Challenges to Economic Resiliency and Performance: Measuring the Regional Impacts of Rurality and Space

It is commonly observed that there are inequalities found in economic growth, development, and performance between different regions. Because of this, it is vital for regional planners to have knowledge to which economic problems are present (Armstrong and Taylor, 2000; Martin, 2005). With such knowledge, planners are able to tailor and implement regional policies in an informed manner that is better suited to address economic problems. Found in this work are two studies that contextualize separate economic problems which have been extensively discussed within regional sciences and rural studies. The first study seeks to assess how a county’s degree of rurality affects its capacity to resist and rebound from economic shocks. Rurality is a variable that challenging to define, but is nonetheless important to understand because identifying how regions can be rural provides necessary context for the justification of policy intervention (Cloke and Edwards, 1986; Beynon et al., 2016). We use county-level data from a series of federal agencies over the period of 2011 through 2015 to statistically estimate and visualize an urban-rural landscape of New England. Using this measure, we further test to see if a county’s degree of rurality had an impact on its relative recovery speed in employment growth. Over the same period of 2011 to 2015, we test how these counties recovered from two years and beyond after the Great Recession. The findings suggest overall a county’s degree of rurality corresponded with slower levels of recovery in terms of employment in comparison to overall U.S. levels. The second study seeks to explain how spatial factors such as market access and geographical remoteness influences a region’s differential economic performance. While the discussion of factors contributing to economic performance is expansive for large areal units like nations, there is a need for more understanding on how factors that dampen economic performance at a granular level can influence the greater region’s performance (Porter, 2003; Agarwal et al., 2009). We use data from the Census Bureau, National Park Service, and Google Geocoding Service in the one-year period of 2016 to: (1) estimate economic output as a proxy for performance in a system of equations, and (2) to see how such performance differentiates across geographic space. To approach this problem, we used a novel method of extracting and translating geographic data into distance measures at the census tract level to investigate how spatial factors influence economic performance. Overall, the findings from our jointly estimated system of equations highlight that larger distances to market access and remoteness negatively influences economic performance at the census tract level. Similarly, higher levels in variables such as workplace disability and the old-age dependency ratio had other dampening impacts