An Examination of Entry and Competitive Performance in Rural Banking Markets

Abstract

This paper explores the change in the level of competition in rural banking markets since the deregulation that occurred following passage of the Riegle Neal Act of 1994. Using an empirical model that utilizes both the number of banks and the value of deposits in a cross-section of rural markets, we decompose the impact of the entry of new banks into resulting changes in per capita demand and the costs/profits of local banks in both 1994 and 2004. We conclude that the banking market is more competitive today despite the fact that the number of banks may have declined; on average fewer banks are now needed to make rural banking markets competitive than were needed in 1994.

Food retail profits, competition, and the Great Recession

“…To capture heterogeneity in variable costs, we use the state‐level commercial retail price of electricity in cents per Kwh from the US Department of Energy; a weekly wage rate calculated from the total annual payroll divided by the number of employees in the grocery sector collected from the CBP; and interaction between electricity price and weekly wage. Feinberg and Reynolds (2010) and Manuszak (2002) similarly use a wage rate to capture some of the relevant, variable costs faced by firms upon entry. In analyses of entry threshold ratios, it is also typical to control for variation of population density between counties using land area (Bresnahan & Reiss, 1991; Cleeren et al, 2006; Gayle et al, 2017).…”

“…Ordinary least squares does not provide consistent estimates of ordered probit models. We, therefore, follow Feinberg and Reynolds (2010), Manuszak (2002), Gayle et al (2017), Bresnahan and Reiss (1991), and others and use a maximum likelihood estimator. Equation ( 13) and ( 14) are nonlinear combinations of the parameters of Equation (8) as described above.…”

“…These variables are expected to have a positive influence on demand, and therefore profits. A measure of income is common to include in B&R type analyses of entry thresholds as theory predicts that income increases increase demand for normal goods (Bresnahan & Reiss, 1991; Chevalier et al, 2008; Cleeren et al, 2006; Feinberg & Reynolds, 2010; Gayle et al, 2017). We expect SNAP coverage to increase the ability of poorer households to spend on food‐at‐home as found by Hastings and Shapiro (2018) and we also include the county‐level poverty rate from PEP to control for the extent of poverty in an area.…”

“…The low data demands of the B&R model have led it to be applied to several differentiated industries; some examples include video rentals (Cleeren et al, 2006), local banking (Feinberg & Reynolds, 2010), and non-profits (Gayle et al, 2017).…”