Recipient Organization
UNIV OF CONNECTICUT
438 WHITNEY RD EXTENSION UNIT 1133
STORRS,CT 06269
Performing Department
Agricultural Resource Economic
Non Technical Summary
The economies of rural America continue to lag those in metropolitan areas and some rural regions are experiencing significant economic hardship. It is increasingly recognized that 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. While social science scholars have begun to examine these issues, it is widely recognized that existing urban-rural place typologies do not adequately capture the profuse economic, sociological and demographic heterogeneity among non-metropolitan areas of the country. This project seeks to use InfoGroup data to generate a new place typology that will complement existing place typologies currently published by USDA ERS by incorporating supply-chain connectedness in a spatial gravity model. These will generate a hierarchy of Economic Catchment Areas (ECAs). Once defined, we will construct standard measures of economic activity and economic dynamism by aggregating InfoGroup establishment data to the ECA-level. We will then use these measures for two purposes. First, we will estimate the relationship between economic growth and supply-chain connectedness with particularly emphasis on the role of establishments in food-and-agricultural industries. We will also develop a web-based platform that will allow users to select the gravity model parameters that define the ECA hierarchy, visualize the spatial distribution of ECAs through a mapping tool, and download data via an extract function for their own research or policy evaluation needs.
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: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. 2017 witnessed the first population increase of the decade. In contrast, the annual population growth rate in metropolitan areas during this period has averaged 0.8 percent.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. It is increasingly recognized that 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. 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), rural development policy (Kandilov and Renkow 2010; Rasker et al. 2009), and the features of rural entrepreneurship (Acs and Malecki 2003; Ring, Peredo, and Chrisman 2010), three deficiencies in the federal statistical system hamper research efforts: 1) a lack of systematic data collection and reporting on economic activity within the food-and-agriculture supply chain; 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. Historically, the connection between farming (economic activity) and rurality (demographic concept) underlies the rationale for USDA administering rural development programs. Increasingly, however, rural areas have become less dependent on agriculture as a source of economic activity. Further,researchers recognize that available urban-rural taxonomic systems increasingly strain to capture the complex interaction of economic, sociological, demographic, and geographic attributes that define place in America. In the past, the delineations between urban and rural were stark along these dimensions. Demographically, urban was synonymous with densely populated, while rural areas were sparsely populated. Economically, urban was synonymous with manufacturing and trade, while rural was associated with agriculture, forestry, and resource extraction. Moreover, urban-rural distinctions based on demographics or economics mostly largely aligned. As a result, both the definitional (sparsity, farming) and consequential attributes (e.g., higher fertility, higher out-migration, limited economic opportunity) summarized by the category rural were widely recognized. Today, the distinctions between rural and urban places are increasingly blurred. Rural communities support a more diverse set of economic activities, while urban agriculture has gained momentum. Further, the problems of population loss and economic stagnation associated with rural America are increasingly experienced by urban areas of all sizes. Consequently, researchers generally agree that the previously clear contrast between place type is now a complex set of interdependent layers, rendering traditional measures of "rural" increasingly less accurate and less useful for social scientists and policymakers (Champion and Hugo 2004 ; Kulcsár and Brown 2011 ; Lichter and Ziliak 2017). Although there is general agreement that binary approaches (urban vs rural) are no longer applicable, there is no consensus in the research community on alternative, improved measures of the rural-urban gradient. Until relatively recently, most analytic attention in the construction of place typologies focused on the urban end of the rural-urban continuum. As a result, available measures fails to account for the multidimensional quality of rural places or are organized at geographic aggregations that masks important sub-unit heterogeneity or both (Cromartie and Bucholtz 2008), e.g. MSAs and Rural-Urban Continuum (RUC) Codes. Despite their widespread use, both these place typologies exhibit recognized shortcomings. First, they mask sub-county heterogeneity, which is problematic in contemporary rural America since many "metropolitan" counties contain rural territories and populations. Second, in both taxonomic systems rural is simply the residual category, i.e., the populations and places that are not included in an urban designation, rather than a class of place defined on its own terms. Acknowledging the complexity of social and economic trends that define place requires researchers to develop taxonomic systems that better organize the economic, sociological, and demographic attributes of where Americans live and work, thereby generating the research structure to better assess the forces underlying the disparate fortunes among rural places (Curtis and Kulcsár 2019).Recognizing these issues, researchers at ERS have taken a leading role in developing more nuanced classification schema: RUCA codes, Typology Codes, andFrontier and Remote (FAR) Area Codes. Although each of these taxonomic systems are valuable improvements as we move away from the overly simplistic rural-as-residual and rural-as-farming frameworks, it is also clear that they do not exhaust all salient environmental-socioeconomic relationships characterizing place in America. Therefore, we propose to create a taxonomy of place based on economic interdependence that complements the systems described above. Specifically, we will offer a hierarchical classification scheme that links geographic units through the supply of intermediate production inputs. This projectscontinues work toward the long-term goals of better documenting the sources of economic growth in rural America by using InfoGroup data to generate new place typologies defined by economic connectedness. Therefore, we propose the following specific project objectives:Revise the InfoGroup dataset to act as a synthetic LBD by redefining key variables of analysis (establishment and firm identifiers, industry codes, location of operation, and employment);Generate Economic Catchment Area (ECA) codes that complement existing place typologies currently published by USDA ERS by incorporating supply-chain connectedness as calculated in InfoGroup using establishment industry codes and BEA input-output tables;Construct measures of economic activity (sales, employment, number of establishments, productivity) for place categories by aggregating InfoGroup establishment data. Further, we will harness the longitudinal nature of InfoGroup to construct measures of economic dynamism (growth rates of employment, sales, and productivity; entry and exit rates) for place categories;Identify ECAs that are strongly associated with the food-and-agricultural supply chain and then estimate the relationship between economic growth in FAI and economic connectedness;Develop a web-based platform that allows users to explore project data and download outputs. An interactive mapping component will allow users to examine the spatial properties of different place typologies and their relationship to one another. Users will be able to adjust the parameters that define ECA codes to see changes to their spatial extent in real time. A data extract function will enable users to download project datasets at a variety of geographic scales for their own use.
Project Methods
1. Revise the InfoGroup dataset to act as a synthetic LBDAlthough the InfoGroup data is collected, organized, and published for very different reasons using very different approaches than federal administrative and survey data, in many important respects it is similarly structured to the Longitudinal Business Database (LBD). We thus propose to build a crosswalk between the LBD and InfoGroup that effectively makes the latter a synthetic version of the former:2. Generate Economic Catchment Area (ECA) codesWe propose to use core units in the RUCA codes system as the basis for building Rural-Urban Economic Catchment Areas codes from InfoGroup and the Census of Agriculture. Specifically, we will define a primary economic catchment area (ECA) centered on each small UC core (RUCA 7) that includes associated commuting Census tracts (RUCA 8 and 9) and nearby rural tracts that include large employers in InfoGroup. We will use the Census of Agriculture to identify the primary crop and livestock production activities and the County Business Patterns to identify the presence of forestry and mining activity in rural Zip codes surrounding the primary ECA (the smallest published unit for both programs is the Zip code). We anticipate that for the overwhelming majority of the country, using the county as the basic unit will suffice for identifying agricultural, forestry, and extraction, but western counties are sufficiently large to benefit from a more disaggregated reporting unit. We will then use the most recent Use Tables published by BEA as part of its Input-Output Accounts program (2018 for 71 two- and three-digit NAICS industries and 2012 for 415 four-digit NAICS industries) to calculate absolute and relative rural use coefficients for each primary ECA with respect to the agricultural, forestry, and extraction (AFE) activity in its vicinity. We then will then assign remaining rural tracts to primary ECAs using a gravity model based on the use coefficients defined above. Gravity models have been applied as far back as the 1850s (Carey, 1858) to describe social phenomena and continue to be used across disciplines (Erlander, 1980; Haynes and Fotheringham, 1984; Sen and Smith, 1995; Wilson 2000), including contemporary analyses of international trade (USITC, 2020). We will then move to assigning the remaining Micropolitan and Metropolitan commuting tracts. We will then move to define secondary ECAs. To do so, we will again treat the United States as a plane with nodes defined by Micropolitan cores and primary ECAs attracted to those nodes by total use coefficients. Primary ECAs are assigned to secondary ECAs centered on Micropolitan cores when gravity is sufficiently strong. Next, contiguous groups of yet-unassigned primary ECAs with sufficiently strong gravity will be aggregated into secondary ECAs independent of a Micropolitan core; those without are classified as isolated. This is repeated with Metropolitan cores to define tertiary ECAs. The approach described above is based on the three categories of urbanized places from the RUCA system, but it generalizes broadly to an N-level hierarchy of nodes where the nth ECA is defined by a parameter pair: the upper limit on the population of the core and the gravity threshold that defines economic islands, respectively. Refinements based on the former would permit the definition of small and large metropolitan cores, or even Megapolitan cores. The latter parameter can be varied according to moments of the use coefficient distribution (the lowest quintile or decile) or absolute values with a priori relevance, e.g., a parameter from a specified decay function in a formal model.3. Construct measures of economic activity and economic dynamism by place type The smallest unit in the typology dataset described above is the Census tract. Every establishment observation in the InfoGroup dataset includes a Census tract identifier. Further, InfoGroup is linked longitudinally at the establishment level. It is therefore possible to construct measures of economic activity, the net change in economic activity, and the gross flows of economic activity at the Census tract level. For example, we can calculate the number of establishments (level), the change in the number of establishments (net), and the number of establishments that started and ceased operation (gross). We can do likewise for employment and sales. Further, because every ECA at every level of the proposed hierarchical typology is constructed by agglomerating Census tracts, these measures can be calculated for every ECA in the hierarchy by summing over their constituent tracts.4. Examine dynamism and FAI activity across place typesWith measures of economic growth at the ECA-level, we propose the following descriptive analyses:-For every level of the ECA hierarchy, we will identify the ECAs in the top and bottom quintile of the growth distribution.-For every level of the ECA hierarchy, we will construct the CDF of the use coefficient (i.e., the extent to which a unit at one level of the hierarchy is economically connected to a unit at a higher level of the hierarchy). We will then calculate the mean net growth rate for ECAs at each quintile of the use coefficient distribution. This will allow us to test whether economic connectedness is related to economic growth.-We will further explore this relationship by estimating regressions of growth rates in ECA i in year t on the relative use coefficient between ECA i and the core of ECA j. To study the specific role of FAI industries on growth, we will also estimate regression models that interact use coefficients with the share of economic activity in FAI industries. Further, we will decompose use coefficients into FAI and non-FAI components, allowing these to enter the regression separately.-Finally, we are interested in how growth in FAI translates through the supply chain to influence regional economic development. We will therefore estimate linear regressions of the growth rate of FAI in ECA i in year t with the growth rate of FAI in ECA j.5. Develop a web platform that stakeholders and researchers can use to download, analyze, and visualize economic activity and dynamism for different place types. We believe the creation of a web-based platform with interactive mapping that allows users to examine the spatial properties of different place typologies is a particularly innovative component of the proposed work that will provide great benefit to both researchers and policymakers. Users will be able to adjust the threshold parameters that generate ECA codes and see changes to their spatial extent in real time. Measures of economic activity and economic dynamism will be generated at the ECA-level and a data extract function will enable users to download shape files, Census tract and ECA identifiers, and these measures of economic activity and dynamism for specified geographies for use in their own analyses. For this project, the web-based interface will draw from a library of datasets created by the project team. Recall, that each typology is defined by model parameters. While the number of levels in the hierarchy, N, is discreet, the population thresholds that define the core of each level and the gravity thresholds are continuous. Beginning with N=3, we will discretize population to provide users options for each threshold with the grid size increasing at each level in the hierarchy. We will similarly discretize the gravity thresholds ten options, but unlike population, these will be based on the quantile of the use parameter distribution.