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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.

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.

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.

In recent years, prices for prime farmland have increased substantially, begging the question is the dramatic increase the result of a speculative bubble or consistent with market fundamentals with increases driven by increased global demand, low interest rates, and recent changes to US agricultural and energy policies. The purpose of this paper is to investigate the impacts of recent agricultural support policies and ethanol policies on farmland values and rental rates.

Farm-level Agricultural Resource Management Survey data collected by the United States Department of Agriculture (USDA) between 1998 and 2008 as well as county-level data collected by the USDA, US Census Bureau, and Bureau of Economic Analysis are used to determine the impacts of recent agricultural support policies and ethanol policies on farmland values and rental rates, while controlling for parcel characteristics and urban pressure. Specifically, weighted ordinary least squares and two-stage least squares are used to investigate the impact of various governmental agricultural support policies, corn ethanol facilities location, and local corn ethanol production capacity on farmland values and rental rates.

The results indicate that government payments, urban pressure, and the proximity of the parcel to an ethanol facility have a positive impact on both farmland values and rental rates. More specifically, parcels located in the same county as at least one corn ethanol facility are more valuable and command higher rental rates. In addition, county-level ethanol production capacity is positively associated with farmland values and rental rates. An inverse relationship between distance of the parcels from an ethanol facility and farmland values is also found; a similar result is found for rental rates.

The findings suggest that agricultural support payments and ethanol policies are capitalized into farmland values. These findings have important implications for the formulation of future farm policy. A limitation of the analyses is that farmland values are estimated by landowners; future research could utilize farmland transaction data to overcome potential biases generated by using landowner estimates. In addition, while our study period covers 11 years, future research could expand the time period further to analyze the effect of more recent agricultural and ethanol policies.

This paper extends prior research pertaining to factors influencing farmland values and rental rates by also examining the proximity of the parcel to an operating ethanol facility using a unique data set.

Argues that US county‐level policing is distinct from municipal policing. Examines differences between them in terms of historical, political, geographical, functional, organizational and regional variations. Suggests how research might be focused to explicate these differences. In particular, presents the idea of a militia, a group organized out of and by a community for its own protection. Contrasts this with the professional paramilitary model associated with large municipal departments. Points out that most police agencies are not large or urban. The greater part of the USA is policed by approximately 3,000 county‐level agencies. Proposes the militia model as a template for further research.

This paper aims to introduce mathematical models to capture the spreading of epidemics to explain the expansion of mortgage default events in the USA.

The authors use the state of infectiousness and death to represent the subsequent steps of payment elinquency and default, respectively. As the local economic structure influences regional unemployment, which is a strong driver of mortgage default, the authors model interdependencies of regional mortgage default rates through employment conditions and vicinity.

Based on a large sample between 2000 and 2014 of loan-level data, the estimation of key parameters of the model is proposed. The model’s forecast accuracy shows an above-average performance compared to well-known approaches such as linear regression or logit models.

The key findings may be useful in understanding the dynamics of mortgage defaults and its spatial spreading.

The purpose of this paper is to investigate electronic benefits transfer (EBT) card reforms in California’s Food Stamp Program, and its impact on food insecurity.

The authors test the hypothesis that EBT cards reduce food insecurity by reducing the food costs associated with loss and theft of benefits, as well as by decreasing fraudulent sales of benefits. The authors use a natural experiment in the form of the time-varying roll-out of EBT card reforms across California counties in conjunction with the California Health Interview Survey, to conduct an event study.

The findings suggest no evidence for a decrease in food insecurity. The authors do, however, find evidence of a transitory increase in food insecurity immediately following implementation of EBT reforms. Reforms increase the likelihood of food insecurity by about 3 percent for up to two months. The result is distinguishable from zero, and robust to changes in specification, inclusion of controls, and measurement choices. The authors posit the increase was due to frictions in the transition to EBT card systems.

Although a considerable literature with regard to the FSP exists, very little has been written investigating a specific linkage between EBT cards and food security. The findings are not supportive of policy makers’ hypothesis that a positive externality of EBT benefits delivery is a lasting reduction in food insecurity.

Little research on the influence of external factors, such as weather and holiday periods, on retail sales on alcoholic beverages is available. This study aims to investigate how weekly retail sales of different alcoholic beverages vary in association with daily maximum temperatures and annual federal holidays across selected US counties in the years 2013 to 2015. The research provides information, which can contribute to better sales forecasts.

Secondary data of weekly retail sales (volume) of alcoholic beverages from 37,346 stores in 651 counties in the USA are analysed. The data cover on average 21% of all existing US counties and 12% of the total US off-trade retail sales of alcoholic beverages in the period studied (Euromonitor, 2017). Additional data of federal holidays and meteorological data are collated for each county in the sample. Seasonal autoregressive integrated moving average models with exogenous regressors (SARIMAX) are applied to develop forecasting models and to investigate possible relationships and effects.

The results indicate that off-trade retail sales of beer, liquor, red and white wine are temperature sensitive throughout the year, while contrary to expectations rosé, sparkling and other wines are not. Sales sensitivities to temperature also differ by geography. In the warmest regions, liquor and white wine sales do not respond to temperature changes, as opposed to the coolest regions, where they are responsive. Public holidays, particularly Easter, Thanksgiving, Christmas and New Year holidays, represent a constant influencing factor on short-term sales increases for all investigated alcoholic beverage categories.

This is the first large-scale study of weather and holiday-related sales variations over time, across geographies and different alcoholic beverage categories. Seasonal and non-seasonal short-term sales variations are important for retailers and manufacturers alike. Accounting for expected changes in demand accommodates efficiencies along the supply chain and has implications for retail management, as well as adjusting marketing efforts in competing categories.