ABSTRACT. Economic conditions vary greatly across the contiguous United States. Native American communities across the US have this same heterogeneity but face additional institutional hurdles such as land tenure issues and lack of financial capital. These disadvantages may not affect all business sectors equally. Therefore, Native American reservations could suffer from a lack of access or supply of businesses integral to burgeoning local economies. Using WholeData and the BEA’s Personal Income Data, we identify leading industries across counties that intersect tribal-owned land and measure how they compare to their non-tribal counterparts in both employment per capita and industry-level average income. We also evaluate the economic diversity of these counties and whether growth in certain industries in tribal counties crowd out other industries more than nontribal counties. Our analysis shows that although leading sectors are similar between the two county types, there are differences in economic diversity in rural counties, suggesting that remote tribal communities are better positioned for future growth. Over the past two decades tribes invested in casino-based entertainment and associated hotel accommodations. Regression analysis shows that tribal interaction term within the entertainment sector is negative with manufacturing, but other sectors such as agriculture, retail, and health care are unaffected. The interaction between accommodations in tribal counties and per capita income in agriculture, manufacturing, health care, and retailing are all negative, indicating broader-based crowding out of these activities in the local economy. Recent moves to online gaming, by disconnecting gaming and accommodations, may help tribes interested in diversifying their local economy.

ABSTRACT. In recent years, the labor force participation rate in the U.S. has been at low levels not experienced since the 1970s – when more women began entering the labor force – even before the economic crisis due to the COVID pandemic. Drops in unemployment rates appear to be masking the problem of people leaving the workforce. There is also significant regional variation in labor force participation as well as persistent differences among men and women, with pre-COVID stagnation in terms of labor force participation by men. The question is what drives labor force participation rates and leads to these regional and gender variations? Despite some previous research, the challenge is that theory tells us that "everything matters." Additionally, from an empirical perspective, we have several ways to measure things we know matter, such as human capital or economic opportunity. Thus, to provide policy insights we must turn to methods that allow us to "model uncertainty," i.e., letting the data tell us what matters. Using Bayesian Model Averaging methods developed by LeSage and Pace, we explore which regional characteristics are associated with labor force participation rates. We use U.S. county-level data from the five-year average American Community Survey. We allow for spatial dependency and spillovers within the data and we explore if the underlying drivers of labor force participation rates vary by gender and across the urban-rural spectrum. We also consider factors that have been exposed by the current economic crisis – such as access to child care for women. Having answers to “what really drives labor force participation” will enable policy makers to design policies that can help bring those people on the sidelines back into the workforce and help communities weather economic crises.

ABSTRACT. Even after more than a half-century of the Civil Rights Act of 1964, racial disparity in America remains a persistent issue in academic research and public policy discourse. Despite some progress in the 1960s and 1970s, huge black-white gaps remain in income, life expectancy, health, educational attainment, teenage pregnancy, and incarceration (Fryer and Katz 2013). Several studies have focused on social class and neighborhood disadvantages to conclude that the racial gap in the crime and incarceration rates can be overwhelmingly explained by the differences in such disadvantages. Using publicly available datasets in the “Opportunity Atlas” (Chetty et. al. 2018), our study evaluates the role of the childhood environment on disparate racial incarceration rates in the US under the fractional response model framework. Black males have higher incarceration rates in the US than any other racial/ethnic group whether we measure the incarceration rates at the level of neighborhoods, communities, or labor market areas. Higher black incarceration has mostly been linked to residential segregation, disproportionately high policing in black neighborhoods, racial bias in the criminal justice system, and contemporaneous measures of neighborhood disadvantages like poverty or lack of jobs. However, this analysis tries to understand if the racial disparity in incarceration rates of children from low-income families has roots in the childhood environment where they grow up. The results of this study suggest that childhood contextual variables are sensitive to the level at which we measure them so one should be careful in selecting the variables at the level of neighborhoods, communities, or labor market areas. The findings suggest that several characteristics of the childhood environment affect differently depending on both the race and the gender of the child as well as whether they grow up in rural or urban areas. We also show that higher social capital helps decrease incarceration rates but only for whites. Furthermore, we show that growing up in areas around households with a stable family structure and educated residents decrease the chances of being incarcerated for low-income children, but the effect is higher for black children compared to white children.

References: Chetty, R., et al. (2018). The opportunity atlas: Mapping the childhood roots of social mobility, National Bureau of Economic Research.

Fryer Jr, R. G. and L. F. Katz (2013). "Achieving escape velocity: Neighborhood and school interventions to reduce persistent inequality." American Economic Review 103(3): 232-237.

ABSTRACT. Occupation segregation explains a significant portion of the gender wage gap with women sorting into lower paid female-dominated occupations, especially care occupations. Economic theory posits women optimally trade off earnings for flexibility - suggesting discrimination does not play a large role. We assess this by modeling how gender role attitudes in a person’s birth state (“background sexism”) affect occupation choice, considering educational attainment and major. We find traditional gender attitudes, which view women’s role as caretakers, influence women and men’s occupation choice and increase the gender care occupation gap.

ABSTRACT. In this paper, we analyse the impact of divorce law reforms on house prices for a sample of ten European countries between 1960 and 2008, taking advantage of the real house price index developed by Knoll et al. (2017). The period of reforms began in 1970, and differences in the timing of entry into force of unilateral divorce laws across countries provide a quasi-experimental setting. We estimate the static and dynamic effect of divorce law reforms, finding a positive and significant effect of these reforms on real house prices, mainly concentrated in years 3 to 6 after the reforms, even after controlling for a set of country-specific variables, as well as country-specific linear and quadratic time trends. The dynamic effect of unilateral divorce law reforms accounts in those years for 22% of the average interannual increment in the real house price index.

ABSTRACT. It has been 18 years since the collapse of the new manufactured homes market. This semi-durable good has an estimated life span of 22 years, yet total sales figures show that they are not being replaced at an adequate rate, despite population estimates remaining constant. Perhaps the unique market structure of manufactured homes may be the issue. Unlike site-built homes, many do not have access to mortgages, are considered depreciating assets, and are often titled as personal property rather than real property. In many ways, it is similar to automobiles with access to financial instruments and legal protections. This is concerning as nearly 20 million Americans live in manufactured homes as their permanent residence. Due to their low purchase price, manufactured homes have become a popular alternative for affordable homes, most notably in rural areas. Despite this, there is little in the economic literature about manufactured housing. This study attempts to look at the demand factors driving millions of Americans to choose manufactured homes over traditional substitutes including rental and site build homes. The data covers all manufactured homeowners within the American Housing Survey from 1997-2017. I use a nested logit model to estimate the factors of demand for the home buyer’s decision in purchasing a manufactured home. In addition, the model considers what factors impact a homeowner’s decision to enter the market over maintaining an existing home and as well as whether to buy a new or used manufactured home. To my knowledge, this is the first paper to consider the difference in purchasing new and used manufactured homes.

ABSTRACT. In a society where people of all races are perceived equally, the neighborhood racial composition should not impact house price, ceteris paribus. However, prior work has robustly identified house price discounts associated with increasing proportions of Black neighborhood residents. These discounts vary across housing markets. We investigated one factor that may explain the variation: quantile effects. We found that house price discounts are most severe in the lower-priced segments of the house price distribution. Mechanisms underlying these results are explored and their policy implications are discussed.

ABSTRACT. In the past several decades, obesity has become an increasingly severe problem in United States. In 2008, the adult obesity rate was 33.8%; however this number increased to 42.4% by 2018. Obesity rates are notably higher in rural America when compared to their urban counterparts, which leads to higher morbidity and mortality of chronic diseases in rural locations. Meanwhile, rural regions have experienced relatively slower employment growth and higher poverty rates during the recovery from the Great Recession. Social scientists are interested in determinants of – and potential solutions to – this rise in obesity rates. The existing literature has largely focused on the relationship between obesity and social / economic factors, such as the number of fast food restaurants, limited physical activity, and unemployment rates. However, one unexplored question is whether the level of economic growth experienced by a rural area plays a role in the obesity problem.

This paper assesses the impact of economic growth (measured by county-level GDP per capita) on obesity rates (measured by the county-level percentage of adults with BMI higher than 30) in rural America. Nationwide, data is collected on a host of demographic and economic characteristics for all non-metropolitan counties from 2012 to 2016, resulting in a county-level panel data set (n=1,948, t=5). Control variables include age, race and ethnicity, unemployment rates, rates of physical inactivity, and an index measuring healthy food availability. Two different econometric approaches were applied: (1) a fixed effects panel regression model and (2) a difference-in-difference model using propensity score matching (PSM). Under the PSM method, our outcome variable was the difference of obesity growth rates between 2014-2016 and 2012-2014, while the treatment was defined as growth rates in GDP per capita between 2012 and 2014. We consider several distinct thresholds (such as 10% and 20% growth) for our treatment to test if specific rates of growth are more closely associated with changes in obesity.

The results of both econometric models suggest that higher levels of economic growth in non-metro counties have a surprisingly positive impact on obesity rates in later years. This result is counter to our hypothesis and suggests that programs focused on rural economic growth may lead to undesirable outcomes in other quality-of-life metrics. These results are important for rural development practitioners who largely emphasize economic growth as the primary goal for policymakers. Our conclusion discusses these competing interests and how regional scientists can play a role in future research in this area.

ABSTRACT. Consequences for traffic safety are a concern associated with continuing liberalization of marijuana use laws. Several studies have found traffic fatalities fall following liberalization; yet we are unaware of any studies of the effect of liberalization on pedestrian fatalities. In addition to its implications for the debate over liberalization, this effect is likely of interest to those working to enhance pedestrian safety or encourage walking as a sustainable mode of travel. We employ a difference in difference design to measure the impact of liberalization on pedestrian fatalities in the US using data from the National Conference of State Legislatures and the Fatality Analysis Reporting System. Since all states with recreational legalization had previously legalized medicinal use, our primary measure of liberalization is the presence of a medical marijuana law. The presence of a medical marijuana law is associated with a ten percent decline in annual pedestrian fatalities for those under age 75 following the effective year, with about half that in the effective year. We find no effect on those 75 and over. There is no evidence of an association between the presence of a medical marijuana law and pedestrian fatalities prior to its effective date which, together with control for state specific time trends, supports a causal interpretation of our results. Controlling separately for recreational legalization does not change our results.

ABSTRACT. The relationship between diabetes and oral health has been well established. People with diabetes tend to have a number of oral health complications including tooth decay, gum disease, and periodontal disease—conditions that may result in the need for extractions.

In 2014, South Carolina implemented an adult dental benefit for its Medicaid recipients. Our study uses individual Medicaid claims data from South Carolina covering 2015–2019 to explore whether medical costs increased for adults with diabetes as a result of tooth extraction(s). Our data includes adults with diabetes that had at least one dental claim. To determine whether extractions increase medical costs in diabetics, we compare medical costs for those with tooth extractions and those without.

Additionally, we explore whether outcomes vary among the four geopolitical regions of South Carolina: Upstate, Midlands, Pee Dee, and the Lowcountry, as well as between urban and rural counties.

The findings will aid in better understanding the true costs associated with tooth extractions in this population, and whether outcomes vary by geographic location or rurality.