Shrinking Networks: A Spatial Analysis of Bank Branch Closures

Abstract

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.

The determinants of bank branch location in India: an empirical investigation

“…For example, Amel and Liang (1997) show that in the US, market with high profit, large population size and high population growth are the prime motivating factors for the entry of banks. Other studies show that low-income areas are more affected in the closing down of bank branches than high-income areas (Tranfaglia, 2018;Bierman et al, 1996). For example, Bierman et al (1996) examine the impact of branching regulations on the number of bank branches in high-and low-income areas in the US.…”

The Financial Services Environment and Schools' Savings Rates in the San Francisco Kindergarten to College Program

“…Manifestations of these variations include the densities and proximities of banks, credit unions, and alternative financial service providers (Dunham 2018;Dunham and Foster 2015;Goodstein and Rhine 2017;Jorgensen and Akee 2017), which are often spatially patterned after neighborhoods' and communities' racial and economic makeup. Neighborhoods of color tend to have lower average densities of bank and credit union branches than their White counterparts, net of socioeconomic controls (Dahl and Franke 2017;Ergungor 2010;Goodstein and Rhine 2017;Graves 2003;Hegerty 2016;Jorgensen and Akee 2017;Morgan, Pinkovskiy, and Yang 2016;Tranfaglia 2018;Smith, Smith, and Wackes 2008;Richardson et al 2017). For instance, census block groups in the 1990s with higher percentages of Black residents had significantly fewer bank branches than the county average in Cook County, Illinois, which includes the city of Chicago (Graves 2003).…”

“…Moreover, forecasts predict that the number of bank and credit union branches will continue to decline amidst bank failures, consolidations, and efforts to increase efficiency and reduce costs via technological advancements such as automated teller machines (Debter ; JLL ; Morgan, Pinkovskiy, and Yang ). The rise in popularity of online banking and mobile payments in the contemporary era is a contributing factor to brick‐and‐mortar bank branch closures (Servon and Kaestner ), as well as profitability and consolidation through mergers and acquisitions (Tranfaglia ).…”

Three Chapters Exploring Themes in Competition and Collusion

“…p<0.1; * * p<0.05; * * * p<0.01Table B.4: Negative Binomial Lagged Regression -Cartel Births (1960-2018 Note: Robust standard errors reported in parentheses. * p<0.1; * * p<0.05; * * * p<0.01Table B.5: Negative Binomial Lagged Regression -Cartel Deaths (1960-2018 Note: Robust standard errors reported in parentheses.…”

“…p<0.1; * * p<0.05; * * * p<0.01Table B.4: Negative Binomial Lagged Regression -Cartel Births (1960-2018 Note: Robust standard errors reported in parentheses. * p<0.1; * * p<0.05; * * * p<0.01Table B.5: Negative Binomial Lagged Regression -Cartel Deaths (1960-2018 Note: Robust standard errors reported in parentheses. * p<0.1; * * p<0.05; * * * p<0.01Table B.6: Negative Binomial Lagged Regression -Cartel Discovery (1960-2018 Note: Robust standard errors reported in parentheses.…”