Recipient Organization
UNIV OF WISCONSIN
21 N PARK ST STE 6401
MADISON,WI 53715-1218
Performing Department
(N/A)
Non Technical Summary
The economies of rural America continue to lag those in metropolitan areas with many experiencing significant hardship. Identifying the attributes of rural economies that explain why they lag their urban counterparts, and the characteristics that cause some rural areas to flourish while others flounder are critically important areas of research.Yet, rural America is not monolithic and modern economies are increasingly less constrained by geography. The goods and services consumers purchase are the end-products of complex supply-chains with numerous highly specialized production and distribution steps. The economic performance of a rural area is intimately connected to economic activity in the upstream and downstream sectors linked to businesses located there. Although some may be located in rural places, many will be in urban areas. Thus, rural economic development policy that ignores urban areas is incomplete.This project seeks to use federal administrative microdata through an approved FSRDC program to construct a new place typology based on supply-chain connectedness. This approach will overcome recognized shortcomings in existing typologies that define rural as the residual, non-urban category, or apply geographic aggregations that combine define rural areas within urban counties as urban. We will measure the economic isolation of rural areas, evaluate how isolation affects economic performance, and how the economic activity in urban cores is related to economic performance in their hinterlands. We will also examine spatial heterogeneity in these relationships based on the demographic profiles of both rural units and urban cores.
Animal Health Component
100%
Research Effort Categories
Basic
(N/A)
Applied
100%
Developmental
(N/A)
Goals / Objectives
The economies of rural America continue to lag those in metropolitan areas, and some rural regions are experiencing significant economic hardship. Statistics reported by the USDA Economic Research Service (ERS) paint a sobering picture (Kusmin 2016; Cromartie 2018):In 2018, total employment in rural America was two percent lower compared to 2008; in contrast, employment grew by eight percent in metropolitan areas over the same period.Between 2015 and 2016, nominal wages in rural America grew by 3.8 percent, compared to 5.5 percent in mid-sized metropolitan areas (250,000 to 1 million residents) and 4.9 percent in small metropolitan areas (less than 250,000 residents).Population growth in rural America was negative every year between 2010 and 2016. In contrast, the population growth rate in metropolitan areas averaged 0.8 percent annually.The labor force in rural America decreased 2.5 percent between 2008 and 2016, compared to a 4.5 percent increase in the labor force in metropolitan areas.The vitality of many rural communities-and the very viability of others-depends critically on reversing their economic decline (Stauber 2001; Goetz, Partridge, and Stephens 2018; Li, Westlund, and Liu 2019). Identifying the attributes of rural economies that explain why they lag their urban counterparts and the characteristics that cause some rural areas to flourish while others flounder are critically important lines of inquiry in public policy research (Isserman et al. 2009). Yet, the temptation to frame this challenge as "fixing the rural economy" is inappropriate.First, rural America is not a monolith (Stauber 2001; Goetz, Partridge, and Stephens 2018). Urban-rural taxonomies increasingly strain to capture the complex interaction of economic, sociological, demographic, and geographic attributes that define place. The previously clear contrast between urban and rural is now a complex set of interdependent layers, rendering traditional measures of "rural" increasingly less accurate and useful for researchers and policymakers (Champion and Hugo 2004; Kulcsár and Brown 2011; Lichter and Ziliak 2017; Curtis and Kulcsár 2019).Second, modern economies are increasingly less constrained by geography. The goods and services consumers purchase are the end-products of complex supply-chains with numerous specialized production and distribution steps. The economic performance of an area is inseparable from activity in the upstream and downstream sectors linked to its local businesses. Though some linkages may be local, e.g., a grain elevator used by farms, many businesses that are supply-chain adjacent to firms in rural areas may be located elsewhere, e.g., a transmission manufacturer sending output to an assembly plant in another state. The economic prospects of a rural place depend on economic activity in many others-some rural, others urban. As a result, rural economic development policy that ignores urban areas is incomplete. As a corollary, because different rural areas are connected to different urban ones through different supply-chain relationships, one-size-fits-all policies are similarly limited.Additionally, supply-chain linkages are bidirectional. While rural economies may depend on urban ones, rural economies also play an important role in supporting urban economies. Rural input suppliers may anchor downstream businesses to urban areas because relocations that break supply-chain linkages, e.g., identifying potential firms, renegotiating contracts, rebuilding relationship capital are costly. This suggests that coordinated regional economic development policy is not only preferable for achieving rural prosperity, but mutually beneficial to urban and rural communities.While social science scholars have begun to examine these issues, including shifting rural-urban boundaries and urban-to-rural migration (e.g., Lichter and Brown 2011; Nelson, Oberg, and Nelson 2010; reverse flow in Rupasingha, Liu, and Partridge 2015), rural development policy (Kandilov and Renkow 2010; Rasker et al. 2009; Schafft 2016; Lobao et al. 2016), and the features of rural entrepreneurship (Acs and Malecki 2003; Ring, Peredo, and Chrisman 2010; Deller, Kures, and Conroy 2019), three deficiencies in the federal statistical system hamper research efforts: 1) a lack of systematic data collection and reporting on economic activity within supply chains; 2)inadequate reporting of economic activity in rural areas; and 3) place typologies that obscure profuse economic, sociological, and demographic heterogeneity among non-metropolitan areas of the country.This proposal advances ongoing research efforts seeking to identify the attributes of rural areas that cause their economies to lag urban ones. The specific scope, scale, and mechanisms which define rurality are of fundamental importance, but extant typologies obscure heterogeneity among non-metropolitan areas of the country across economic, sociological, demographic, and geographic dimensions. Therefore, we will use recently developed urban-rural place typologies based on economic connectedness through supply chains to examine how connectedness affects economic outcomes in rural communities, including how economic activity in the urban cores influences outcomes in the rural areas to which they are connected. Bringing together an interdisciplinary team of social science researchers representing economics, sociology, demography, and geography with decades of combined experience in data management, analysis, and visualization, we propose the following specific project objectives:1. Construct a measure of supply-chain connectedness between every pair of US counties using establishment output from the Longitudinal Business Database, crop sales from the Census of Agriculture, and BEA input-output tables;2. Use this measure to construct a hierarchical Economic Catchment Area (ECA) typology and categorize non-metropolitan counties according to their economic isolation;3. Construct measures of economic activity and dynamism for urban and rural places categories under the OMB and ECA place typologies;4. Estimate the relationship between rural economic isolation and measures of economic activity and dynamism, including whether these relationships changed after the COVID-19 pandemic;5. Estimate the relationship between the economic activity in urban cores and measures of economic activity and dynamism in economically connected rural hinterlands, including whether these relationships changed after the COVID-19 pandemic.
Project Methods
1. Measure supply-chain connectednessUsing data from the BEA Input-Output tables, the Census Bureau Longitudinal Business Database (LBD), and the USDA Census of Agriculture county product sales production statistics, we will calculate the input needs of every county given the observed level of production. For every industry, we will calculate either the production in excess of input need (net input supply) or the remaining input need in excess of local production (net input demand). For each county, we will then calculate the share of net input supply that can be absorbed by the net input demands of every other county.2. Construct Economic Catchment Area (ECA) hierarchy and economic isolationTo construct an ECA hierarchy, we will follow the following process at each level, L:a. Define the set of geographic units with dimension n(L);b. Construct the n(L) x n(L) absorption matrix;c. For each of the n(L)units, identify the unit in the matching set with the largest absorption share;d. If this share is less than the isolation threshold, T(L), the unit is categorized as L-isolated. If this share is greater than the isolation threshold, the absorbing unit is defined as an ECA-L core;e. An ECA- is defined as the core and all units for which the core unit absorbs the largest share so long as those units are not themselves ECA-L cores;f. Aggregate economic activity over the ECA-L level and repeat 1-5 for the ECA-(L+1) level.Finally, there is an implicit parameter assumption in the process described above: within the matching set, the value of one unit of input does not depend on its source (no transportation costs). Thus, we will also implement geographic impedance functions that weight absorption shares by distance. Lacking an a priori best weighting function, we will examine robustness to a range of reasonable values (we have successfully incorporated impedance into our state-by-state approach).3. Construct measures of economic activity and dynamismThe unit of observation in the LBD is an establishment-year pair with identifiers that allow for linkage over time with variables that provide locational information, including the county of operation. Thus, we will construct the following annual county-level measures of economic activity by summing values over the establishments in a given county: a) the number of establishments with employees (N) b) the number of establishments starting operation (births) c) the number of establishments ceasing operation (deaths) d) the annual payroll to workers employed by businesses (payroll) e) the number of workers employed by business during the March 12 pay period (employment) f) the annualized mean wage for employees based on first quarter employment and payroll (wage)g) the annual full-time-equivalent employment (annualized employment).We will use these variables to calculate measures of economic activity and dynamism along three domains: number of establishments, employment, and payroll.4. Estimate relationship between economic isolation and economic activityTo satisfy disclosure avoidance standards, for each of the outcomes x in the set of outcomes defined above, we will estimate x=f(isolation) where f(.) is a basis spline specified to minimize the difference to the results defining isolation as a categorical variable. Because spline coefficients are not interpretable directly, we will rely on graphical presentation of marginal effects at salient values of the explanatory variables, which we believe will make patterns easier to recognize and thus easier to incorporate into policy discussions.Our data span two seismic events that challenged economic, social, and public health systems-the Great Recession and the COVID-19 pandemic. Both events were once-in-a-century global shocks that affected economic activity, the allocation of government resources, and population health outcomes. As systematic challenges that tested the capacity of communities to minimize harm to residents in the midst of sometimes profound stress and then recover in their aftermath, they can illuminate the attributes of communities that are associated with greater resiliency. We will define the pre-shock period as the five years prior to the shock: 2003-2007 for the Great Recession and 2015-2019 for COVID-19. In addition to contemporaneous effects (absorption), both events may have precipitated longer-term changes (recovery). Thus, we define two post-periods for each shock: short-run (2008-2010 for the Great Recession, 2020-2022 for COVID-19) and medium-run (2011-2019 for the Great Recession, 2023-2024 for COVID-19) for a basis spline specification:x=f(isolation, Short, Medium)Rural areas of America are racially and ethnically diverse. Because of institutional barriers that have generated ingrained disparities across racial, ethnic, and socioeconomic groups, spatial heterogeneity in the isolation-activity relationship that is systematically related to the demographic profile may exist in rural areas. Therefore, we use counties estimates of the population share that is Black, Hispanic, Native American, Asian or Pacific Islander, and non-US citizens from the ACS and the share living below the poverty line from the SAIPE to include as variables in the outcome function:x=f(isolation, demographics)5. Estimate relationship between the economic activity in urban cores and measures of economic activity and dynamism in rural hinterlandsThe analysis is structured identically to the one described above, replacing economic isolation of the hinterland county with relevant measures of economic dynamics for ECA-1 cores. As in Objective 4, we wish to identify whether connectivity to economically dynamic urban areas affects the resiliency of rural areas. Thus, we will augment the specification of f(.) to flexibly allow the relationship between dynamism in the core and outcomes in rural areas to vary in the short and medium run following the Great Recession and COVID-19 pandemic. Finally, we will consider whether the potential benefits of being connected to a highly dynamic urban core depend upon the demographic profile of rural counties, e.g., are rural counties with higher shares of historically disadvantaged communities more or less likely to benefit from their connectedness to urban places.