ABSTRACT. Oklahoma Economic Forecast

ABSTRACT. West Virginia Economic Forecast

ABSTRACT. South Carolina Economic Forecast

ABSTRACT. Virginia Economic Forecast

ABSTRACT. This study constructs a composite housing livability index for U.S. counties across three dimensions: affordability (cost-to-income ratios and cost burdens), market dynamism (construction, residential turnover, vacancy), and housing quality (overcrowding, mobile homes). Using principal component analysis with data from American Community Survey 5-year estimates, we compare 2013 and 2023—two post-crisis periods separated by economic recovery and pandemic disruption. The preliminary results reveal a paradoxical pattern: affordability improved in 74% of counties, while market dynamism declined, leaving overall livability slightly worsened. Market dynamism contracted to approximately 59% of 2013 levels, with a negative ranking correlation (r = −0.57) indicating that previously high-activity markets experienced the sharpest reversals—suggesting post-Great Recession recovery momentum had exhausted itself. Affordability rankings remained stable (r = 0.83), and housing quality showed minimal change. Regression analysis by county type shows metro counties consistently outperform non-metro counties across all dimensions, with non-metro counties scoring 4-9 points lower on overall livability. Persistent poverty counties show particularly large deficits (3-4 points lower on quality), while recreation and retirement counties score higher. Farming counties show lower scores across most dimensions. These findings reveal complex tradeoffs between affordability gains and market vitality during a decade marked by economic recovery, housing boom, and post-COVID adjustments, with outcomes strongly shaped by local economic structure and demographic composition.

ABSTRACT. Rural livability depends on an ecosystem of "critical institutions" that provide essential services, amenities, and civic infrastructure, yet there has been no systematic effort to quantify these assets over time. This article introduces a new publicly available dataset and index that measure critical institutions at the county and municipality levels for rural U.S. communities from 1998 to 2025. The Rural Critical Institutions (RCI) index combines per-capita counts of anchor institutions, essential businesses, and civic infrastructure establishments, derived from longitudinal YourEconomy Time Series (YTS) microdata linked to NAICS codes and geocoded to counties and municipalities. After describing the conceptual framework, data, and index construction, the article presents descriptive maps and figures documenting spatial and temporal patterns in institutional presence across rural America. As a proof of concept, it examines whether some municipalities accumulate critical institutions at the expense of other municipalities within the same county. The article concludes with potential applications for research, policy, and Extension practice.

ABSTRACT. This paper examines differences in quality of life (QOL) between metropolitan and rural counties in the United States over the period 1970–2019. Using a county-level implementation of the Rosen–Roback spatial equilibrium framework, we construct a QOL index from wage and housing price differentials and evaluate both its association with population growth and the amenity composition underlying cross-sectional variation in inferred welfare. The analysis documents a persistent metropolitan advantage in QOL, alongside increasing dispersion in both population and amenities over time. Regression results indicate a positive and statistically significant relationship between QOL and subsequent population growth in both metropolitan and rural counties, though the strength of this association varies across decades and settlement types. Penalized regression (LASSO) estimates reveal that natural amenities and public goods—including climate, topography, forest cover, inequality, crime, mortality, and school spending—are consistently associated with QOL, while private consumption amenities display greater heterogeneity across contexts. The findings underscore the evolving role of amenities in shaping regional population dynamics and contribute to the literature on spatial equilibrium, regional disparities, and rural–urban divergence in the United States.

ABSTRACT. Over the past several decades the U.S. economy has become less dynamic along several dimensions. For example, the rate of business entry and exit and rates of domestic migration have declined. Prior research has shown that rural and small urban areas of the country have experienced larger declines in these measures compared to larger urban areas. Economic downturns often lead to a redistribution of resources and activity across sectors in the economy, and areas with lower entry and exit of businesses and movement of people may have less ability to adjust to these changes. If the gap between rural and urban area dynamics persists or widens further, the gap between their economic fortunes may widen as well.

One way to measure the gap in economic fortune is to estimate the rate at which average income is converging across areas over time. Historically, rural areas have lower income compared to urban areas. However, rural areas also tend to experience higher income growth. Consequently, we should expect rural area incomes to “catch-up” or converge over time to urban areas incomes if this growth is high enough. However, the rate of income convergence in rural areas has likely declined due partially to declining dynamism.

We investigate whether rural areas experienced higher income growth over the past 50 years and whether this growth was high enough for incomes to narrow the gap in income between rural and urban areas. We use county-level data to estimate real income growth and convergence between 1970 to 2023 over three time periods. Local growth regressions are used to determine if the rate of convergence has differed in rural versus urban areas and if the rate has changed over time. We find that while lower income counties tended to grow faster than higher income ones, this “catch-up” effect was significantly higher in rural areas. In other words, a county that had lower income and was rural tended to have an extra boost in growth compared to being only lower income or only rural. However, the rate of income convergence slowed over time and slowed more in rural areas, declining by over half relative to what was observed between 1970 and 1990.

We explore if all rural areas had similar rates of growth and convergence or if proximity to urban populations made a difference. Rural areas in closer proximity to urban areas likely experienced more suburbanization, leading them to grow faster and experience a higher rate of income convergence relative to urban areas. We find that, compared to urban areas, rural areas adjacent to urban counties did grow faster and had a higher rate of income convergence in 1970-1990 and 1990-2010. Moreover, rural areas non-adjacent to urban ones also experienced similar rates of growth and convergence as adjacent rural counties over the same time frames. From 2010 and onwards, income growth and the rate of convergence were no longer significantly higher in either rural setting. Thus, the larger declines in dynamism in rural areas have coincided with slowing growth and slowing income convergence. As a result, the relative gap between average rural and urban incomes is likely to persist.

ABSTRACT. Recent macroeconomic research on labor market outcomes has emphasized the role of nationally dominant firms, often referred to as “superstars.” Still, much less is known about how extreme observations within local employer size distributions shape labor outcomes in regional labor markets. This work uses County Business Patterns data on employment by establishment size in the United States and an outlier-based methodology to identify unusually large establishments relative to the overall distribution of establishment sizes in all other U.S. counties. These Sector-County Outlier Relative Employment (SCORE) Establishments are linked to the commonly-used Gini Index as a measure of local inequality, with Gini Indices constructed using wage and salary data from the Public Use Microdata Sets. Across a range of specifications and with several controls for macroeconomic shocks included, little evidence is found that counties with a greater presence of SCORE Establishments experience systematically greater inequality. Indeed, several specifications suggest the presence of SCORE Establishments is weakly associated with lower inequality.

These findings contrast the macroeconomic findings regarding large-scale firms to a more localized level. More broadly, the results illustrate how outlier-based approaches, which remain uncommon in regional labor market studies, can provide a complementary perspective on the relationship between employer structure and labor outcomes.

ABSTRACT. Settlement patterns vary across rural America. In some regions, population is concentrated in small towns and cities. In others, it is dispersed across the open countryside. Analyses of rural areas frequently rely on county-level data as the smallest areal unit of observation for which data are widely available, yet county-level measures of population distribution - such as population density and "percent urban" - do not adequately capture the variation in settlement patterns in counties. This paper develops a measure to capture these varying settlement patterns - rural agglomeration - then uses the measure in a demand-threshold analysis to examine how settlement patterns in rural counties affect the number of business establishments in various industries in rural counties. The paper concludes with a discussion on how rural agglomeration and demand-threshold analysis may be useful in describing and classifying counties across the rural-urban continuum.