Mapping Economic Opportunity in Rural America: Mining Big Data for Decision Making in Business Development Grant uri icon


  • Business owners and communities are often interested in expanding an industry in their area. Despite the growing body of literature highlighting effective econometric methods to measure demand thresholds on establishment counts in rural areas, there is a dearth of studies using more accurate measures of industry size (e.g., employment or payroll), or using big data to find more accurate and detailed measures of community thresholds for businesses of various types in the rural United States. Extant research into demand thresholds has focused on retail businesses almost exclusively and is limited in detail due to a lack of access to data, or limited in scope due to reliance on state tax data. Our study will advance research on thresholds in notable ways. First, almost all demand threshold analysis conducted to date has used the Census' publicly available County Business Patterns (CBP) data, resulting in several limitations:In large part due to disclosure concerns, CBP data is often suppressed, especially when examining very specific industrial classifications.The universe of CBP businesses is only for employers. Thus small self-proprietary establishments with no formally paid employees - common to rural areas - are not included. As a result of this omission, estimated thresholds are biased upwards.Thresholds based on CBP ignore the fact that businesses of the same industry code are of different sizes. For example, a community may be able to support three small grocery stores, but only one large grocery store. Hence, this technique could be improved if "number of businesses in an industry" were replaced with more descriptive information such as "employment in an industry" or "payroll in an industry," for example.Most prior studies have focused on population-based estimates, which is sensible for retail trade and other service industries, but perhaps less germane to other types of industries. For example, to be viable, a meat processing plant may need local animal production more than it needs local consumers. Thus, this project will also consider supply-based thresholds, thereby expanding current research in such a way that is only feasible with "big data."Although the Census Bureau provides Nonemployer Statistics publicly, researchers encounter the same problem as the CBP data: the exact counts of nonemployers in a particular NAICS code are often suppressed to avoid disclosure. Further, even seemingly small employment ranges in CBP size classes (e.g. 10-19 employees) are relatively large in rural areas. Below we show two maps below to emphasize the significance of this limitation. Counties in the maps are colored dark red if the publicly available CBP does not disclose a count of the employment and annual payroll in the food manufacturing industry or florist industry, respectively. The limitations in other industries are similar and worsen with industry specificity.Given the extensiveness of the disclosure limitations, an exact count would yield much more precise models..........

date/time interval

  • 2017 - 2021