Search

The opioid crisis has impacted many regions of the United States, transcending socioeconomic, demographic, and political divides and leading to urgent calls for public health and law enforcement interventions. It has hit both micropolitan and rural smaller communities especially hard, with severe increases in prescription drug-induced fatalities over time. This paper explores socioeconomic determinants and spatial disparities of fatalities caused by drug and opioid overdose (both intentional and unintentional), focusing specifically on rural-urban differences and understanding the separate role of net farm income in the drug overdose crisis. Our panel data analysis of all US counties spanning the 1970-2016 period indicates that rurality, as measured by lower population density, is associated with higher deaths rates. Importantly, while our research suggests that the opioid crises went hand in hand with declining net farm income, the effect appears to be small. Specifically, we estimate that each $10,000 reduction in net income per farm is associated with a 0.04 increase in overdose age-adjusted deaths per 100,000 people. This effect is more prominent in counties with persistent poverty, which also is predominantly rural. Given the pronounced variability in farm financial indicators over time, the relative health of the farm sector in rural areas may warrant more attention if the pressing health crisis is to be addressed effectively. [ABSTRACT FROM AUTHOR]

The article offers information on the sociodemographic factors and distance from metro areas in the U.S. which causes problem of low 2020 census participation. It further discusses raising the response rate significantly through outreach and by using on-line survey forms; lack of sufficient broadband internet in rural areas affects the 2020 census; and also mentions that effects of the sociodemographic variables also differ by county type.

Recent years have seen tremendous advances in the scientific study of networks, as more and larger data sets of relationships among nodes have become available in many different fields. This has led to pathbreaking discoveries of near-universal network behavior over time, including the principle of preferential attachment and the emergence of scaling in complex networks. Missing from the set of network analysis methods to date is a measure that describes for each node how its relationship (or links) with other nodes changes from one period to the next. Conventional measures of network change for the most part show how the degrees of a node change; these are scalar comparisons. Our contribution is to use, for the first time, the cosine similarity to capture not just the change in degrees of a node but its relationship to other nodes. These are vector (or matrix)-based comparisons, rather than scalar, and we refer to them as “rewiring” coefficients. We apply this measure to three different networks over time to show the differences in the two types of measures. In general, bigger increases in our rewiring measure are associated with larger increases in network density, but this is not always the case. [ABSTRACT FROM AUTHOR]

Many urban areas around the world have expanded from monocentric to polycentric regions forming larger hierarchical structures. These regions represent functional regions or labour market areas (LMAs) that are classified as groups of counties representing mutually exclusive, non‐overlapping geographies. In practice, however, a county may belong to multiple overlapping LMAs. To model this overlapping across urban hierarchies, we extend the link community method from network theory and apply it to US commuting data. Our proposed approach reliably identifies the LMAs that we would expect to find as well as core and periphery areas within them. [ABSTRACT FROM AUTHOR]

Poor mental health creates significant economic costs, in addition to human suffering, and is a growing world-wide concern, especially with an aging population. To estimate the cost of this disease in the U.S., we adopt a conventional economic growth model and include the number of poor mental health days (PMHD) as a right-hand side variable. Controlling for various county-level variables associated with income growth, our results suggest that one additional PMHD is associated with a 1.84 percentage point lower per capita real income growth rate, or $53 billion less total annual income, across the U.S. between 2008 and 2014. This effect is in addition to the income losses associated with the Great Recession. [ABSTRACT FROM AUTHOR]

A regional economy perturbed by a shock may move onto a new growth path by reestablishing economic linkages both internally and with other regions. This dynamic property of regions has recently been explained in terms of regional economic resilience. In this paper, we introduce a more refined measure of economic resilience and then apply it to monthly employment data for U.S. counties in the 2007-2009 downturn. We suggest that describing and analyzing the distinct response patterns during this downturn are important starting points for policy makers to understand the spatial resilience of the US economy. [ABSTRACT FROM AUTHOR]

The article focuses on the importance of federal statistics data in determining the occurrences of demographic and socioeconomic changes in the regional and local areas of the U.S. Topics include the economic impacts of the proposed public spending cutbacks, the possible removal of the American Community Survey (ACS), the state's demographic and income data source, and the suspension of the statistical reports of the Agricultural Statistics Service of the U.S. Department of Agriculture (USDA).

A U. S. county workplace-to-workplace or latent migration data set is generated from overlapping migration and commuting networks. The latent migration network is the estimated number of movers between places of work, which is then compared with the actual number of migrants between places of residence. This allows both employment-related and amenity-related migration and pull/push factors as causes of migration flows to be identified and contrasted. Certain counties and cities that are not important migration destinations (e.g., with <200,000 net in-migrants between 1995 and 2000) according to official data are in fact important targets when additional in-migrants who commute into surrounding counties also are considered. An econometric analysis is then used to examine whether different regressors have different effects on the residence- versus employment-based migration patterns. This is a first assessment of whether or not the proposed approach has merit. Results are consistent with prior expectations regarding the factors that would motivate latent versus actual migration. [ABSTRACT FROM AUTHOR]

This study explores the relationship between self-employment and income growth, employment growth, and change in poverty in metro and non-metro areas in the United States using county-level panel data. We investigate the impact of the relative size of the self-employment sector measured by the share of non-farm proprietorships (NFPs) in total full and part-time employment on three key economic performance indicators. We first estimate an income growth model to analyse the effects of self-employment on income growth. Then we investigate the independent effects of self-employment on employment growth and changes in family poverty rates. Our results indicate that higher self-employment rates are associated with statistically significant increases over time in income and employment growth, and reductions in poverty rates in non-metro counties. We find similar effects on metro county income and employment, but not on poverty dynamics. [ABSTRACT FROM AUTHOR]

Using the fact that connections between vertices of a network often represent directed and weighted flows, we apply hydraulic principles to develop novel insights into network structure and growth. We develop a network model based on Bernoulli's principle and use it to analyze changes in network properties. Simulation results show that velocity of flow, resistance, fitness and existing connections in a system determine network connections of a vertex as well as overall network structure. We demonstrate how network structure is affected by changes in velocity and resistance, and how one vertex can monopolize connections within a network. Using Bernoulli's principle, we are able to independently reproduce key results in the network literature. [ABSTRACT FROM AUTHOR]

Objective The recent surge in hate group activity is a concern to many citizens and policymakers. We examine the roles of socioeconomic factors measured at the county level that are hypothesized to account for the presence of such groups, including social capital and religious affiliations. Methods We estimate a Poisson regression model using counts of hate groups provided by the Southern Poverty Law Center for each of the over 3,000 U.S. counties. Our regressors include a wider set of variables than has been considered in previous studies, such as Jefferson and Pryor (1999). Results Our approach produces a better statistical fit than that in Jefferson and Pryor's paper, and the additional regressors contribute significantly to our understanding of hate groups. Conclusion Both social capital stocks and religious affiliation exert an independent and statistically significant influence on the number of hate groups, as does the presence of Wal- Mart stores, holding other factors constant. [ABSTRACT FROM AUTHOR]

Labor market areas (LMAs) have long been a staple of regional and urban analysis. As commuting patterns have expanded over time, these areas have become larger and more complex, and the dichotomous designation of a county either belonging to an LMA or not may no longer be adequate. We apply recent advances in network science to conduct a more refined analysis of U.S. commuting patterns, and examine their effects on local economic growth. Results show that network degree and entropy measures explain variations in county per capita income growth patterns. Higher in- and out-commuting entropies are associated with lower per capita income growth, but their interaction enhances economic growth in places simultaneously open to both in- and out-commuters. Using these results, common ground may be found for creating new forms of regional governance that better reflect local realities of cross-county border flows of workers and economic activity. [ABSTRACT FROM AUTHOR]

Objectives. Two powerful socioeconomic innovations are sweeping the nation, led by Wal-Mart Stores, Inc. and Starbucks Corp. These innovations both affect and are driven by profound labor market changes, but exactly how they affect self-employment or entrepreneurship has not been investigated. We examine the independent effects of these phenomena on the returns to self-employment, which is itself an underresearched topic in labor economics. Methods. We apply spatial econometric analysis to data from more than 3,000 U.S. counties to analyze how big-boxes and drinking places that facilitate social networking affect self-employment earnings. Findings. The presence of Wal-Mart stores is associated with higher returns to self-employment, whereas the results for coffee shops and drinking places are mixed. A negative interaction effect on earnings emerges when Wal-Mart stores and drinking places exist in the same county. Conclusions. We confirm both Schumpeter's and Putnam's assertions about the importance of creative destruction and social networking in raising the productivity of entrepreneurs, although the latter effect is not as clear-cut as the former. [ABSTRACT FROM AUTHOR]

Despite the surging interest in entrepreneurship as an economic development strategy, studies of the independent relationship between proprietorship formations and job growth are virtually nonexistent. We find that self-employment or proprietorship rates are associated with faster job growth in the wage-and-salary sector, and the effect is statistically significant. The relative magnitude of this effect varies with the business cycle, being stronger during economic expansions and weaker during contractions. Further, the effect is stronger in metropolitan than in non-metropolitan counties. [ABSTRACT FROM AUTHOR]

Abstract: This study contributes to basic knowledge of the structural determinants of poverty in the US by analyzing an expanded set of determinants of poverty, namely factors related to economic, social, and political influence using spatial data analysis techniques. New data sets and creative use of existing data sets make it possible to measure some of these county-wide social and political factors that have previously been excluded from formal investigation. Social capital, ethnic and income inequality, local political competition, federal grants, foreign-born population, and spatial effects are found to be important determinants of poverty in US counties along with other conventional factors. [Copyright &y& Elsevier]

Objectives. This study seeks to identify the independent effect of Wal-Mart stores on changes in U.S. family-poverty rates at the county level. We draw on the contributions of a number of disciplines to enhance our understanding of the broader forces that influence poverty. Methods. A key innovation is that we estimate a two-stage regression model, in which an instrument is created for new Wal-Mart stores from a location equation; this reduces any potential endogeneity bias in the poverty-change equation. In addition, we use spatial econometric methods to correct for spatial dependence bias. Results. After controlling for other factors determining changes in the poverty rate over time, we find that counties with more initial (1987) Wal-Mart stores and counties with more additions of stores between 1987 and 1998 experienced greater increases (or smaller decreases) in family-poverty rates during the 1990s economic boom period. Conclusions. Wal-Mart creates both benefits and costs to communities in which the chain locates. These benefits and costs need to be weighed carefully by community decisionmakers in deciding whether to provide public subsidies to the chain. [ABSTRACT FROM AUTHOR]

Abstract: A growing belief exists that social capital contributes to economic growth of communities. In this paper, we identify inputs into the production of social capital at the level of US counties, using an array of individual and community factors that are theoretically important determinants of social capital. We use data from the Bureau of the Census, County Business Patterns, USA Counties on CD, National Center for Charitable Statistics, and the Regional Economic Information System for two time periods. Ethnic homogeneity, income inequality, attachment to place, education, age, and female labor force participation are strongly associated with levels of social capital across US counties. [Copyright &y& Elsevier]

We compare the returns to human capital in rural areas with those in urban areas, and across the different regions of the US. A key finding is that these returns are considerably lower in rural than in urban areas. Furthermore, failure to correct for spatial dependence bias produces an overestimate of the returns to education at the county-level. We also examine various interactions among the regressors to identify the reasons why the returns to education vary between rural and urban areas. [ABSTRACT FROM AUTHOR]

Understanding the factors that account for differences in food stamp expenditure dynamics over space is important not only for potentially improving public policy but also for clarifying the relative roles of factors that are and are not under the control of welfare recipients in determining their ability to move off welfare rolls. This study contributes to the literature by including a better measure of employment opportunities for welfare recipients, allowing for potential endogeneity in the unemployment rate, and by correcting for spatial dependence bias that may result from the clustering of poverty in "pockets." [ABSTRACT FROM AUTHOR]

We examine the relationship between county per capita income and toxic pollutants using a comprehensive model of the Environmental Kuznets Curve. The model incorporates ethnic diversity, spatial effects and most independent variables used in previous studies. Initial results suggest that the relationship follows an inverted-U-shape. However, incorporation of a cubic term for income reveals that toxic pollution eventually increases again as income continues to rise. Ethnic diversity and spatial effects are found to be important in understanding toxic pollution in US counties. We also detect an EKC-type relationship with respect to income inequality. [ABSTRACT FROM AUTHOR]

Linkages among changes in employment, earnings per worker, and pollution per square mile are estimated for 3,036 U.S. counties for the period 1987 to 1995 using a three-equation disequilibrium adjustment model. Counties with higher shares of African-Americans experienced higher earnings growth rates over the period 1987-1995, as did counties with proportionally more females. Counties in states with higher shares of unionized workers had higher earnings growth rates but generated fewer new jobs. Firm size had a significant and negative effect on earnings growth while higher costs of living were associated with higher earnings growth. Also, metro counties and counties in the Northeastern U.S. experienced higher earnings growth than their non-metro counterparts and counties in other geographic regions. Statistically, faster job growth was found to accelerate the rate of earnings growth per worker. The authors conclude that counties concerned with job growth should recruit or attempt to spawn the creation of larger firms, recognizing that for some firms such a strategy may come at the cost of more rapid increases in pollution. Counties concerned with increasing the rate of growth in per worker earnings should instead focus on the creation of smaller firms. [ABSTRACT FROM AUTHOR]

In the search for explanations of persistent differences in economic growth rates, the conditional convergence growth model has introduced the possibility of incorporating a wide set of factors as determinants of growth. Controlling for spatial dependence, we assess the contribution of differences in social and institutional variables on growth rates of per capita income for counties in the United States. The empirical results indicate that, ceteris paribus, social and institutio variables explain some of the differences in convergence rates among counties. In particular, (i) ethnic diversity is associated with faster rates of economic growth; (ii) higher levels of income inequality are associated with lower rates; and (iii) higher levels of social capital have a positive effect on economic growth rates. [ABSTRACT FROM AUTHOR]

• U.S. female farmers pursue agricultural practices distinctly different from male farmers. • Push-pull factors account for most of the variation in female farmer shares. • Inherent differences in networking (via social capital) are important. • More research is needed to analyze the increasing importance and impact of female farmers in the U.S. The literature on women in agricultural production in developing countries is vast. While female farmers in the United States have recently received more attention, their general characteristics and practices pursued have not received as much consideration by agricultural economists. Here we examine U.S. female farmers' characteristics and factors associated with county level female farm shares using Census data. We find that these shares are higher near metropolitan core counties and that their presence is associated with agritourism activity as well as horticultural and small livestock production. We conclude with several policy questions and future research needed to assess the roles and impacts of female farm operators in the U.S. [ABSTRACT FROM AUTHOR]

Estimates a Barro-type economic growth models for the years 1982 to 1991 to determine empirically the effect of environmental conditions and policies in state-level per capita income growth in the United States. Measures of environmental policy and quality for individual states; Model of economic growth.

This study tests for potential bias in self-reported innovation due to the inclusion of a research and development (R&D) module that only microbusinesses (less than 10 employees) receive in the Annual Business Survey (ABS). Previous research found that respondents to combined innovation/R&D surveys reported innovation at lower rates than respondents to innovation-only surveys. A regression discontinuity design is used to test whether microbusinesses, which constitute a significant portion of U.S. firms with employees, are less likely to report innovation compared to other small businesses. In the vicinity of the 10-employee threshold, the study does not detect statistically significant biases for new-to-market and new-to-business product innovation. Statistical power analysis confirms the nonexistence of biases with a high power. Comparing the survey design of ABS to earlier combined innovation/R&D surveys provides valuable insights for the proposed integration of multiple Federal surveys into a single enterprise platform survey. The findings also have important implications for the accuracy and reliability of innovation data used as an input to policymaking and business development strategies in the United States. [ABSTRACT FROM AUTHOR]

This paper builds on a recent National Science Foundation working paper that identified a strong association between cloud use and various types of innovation but did not consider whether 1) cloud adoption is a reliable indicator of the innovation orientation of a firm, or 2) cloud adoption enables various types of innovation. We estimate propensity score matching and endogenous treatment effect models to control for innovation orientation, producing evidence to test the second explanation. Findings support an enabling effect of the cloud on innovation providing concrete evidence of the adverse impact of the digital divide. • New data allows examining effects of the digital divide on cloud computing. • Earlier analysis identified a strong association between the cloud and innovation. • It is not clear if cloud adoption enables innovation or is merely an indicator of it. • Controlling for endogeneity allows testing these alternative explanations. • Evidence of an enabling effect suggests the digital divide may inhibit rural innovation. [ABSTRACT FROM AUTHOR]

An introduction to the journal, which outlines ideas for communities struggling with jobs creation or recovery, is presented.

The article discusses various papers submitted for the issue, including one by JunJie Yu on the effects of recent reductions in total natural land areas associated with urbanization and another by Joshua Duke examining the types of environmental amenities provided by the farmland.

Access to affordable and nutritious food is a widely-debated public policy issue in the U.S. In non-metro (including semi-urban and rural) U.S. areas, poor food access is mostly the result of lack of food stores, and in particular the absence of large ones ( e.g. , full-line groceries or superstores). Any policy designed to improve food access in non-metro U.S. areas should recognize that improving stores’ profitability is crucial to policy success in the long-run. We adapt an empirical entry model to assess the effectiveness of two types of policies to improve food access – demand-stimulating policies (DSP), such as increases in Supplemental Nutrition Assistance Program dollars, and supply-side policies (SSP), such as subsidies to reduce establishment costs – by estimating the minimum market size needed for one or more large food stores in non-metro U.S. counties to be profitable. We find that neither type of intervention is preferred a priori and that the cost-effectiveness of each policy type depends upon the presence of an adjacent metropolitan county, the number of pre-existing stores, and the duration of the demand-side stimuli. Additionally, our results suggest that the cost-effectiveness of broad-based policy solutions to improve physical access to large food stores or to stimulate demand may be limited when it comes to easing entry in areas with multiple stores. [ABSTRACT FROM AUTHOR]

Since 1980, average life expectancy in the United States has increased by roughly five years; however, in recent years it has been declining. At the same time, spatial variation in life expectancy has been growing. To explore reasons for this trend, some researchers have focused on morbidity factors, while others have focused on how mortality trends differ by personal characteristics. However, the effect community characteristics may play in expanding the spatial heterogeneity has not yet been fully explored. Using a spatial Durbin error model, we explore how community and demographic factors influence county-level life expectancy in 2014, controlling for life expectancy in 1980 and migration over time, and analyzing men and women separately. We find that community characteristics are important in determining life expectancy and that there may be a role for policy makers in addressing factors that are associated with lower life expectancy in some regions. • There is statistically significant spatial clustering in county life expectancy. • Community characteristics are associated with spatial variation in life expectancy. • Community factors may influence life expectancy in surrounding locations. • Individual characteristics continue to be strongly correlated with life expectancy. [ABSTRACT FROM AUTHOR]