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The US fertility transition in the nineteenth century is unusual. Not only did it start from a very high fertility level and very early in the nation’s development, but it also took place long before the nation’s mortality transition, industrialization, and urbanization. This paper assembles new county-level, household-level, and individual-level data, including new complete-count IPUMS microdata databases of the 1830–1880 censuses, to evaluate different theories for the nineteenth-century American fertility transition. We construct cross-sectional models of net fertility for currently-married white couples in census years 1830–1880 and test the results with a subset of couples linked between the 1850–1860, 1860–1870, and 1870–1880 censuses. We find evidence of marital fertility control consistent with hypotheses as early as 1830. The results indicate support for several different but complementary theories of the early US fertility decline, including the land availability, conventional structuralist, ideational, child demand/quality-quantity tradeoff, and life cycle savings theories.

Like many countries, the US government-imposed travel restriction policies on selected countries with a high spread of COVID-19 airports to prevent the introduction and spread of COVID-19. Between March 2020 and October 2021, travellers from China, Iran, European Schengen countries, the United Kingdom, Republic of Ireland, Brazil, South Africa and India were restricted with some exceptions. The main objective with this study was to explore the associations between COVID-19 cases and death rates, and the proximity to airports, train stations and time of public transportation. To address the study objective, the authors used the most recent JHU COVID-19 database, the American Community Survey and Airport and Amtrak data from the Bureau of Transportation Statistics from 3,132 US counties. The authors categorised the counties into three groups according to their distance from an airport: less than 25 miles, between 25 and 50 miles and more than 50 miles. The authors then ran negative binomial regressions and Cox regression models, adjusted for population density, population race/ethnicity, travel time, being close to an international airport and the main sources of commutes. The findings showed that the number of airports, the number of train station and the length of commuting time were predictors for the number of deaths and cases in a county. The authors found that counties within 25 miles of an airport had 1.372 times the rate of COVID-19 cases and 1.338 times the rate of COVID-19 deaths compared to the counties that were more than 50 miles from an airport. To prevent the introduction and spread of COVID-19 and any similar pandemic that transfers by air, the timing of the travel restriction policy is a crucial element. Policymakers and officials in transportation and public health should collaborate to promulgate policies and procedures to prevent the spread of airborne infectious diseases.

The purpose of this study is to evaluate the impact of food access and other vulnerability measures on the COVID-19 progression to inform the public health decision-makers while setting priority rules for vaccine schedules.

In this paper, the authors used the Supplemental Nutrition Assistance Program (SNAP) data combined with the Centers for Disease Control and Prevention (CDC)’s social vulnerability score variables and diabetes and obesity prevalence in a set of models to assess the associations with the COVID-19 prevalence and case-fatality rates in the United States (US) counties. Using the case prevalence estimates provided by these models, the authors developed a COVID-19 vulnerability score. The COVID-19 vulnerability score prioritization is then compared with the pro-rata approach commonly used for vaccine distribution.

The study found that the population proportion residing in a food desert is positively correlated with the COVID-19 prevalence. Similarly, the population proportion registered to SNAP is positively correlated with the COVID-19 prevalence. The findings demonstrate that commonly used pro-rata vaccine allocation can overlook vulnerable communities, which can eventually create disease hot-spots.

The proposed methodology provides a rapid and effective vaccine prioritization scoring. However, this scoring can also be considered for other humanitarian programs such as food aid and rapid test distribution in response to the current and future pandemics.

Humanitarian logistics domain predominantly relies on equity measures, where each jurisdiction receives resources proportional to their population. This study provides a tool to rapidly identify and prioritize vulnerable communities while determining vaccination schedules.

The 1896 presidential election between William Jennings Bryan and William McKinley has new salience in the wake of the 2016 presidential contest. We provide the first systematic analysis of presidential voting in 1896, combining county-level returns with economic, financial, and demographic data. We show that Bryan did well where interest rates were high, railroad penetration was low, and crop prices had declined. We show that further declines in crop prices or increases in interest rates would have been enough to tip the Electoral College in Bryan’s favor. But to change the outcome, the additional changes would have had to be large.

Some US counties are more likely to generate entrepreneurial opportunities. This paper aims to determine whether US micropolitan counties with disproportionately high nonfarm proprietorship (NFP) employment levels are systematically linked to specific attributes of the entrepreneurial ecosystem. A limited amount of research has been conducted on the geography of entrepreneurship in small to medium-sized micropolitan counties where rates of growth and change can be quite dramatic.

NFP employment data from the US Bureau of Economic Analysis (BEA) is used as a dependent variable proxy for entrepreneurship. NFP data are widely used in the entrepreneurship literature. Data on all independent variables were obtained from the US Census Bureau’s American Community Survey and BEA by county and subject to stepwise linear regression.

Results revealed a strong positive relationship between the percent of NFP employment by micropolitan county and percent construction employment, percent real estate, and rental and leasing employment, and the percent elderly. It is argued that the combination of predictors captures primarily a self-employment of opportunity (e.g., thriving land and real estate markets).

In attempting to encourage NFP employment, policymakers should be more alert to the key predictors that shape micropolitan entrepreneurial ecosystems when attempting to enhance competitive advantage in small- to medium-sized communities. Better understanding how micropolitan counties function relative to larger metropolitan places can help local policymakers more efficiently enhance the overall quality of life in smaller communities.

The focus on smaller micropolitan communities and the explicit spatial context of this paper has sometimes been overlooked in the traditional entrepreneurship literature and this research helps to fill that gap.

Some US counties are more likely to generate entrepreneurial opportunities than others. This paper aims to determine the linkages between US counties with disproportionately high shares of entrepreneurs and specific attributes of the entrepreneurial support system.

Non-farm proprietorship (NFP) has been used as a proxy for entrepreneurship and self-employment. NFP employment data were collected from the US Bureau of Economic Analysis by county. Data on all independent variables were obtained from the US Census and Bureau of Economic Analysis by county and subject to stepwise linear regression analysis.

Results revealed a strong positive relationship between the percent of NFP employment by county and the percent real estate, rental and leasing employment and construction employment as well as percent Hispanic and median age.

In attempting to encourage NFP employment, policymakers should be more aware of the key predictors that shape county-wide entrepreneurial ecosystems to enhance competitive advantage. Better understanding of the needs and experiences of different types of entrepreneurs and ecosystems can enhance overall quality of life and economic opportunity levels in a community.

The explicit spatial context of this paper has sometimes been overlooked in the traditional entrepreneurship literature, as such, this paper helps fill that gap. The findings provide a disaggregated analysis that can help better understand the key predictors that can drive the local choices of entrepreneurs and help local policymakers to build more competitive communities.

Since the development of the COVID-19 vaccinations, questions surrounding race have been prominent in the literature on vaccine uptake. Early in the vaccine rollout, public health officials were concerned with the relatively lower rates of uptake among certain racial/ethnic minority groups. We suggest that this may also be patterned by racial/ethnic residential segregation, which previous work has demonstrated to be an important factor for both health and access to health care.

In this study, we examine county-level vaccination rates, racial/ethnic composition, and residential segregation across the U.S. We compile data from several sources, including the American Community Survey (ACS) and Centers for Disease Control (CDC) measured at the county level.

We find that just looking at the associations between racial/ethnic composition and vaccination rates, both percent Black and percent White are significant and negative, meaning that higher percentages of these groups in a county are associated with lower vaccination rates, whereas the opposite is the case for percent Latino. When we factor in segregation, as measured by the index of dissimilarity, the patterns change somewhat. Dissimilarity itself was not significant in the models across all groups, but when interacted with race/ethnic composition, it moderates the association. For both percent Black and percent White, the interaction with the Black-White dissimilarity index is significant and negative, meaning that it deepens the negative association between composition and the vaccination rate.

The analysis is only limited to county-level measures of racial/ethnic composition and vaccination rates, so we are unable to see at the individual-level who is getting vaccinated.

We find that segregation moderates the association between racial/ethnic composition and vaccination rates, suggesting that local race relations in a county helps contextualize the compositional effects of race/ethnicity.

This chapter addresses the growing need to analyze the relationship between COVID-19 vulnerability and disability status at the national scale in the US. It presents a quantitative study that seeks to determine whether US counties more vulnerable to the COVID-19 pandemic contain significantly higher percentages of people with disabilities (PwDs), in general, and socially disadvantaged PwDs (based on their ethnicity/race, biological sex, age poverty, and employment status), in particular.

Vulnerability to COVID-19 is measured using the COVID-19 Pandemic Vulnerability Index (PVI) model developed by the National Institute of Environmental Health Sciences, which integrates multiple variables into relevant indicators that are weighted and combined to formulate a county-level PVI score. These scores are linked to a wide range of disability-related variables from the 2019 American Community Survey five-year estimates. Statistical analyses are based on multivariable generalized estimating equations that extend the generalized linear model to account for spatial clustering.

US counties more vulnerable to the COVID-19 pandemic are characterized by significantly higher percentages of PwDs, when vaccination is considered in estimating the PVI. These counties also contain significantly higher percentages of ethnic/racial minority, female, below poverty, and unemployed PwDs, in multiple timeframes of the pandemic.

The findings provide important insights and new knowledge on the relationship between COVID-19 vulnerability and socially disadvantaged PwDs in the US. The county-level associations highlight the need for additional data and more detailed analysis to examine the differential impacts of this pandemic on PwDs, as well as formulate appropriate intervention strategies.

Despite 2016 legalization of recreational cannabis cultivation and sale in California with the passage of Proposition 64, many cannabis businesses operate without licenses. Furthermore, federal regulations disincentivize financial institutions from banking and lending to licensed cannabis businesses. The authors explore the impact of legal cannabis business activity on California financial institutions, the barriers to banking faced by cannabis businesses, and the nontraditional sources of financing used by the industry.

The authors use a mixed methods approach. The authors utilize call data for banks and credit unions headquartered in California and state cannabis licensing data to estimate the impact of the extensive and intensive margins of licensed cannabis activity on key banking indicators using difference-and-difference and fixed effects regressions. The qualitative data come from interviews with industry stakeholders in northern California's “Emerald Triangle” and add important context.

The quantitative results show economically and statistically significant impacts of licensed cannabis activity on banking indicators, suggesting both direct and spillover effects from cannabis activity to the financial sector. However, cannabis businesses report substantial barriers to accessing basic financial services and credit, leading to nontraditional financing arrangements.

The results suggest opportunities for cannabis businesses and financial institutions if regulations are eased and important avenues for further study.

The authors contribute to the nascent literature on cannabis economics and the literature on banking regulation and nontraditional finance.

The purpose of this paper is to examine the influence of the institutional environment on firm birth and death rates. It is hypothesized that high taxation levels, large government size, high levels of unionization and high minimum wages will be associated with relatively low firm birth and death rates.

This study makes use of a set of custom tabulations from the US Census Bureau that contain data on county-level firm births and deaths. To account for differences in state policies, matched contiguous counties located on state borders are used to calculate matched birth and death ratios.

In the sample of eastern US state border counties, state taxation levels and minimum wages had no significant relationship with firm birth rates, but there was a negative relationship between state union densities and firm birth rates. Both state education and public welfare expenditures were marginally negatively related to firm birth rates. State public welfare expenditures were negatively related to firm death rates, while a marginally significant negative relationship between hospital/health expenditures and firm death rates was observed.

These results indicate that state government expenditures may have varying influences on firm birth and death rates, and that high union densities may deter new firm entry.

This paper makes use of a county matching technique to help control for confounding variables, allowing for differences in state policies to be better accounted for.