Electronic copy available at: http://ssrn.com/abstract=2505036

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Banks and Payday Lenders: Friends or Foes?

James R. Barth, Jitka Hilliard and John S. Jahera, Jr.1

Most people find it convenient, and in many cases essential, at one time or another to borrow money to cover a variety of expenditures. This is almost always the case for big ticket items like homes or automobiles. Of course, such borrowers are charged an interest rate, taking into account the costs and riskiness associated with a loan, to compensate the lender for the service provided. To prevent what some consider to be unreasonable or excessive interest rates most states have established usury laws that set maximum rates that can be charged on specific types of consumer loans, usually rates that are less than 25 percent. Banks are the most heavily involved and widely known of all the different types of financial firms when it comes to offering such loans. They are facilitated in this regard by relatively recent changes in the law, which allow banks today to open or acquire branches anywhere they wish throughout the Unites States. There are people who also borrow money but in quite small amounts and for exceedingly short periods of time. Instead of being charged an interest rate with recurring interest payments borrowers are charged a flat fee, such as $15 per $100 borrowed. The type of financial firms offering such loans are commonly known as payday lenders and the product offered is most commonly known as a payday loan.2 A payday loan is typically structured with a due date that coincides with the borrower’s next payday, which is most frequently two weeks. An individual obtaining a payday loan is required to provide a post-dated personal check to the lender or an authorization to electronically debit the person’s deposit account for the loan amount and associated fee. The borrower usually agrees to return to the store of the payday lender when the loan is due to make payment. If the borrower does not do so, the lender has the option of depositing the person’s check or initiating an electronic withdrawal from the person’s deposit account. To obtain a payday loan an individual typically only needs a bank account and a job (i.e., a regular source and minimum level of income). In comparison to banks, payday lenders have one advantage as well as one disadvantage. The advantage is they are allowed to charge flat fees that when converted to interest rates always exceed the usury rate, while the disadvantage is payday lenders are restricted as to where they can open or acquire stores in the United States. Payday lenders are frequently a source of controversy centering on the fees they charge and their typical customer base. Consider the allowable fees on payday loans in two states. A fee of $15 for a $100 loan to be repaid in 14 days, which is allowed in Indiana, is equivalent to an annual percentage

1 James R. Barth is a Lowder Eminent Scholar at Auburn University and a Senior Fellow at the Milken Institute, Jitka Hilliard is an Assistant Professor in Finance at Auburn University, and John S. Jahera, Jr. is Lowder Professor of Finance at Auburn University. 2 Payday lenders are also referred to as deferred deposit originators and their product as payday advances, cash advances, deferred deposits, among other terms.

Electronic copy available at: http://ssrn.com/abstract=2505036

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rate of 390 percent. A larger fee of $75 for the same loan, which is permitted in Missouri, translates into an annual percentage rate of 1,950 percent.3 Some individuals consider the fact that persons borrowing money pay such high interest rates to be an outrage. This is no doubt a factor in the decision of some states to either explicitly prohibit this type of financial firm from offering such loans or to de facto do so by setting much lower interest rate caps on small loans. In addition, there is a concern by some that payday lenders may engage in so-called predatory lending by locating their stores in geographical areas with higher percentages of people in poverty, lower-income individuals, unemployed and less-educated individuals. These are the groups that may fall prey to the less scrupulous firms as well as suffer more of a hardship when confronted with the high interest rates associated with payday loans. There is a related concern that the same geographical areas consists of disproportionately high percentages of African Americans and Hispanics. Indeed, it is reported that Senator Sherrod Brown, at a recent Senate Banking Committee hearing, said “… he was concerned that payday companies are marketing their high-cost loans to the very people who can least afford them, much like predatory mortgage lenders did in the run up to the housing crisis.”4 The purpose of this paper is to examine in some detail the different business characteristics of the payday lenders that operate throughout the United States. The examination is based on state-level data to emphasize differences in the regulatory environment across the states that constrains the prices and other aspects of the loan products that the firms are allowed to offer. Importantly, an empirical analysis is conducted to determine the extent to which the number of stores operated by payday lenders in the different states are related to various demographic and economic characteristics of those states in an attempt to address the concerns noted above.5 Since banks also offer consumer loans, but mainly to a different clientele and on different terms, the analysis also examines whether there is an association between the location of the branches of banks and the location of the stores of payday lenders. Some banks do, however, offer deposit advances to customers, which are typically structured as short-term loans but without a predetermined repayment date. This product is only offered to existing customers and the bank can take action to be repaid by debiting incoming electronic deposits before paying other transactions of the customer. To the extent that payday lenders and banks locate offices in the same geographical areas there may be an opportunity for some individuals to switch from being customers of payday lenders to banks and thereby obtain lower-cost loans.6 The remainder of the paper proceeds as follows. In the next section, a review of selected related literature is provided. This is followed by an overview of the payday lending industry, emphasizing 3 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. 4 See Douglas (2014, p.2). 5 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. 6 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.”

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two somewhat unique issues that arise when studying this industry. The third section presents and discusses our approach to analyzing some of the determinants of the location and concentration of payday lending stores operating in the various states throughout the United States. The last section focuses on plans for future research on payday lending.

1. Selected Literature Review

The literature regarding the role that payday lending plays in the financial marketplace is quite diverse in terms of sample data and broad-based policy conclusions. As already noted, payday lending has always been and continues to be controversial due to both the high rates of interest that are allowed in states in which payday lenders operate and the demographic and economic characteristics of the typical customers to which the firms cater. Much of the existing literature provides results that reinforce the view that payday lending is indeed predatory by targeting economically struggling and less educated individuals in the United States. Of course, there are other studies that report benefits associated with payday lending, such as fewer and more costly bounced checks and bankruptcy fillings. Given the importance of the issue and the lack of a consensus based on empirical results, it is clear that additional research is worthwhile.

In a relatively early study, Stegman and Faris (2003) analyze a database of 142 (165) payday lenders operating 807 (902) outlets for the year 1999 (2000) in North Carolina. Their data shows that there were double-digit increases in the number and value of deferred deposit checks as well as the payday loan fees collected over the two years. During the same period, it is noted that net charge-offs increased by 54 percent reflecting the higher risk of such loans. The authors use a logistic regression to examine the determinants of whether or not households used payday lending during the two-year period and an ordered logistic regression to examine the determinants of the frequency of use of the service. Their results indicate that lower-income African Americans are more than twice as likely to have taken out a payday loan as White non-Hispanics. Interestingly, they find that Hispanics are less likely to utilize payday loans than other groups. Although they find that race matters with respect to using a payday lender, once a customer, race does not matter with respect to the frequency of use. The authors also find that households with impaired credit are much more likely both to use payday lenders and to be more frequent borrowers than others. Older individuals, however, were less likely to be found as customers of payday lenders than younger individuals. Furthermore, the results indicate that the number of banks and thrifts in a household’s neighborhood has a small but significantly negative effect on the use of payday lenders. One of their policy conclusions is that action should be taken to encourage financial institutions like banks to provide less expensive and more consumer friendly products similar to payday loans.

Morgan and Strain (2008) also perform an examination of payday lending, focusing on Georgia and North Carolina, two states that had banned such loans in 2004 and 2005, respectively. Based upon an analysis of data for returned checks at Federal Reserve processing centers from 1997 to 2007, complaints filed with the Federal Trade Commission (FTC) between 1997 and 2007, and bankruptcy fillings between 1998 and 2007, they found that compared with households in states where payday lending is permitted, households in Georgia have bounced more checks, complained more to the FTC about lenders and debt collectors, and filed for bankruptcy protection at a higher rate. The authors also

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found that North Carolina households fared about the same. In a related nationwide study, Morgan, Strain and Seblani (2012) find some evidence that bankruptcy rates decrease after payday loan bans, but at the same time complaints against lenders tend to increase. Moreover, the authors report that their most robust finding is that returned check numbers and overdraft fee income at depository institutions decline when payday credit supply expands.7

Rather than focus on an entire state or states, Gallmeyer and Roberts (2009) conduct a study of payday lenders in the Front Range area of Colorado. According to them, this part of the state has 638 payday lending stores. The locations of the stores are matched with census block groups or communities that serve as hosts. An analysis is then performed on the socio-demographic characteristics of the communities, as measured by median household income, the percent of the population falling substantially below the federal poverty line, and the labor force composition, Based on comparison of means tests and multivariate logistic regression techniques, the authors find that payday lenders are more likely to concentrate in neighborhoods that have lower income, moderate poverty and higher percentages of ethnic minorities, immigrants, young adults, elderly, military personnel, and those working in non-management/professional occupations. They conclude that even though the presence of a payday lender does not directly measure use by those living in the community, it does represent a financial hazard and functions as both a signal and aggravating factor of economic distress in those communities.

In another study focusing on two states, Zinman (2010) examines some of the effects of restricting access to expensive credit using data from two phone surveys conducted in 2007 of 1,040 payday borrowers residing in Oregon and Washington. Oregon imposed a binding rate cap on such credit in that year, whereas the neighboring state of Washington did not. Zinman finds that access to payday loans declined in Oregon relative to Washington, while many borrowers in Oregon shifted into plausibly inferior substitutes. Furthermore, the author finds that such restrictions were significantly more likely to lead to an adverse change of the financial condition of Oregon borrowers, such as becoming unemployed. In a related and more recent study, Carrell and Zinman (2014) analyze the impact of payday loan access on three different measures of military job performance in 35 states that both allow and prohibit payday lending for the period 1995 to 2007. Their empirical results indicate that payday loan access adversely affects overall job performance, retention and readiness.

Combining household survey data and county-level data for thirteen states, three of which prohibit payday lending, Melzer (2011) examines whether payday loan access mitigates financial distress, as some claim. Although he does not have information on actual payday borrowing by households, his results indicate that access to payday lending stores leads to increased difficulty paying mortgage, rent and utilities bills as well as delaying needed health care. Further, borrowers find themselves deferring medical and dental care. On the basis of these results, Melzer concludes that for some low-income households, the debt service burden that arises when borrowing from payday lenders inhibits their ability to pay important bills. 7 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, has a check on whether the state bans are binding.

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Morse (2011), like Melzer (2011), also examines whether payday lending exacerbates or mitigates financial distress. Specifically, he considers whether the adverse effects of natural disasters on home foreclosures and small property crimes are mitigated when individuals have access to payday lenders, His analysis is based on data at the zip-code level for California over the period 1996 to 2002. In contrast to Melzer, however, Morse finds that payday lenders offer a positive service to individuals facing unexpected financial distress. He does qualify his finding by saying that the results do not address the issue of any adverse effects on those persons who are habitual borrowers from payday lenders. Given these conflicting findings, Bhutta (2014) uses zip-code business data to analyze the socioeconomic factors correlated with payday lender concentration. Unlike the two studies that find both positive and negative effects of payday loans on financial well-being, his empirical results indicate little connection in terms of such loans and credit scores.

Quite recently, the Consumer Financial Protection Bureau (CFPB) (2013, 2014) that was established by the Dodd-Frank Act in 2010 has devoted attention to payday lending. This is consistent with its obligation to promote markets that are fair, transparent and competitive. The focus of its two “white papers” is on the long-term use of short-term loans evidenced by a pattern of repeatedly rolling over or consistently re-borrowing by individuals. In the 2013 white paper, the CFPB gathered data from a sample of payday lenders to create a dataset of all payday loans extended by each lender for a 12-month period. The dataset allowed CFPB to identify the loans that were made to the same customer at a given lender. The agency finds that the median amount borrowed was $350 with about a third of borrowers having six loans or less and a total dollar amount borrowed of $1,500 during the year-long period. The CFPB concluded in this work that consumer usage was moderate with the caution that some borrowers may overuse such payday loans. In the 2014 white paper, using the same data as in the 2013 study, the CFPB finds that approximately 80 percent of loans are renewed with another loan with 14 days.

In a fairly recent paper, Caskey (2010) surveys research that attempts to answer what he describes as the “big question”, which is whether payday lenders exacerbate or relieve customers’ financial difficulties. He concludes that despite the research efforts prior to the writing of his paper, we still do not know the answer to that question. The author does offer several specific suggestions for future research, including the effect that financial education can have on the demand for payday loans and the role of other financial service providers intended to serve low-to-moderate households.

2. Overview of the Payday Lending Industry

In our and other studies of payday lenders two important issues arise. First, one must identify the legal status of payday lenders in the different states as well as the regulatory environment in those states in which such firms are allowed to operate. Appendix 1 provides the necessary information in this regard for our study. The appendix indicates there are 13 states and the District of Columbia that actually prohibit payday lenders. These states are Arizona, Arkansas, Connecticut, Georgia, Maine, Maryland, Massachusetts, New Jersey, New York, North Carolina, Pennsylvania, Vermont, and West Virginia, as shown in Figure 1.

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Figure 1: States that prohibit payday lending

Three states set maximum payday loan rates based on a finance charge for a 14 day $100 loan

that are far below the typical rates so as to discourage this type of product being offered within their borders. The states and their rates are as follows: Montana at 36 percent, New Hampshire also at 36 percent, and Ohio at 28 percent. At the other end of the spectrum, there are six states that set no limit on the rate that may be charged on payday loans. In short, the sky is the limit. These states are Delaware, Idaho, Nevada, South Dakota, Utah and Wisconsin. The remaining 28 states allowing payday lending explicitly specify that triple-digit rates may be charged (see Appendix 1 for the names of these states).8 Among these states, Missouri specifies the highest maximum interest rate that may be charged at 1,950 percent.9 Figure 2 shows the fairly wide distribution of the rates of interest that may be charged by payday lenders in the states in which they are allowed to operate.

Other interesting information about the regulatory constraints placed on the payday lending industry is also included in Appendix 1. There are limits on the loan amount in all but 3 states: Oregon, Texas and Utah. The stated maximum loan amount that is the lowest is $300 and is found in both California and Montana, while the stated maximum loan amount that is the highest is $2,500 and is found in New Mexico. The most frequent loan amount limit is $500 and is found in 18 states. In addition to limits being placed on loan amounts, there are specified limits on the terms on loans in all but 3 states: Idaho, Nevada and South Dakota. Fifteen states specify a maximum loan term but at the same time do not specify a minimum, including California, Delaware, Hawaii, Iowa, Louisiana, Michigan, Minnesota, Montana, Nebraska, North Dakota, South Carolina, Tennessee, Utah, Washington, Wisconsin and Wyoming. Wisconsin specifies the longest allowable loan term at 90 days, whereas Florida, Kansas,

8 Colorado, as shown in Appendix 1, has a tiered structure of rates and fees based upon the loan amount. 9 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.

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New Hampshire and Texas all specify the shortest allowable loan term at 7 days. The most frequent maximum loan term that is specified is 31 days. Interestingly enough, Colorado specifies a minimum loan term of 6 months.

Figure 2: Distribution of maximum allowable interest rates by payday lenders

There are also regulatory limits on the number of loans that an individual may have outstanding at one time and the number of time a loan may be rolled over. There are 12 states that either do not specify or set a limit on the number of outstanding loans, including Alaska, Louisiana, Minnesota, Mississippi, Nevada, Oregon, South Carolina, South Dakota, Texas, Utah, Wisconsin and Wyoming. Some states do not limit the number of outstanding loans but instead limit the dollar amount outstanding at any one time, such as Alabama, Delaware and Idaho. The most common limits set by states are 1 or 2 loans outstanding at any one time. As regards rollovers, 22 states prohibit any rollovers at all. The other 11 states allow between 1 and 4 rollovers, with the exception that Kansas, Nevada and Utah do not specify a limit. In a study of the borrowing patterns of over 12 million loans in 30 states, the Consumer Financial Protection Bureau (2014, p.4) found that over 80 percent of payday loans are rolled over or followed by another loan within 14 days. The second issue that arises in studies of payday lenders is determining the number of firms in the different states. Unfortunately, there is no central database for such information nor is such information readily available from the various state regulatory authorities of payday lenders. Nonetheless, estimates by Stephens Inc. (2013) indicate that there were 18,273 payday lending stores in 2012. A few fairly large firms, moreover, play a major role in the industry. Advance America is the largest such firm in the United States and was acquired by Grupo Elektra, a company owned by Ricardo Salinas Pliego of Mexico, in 2012. Advance America has roughly 2,400 stores throughout the nation. However, these are not exclusively payday lenders, with some of the stores offering pawn services, check cashing and other services. As of mid-2014, we have only been able to identify the following firms as publicly-traded entities: Cash America International (CSH), QC Holdings (QCCO),

Less than 300%, 4

300%-400%, 8

400%-500%, 10

500%-600%, 3

600%-700%, 1

700%-800%, 2

Above 800%, 1

No limit, 6

APR FOR 14-DAY $100 LOAN

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EZCORP Inc. (EXPW), First Cash Financial Services (FCFS) and DFC Global (DLLR). All of these firms engage not only in payday lending but offer other short-term financial services, such as pawn lending and check cashing. Cash America International has more than 1,000 stores; QC Holdings has about 500 outlets, while EZCORP Inc. has about 900 U.S. outlets, with roughly 500 being financial service stores. DFC Global operates in a number of countries, with about 293 outlets in the United States. And First Cash Financial has 309 U.S. stores and more than that number in Mexico.

To obtain the number of payday lending stores in the United States, one usually relies on a proxy measure for such firms. In this regard, we follow the approach of Bhutta (2014), who relies on two North American Industrial Classification System (NAICS) codes to capture payday lending firms. Specifically, these two codes include firms primarily engaged in making unsecured cash loans to consumers and in facilitating credit intermediation, including check cashing services and money order issuance services.10 These firms encompass nondepository consumer lending and other activities related to credit intermediation.

The number of payday lenders based upon these codes for each state is provided in Appendix 2.

The total for all the states is 29,044. As may be seen, numbers are included even for the 14 states that prohibit payday lending. Of the total number of firms in the table, 3,952 or 13.6 percent are located in these 14 states. Georgia has the largest number of such firms at 1,208 or 30.6 percent for the total of these states. Upon checking with the regulatory authorities and the appropriate statutes in Georgia, we have concluded that almost all of the firms for this state listed in the table are industrial loan firms. Importantly, these firms are allowed to charge an interest rate and a fixed loan fee for a small loan that based on our interpretation enables them to charge a maximum interest rate of 218 percent, which helps explain the large number of firms in the table for Georgia. Excluding this state, the remaining 2,744 firms in the other 13 states that prohibit payday lending offer similar services as payday lenders. In any event, our empirical analysis below will take into account all the firms as well as only those firms operating in states that allow payday lending (Appendix 2). For convenience we will use the term “payday lenders” for the firms throughout the remainder of the paper.11

The distribution of payday lenders by state is shown in Figure 3. As may be seen, the largest

number of payday lenders are found in California, Illinois and eight southeastern states, which includes Alabama, Florida, Georgia, Louisiana, Mississippi, South Carolina, Tennessee and Texas. Each of these states has more than 1,000 payday lending stores. The state that has the most payday lenders is Texas with 4,623, while the state that has the fewest is Vermont with 4 (see Appendix 2 for the number of payday lenders for each of the states). Appendix 3 shows that the mean (median) number of payday lenders in states allowing such firms is 678 (432), whereas the mean (median) number of payday lenders in states prohibiting such firms is 282 (173). A t-test of the difference in means of the number of payday lenders (as well as the number per 10,000 people) between states allowing payday lending and those prohibiting it indicate that the former states have significantly more payday lenders, which is what one would expect. 10 The codes are 522291 (consumer lending) and 522390 (other activities related to credit intermediation). 11 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.

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Figure 3: Number of payday lenders by state

Appendix 2 also provides information on the number of banks and bank branches. There are 97,670 bank’s branches nationwide. Texas has the largest number of banks and branches at 6,875, while Alaska has the fewest at 132. On average, there are roughly three bank branches per payday store for all the states (see Appendix 2). In every state, there are more bank branches than payday stores. South Carolina has the largest ratio of payday stores to bank branches at 0.94, or nearly one store per branch. Rather than compare the number of payday lender stores to the number of banks and bank branches, Figure 4 shows the relationship of stores to branches for states when each is expressed per 10,000 people. As may be seen, Mississippi has the highest number of payday lending stores on this basis, followed by Louisiana, South Carolina, Oklahoma, Alabama, Tennessee and New Mexico. All of these states have more than 2 stores per 10,000 people. Vermont has the smallest number of stores per 10,000 people, followed by Maine, New Hampshire, Arkansas, West Virginia and Alaska, with each having less than 0.15 stores per 10,000 people. In the case of banks, North Dakota has the largest number of banks and branches per 10,000 people, followed by Nebraska, South Dakota, Kansas, Iowa and Arkansas. All these states have more than 5 banks and bank branches per 10,000 people. Alaska has the smallest number of banks and branches per 10,000 people with California a close second, with both states being the only states having fewer than 2 banks and branches per 10,000 people.

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Figure 4: Number of payday lenders and bank branches by state

3. Empirical Model and Results

To address the issue of the concentration of payday lending stores in states throughout the United States, we specify the following model with states being units of observation:

(1) 𝑦 = 𝛼 + 𝛽1(𝑏𝑎𝑛𝑘 𝑏𝑟𝑎𝑛𝑐ℎ𝑒𝑠) + 𝛽2 (𝑓𝑖𝑛𝑎𝑛𝑐𝑖𝑎𝑙 𝑓𝑎𝑐𝑡𝑜𝑟𝑠) + 𝛽3(𝑑𝑒𝑚𝑜𝑔𝑟𝑎𝑝ℎ𝑖𝑐 𝑓𝑎𝑐𝑡𝑜𝑟𝑠) +

𝛽4(𝑒𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛𝑎𝑙 𝑓𝑎𝑐𝑡𝑜𝑟𝑠) + 𝜀, where 𝑦 is the number of payday lending stores; bank branches are the number of banks and bank branches; financial factors include income per capita, the poverty rate and the unemployment rate; demographic factors include the percentages of the population that are African American, Asian, Hispanic, aged 15 and under and aged 65 and over; educational factors include the percentages of the population that have a high school degree or higher and have a bachelor degrees or higher; and 𝜀 is a random error term.12 Information on the various variables included in our model by state are provided in Appendices 2 and 4, while descriptive statistics for the same variables are provided separately for all states, the states allowing payday lenders, and the states prohibiting payday lenders.

12 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.

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Payday lenders per 10,000 peopleBank branches per 10,000 people

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Before presenting and discussing the empirical results based upon the estimation of equation (1), the simple correlations among the various variables used in our analysis are shown in Table 1. In this table, given the substantial variation in population among the different states, the focus is on the number of payday lending stores per capita. As may be seen, there is no significant correlation between the number of bank branches and the number of payday lending stores, suggesting they are neither friend nor foe. Perhaps not surprisingly, the number of payday lending stores is positively and significantly correlated with the percentages of the population that are African American and aged 15 and under (indicating a larger family size). It is also found that the correlations between the number of payday lending stores and the percentages of the population that have high school and bachelor degrees are significantly negative, which also does not seem surprising. Turning to the financial factors, there is a significantly negative correlation between the number of payday lending stores and income per capita, while a significant and positive correlation between the number of stores and the poverty rate. Again, neither of these correlations are surprising. At the same time, there is no significant correlation between the number of payday lending stores and the unemployment rate, which does seem surprising. Table 1: Correlations between the number of banks and payday stores and selected demographic and financial characteristics at state level State (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13)

Bank’s branches per capita (1) 1.00

Payday loan stores per capita (2) 0.002 1.00

% White (3) 0.37** -0.24 1.00

% Black or African American (4) -0.07 0.40** -0.74** 1.00

% Asian (5) -0.36* -0.21 -0.52** -0.10 1.00

% Hispanic (6) -0.48** 0.08 -0.22 -0.11 0.15 1.00

15 years and under (7) -0.15 0.40** 0.10 -0.18 -0.07 0.28* 1.00

% aged 65+ (8) 0.36* -0.15 0.29* -0.16 -0.04 -0.25 -0.60** 1.00

Income per capita (9) -0.09 -0.51** -0.33* 0.21 0.22 0.07 -0.48** -0.19 1.00

Poverty rate (10) -0.03 0.61** -0.26 0.44** -0.26 0.15 0.11 0.05 -0.57** 1.00

% High school degree (11) 0.27 -0.55** 0.43** -0.50** 0.09 -0.38** -0.12 0.04 0.36** -0.77** 1.00

% Bachelor's degree or higher (12) -0.09 -0.46** -0.28* 0.18 0.18 0.12 -0.39** -0.22 .90** -0.47** 0.40** 1.00

Unemployment rate (13) -0.58** 0.20 -0.42** 0.49** -0.02 0.29* -0.06 -0.07 -0.06 0.50** -0.57** -0.06 1.00

*Denotes significance at 5% level, **denotes significance at 10% level.

In addition to the correlations just discussed, Table 1 provides the corresponding correlations

for the number of bank branches and the same variables. Briefly, there are significantly positive correlations between the number of branches and both the percentage of the population that is white and aged 65 and over, which do not seem surprising. The correlations for both Asian and Hispanic, moreover, are both significantly negative. The only other significant correlation is the one for the

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number of branches and the unemployment rate, and it is negative. This correlations would seem to indicate that more bank branches are found in states with lower unemployment rates.13

Turning from the bivariate to the multivariate empirical results, there are three sets of tables

that are presented and discussed. The tables differ in that three different dependent variables are employed: the number of payday loan stores per 10,000 people (Table 2) and the ratio of the number of payday loan stores to the number of banks and bank branches (Table 3). In Table 2, the ordinary least squares results indicate that the only significant explanatory variable is the percentage of the population that is African American, and its coefficient is positive. This indicates that there is on average a greater concentration of payday lender stores in those states with a higher concentration of

Table 2: OLS and ridge regressions: Number of payday lenders per 10,000 people on selected demographic and financial characteristics at state level OLS VIF OLS VIF Ridge

Regression VIF

Constant 20.314 -0.782 13.670 (0.481) (0.685) (0.042)

Bank’s branches per 10,000 people -0.053 3.27 -0.049 2.596 -0.007 1.37 (0.707) (0.693) (0.937) % Black or African American 0.043 4.22 0.036 2.241 0.029 1.34

(0.006) (0.001) (0.001) % Asian -0.005 1.35 -0.005 1.331 -0.004 0.97 (0.669) (0.684) (0.704) % Hispanic or Latino origin 0.013 4.02 0.008 1.880 0.005 1.14

(0.436) (0.471) (0.552) % Age under 15 0.092 8.59 0.122 1.531 0.100 1.30

(0.467) (0.023) (0.050) % aged 65+ 0.009 5.27 -0.005 1.26

(0.934) (0.931) Ln(Income per capita) -1.972 22.34 -1.196 1.23

(0.428) (0.046) Poverty rate 0.031 10.63 0.085 2.925 0.046 1.47

(0.716) (0.055) (0.154) % High school degree or higher -0.007 5.17 -0.027 1.65

(0.901) (0.403) % Bachelor's degree or higher -0.015 7.16 -0.044 2.449 -0.016 1.59

(0.681) (0.045) (0.357) Unemployment rate -0.114 3.17 -0.113 3.014 -0.071 1.46

(0.133) (0.117) (0.170) Adjusted R2 0.518 0.539 k 0 0.099 Root MSE 0.559 0.572 African Americans. However, the correlations discussed above and the variation inflation factors (VIFs) provided in the table indicate a high degree of multicollinearity among some of the variables, which can lead to the insignificance of variables. It was therefore decided to omit three of the collinear

13 Rank order correlations were also calculated for the same variables as in Table 1. The results are presented in Appendix 5 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.

13

variables, with the results reported in column three of the table. In this case, not only is the African American variable significant, but also three other variables. The poverty rate enters with a significantly positive sign, which one might expect. Also, as one might expect, the percentage of the population that is 15 and under enters with a significantly positive sign, while the percentage of the population that has a Bachelor’s degree or higher enters with a significantly negative sign. As a final check a ridge estimation technique is employed to address the multicollinearity issue.14 The results of this estimation are reported in column three of the table. In this case the difference as compared to dropping variables is that per capita income now enters with a significantly negative sign and the poverty rated and educational variables lose their significance.

Rather than using the number of payday lending stores per 10,000 people, the ratio of the number of stores relative to the number of banks and bank branches is employed as the dependent variable. The results of doing this are reported in Table 3. In this case, the ordinary least squares results

Table 3: OLS and ridge regressions: Ratio of number of payday stores to number of bank branches on selected demographic and financial characteristics at the state level

OLS VIF OLS VIF Ridge Regression VIF

Constant 13.729 0.065 5.679 (0.079) (0.912) (0.005)

ln(Population) -0.045 1.86 -0.042 1.549 -0.036 1.12 (0.132) (0.123) (0.132) % Black or African American 0.013 3.89 0.011 2.280 0.008 1.33

(0.003) (0.001) (0.002) % Asian

-0.0003 1.13 0.00009 1.133 -0.00003 0.89 (0.925) (0.975) (0.991)

% Hispanic or Latino origin 0.011 3.32 0.009 1.881 0.006 1.14 (0.016) (0.008) (0.015) % Age under 15 0.013 7.65 0.044 1.485 0.032 1.31

(0.725) (0.005) (0.026) % aged 65+ -0.015 4.27 -0.007 1.23

(0.609) (0.672) Ln(Income per capita) -1.157 17.04 -0.429 1.25

(0.086) (0.013) Poverty rate -0.019 7.84 0.015 2.810 0.005 1.51

(0.383) (0.217) (0.591) % High school degree or higher -0.011 5.39 -0.013 1.67

(0.497) (0.155) % Bachelor's degree or higher 0.001 6.50 -0.015 2.483 -0.004 1.58

(0.956) (0.020) (0.412) Unemployment rate -0.002 2.12 -0.002 2.057 0.001 1.30

(0.887) (0.909) (0.972) AdjR2 0.596 0.589 k Root MSE

0.000 0.099 0.156 0.161

indicate that four variables are now significant. Again, the percentage of the population that is African American has a significantly positive sign, but now so too does the percentage of the population that is Hispanic. And income per capita also is now significant with the expected negative sign. When some 14 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 (see Appendix 6).

14

of the collinear variables are dropped, the results are similar to the comparable results in Table 2, except that the poverty rate is no longer significant, while the percentage of the population that is Hispanic is now significant and has a positive coefficient. The results for the ridge estimation are nearly identical to those in Table 3, except that now the percentage of the population that is Hispanic becomes positive and significant, which is not unexpected. Overall, the empirical results indicate the following: (1) in all six of the estimated equations, there is a significantly positive relationship between the number of payday lending stores as well as the ration of the number of payday lending stores to the number of banks and bank branches and the percentage of the population that is African American; (2) in four of the estimated regressions (two from each of the tables with the two different dependent variables) the percentage of the population that is aged 15 and under (larger family size) also enters with a positive and significant coefficient; (3) income per capita is significant and positive for both dependent variables with the ridge estimation and also with the ordinary least squares estimation when the ratio of the numbers of the two types of firm is used as the dependent variable; (4) the percentage of the population that has a Bachelor’s degree or higher is significantly negative for both dependent variables, but only in the case when some collinear variables are dropped, and (5) the poverty rate is only significantly positive when an employing ordinary least squares estimation with the dependent variable being the number of payday lending stores per capita.

4. Plans for Future Research

It is clear that the payday lending industry receives mixed reviews. Some believe that payday lenders prey on lower-income and less financially literate individuals, frequently African Americans and Hispanics, charging exorbitant interest rates for extremely short-term loans. Still others believe that these lenders cater to individuals who benefit by gaining access to otherwise unavailable short-term credit for unexpected needs, such as a medical emergency. Unfortunately, despite several fairly recent and careful empirical studies of payday lending, there has been no consensus reached as to whether there has been a net gain in welfare to borrowers. Given the importance of these particular financial firms to a significant segment of the population, there is always the need for more research to better understand their role in the financial system. In terms of future research, we are in the process of collecting more detailed information on payday lenders at the county and zip-code level. Based on our review of previous research, it appears that many of the studies have not actually been able to rely solely on data for licensed payday lenders. We are therefore trying to obtain such information through a fairly intensive and comprehensive collection effort. Already, our current paper has the most up-to-date information on the regulatory restrictions on a state-by-state basis of the payday lending industry. Such information should prove useful in better assessing the implications of the payday lending industry for those individuals using its services.

15

References

Bhutta, Neil. (2014) ”Payday Loans and Consumer Financial Health.” Journal of Banking and Finance, 47, 230-242.

Carrell, Scott and Jonathan Zinman. (2014) “In Harm's Way? Payday Loan Access and Military Personnel Performance.” Review of Financial Studies, 27, 2805-2840.

Caskey, John P. (2010) “Payday lending: New research and the big question.” Federal Reserve Bank of Philadelphia Working Paper No. 10-32.

Consumer Financial Protection Bureau. (2013) “Payday loans and deposit advance products.”

Consumer Financial Protection Bureau (2014) “”CFPB Data point: Payday lending.”

Douglas, Danielle. (2014) “There are almost as many payday lenders as McDonald’s and Starbucks. No, really.” Washington Post, March 26, 2014, p. 2.

Gallmeyer, Alice, and Wade T. Roberts. (2009) “Payday Lenders and Economically Distressed Communities: A Spatial Analysis of Financial Predation.” The Social Science Journal, 46, 521-538.

Melzer, Brian T. (2011) “The Real Costs of Credit Access: Evidence from the Payday Lending Market.” Quarterly Journal of Economics, 126, 517–55.

Morgan, Donald P., and Michael R. Strain. (2008) “Payday holiday: How households Fare after Payday Credit Bans.” Federal Reserve Bank of New York Staff Report No. 309.

Morgan, Donald P., and Michael R. Strain and Ihab Seblani. (2008) “How Payday Credit Access *Affects Overdrafts and Other Outcomes.” Journal of Money, Credit and Banking, 44((2-3), 519–531.

Morse, Adair. (2011) “Payday Lenders: Heroes or Villains?” Journal of Financial Economics, 102, 28–44.

Prager, Robin A. (2009) “Determinants of the Locations of Payday Lenders, Pawnshops and Check Cashing Outlets.” Federal Reserve Board Finance and Economics Discussion Series # 2009-33.

Sorrell, William H., Attorney General-State of Vermont. (2014) Letter to DISH Network regarding advertisements for payday lenders in Vermont.

Stegman, Michael A. (2007) “Payday Lending.” The Journal of Economic Perspectives, 21, 169-190.

Stegman, Michael A., and Robert Faris. (2003) “Payday Lending: A Business Model that Encourages Chronic Borrowing.” Economic Development Quarterly, 17, 8-32.

Zinman, Jonathan. (2010) “Restricting consumer access: Household survey evidence on effects around the Oregon rate cap.” Journal of Banking and Finance, 34, 546-556.

16

Appendix 1: Legal status of payday lenders and allowable financial terms of payday loans by state Loan terms Debt limits State Legal status Max loan amount Loan

term Max finance rate and fees Finance charge

for 14-day $ 100 loan

APR for 14-day $100 loan

Max number of outstanding loans at one time

Rollovers permitted

Alabama Legal $500 10-31 days

17.50%, 3% per month after default $17.50 456.25% None (max $ amount) One

Alaska Legal $500 14 days $5 + the lesser of $15 per $100 or 15% $20 520% Not specified Two Arizona Prohibited Arkansas Prohibited California Legal $300 Max: 31

days 15% of check $17.65 459% One None

Colorado Legal $500 Min: 6 months

20%: $0-$300 + 7.5%: $301-$500 plus 45% per annum interest plus monthly maintenance fee $7.50 per $100 borrowed, up to $30, after first month.

Not applicable Not applicable

No limit if total debt does not exceed $500 and 30-days between loans

One renewal at 45% interest allowed.

Connecticut Prohibited Delaware Legal $500 Max: 60

days Not specified No limit No limit Not Specified ($1000

aggregate loans outstanding to all licensees)

Four

District of Columbia

Prohibited

Florida Legal $500 7-31 days 10% of check + verification fee not to exceed $5

$16.11 419% One None

Georgia Prohibited Hawaii Legal (applies

to check cashers only)

$600 Max: 32 days

15% of check $17.65 459% One None

Idaho Legal $1,000 Not specified

Not specified No limit Not Specified ($1000 aggregate loans outstanding to all licensees)

Three

Illinois Legal Lesser of $1000 or 25% gross monthly income

13-45 days

$15.50 per $100 $15.50 403% Two None

Indiana Legal $550 (not to exceed 20% of borrower's monthly gross income)

Min: 14 days

15%: $0-$250; 13%: $251-$400; 10%: $401-$500

$15 390% One per lender; Two total

None

Iowa Legal $500 Max: 31 days

$15: $0-$100 on face of check; $10 per $100 thereafter

$16.67 433% Two None

Kansas Legal $500 7-30 days 15% $15 390% Two Not specified Kentucky Legal (Applies

to check cashers only)

$500 14-60 days

$15 per $100 on face value of check + $1 database fee

$17.65 459% Two ($500 aggregate loans outstanding to all licensees)

None

17

Appendix 1: Legal status of payday lenders and allowable financial terms of payday loans by state: Continued Loan terms Debt limits State Legal status Max loan

amount Loan term

Max finance rate and fees Finance charge for 14-day $ 100 loan APR for 14-day $100 loan

Max number of outstanding loans at one time

Rollovers permitted

Louisiana Legal $350 60 days or less 16.75% of face-value of check, not to exceed $45; + $10 documentation fee (After default: months 1-12: 36% per year; months 13 and beyond: 18% per year)

$30 780% Not specified None

Maine Prohibited Maryland Prohibited Massachusetts Prohibited Michigan Legal $600 Max: 31 days 15% of first $100, 14% of second

$100, 13% of third $100, 12% of fourth $100, 11% of fifth $100, 11% of sixth $100 + any database verification fee

$15 390% One with licensee or more than one with any other licensee

None

Minnesota Legal $350 Max: 30 days $5.50: $0-$50; 10%+$5: $51-$100; 7% (min. $10) + $5: $101-$250; 6% (min. $17.50) + $5: $251-$350 (After default: 2.75% per month)

$15 390% Not specified None

Mississippi Legal $500 Under $250: maximum of 30 days; $250-$500: 28-30 days

Under $250: $20 per $100 advanced; $250-500: $21.95 per $100 advanced

$20 520% Not specified None

Missouri Legal $500 14-31 days Not specified $75 1,950% No more than $500 to any one lender at one time

Six

Montana Legal $300 Max: 31 days 36% APR $1.39 36% Nebraska Legal $500 Max: 34 days $15 per $100 or pro rata for any part

thereof on amount of check $17.65 459% Two None

Nevada Legal 25% of expected gross monthly income

Not specified No limit No limit No limit Not specified Not specified

New Hampshire Legal $500 7-30 days 36% annual interest $1.38 36% One None New Jersey Prohibited New Mexico Legal $2,500 14 to 35 days, can

be shorter by written agreement

$15.50 per $100; $.50 verification fee per $100

$16 416% Total capped at 25% gross monthly income

None

New York Prohibited North Carolina Prohibited North Dakota Legal $500 Max: 60 days 20% + databasing fee $20 520% Not Specified ($600

aggregate loans outstanding to all licensees)

One

Ohio Legal $500 Min: 31 days 28% annual interest $1.08 28% One, four per year None Oklahoma Legal $500 12-45 days $15 per $100: $0-$300; an

additional fee of $10 per $100: $301-$500

$15 390% Two None

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Appendix 1: Legal status of payday lenders and allowable financial terms of payday loans by state: Continued Loan terms Debt limits State Legal status Max loan amount Loan term Max finance rate

and fees Finance charge for 14-day $ 100 loan

APR for 14-day $100 loan

Max number of outstanding loans at one time Rollovers permitted

Oregon Legal Not specified Min: 31 days

36% APR interest, $10 per $100 of loan amount as fee, up to $30

$13 for 31 day loan

156% APR for 31 day loan

No limit Two

Pennsylvania Prohibited Rhode Island Legal $500 Min: 13

days 10% $10 260% 3/$500 One

South Carolina

Legal $550 Max: 31 days

15% of principal $15 390% Not specified None

South Dakota

Legal $500 Not specified

Not specified No limit No limit Not specified Four

Tennessee Legal $425 ($500 check) Max: 31 days

15% of the face value of the check $17.65 459% 3 (2 per licensee) None

Texas Legal Not specified 7-31 days May not exceed rates authorized in Tex. Fin. Code §§ 342.251-342.259. 7 Tex. Admin Code § 83.604 provides a chart stating that maximum APRs for payday loans range from 83.43% for a 30-day, $350 loan to 569.92% for a 7-day, $100 loan.

$11.87 309.47% Not Specified None

Utah Legal (Applies to check cashers only)

No limit May not exceed 10 weeks

No limit No limit No limit No limit Not specified

Vermont Prohibited Virginia Legal $500 Min: 2 pay

periods 36% annual interest + $5 verification fee + 20% of loan

$26.38 687.76% One None

Washington Legal $700 or 30% of gross monthly income, whichever is less

Max: 45 days

15%: first $500; 10%: remaining portion of the loan in excess of $500 up to the $700 maximum

$15 390% A company cannot hold a check or checks in an aggregate face amount of more than $700 plus allowable fees from any one borrower at any one time.

None

West Virginia

Prohibited

Wisconsin Legal Lesser of $1,500 including fees or 35% gross monthly income

90 days or less

No limit No limit No limit No limit One

Wyoming Legal Not specified 1 calendar month

The greater of 20% per month or $30 $30 780% Not Specified None

Source: http://www.paydayloaninfo.org accessed on September 3, 2014.

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Appendix 2: Number of payday lenders and bank’s branches and the ranking by state Ranking

State Number of payday lenders **

Number of bank’s branches

By number of payday lenders

By number of bank’s branches

By payday lenders per

capita

By bank’s branches per capita

Alabama 1,035 1,571 9 25 9 24 Alaska 9 132 50 51 47 46 Arizona* 436 1,362 21 30 30 49 Arkansas* 36 1,484 44 27 45 8 California 2,427 7,275 2 1 33 51 Colorado 432 1,641 23 23 25 27 Connecticut* 82 1,301 39 31 46 34 Delaware 126 287 36 45 19 41 District of Columbia* 29 244 45 49 37 31

Florida 1,520 5,616 3 3 28 43 Georgia* 1,208 2,711 8 11 15 28 Hawaii 41 283 43 46 43 50 Idaho 212 537 31 38 12 15 Illinois 1,248 4,926 7 5 22 23 Indiana 520 2,386 20 14 23 19 Iowa 212 1,613 32 24 18 6 Kansas 305 1,559 27 26 14 9 Kentucky 648 1,808 15 20 7 11 Louisiana 1,342 1,646 6 22 3 20 Maine* 15 519 49 39 50 16 Maryland* 232 1,785 29 21 38 40 Massachusetts* 126 2,223 37 17 48 36 Michigan 555 2,978 17 9 32 35 Minnesota 132 1,821 35 19 41 21 Mississippi 1,004 1,215 10 32 1 7 Missouri 972 2,436 12 13 11 14 Montana 57 392 40 44 24 12 Nebraska 147 1,093 34 34 16 5 Nevada 316 549 26 37 20 48 New Hampshire 15 435 48 43 51 30 New Jersey* 333 3,320 24 8 39 29 New Mexico 435 512 22 40 10 39 New York* 685 5,498 14 4 40 44 North Carolina* 524 2,760 19 10 31 33 North Dakota 22 439 47 42 29 3 Ohio 950 4,044 13 7 27 25 Oklahoma 977 1,408 11 29 2 13 Oregon 151 1,132 33 33 36 37 Pennsylvania* 219 4,710 30 6 49 26 Rhode Island 53 260 41 48 34 47 South Carolina 1,348 1,441 5 28 5 32 South Dakota 110 480 38 41 4 1 Tennessee 1,370 2,293 4 16 6 18 Texas 4,623 6,875 1 2 13 38 Utah 320 604 25 36 17 45 Vermont* 4 266 51 47 44 2 Virginia 577 2,687 16 12 26 22 Washington 293 1,891 28 18 35 42 West Virginia* 23 671 46 35 42 10 Wisconsin 536 2,320 18 15 21 17 Wyoming 52 231 42 50 8 4 Total 29,044 97,670

*States where payday lenders are illegal. **Payday lenders stores are proxied by NAICS codes 522291 (Consumer lending) and 522390 (Other activities related to credit intermediation).

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Appendix 3: Descriptive statistics Full data States with legal payday lending States with prohibited payday lending

Mean Median Min Max Mean Median Min Max Mean Median Min Max

Number of bank’s branches 1,915 1,559 132 7,275 1,860 1,559 132 7,275 2,061 1,635 244 5,498

Number of payday lender stores 569 316 4 4,623 678 432 9 4,623 282 173 4 1,208

Number of payday stores per 10,000 people 0.95 0.72 0.06 3.38 1.17 0.92 0.11 3.38 0.36 0.29 0.06 1.24

Number of bank’s branches per 10,000 people 3.54 3.43 1.86 6.49 3.55 3.43 1.86 6.49 3.51 3.63 2.12 5.09

Ratio of payday stores to bank’s branches 0.29 0.21 0.02 0.94 0.35 0.23 0.03 0.94 0.12 0.08 0.02 0.45

% White 79.85 81.80 41.00 97.10 81.01 83.70 42.20 95.70 76.78 80.35 41.00 97.10

% Black or African American 12.05 8.70 0.80 52.20 10.44 7.10 0.80 37.70 16.30 13.30 1.50 52.20

% Asian 4.66 2.80 0.90 57.10 4.88 2.70 1.10 57.10 4.07 3.65 0.90 9.10

% Hispanic 10.58 8.20 1.20 46.30 10.83 7.90 2.10 46.30 9.91 8.55 1.20 29.70

% 15 years and under 19.56 19.50 14.30 26.60 19.98 19.70 17.30 26.60 18.43 18.35 14.30 21.40

% aged 65+ 13.36 13.60 7.80 17.50 13.18 13.50 7.80 17.50 13.84 13.90 10.80 16.20

Income per capita ($) 27,894 26,545 20,670 45,004 26,920 26,523 20,670 33,326 30,467 28,518 22,007 45,004

% Poverty rate 14.35 14.20 8.40 22.30 14.39 14.20 8.40 22.30 14.24 14.10 9.40 18.70

% High school degree 87.30 87.90 80.80 92.10 87.41 88.20 80.80 92.10 87.01 87.70 83.30 91.30

% Bachelor's degree or higher 28.23 27.10 17.90 51.20 27.06 26.30 20.00 36.70 31.31 30.30 17.90 51.20

% Unemployment rate 8.51 8.50 3.40 12.60 8.38 8.40 3.40 12.60 8.86 8.65 6.5 10.7 Source: United States Census Bureau (http://www.census.gov) and the Federal Deposit Insurance Corporation (https://www.fdic.gov).

21

Appendix 4: Selected demographic, educational and financial characteristics State

% White

% Black or

African America

n

% Asian %

Hispanic

% 15 years and

under

% aged 65+

Income per

capita

Poverty rate (%)

% High school degree

% Bachelo

r's degree

or higher

Unemployment rate (%)

Alabama 70.7 26.9 1.4 3.8 19.6 13.8 23,587 18.1 82.6 22.3 10.3 Alaska 74.2 4.9 7.0 5.7 21.8 7.8 32,537 9.6 91.6 27.5 8.4 Arizona 81.8 4.9 3.6 29.7 21.1 13.9 25,571 17.2 85.4 26.6 9.8 Arkansas 80.2 16.2 1.5 6.4 20.2 14.4 22,007 18.7 83.3 19.8 8.6 California 65.7 7.1 14.9 37.6 20.4 11.5 29,551 15.3 81.0 30.5 11.0 Colorado 87.1 4.9 3.7 20.6 20.3 11.1 31,039 12.9 89.9 36.7 8.0 Connecticut 80.5 11.3 4.4 13.4 18.5 14.3 37,807 10.0 89.0 36.2 9.2 Delaware 72.4 22.8 3.8 8.1 18.8 14.5 29,733 11.5 87.7 28.5 8.4 District of Columbia 41.0 52.2 4.4 9.3 14.3 11.4 45,004 18.5 87.5 51.2 10.5 Florida 78.3 16.9 3.0 22.5 17.4 17.5 26,451 15.6 85.8 26.2 11.3 Georgia 62.3 31.6 3.7 8.8 21.4 10.8 25,309 17.4 84.4 27.8 10.7 Hawaii 42.2 3.0 57.1 9.0 18.5 14.5 29,227 10.8 90.3 29.6 6.7 Idaho 94.6 1.0 1.9 11.2 22.7 12.6 22,581 15.1 88.6 24.7 8.5 Illinois 74.2 15.4 5.2 15.8 20.0 12.6 29,519 13.7 87.0 31.1 9.9 Indiana 86.7 10.0 1.9 6.0 20.4 13.0 24,558 14.7 87.0 23.0 9.4 Iowa 93.4 3.7 2.1 5.0 19.7 14.9 26,545 12.2 90.7 25.3 5.6 Kansas 88.2 7.1 2.9 10.5 21.2 13.2 26,845 13.2 89.7 30.0 6.7 Kentucky 89.7 8.8 1.4 3.0 19.6 13.4 23,210 18.6 82.4 21.0 9.5 Louisiana 64.4 32.8 1.8 4.3 20.5 12.5 24,264 18.7 82.2 21.4 8.4 Maine 97.0 1.5 1.4 1.3 16.7 16.1 26,464 13.3 90.6 27.3 7.4 Maryland 61.0 30.8 6.4 8.2 19.2 12.4 36,056 9.4 88.5 36.3 7.8 Massachusetts 83.2 8.2 6.0 9.6 17.6 13.9 35,485 11.0 89.1 39.0 8.5 Michigan 81.4 15.2 2.9 4.4 19.2 13.8 25,547 16.3 88.7 25.5 12.6 Minnesota 88.1 6.2 4.7 4.7 20.0 13.0 30,656 11.2 91.9 32.2 7.0 Mississippi 60.6 37.7 1.1 2.6 21.0 12.9 20,670 22.3 81.0 20.0 10.6 Missouri 85.1 12.5 2.0 3.5 19.5 14.1 25,546 15.0 87.2 25.8 8.5 Montana 92.0 0.8 1.1 2.9 18.5 14.9 25,002 14.8 91.9 28.5 6.9 Nebraska 90.1 5.4 2.2 9.1 21.0 13.5 26,523 12.4 90.4 28.1 5.6 Nevada 76.0 9.3 8.9 26.5 20.4 12.2 27,003 14.2 84.4 22.2 11.9 New Hampshire 95.7 1.6 2.7 2.8 17.5 13.7 32,758 8.4 91.4 33.4 6.6 New Jersey 71.5 14.7 9.1 17.7 19.2 13.6 35,928 9.9 87.9 35.4 9.5 New Mexico 75.3 2.7 1.8 46.3 20.8 13.4 23,749 19.5 83.4 25.6 9.1 New York 67.9 16.9 8.1 17.7 18.2 13.6 32,104 14.9 84.9 32.8 8.7 North Carolina 71.6 22.6 2.6 8.3 19.9 13.0 25,285 16.8 84.5 26.8 10.5 North Dakota 91.7 1.7 1.4 2.1 18.4 14.4 28,700 12.1 90.5 27.1 3.4 Ohio 85.0 13.4 2.1 3.1 19.4 14.2 25,857 15.4 88.2 24.7 9.7 Oklahoma 80.8 8.7 2.2 8.8 20.7 13.7 24,046 16.6 86.2 23.2 6.8 Oregon 88.7 2.6 4.9 11.7 18.6 14.1 26,702 15.5 89.2 29.2 10.8 Pennsylvania 84.1 11.9 3.2 5.7 17.9 15.5 28,190 13.1 88.3 27.0 8.5 Rhode Island 83.7 7.5 3.5 12.5 17.3 14.6 30,005 13.2 84.8 30.8 9.5 South Carolina 68.6 28.8 1.6 5.0 19.3 13.8 23,906 17.6 84.0 24.6 11.1 South Dakota 87.9 1.8 1.2 2.8 20.5 14.4 25,570 13.8 90.1 26.0 4.9 Tennessee 80.0 17.4 1.8 4.5 19.5 13.6 24,294 17.3 83.9 23.5 9.8 Texas 76.0 12.5 4.4 37.6 22.7 10.4 25,809 17.4 80.8 26.3 7.7 Utah 91.2 1.5 2.8 12.9 26.6 9.2 23,794 12.1 90.6 29.9 7.1 Vermont 97.1 1.5 1.7 1.5 16.6 14.7 28,846 11.6 91.3 34.2 6.5 Virginia 71.9 20.8 6.5 7.9 19.0 12.3 33,326 11.1 86.9 34.7 6.9 Washington 82.8 4.8 9.0 11.2 19.4 12.4 30,661 12.9 90.0 31.6 8.9 West Virginia 95.7 3.9 0.9 1.2 17.2 16.2 22,482 17.6 83.4 17.9 7.9 Wisconsin 89.0 7.0 2.7 5.9 19.2 13.7 27,426 12.5 90.2 26.4 7.5 Wyoming 93.8 1.2 1.1 8.9 20.0 12.5 28,858 11.0 92.1 24.3 5.1 Source: United States Census Bureau (http://www.census.gov).

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Appendix 5: Rank order correlations between the number of banks and payday stores and selected demographic and financial characteristics at the state level State (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13)

Bank’s branches per capita (1) 1.00

Payday loan stores per capita (2) 0.02 1.00

% White (3) 0.44** -0.25 1.00

% Black or African American (4) -0.10 0.33* -0.78** 1.00

% Asian (5) -0.58** -0.28* -0.46** 0.19 1.00

% Hispanic (6) -0.53** 0.10 -0.34* 0.05 0.65** 1.00

15 years and under (7) -0.12 0.58** -0.12 -0.01 -0.04 0.28* 1.00

% aged 65+ (8) 0.32* -0.33* 0.36** -0.22 -0.36** -0.40** -0.61** 1.00

Income per capita (9) -0.13 -0.53** -0.13 -0.02 0.71** 0.30* -0.42** -0.13 1.00

Poverty rate (10) -0.02 0.50** -0.26 0.38** -0.42** -0.04 0.25 -0.03 -0.74** 1.00

% High school degree (11) 0.25 -0.47** 0.58** -0.66** 0.05 -0.20 -0.20 0.16 0.44** -0.76** 1.00

% Bachelor's degree or higher (12) -0.17 -0.50** -0.079 -0.10 0.65** 0.34* -0.35* -0.15 0.84** -0.63** 0.44** 1.00

Unemployment rate (13) -0.45** 0.21 -0.53** 0.57** 0.22 0.31* 0.010 -0.15 -0.17 0.54** -0.63** -0.17 1.00

*Denotes significance at 5% level, **denotes significance at 10% level.

Appendix 6: The coefficients for ridge regression stabilize fairly quickly