialization increased long-run economic inequality at the county level. Moreover, evidence from the census data indicates that cotton specialization increased wage inequality exclusively in the local service sector, without any effects on the other non-agricultural sectors. As an explanation, I argue that wage inequality in the service sector increased due to expansion of employment in low-wage occupations followed by a decrease in their wage level.

The expansion of public insurance eligibility that occurred with the Affordable Care Act (ACA) Medicaid expansions may have spillover effects to other public assistance programs. We explore the impact of the ACA on two large safety net programs: the Earned Income Tax Credit (EITC) and the Supplemental Nutrition Assistance Program (SNAP). We use a county border-pair research design, examining county-level administrative measures of EITC and SNAP participation in contiguous county pairs that cross state lines where the county on one side of the border experienced the Medicaid expansion and the county on the other side did not. This approach allows us to focus narrowly on differences arising from the ACA Medicaid expansion choice, implicitly controlling for local economic trends that could affect safety net participation. Our results suggest that the Medicaid expansion increased participation in SNAP, and possibly in the EITC, in counties that expanded relative to nearby counties that did not expand. We corroborate and extend these results using individual level data from the American Community Survey (ACS). Our results show that access to one safety net program may increase take-up of others.