Abstract
This paper investigates the geographic distribution of payday lenders and banks that operate throughout the United States. State-level data are used to indicate differences in the regulatory environment across the states. Given the different constrains on interest rates and other aspects of the payday loan products, we empirically examine the relationship between the number of payday lender stores and various demographic and economic characteristics. Our results indicate that number of stores is positively related to the percentage of African American population, the percentage of population that is aged 15 and under and the poverty rate. The number of stores is also negatively related to income per capita and educational levels.
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Notes
Payday lenders are also referred to as deferred deposit originators and their product as payday advances, cash advances, deferred deposits, among other terms.
The interest rates in both cases are calculated assuming the two loans are outstanding for a year and the fees are paid every 14 days. Of course, the rates are much higher if one assumes a new loan is taken out every 14 days and the same fees charged.
See Douglas (2014, p.2).
Due to limited availability of data, the paper focuses on actual storefronts to the exclusion of online payday lenders. However, William H. Sorrell (2014, p.1), Attorney General of Vermont, recently stated that “Online lenders nationwide (currently numbered at over 200) earned over $18 billion dollars in income from high-interest, small-dollar loans made in 2012.” Yet, according to the Consumer Financial Protection Bureau (2013), these payday loans still make up a minority of the total loan volume, and the loans are offered with fees equal to or higher than storefront loans.
It should be note that in the late 1990s some payday lenders began partnering with nationally chartered banks and payday loans became “bank loans” because such banks were not subject to state-imposed fee caps or usury laws. However, the Federal Deposit Insurance Corporation took actions in 2003 and 2005 that, according to Stegman (2007, p. 179) “… rendered the rent-a-bank model obsolete.”
Changes in credit supply are proxied by two dummy variables, with 0 before a state banned payday lending and also a 0 before a state passed enabling legislation for payday lending, and a 1 in both cases after the banning and enabling changes. They rely on annual store counts obtained from Stephen Inc., which is an investment bank that tracks the payday lending industry.
Colorado has a tiered structure of rates and fees based upon the loan amount.
As a result of the Talent-Nelson Amendment to the John Warner National Defense Authorization Act of 2007, a 36 percent annual percentage rate cap took effect on October 1, 2007, for all payday loans made to military borrowers on active duty.
The codes are 522291 (consumer lending) and 522390 (other activities related to credit intermediation).
It should be noted that when we refer to the number of payday lenders, we are referring to the number of stores since each store must have a separate license.
Our study is related to that of Prager (2009) and several of the papers he discusses, but relies on more recent data, a somewhat different set of variables to explain the concentration of payday lending stores, and a different estimation technique to deal with multicollinearity.
Rank order correlations were also calculated for the same variables as in Table 1. The results are quite similar to those already reported, with one notable exception. The correlations between the percentage of the population that is Asian and the income and education variables are now significantly positive, and significantly negative for the poverty rate and the percentage of the population that is aged 65 and over. These correlations are not unexpected.
A check on the stability of the estimated coefficients in the ridge regression was conducted and the results indicate that the coefficients are quite stable.
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Acknowledgment
The authors are extremely grateful to Richard Cebula for inviting us to write and present this paper as well as helpful comments. Thanks are also due to Kang Lee for assistance with the ridge estimation application.
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Barth, J.R., Hilliard, J. & Jahera, J.S. Banks and Payday Lenders: Friends or Foes?. Int Adv Econ Res 21, 139–153 (2015). https://doi.org/10.1007/s11294-015-9518-z
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DOI: https://doi.org/10.1007/s11294-015-9518-z