Abstract
This study tests the degree to which payday and title lenders differentiate their store location and interest rates based on the socioeconomic characteristics of the areas in which they operate. We use store-level lender data, geographically matched IRS income data, and Census Bureau demographic data to answer these questions. In the case of lender location, we find that payday and title lenders tend to locate in areas with lower median age, a larger population of not married households, more restaurants, and more pawn shops. We also find a nonlinear relationship between lender location and individual incomes in the surrounding area. Regarding lender interest rates, we find that competition among lenders reduces average interest rates and that riskiness of borrowers, as measured by defaults, increases average interest rates. We also find that payday and title lenders have higher interest rates in areas with lower educational attainment, smaller proportions of Black residents, and fewer married households. This evidence seems to contradict the argument that payday and title lenders prey on minorities.
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Notes
See Morgan-Cross and Klawitter (2011). Delaware, Idaho, Nevada, South Dakota, Utah, and Wisconsin do not limit interest rates or fees on short-term loans. All the other 44 U.S. States and Washington, D.C. have some form of usury laws that limit the size of interest rates or the amount of small-dollar loans.
Although some of the papers cited here find that short-term lenders’ location is correlated with demographics, Donald Morgan and Kevin Pan have a post on the Federal Reserve Bank of New York blog (http://libertystreeteconomics.newyorkfed.org/2012/02/do-payday-lenders-target-minorities.html) in which they use the Survey of Consumer Finances and find that minorities are no more likely to use payday or pawn loans once financial characteristics of the individual are controlled for.
In effect, the average bank overdraft fee in 2008 was $20, for which the average overdraft amount was $66, and the average duration the account was overdrawn was two weeks. That amounts to the implied APR of 1,067 %. See Bachelder et al. (2008 p. v).
In the Mosiac Law of the Old Testament (see James 2000, Exodus ch. 22, vv. 25-27), which conservative estimates date to 1290 B.C., Moses prohibits the Israelites from making loans among themselves based on collateral such as clothing. Whelan (1979, p. 1) states that the “pawnshop in China dates from the last quarter of the fifth century A.D.... (A.D. 479-502).”
This number is reported in the Pawn Shops Today: the national voice of the pawn industry website at http://www.pawnshopstoday.com.
Per shop amount of loans made and renewed $863,058 divided by the per shop number of loans made and renewed 12,786 equals $67.50. See Caskey (1994, Table 3.2).
Utah has 353 zip codes that are either entirely or partially within the state’s boundaries. In the analysis in this section, we uses 283 of those zip codes because they each have a complete set of explanatory and dependent variables.
The IRS zip-code-level tabulations data for the State of Utah were provided by a U.S. Treasury employee from the Office of Tax Analysis. The median statistics use “smeared” data in which the 9 middle observations were averaged to protect confidential information of individual taxpayers.
The website is http://health.utah.gov/myhealthcare/facility.htm.
The website is http://research.fdic.gov/bankfind.
The website is http://fflgundealers.net/utah-zip-codes.html.
The website is http://www.manta.com/mb_43_A9_45/warehousing_storage/utah.
The negative binomial regression model uses the NB2 distribution and has the same mean structure as a standard Poisson regression model for count data, but the negative binomial model accounts for overdispersion in the data by adding a parameter that reflects the unobserved heterogeneity among observations. See Cameron and Trivedi (2013, Section 3.3) and Long and Freese (2001, Section 7.3).
Let count of payday and title lenders be y i , let the median income variable be z i , and let all the other explanatory variables be in the vector x i . Then the model (3.1) and the estimates in specification (1) in Table 3 imply the following relationship \(y_{i} = \beta \mathbf {x}_{i} + 0.347z_{i} - 0.005{z_{i}^{2}}\). The count y i is concave in z i , and the maximum count is z i,max=34,700, by solving \(\frac {\partial y_{i}}{\partial z_{i}}=0\).
This regression table is available in the Supplementary Material, which is available upon request.
In specification (3), the median income associated with the highest predicted number of payday and title lenders is $52,421. See footnote 13 calculation for specification (1).
We report the first stage regression described here in the Supplementary Material, which is available upon request. The gun shops variable is highly significant in predicting the number of pawn shops.
We also surveyed Utah pawn lenders, but their response rate was 6.5 %. For this reason, we do not include the pawn lenders’ survey data on interest rates, loan amounts, and loan terms in this analysis.
The Supplementary Material (available upon request) contains a copy of the initial e-mail that we sent to each lender, the survey instructions that were attached to the initial e-mail, and the nondisclosure agreement.
The Supplementary Material (available upon request) includes a copy of the survey instruction sheet that was given to each lender and describes the data that we were requesting.
Because we have socioeconomic and market concentration data on all lenders, including those who did not respond to the survey, we run a regression of total principal lent in 2010 on the socioeconomic and market concentration variables for the survey respondents. Then we use the estimated coefficients to impute the total principal lent for the nonrespondents. The estimates of total market size are the sums of the survey responses of total principal lent and the imputed values.
We actually use the Census’ zip code tabulation area (ZCTA), which is made to correspond roughly (although not exactly) to postal zip codes.
The IRS zip-code-level tabulations data for the State of Utah presented in Table 8 were provided by a U.S. Treasury employee from the Office of Tax Analysis. The median statistics use “smeared” data in which the 9 middle observations were averaged to protect confidential information of individual taxpayers.
We provide a Table of regression results in the Supplementary Material corresponding to Table 9 in which we included four firm fixed effects indicator variables for the four largest firms. However, many of the coefficients lost their statistical significance due to the scarcity of data. We need more data in order to effectively include firm fixed effects.
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Acknowledgments
Thanks to Kerk Phillips, Dave Spencer, and Lars Lefgren for helpful comments and suggestions. Special thanks to Jason Debacker for providing access to the IRS Compliance Data Warehouse’s Individual Return Transaction File. This project benefited from the excellent research assistance of Benjamin Tengelsen and from a grant from the Consumer Credit Research Foundation. The Foundation played no role in the collection or interpretation of the data employed in this project. All errors are the authors’.
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Canann, T.J., Evans, R.W. Determinants of Short-term Lender Location and Interest Rates. J Financ Serv Res 48, 235–262 (2015). https://doi.org/10.1007/s10693-014-0202-x
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DOI: https://doi.org/10.1007/s10693-014-0202-x