As more consumers take advantage of online banking services, branch networks are declining across the country. Limited attention has been given to identifying any possible spatial patterns of branch closures and, more importantly, the community demographics where branches close their doors. This analysis uses an innovative spatial statistics concept to study financial services: Using data from 2010 to 2016, a random labelling test is conducted to understand branch closure clustering in the Philadelphia, Chicago, and Baltimore metropolitan statistical areas (MSAs). Additionally, spatial autocorrelation is tested, and an MSA-level spatial regression analysis is done to see if there is a pattern to branch closures in metropolitan areas. I find evidence of branch closure clusters in the Chicago and Philadelphia MSAs; however, this spatial pattern is only observable within the suburbs, not the primary city itself. Using a random labelling test is a methodological innovation in regional economic studies and propels our understanding of banking deserts and underserved neighborhoods.
For much of the 20 th century, America's central cities were viewed as synonymous with economic and social hardship, often used as proxy for low-income communities of color. Since the 1990s, however, many metropolitan areas have seen a resurgence of interest in central city neighborhoods. Theoretical models of income sorting lead to ambiguous predictions about where households of different income levels will live within metropolitan areas. In this paper, we explore intra-city spatial patterns of income and race for U.S. metropolitan areas, focusing particularly on the locations of low-income and minority neighborhoods. Results indicate that, on average, income and white population shares increase with distance to city centers. However, many centrally located neighborhoods are neither low-income nor majority non-white, while low-income and minority neighborhoods are spatially dispersed across most metropolitan areas.
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