Alabama is a high-poverty state with permissive small-dollar credit regulation and one of the largest licensed alternative financial service provider markets in the United States. Using Alabama State Banking Department licensing data for 2011, 2014, 2015, 2016, and 2020, new research by Turner College economist Wen Shi, Tuskegee University's Rui Chen and Robert Zabawa, and Jiayi Xu of Siena University estimates Poisson pseudo-maximum likelihood models with high-dimensional fixed effects in order to examine conditional spatial associations between traditional financial institutions, socioeconomic and demographic characteristics, and counts of payday lenders, small-loan providers, and pawnshops. Their models are estimated at census-tract, ZIP-code, and county levels, and two-part hurdle models separate alternative financial service provider presence from conditional intensity. According to their results, lagged credit-union intensity shows the most consistent positive association with alternative financial service provider counts across specifications, especially at the ZIP-code level, while lagged bank intensity is positive in most specifications, with significance concentrated on the ZIP-code intensity and tract-level entry margins. Population size and an inverted-U income gradient are the strongest predictors. Demographic associations are strongest at the tract level, especially the Hispanic share of the population, share of the population aged 65 and over, and lower educational attainment, while the Black share of the population is not significant in the main models. The findings, which are discussed in greater depth in their article set to appear in a future issue of Applied Economics, underscore the importance of spatial scale for interpreting AFSP location.
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