Small banks and consumer satisfaction

Journal Pre-proof Small banks and consumer satisfaction

John Sedunov PII:

S0929-1199(18)30746-6

DOI:

https://doi.org/10.1016/j.jcorpfin.2019.101517

Reference:

CORFIN 101517

To appear in:

Journal of Corporate Finance

Received date:

22 October 2018

Revised date:

2 July 2019

Accepted date:

22 September 2019

Please cite this article as: J. Sedunov, Small banks and consumer satisfaction, Journal of Corporate Finance(2018), https://doi.org/10.1016/j.jcorpfin.2019.101517

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© 2018 Published by Elsevier.

Journal Pre-proof

Small Banks and Consumer Satisfaction John Sedunov Villanova University1

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Abstract: This paper examines the impact of the composition of local banking markets on customer satisfaction. I measure customer satisfaction at the county level using complaint data filed with the Consumer Financial Protection Bureau (CFPB) from 2012-2017. I find that there are fewer customer complaints in counties where there is a larger presence of small banks, holding constant the level of bank competition. This effect holds for various complaint types, market types, market demographics, and alternative econometric specifications. Additionally, I provide a detailed description of the CFPB complaint data.

July 2019

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Keywords: Consumer Satisfaction, CFPB, Bank Regulation, Small Banks JEL Codes: G20, G21, G23, G28

1. Introduction Do small banks serve consumers better than their larger counterparts? On one hand the 2015 Chicago Booth/Kellogg School Financial Trust Index Survey suggests that local banks are trusted more by households relative to large national banks by a nearly two-to-one margin. On the other hand, Berger, Irresberger, and Roman (2019) provide evidence to the contrary, showing that small banks have 1

Michele and Christopher Iannaccone Assistant Professor of Finance, Finance Dept., Villanova School of Business. E-mail: [email protected]. I am grateful for helpful comments from Caitlin Dannhauser, Rabih Moussawi, and Mike Pagano and research assistance from Charlotte Bern. All errors are my own.

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Journal Pre-proof comparative disadvantages relative to large banks regarding household sentiment. Other strands of the literature suggest that small businesses are better off when small banks are present (e.g., Berger, Cerqueiro, and Penas (2015)). Ultimately it is unclear whether the presence of small banks is beneficial for consumer outcomes and satisfaction. In this paper, I seek to break this tension in the literature, and investigate the role of small banks in boosting consumer satisfaction using new data from the Consumer Financial Protection Bureau (CFPB) on consumer complaints. I find that small banks are related to

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improved customer satisfaction.

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The CFPB began operations in 2011. The bureau was a major part of the Dodd-Frank financial reform bill that was signed into law in 2010. The CFPB acts as an independent unit funded by the Federal

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Reserve, and its director is nominated by the President and confirmed by the Senate (Reuters, 2010),

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though recently there has been some controversy surrounding this position (Bernard, 2017). The bureau

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began with a wide-ranging array of powers, including the ability to write and enforce rules for banks, conduct bank examinations, monitor markets for consumer financial goods and services, and – notably – The CFPB’s

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to collect and track consumer complaints about financial institutions (Levitin, 2013).

complaint system has become an important way for consumers to bring attention to problems throughout

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various parts of the financial system. Complaints filed with the CFPB span a variety of topics, ranging

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from mortgages and checking accounts to payday loans and digital currency. Complaints filed at the CFPB can provide evidence as to the level of consumer satisfaction with financial services in the United States and give evidence as to what types of markets experience comparatively lower levels of satisfaction. In this paper I focus on CFPB complaints comprehensively, examining all types of complaints throughout the United States, and studying which banking markets are most likely to have higher or lower levels of customer satisfaction. Importantly, I investigate the role of small banks, competition, local regulation, and local banking characteristics in consumer satisfaction. I find that the within-state variation of the small bank share is correlated with the variation in complaints within the state filed at the CFPB. Moreover, I find that this result persists when local market 2

Journal Pre-proof competition and regulation are included in the analysis. When adding competition measured by the local market Herfindahl-Hirschmann Index (HHI) to the regression, I find that less competitive local banking markets file fewer CFPB complaints. These findings suggest that smaller institutions may be more attentive to their customers, perhaps due to their focus on soft information, relationship banking, and small business lending (Cole, Goldberg, and White, 2004; Berger, Miller, Petersen, Rajan, and Stein, 2005; Degryse and Ongena, 2005; among many others). Additionally, customers in less competitive

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markets may not have alternative choices for financial services, and accordingly may choose not to complain to the CFPB, as they may not feel that a complaint will affect meaningful change in an

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uncompetitive market. Moreover, institutions in more competitive markets may engage in a “race to the

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bottom,” in which financial services providers may adopt a short-term focus, as they do not wish to invest

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in long-term relationships with customers for fear that the customers will leave for a competing institution. Banks may sacrifice long-term benefits for short-term profits in this case. (see, e.g., Petersen

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and Rajan, 2005).

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This paper is also the first to report a comprehensive summary of the CFPB complaint data. Other papers use this same data, but restrict their analysis and reporting to one subset of the database

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(Begley and Purnanandam (2018) focuses solely on mortgage complaints) or on a very early sample of

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the data (Ayres, Lingwall, and Steinway (2014) study 110,000 complaints from a legal perspective). In contrast, my study includes over 939,000 complaints covering nine broad product areas. I report the summary of complaints by product class, by year, and by state, providing a view of the range and scope of the complaints filed with the CFPB. I add to a small but emerging literature on the effectiveness and impact of CFPB regulation. Levitin (2013) provides an overview of the CFPB’s role and powers. Fuster, Plosser, and Vickery (2018) study the effects of the CFPB’s oversight on credit supply, bank risk-taking, growth, and operating costs but do not focus on complaints.

Additionally, DeFusco, Johnson, and Mondragon (2017) study the

effects of the “ability-to-repay/qualified mortgage” rules implemented by the CFPB. Finally, Begley and 3

Journal Pre-proof Purnanandam (2018) use mortgage-related CFPB complaints to study the effects of competition and consumer satisfaction in high-minority markets. Their paper has the most similarity to the analysis I present below. In their work, Begley and Purnanandam (2018) focus on Community Reinvestment Act regulation and the prevalence of complaints in markets with varying socioeconomic characteristics, and do not consider the impact of small banks nor the impact of branching and regulation. Further, their analysis concentrates on local market demographics (e.g., race, income, and education) and focuses only

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on mortgage-related complaints, while my analysis focuses rather on local banking market characteristics related to lending, financial institution health, and market composition, and considers complaints related

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to all types of banking services.

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This paper contributes to several other areas of the banking literature. Notably, there is a large

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literature that studies community banks and small banks. One segment focuses on the comparative

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advantage of small banks in relationship lending. Small banks enjoy local information advantages that have proved beneficial to their growth over time (Diamond 1984, 1991; Ramakrishnan and Thakor, 1984;

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Boyd and Prescott, 1986). Small banks are typically thought of as better at handling soft, qualitative information (see, e.g., Berger and Udell, 2002; Stein, 2002; Liberti and Mian, 2009). A number of papers

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lend empirical support to this viewpoint (e.g., Petersen and Rajan, 1994; Berger and Udell, 1995; Boot

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and Thakor, 2000; Cole, Goldberg, and White, 2004; Berger, Miller, Petersen, Rajan, and Stein, 2005; Kysucky and Norden, 2016, among others). Additionally, Berger, Cerqueiro, and Penas (2015) and Berger, Bouwman, and Kim (2017) present evidence that suggests small businesses fare better when there are more small banks in the local market. Moreover, depositors tend to prefer banks located nearby (Brevoort and Wolken, 2008). Small bank relationship lending is also important during times of financial crisis and economic downturn, due to increased information inefficiencies (Iyer and Puri, 2012). Further, Beck, Degryse, de Haas, and Van Horen (2018) show that firms in close proximity to relationship lenders had better access to credit during the financial crisis. Alternatively, Berger, Goulding, and Rice (2014) use the Survey of Small Business 4

Journal Pre-proof Finance to show that small businesses may not prefer small banks at the same level that they once did. Other studies (e.g., Frame, Srinivasan, and Woosley (2001) and Berger and Udell (2006)) show that large banks may be able to serve opaque firms using credit scoring technologies, thus reducing small firms’ reliance on small banks for financing. On the household level, the aforementioned Chicago Booth/Kellogg School Financial Trust Index Survey that suggests that small banks may also be trusted more by households relative to large banks,

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while Berger, Irresberger, and Roman (2019) find opposite conclusions. This former is consistent with

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the analysis I show below – more small banks in a local market lead to fewer complaints filed with the

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CFPB.

Additionally, this paper contributes to the broad literature on the impact of banks in the real

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economy. Consumer satisfaction and consumer outcomes are an important facet of determining how

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financial institutions affect the development of local markets and economies. This broad literature also includes work related to bank regulation through capital standards (e.g., Allen, 2004) or bank bailouts

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(e.g., Duchin and Sosyura, 2014; Berger and Roman, 2017). Additionally, it includes investigations into

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the consequences of bank branching deregulation (e.g., Jayaratne and Strahan, 1996; Morgan, Rime, and Strahan, 2004; Huang, 2008; Levine, Levkov, and Rubinstein, 2008; and Beck Levine, and Levkov,

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2010). Finally, this literature also includes studies related to bank deposits (Gilje, Loutskina, and Strahan, 2016), the structure of the banking industry and its impact on small business (Berger, Bouwman, and Kim, 2017), and bank liquidity creation and real economic growth (Berger and Sedunov, 2017). Finally, this paper contributes to the literature on bank regulation and oversight. In addition to the aforementioned work related to the CFPB, the banking literature also includes work related to the FDIC and deposit insurance (e.g., Flannery, 1991; Ashcraft, 2005; Cowan and Salotti, 2015; Calomiris and Jaremski, 2016; Koch and Okamura, 2017), central banking and the Federal Reserve (e.g., Romer and Romer, 2000; Bernanke and Kuttner, 2005; Meltzer and Goodhart, 2005; Chakraborty, Goldstein, and MacKinlay, 2017; Sedunov, 2018), and other forms of regulation and government agencies (e.g., 5

Journal Pre-proof Dimitrov, Palia, and Tang, 2015; Frame, Fuster, Tracy, and Vickery, 2015; Bao, O’Hara, and Zhou, 2018; Berger, Roman, and Sedunov, 2018). To investigate the impact of small banks on customer satisfaction, I gather data on all complaints filed with the CFPB between 2012 and 2017. I aggregate the complaints and run both cross-sectional regressions using county-level banking market characteristics based in 2011 and panel regressions using all county-year observations. In both settings, I find that increased small bank presence in a county is

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linked to lower CFPB complaints, and thus improved customer satisfaction. This finding is robust to the

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inclusion of the county’s Herfindahl-Hirschmann Index (HHI) for bank competition, and several other

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variables that control for the county’s banking environment and demographic characteristics. My main findings also persist when I sort counties by higher or lower competition or small bank

I find that the biggest disparity comes from sorting by minority

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population, poverty, and education.

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share. In the spirit of Begley and Purnanandam (2018), I also sort counties by high and low minority

population – counties with higher numbers of minorities see the greatest benefit from the presence of Within the CFPB database, I am also able to sort complaints based on their product

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small banks.

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classification. These categories include complaints about mortgages, credit cards, debt collectors, payday loans, and student loans, among others. I find that complaints in categories most closely related to

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services provided by small banks (e.g. checking or mortgage lending) are reduced in markets with a greater presence of small banks.

I also include robustness checks to support the main findings of the paper. First, to preserve anonymity, the CFPB does not report ZIP codes for areas with fewer than 20,000 residents. Thus, my sample may underreport the number of complaints in small-population locations. To mitigate this issue, I repeat my analysis dropping all counties with zero complaints and dropping all counties with belowmedian population and find similar results. I also allow the definition of small bank presence to change and find that the main results are robust to these alternative methods.

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Journal Pre-proof The findings I present here also have policy implications. Regulators may wish to consider consumer outcomes as it relates to large banks’ services and customer relations. This implication is especially important considering the number of bank mergers over time and the increasing size of financial institutions. Additionally, regulators may also consider the regulatory burden placed on small banks, as additional time and resource costs can redirect the focus of small banks away from their local customers.

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The rest of the paper proceeds as follows. Section 2 describes the data while Section 3 provides

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more details about the CFPB complaints and discusses the characteristics of the CFPB data. Section 4 develops the hypotheses I test in the main analysis. Section 5 presents the main results of the paper, while

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Section 6 presents robustness checks. Section 7 concludes.

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2. Data and Sample

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I gather data from several sources. The primary source of data is the Consumer Financial Protection Bureau (CFPB) complaint database. The CFPB publishes all complaints it has received since To date, this dataset includes over 1 million complaints.

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its inception in 2011.

I provide more

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information about these complaints, the CFPB’s process for collecting complaints, and summary statistics related to these complaints in Section 3. I conduct the main analysis in this paper at the county level. This is the most reasonable level of consolidation for the data, considering that the focus of the analysis is on local banking market composition.

The ZIP code level is too granular, as it is easy for a consumer to simply exit to a

neighboring ZIP code for banking needs. In the other direction, the state level is too extreme, as a state can be substantial in size, and market composition on a statewide basis isn’t as relevant for local consumers. The CFPB provides the only the ZIP codes of the complainants. Thus, I match ZIP codes to

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Journal Pre-proof county-level FIPS codes using data from the Department of Housing and Urban Development. 2 Because a ZIP code can exist in multiple counties, this dataset provides information on what proportion of a ZIP code’s residential population lies in each county it may straddle. In mapping ZIP codes to counties, I assign a ZIP code to the county that has the largest share of the ZIP code’s population. I also gather bank-level data from the FDIC’s Summary of Deposits (SoD) and Statistics on Depository Institutions (SDI). These databases allow me to calculate county-level estimates of the HHI

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(based on deposits), Small Bank Share (based on bank assets), bank efficiency ratio and capital adequacy,

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and bank lending. For the efficiency and capital adequacy ratios, I multiply the ratio for each bank (gathered from the SDI) by the bank’s share of deposits in a given county (gathered from the SoD). I then

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sum these values at the county level to get a weighted average of the ratios for each county. For aggregate

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measures related to bank lending, I multiply a bank’s total lending (gathered from the SDI) by the

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proportion of a bank’s deposits in a given county (gathered from the SoD). This allows me to allocate lending behavior among the multiple counties in which a bank may do business. I then sum the total

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estimated bank loans in a county and normalize it by the total estimate bank assets in a county. In total,

and bank performance.

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these control variables capture competition, the accessibility of banking to local consumers, bank safety,

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Finally, I gather some basic county demographic data to use in sample splits. This data includes information on the education level, poverty level, and ethnic composition of a given county. This data comes from the American Community Survey, which is part of the U.S. Census. 3

3. CFPB Consumer Complaints The Consumer Financial Protection Bureau (CFPB) was a major part of the Dodd-Frank financial reform bill that was signed into law in 2010. The CFPB officially began operations in July of 2011 under 2 3

https://www.huduser.gov/portal/datasets/usps_crosswalk.html https://www.census.gov/programs -surveys/acs/

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Journal Pre-proof the supervision of Richard Cordray (Eaglesham, 2011). The bureau began with a wide-ranging array of powers, including the ability to write and enforce rules for banks, conduct bank examinations, monitor markets for consumer financial goods and services, and to collect and track consumer complaints about financial institutions.

More complete institutional descriptions of the CFPB are available in Levitin

(2013) or Fuster, Plosser, and Vickery (2018). The CFPB acts as an independent unit funded by the Federal Reserve, and the bureau’s director is nominated by the president and confirmed by the Senate

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some power to block new regulations emanating from the CFPB.

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(Reuters, 2010). There is some oversight for the agency, as the Financial Stability Oversight Council has

As mentioned above, one of the CFPB’s central functions is to collect consumer complaints about

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a variety of issues related to financial services. The CFPB makes all the complaints they receive public

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on their website.4 Data on consumer complaints starts on November 30, 2011, not long after the agency

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began operation. Figure 1 provides an example complaint found on the CFPB’s website. Each complaint provides the name of the institution in question, along with geographic details on the complainant, and a

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narrative about the problem written by the complainant. The CFPB does not verify that the entirety of the account provided by the consumer is factual. However, the CFPB does work to ensure that a commercial

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relationship between the company and the complainant exists.

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The CFPB also takes steps to protect the identity of complainants. When a claim is submitted, several pieces of personal data are scrubbed from the entry. These data include names, ages, gender, ethnicity, and medical conditions, among other items. Moreover, the CFPB does not publish the full ZIP code of any complainant that lives in a ZIP code with fewer than 20,000 residents. This means that for the county-level analysis below, these complaints are dropped from the sample. Importantly, the CFPB removes any account numbers, social security numbers, or vehicle numbers from its complaints. Finally,

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https://www.consumerfinance.gov/data-research/consumer-complaints/

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Journal Pre-proof the CFPB removes any information related to employment, e.g., names of employers, occupations, or student status.5 Between 2011 and 2017, the CFPB collected 939,168 complaints across nine product classes. Table 2 presents summary statistics about these complaints over time. It is important to note that in 2017, the CFPB reorganized the way the product and sub-products under which complaints are classified (CFPB, 2017). Before this change, there were 18 different product classes. Throughout this paper, I

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reorganize the complaints according to these new guidelines, such that the classifications are consistent

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through all years.

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Through every year of the sample, the number of complaints received by the CFPB increases. In 2012, the first full year of the CFPB’s operation, the bureau received 72,373 complaints. Most of these

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complaints were related to mortgages. The total number of complaints grew to 242,992 in 2017, with

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most complaints falling in the “Credit Reporting, Credit Repair Services, or Other Personal Consumer Reports” category. This increase in credit reporting complaints correlates with Equifax’s data breach,

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which was announced in September of 2017 and impacted as many as 143 million Americans (Haselton, 2017). The number of credit reporting complaints received by the CFPB is nearly double the number of

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complaints received in the second largest category, “Debt Collection.”

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Despite the large increase in credit reporting complaints in 2017, “Credit Reporting, Credit Repair Services, or Other Personal Consumer Reports” typically ranks among the top product classes for complaints. Other product classes that remain consistently large relative to the rest of the sample are “Mortgage” and “Debt Collection.” Together, these three product classes comprise 68% of the total number of complaints submitted to the CFPB between 2011 and 2017. Additionally, complaints related to the “Checking or Savings Account” or “Credit Card or Prepaid Card” groups make up approximately 22% of the complaint sample. The remaining four categories, “Money Transfer, Virtual Currency, or 5

Full documentation of the CFPB’s scrubbing standards are found at: http://files.consumerfinance.gov/a/assets/201503_cfpb_Narrative-Scrubbing-Standard.pdf. This document is also linked from the main CFPB data site: https://www.consumerfinance.gov/complaint/data-use/.

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Journal Pre-proof Money Service,” “Payday Loan, Title Loan, or Personal Loan,” “Student Loan,” and “Vehicle Loan or Lease” represent 10% of the sample, and none of these four product classes makes up more than 4.25% of the total sample. Table 3 reports the percentage of complaints by sub-product. Within each product group, the CFPB also classifies a sub-product, which gives a clearer description of what the complaint involves. In each category, the largest sub-products are: checking accounts (68,605, Checking or Savings Account);

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general-purpose credit card or charge card (101,098, Credit Card or Prepaid Card); credit reporting

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(212,242, Credit Reporting, Credit Repair Services, or Other Personal Consumer Reports); other debt (55,296, Debt Collection); domestic money transfer (3,457, Money Transfer, Virtual Currency, or Money

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Service); conventional home mortgage (106,326, Mortgage); installment loan (9,837, Payday Loan, Title

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Loan, or Personal Loan); private student loans (25,172, Student Loan); and vehicle loans (20,883, Vehicle

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Loan or Lease). Again, it is clear that credit reporting, credit cards, and mortgages are among the leading topics of CFPB complaint filings. Aside from credit reporting, the proportional distribution of complaints

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among sub-products is relatively constant over time.

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Table 4 presents a geographical summary of complaints filed with the CFPB, and Figure 2 presents a graphical representation of this data. Though the analysis below is conducted at the county

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level, I report these summary statistics at the state level, for ease of interpretation. Here, I report the total number of complaints filed between 2011 and 2017 on an absolute and per capita basis. Unsurprisingly, the state with the most total complaints filed is California, however, on a per capita basis the largest contribution comes from the District of Columbia, with 880 complaints per 10,000 residents. The largest states on the list after this are: Delaware (529), Maryland (497), Georgia (482), and Florida (474). Likely, this high concentration of complaints in the District of Columbia and Maryland (relatedly, Virginia ranks 8th , with 363 complaints per 10,000 residents) is that the DC/Maryland/Virginia area is a hub for those

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Journal Pre-proof involved closely with crafting and enforcing regulation. 6 Those who live in the area may either be affiliated with the government and its agencies, or more likely to be aware of upcoming regulatory action. Delaware’s 2nd ranking also makes sense, as Delaware is a hub for financial activity, and home to some of the most relaxed state-level banking rules in the United States. In contrast, the five states with the lowest ranks are Alaska (146), Arkansas (145), North Dakota (139), West Virginia (128) and Iowa (128). Additionally, the CFPB adds special flags to each complaint to designate other characteristics. I

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report a summary of these designations in Table 5. Panel A reports the proportion of timely responses

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from financial institutions. Each institution that has a complaint filed against it has the opportunity to provide a response to the consumer. According to the CFPB, complaints are listed in the online database

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once an institution has responded, or after the institution has had a complaint for 15 days. 7 Over 97% of

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complaints filed with the CFPB receive a timely response. This proportion has remained steady over the

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seven years of data I collect and report.

Further, Panel B of Table 5 reports designations regarding the consumer. 7.1 % of complaints are

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filed by “Older Americans.” The CFPB defines an older American as one who is 62 years of age or older. Additionally, 5.71% of all complaints are filed by “Servicemembers,” whom the CFPB defines as

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“anyone who is active duty, National Guard, or Reservist, as well as anyone who previously served and is

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a Veteran or retiree.” Complaints filed by servicemembers spiked in 2017, when this group collectively filed 20,019 complaints (in 2016, servicememebers filed 9,934 complaints).

Finally, 1.11% of

complainants fall in to both the “Older American” and “Servicemember” categories. Panel C of Table 5 reports the percentage of time when the consumer disputes the response provided by the financial institution. Prior to 2017, 19.87% of complaint responses were disputed by the

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This assertion is backed, anecdotally, by evidence from Google Trends. An analysis of Google search interest for the term “CFPB” from Jan. 1, 2012 through Dec. 31, 2012 (the CFPB’s first full y ear of existence) shows that Virginia, Maryland, and Delaware were ranked #2-#4, while Washington, D.C. was ranked #1. These data are found at: https://trends.google.com/trends/explore?date=2011-01-01%202012-12-31&geo=US&q=cfpb. 7 https://data.consumerfinance.gov/dataset/Timely-vs-Untimely-Complaint-Responses/bwmc-xdj7/about

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Journal Pre-proof complainant. However, on April 24, 2017, the option to dispute responses was discontinued. Thus, the remaining 170,620 complaints filed in 2017 cannot be classified with this designation.

4. Hypotheses and Methodology The primary empirical question in the analysis that follows is whether the composition, strength, and regulation of the local banking market impacts the number of complaints that residents file with the

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CFPB.

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The following sets of hypotheses inform and clarify the questions that are central to the analysis

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below. The first set of hypotheses examines the small bank share in local banking markets:

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: A greater small bank presence decreases the number of complaints filed with the CFPB. : A greater small bank presence increases the number of complaints filed with the CFPB. suggests that when there are more small banks in a county, customers may be happier

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First,

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with their service and file fewer complaints. It is likely the case that small banks are community-oriented and more likely to provide personalized service to their customers. This hypothesis relates to some of the

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extant literature on small banks, like Berger, Cerqueiro, and Penas (2015) and Berger, Bouwman, and

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Kim (2017) who suggest that small businesses fare better when there are more small banks in the local market and Brevoort and Wolken (2008), who show that depositors tend to prefer banks located nearby. Moreover, these hypotheses are related to households, as evidence from the Chicago Booth/Kellogg School Financial Trust Index Survey suggests that small banks may also be trusted more by households relative to large banks. This outcome may arise from two channels related to small banking, as described in Berger, Irresberger, and Roman (2019). One is the relationship channel, in which small banks may alleviate household financial constraints via relationship lending, and the other is the trust channel, in which households may trust small banks more relative to large banks.

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Journal Pre-proof Alternatively,

indicates that as there are more small banks in a given community, customer

satisfaction decreases, meaning that more complaints to the CFPB are likely. This alternative hypothesis is supported by other studies in the literature. Berger, Irresberger, and Roman (2019), who show that small banks do not have a competitive advantage in promoting consumer satisfaction. Moreover, Berger, Goulding, and Rice (2014) show that small businesses may not prefer small banks at the same level that they once did, and studies such as Frame Srinivasan and Woosley (2001) and Berger and Udell (2006)) show that large banks may be able to serve opaque firms using credit scoring technologies, thus reducing

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small firms’ reliance on small banks for financing. This outcome may arise from two additional channels,

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also described by Berger, Irresberger, and Roman (2019). The first is the economies of scale channel, in

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which large banks simply outperform small banks due to their economies of scale advantage. As large

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banks are able to better compete on price terms, consumers may be more satisfied. A second channel is the safety channel, by which large banks are viewed as safer than small banks as they are better

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diversified, subject to more stringent regulation, and have access to implicit government guarantees.

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Next, I examine the role of competition in local markets:

the CFPB.

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: Increased local banking market competition decreases the number of complaints filed with

CFPB.

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: Increased local banking market competition increases the number of complaints filed with the

This set of hypotheses suggests that local competition may be related to consumers’ satisfaction with the financial services that are provided to them.

suggests that local competition may force banks to

compete not only on price terms, but also in how they treat customers. If competition in a given market is higher and this hypothesis is true, the local market competition will lead to fewer complaints at the CFPB. Alternatively, the truth of

may indicate that as consumers have fewer local market options for

financial services, they file fewer complaints with the CFPB. In this scenario, financial services firms do not have to compete against one another to offer better service, and consumers may resign themselves to

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Journal Pre-proof the fact that they have no other or better options in the near vicinity. Alternatively, as competition increases, banks may not be interested in forming long-term relationships with customers, given the threat that the customer may move to a competing institution. As a result, banks may take a short-term focus, in which the quality of services offered to consumers declines. In this case, increased competition can negatively affect consumer outcomes (see, e.g., Petersen and Rajan, 1995). Both hypotheses relate to the trust and relationship channels described above, as well as to the potential willingness of large banks to

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ignore their economies of scale benefits due to decreased competition. To test the hypotheses described above, I run my main tests as both panel and cross-sectional The cross-sectional regressions allow me to view the entirety of complaints from a

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OLS regressions.

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given county over the 2012-2017 sample period. There are several county-year observations that have

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zero complaints, however, in the cross-section I am better able to account for counties have complained at

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represents the number of complaints per 10,000 residents filed from a

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In this regression,

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any point in the sample period. First, the cross-sectional regressions take the form:

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certain county to the CFPB between 2012 and 2017.

represents one or both of two

measures of county-level banking market composition: either Small Bank Share or HHI. Small Bank Share measures the proportion of banking assets in a county controlled by banks that are less than $1 billion in size. HHI is the Herfindahl-Hirschmann Index measured at the county level using deposits. Next, the vector Bank Characteristics includes several indicators of local banking market conditions. This vector includes Bank Efficiency, Capital Adequacy, Loans/Assets, and Noncurrent Loans/Assets. Following this, the vector Demographic Characteristics includes measures that describe the county’s socioeconomic composition. These variables include County Education, County Minority Population, and County Poverty. Finally, the regressions include MSA fixed effects, which should capture local 15

Journal Pre-proof conditions that are not captured by the other independent variables. Note that if a county is not part of an MSA, it is considered a rural county. That is, I do not drop any counties that are not in an MSA, but rather count them among their own group of rural counties. Additionally, I cluster all standard errors at the MSA level. All independent variables are measured as of 2011. Thus, the resulting regression is a cross-sectional regression that examines differences in the snapshot of complaints among different counties.

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Next, the panel regressions take the form:

.

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The vectors Composition, Bank Characteristics, and Demographic Characteristics contain the same

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variables as do the vectors in the cross-sectional regressions. In the panel regressions, I include year fixed effects in addition to MSA fixed effects, and cluster standard errors at the MSA and year levels. In some

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regressions, I replace MSA and year fixed effects with state, year, and state x year fixed effects, to further

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5. Main Results

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test the robustness of the main results.

5.1 Baseline Results

Table 6 presents the results from the main analysis. Here, I test the baseline effect of local market concentration, small bank presence, and regulation on consumer satisfaction. Panel A presents crosssectional regressions and Panels B and C present panel regressions. In Panel A, regressions (1)-(3) examine the effect of the HHI and Small Bank Share individually and then together. Regressions (1) and (3) show a negative and statistically significant coefficient on the HHI. As HHI grows larger, a local market is less competitive, as a single bank controls a greater proportion of

16

Journal Pre-proof the market.

The negative coefficient indicates that as banking markets become less competitive,

consumers are less likely to file complaints with the CFPB and lends support to hypothesis

. This

outcome may result from several causes detailed above. First, consumers in the local market may not feel as if they have other options within the market, and do not bother to file complaints against a local “monopoly” because they feel that it would not make a difference to do so. Second, local customers, instead of filing a complaint, may instead simply switch banks and leave for a different county and a

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different institution. Finally, in more competitive markets, banks may enter a race to the bottom as they compete with one another, thus detracting from the quality of service provided to consumers (Petersen

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and Rajan, 1995). This activity may lead to banks sacrificing long-term relationships and benefits for

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short-term prospects, given the threat of a customer leaving for a competitor. The coefficient on HHI is

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also economically meaningful. The coefficient from regression (3), -2.561, implies that a one standard deviation increase in the HHI (0.21) corresponds to a 4.925% decrease in Comp./Capita relative to its

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mean of 10.92.

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In Panel A, regressions (2) and (3) show similar results for Small Bank Share. The coefficient on Small Bank Share is negative and statistically different from zero. This coefficient is also economically

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meaningful, as using the coefficient from regression (3), -1.636, a one standard deviation in Small Bank

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Share (0.34) corresponds to an 4.79% decrease in Comp./Capita relative to its mean level. This outcome gives support to hypothesis

.

Likely, smaller banks are more community-oriented, and provide

customized and superior customer service to local customers relative to larger institutions. This outcome can result from the trust channel or the relationship channel, as described above.

Accordingly, an

increased presence of small banks correlates to fewer complaints and greater customer satisfaction. The results in Panels B and C are in line with those presented in Panel A. Panel B presents panel regressions that include either MSA and year fixed effects or state, year, and state x year fixed effects. Across all specifications, the coefficients on HHI and Small Bank Share are negative and statistically different from zero. In Panel C, I also include Urban, which is a dummy that equals one if a county is in 17

Journal Pre-proof an MSA in place of the MSA fixed effects, to include all cities together to search for commonalities among them. Once again, I continue to find a negative and significant relation between bank complaints and Small Bank Share or HHI. Taken together, the results in Table 6 lend strong support to hypotheses

and

. This

indicates that markets that are less competitive or have a greater presence of small banks have fewer complainants to the CFPB.

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5.2 Sorts by Market-Type

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Building on the results from Table 6, I sort the sample by Small Bank Share and HHI for the next

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analysis. Table 7 provides summary statistics (Panel A) and cross-sectional regression results (Panel B)

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based on these sorts. In Panel A of Table 7, I examine the total complaints from 2012-2017 of above- and below-median Small Bank Share and HHI counties. As HHI increases from below-median (most

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competitive) to above-median (least competitive), the mean number of total complaints per 10,000

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residents decreases from 13.35 to 8.5. Moreover, as Small Bank Share increases from below-median (low small bank presence) to above-median (high small bank presence), the mean number of total complaints

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per 10,000 residents decreases from 14.23 to 7.62. These summary statistics provide more support for the

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regression results shown in Table 6 and present a clearer picture of the impact of competition and small bank presence on bank complaints. Panel B of Table 7 presents regression results based on the sorts shown in Panel A. Here, I try to understand whether the effects of Small Bank Share or HHI are more pronounced in different markets. First, regressions (1) and (2) split the sample by low and high HHI. In these regressions, the independent variable of interest is the Small Bank Share for the local market. Whether the level of competition is high or low, the coefficient on Small Bank Share is negative, however it is only significant in competitive (low HHI) markets. Moreover, the coefficient in the low HHI sample is nearly double that in the high HHI sample.

18

Journal Pre-proof Regressions (3) and (4) in Panel B of Table 7 show the impact of HHI in different Small Bank Share terciles. The coefficients on HHI are negative and statistically significant regardless of the level of Small Bank Share in a market. Like in regressions (1) and (2), the effect of HHI on Comp./Capita is heightened in low Small Bank Share markets. Taken together, the results from Table 7, Panel B imply that there are differential effects across different types of markets. When competition is low (high HHI markets), the magnitude and significance

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of the Small Bank Share effect is reduced, and when competition is high (low HHI markets), the

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magnitude and significance of the Small Bank Share effect is increased. Similarly, in markets with a higher Small Bank Share, the magnitude of the coefficient on HHI is reduced, and when Small Bank

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Share is low, the HHI effect is increased. In sum, there are fewer complaints in markets with many small

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banks and high competition, and markets with few small banks and low competition.

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This outcome may relate to the interaction between the two sets of hypotheses I propose above. When a banking market is more competitive, banks may behave in such a way that satisfies a short-run

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profit maximization goal, rather than focus on establishing long-run relationships with their customers. In ).

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turn, this leads to a reduction in the quality of banking services in the market (hypothesis

However, small banks are proficient in relationship banking and soft information lending (Berger, Miller,

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Petersen, Rajan, and Stein, 2005), which may have a positive effect on consumer satisfaction (hypothesis ). Thus, in more competitive markets, the presence of smaller banks that are focused on this type of lending may serve to alleviate some of the negative impact on service quality that may come with higher levels of competition.

5.3 Demographic Characteristics In Table 8, I present results in which I sort counties based on demographic characteristics. Begley and Purnanandam (2018) show that these demographic characteristics are important for mortgage 19

Journal Pre-proof complaints. Following this work, it is natural to investigate whether competition or small bank presence matters more or less in areas with high minority populations, lower educational attainment, or higher poverty levels. Throughout the paper, I include these demographic control variables in my regressions. However, in this section, I omit them in favor of using them to sort the sample. In Table 8, Panel A sorts by a county’s nonwhite population, Panel B sorts by the proportion of county residents that live below the poverty line, and Panel C sorts by the proportion of county residents who have not obtained a high school

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American Community Survey, which is part of the U.S. Census.

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diploma. All sorts are based on the median levels of demographic variables, and all data comes from the

Across all types of market, I find a similar impact on Comp./Capita for both HHI and Small Bank

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Share as in the baseline results presented above. However, it is notable that the coefficient on the Small

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Bank Share is larger in high-nonwhite counties (Panel A), in low-poverty counties, and in high-education

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counties (Panel C). This result may be a product of the trust channel. Though the evidence in the literature is not conclusive, there is some empirical support for the idea that higher levels of education and

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income predict a greater level of trust in others (Guiso, Sapienza, and Zingales (2003)) and that higher income predicts greater trust in financial institutions (Fungacova, Hasan, and Weill (forthcoming)).

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Increased trust for institutions among these groups can magnify the impact that small banks have on

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customer satisfaction, given their ability and likelihood to engage in relationship banking.

6. Robustness 6.1 Complaint Types The first robustness check I present examines the impact of HHI and Small Bank Share on various types of complaints. It may be the case that the observed effect above is driven by complaints based on a specific product type, rather than a general trend. It may also be the case that small banks have a greater impact on complaints types that are more relevant for the types of services offered by small banks. 20

Journal Pre-proof Regardless, this analysis may present an additional challenge, as splitting the overall number of complaints by complaint type will create many observations in which the number of complaints for a given complaint type is equal to zero. I separate Comp./Capita into nine different product categories, based on the CFPB’s April 2017 reclassification, and described above. I then run a separate regression for each category of complaints and include both HHI and Small Bank Share together in each regression. I report the results of this test in

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Table 9 – Panel A presents cross-sectional regressions and Panel B presents panel regressions. The

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results of the tests are largely similar whether they are run in the cross-section or as a panel. Here, the coefficient on Small Bank Share is negative for all product types except for “Credit Reporting” in Panel A

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debt collection, mortgages, and payday or title loans.

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(where it is not significant). The coefficient is significant for complaints related to checking accounts,

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The coefficient on HHI is also negative in all regressions except for regression (5) in Panel A, which corresponds to complaints related to money transfers or virtual money. This category contains digital

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currencies and other online forms of banking. Competition may not be relevant to the online market as

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much as a brick-and-mortar market. The coefficient on HHI is significant for complaints related to credit reporting, debt collection, mortgages, payday or title loans, and student loans in Panel A, and for

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complaints related to debt collection in Panel B. 6.2 Population

The second robustness check I report relates to county population. The CFPB does not report the ZIP codes for areas with less than 20,000 residents. This practice serves to maintain the anonymity of complainants. Accordingly, I cannot map claims that come from low-population ZIP codes to counties. As a result, some low-population areas may have an under-reported number of CFPB complaints in my sample, which could influence the regression results presented above.

21

Journal Pre-proof Thus, I run two robustness tests to compensate for this issue. First, Table 10, regressions (1)-(3) reports the regressions that replicate Table 6, Panel A, but drop all counties with zero complaints. The sample size decreases by around 150 counties, but the results shown above remain for Small Bank Share, but do not remain for HHI. The second test, reported in Table 10, regressions (4)-(6) restricts the sample to counties with above-median populations.8 Once again, the coefficients on Small Bank Share remain negative and statistically significant at the 1% level, but the coefficients on HHI are negative but not

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significant. Altogether, the results reported in Table 10 show that the removal of low-population counties

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6.3 Alternative Definitions of Small Bank Share

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does not impact the main findings of the paper as they relate to Small Bank Share.

I also check that my findings are robust to alternative definitions of Small Bank Share. I include four

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definitions: 1) Small bank asset share where small banks are defined as those with $3 billion or less in

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assets, 2) Small bank asset share where small banks are defined as those with $5 billion or less in assets, 3) Small bank branches normalized by total bank branches, and 4) Small bank branches normalized by a

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county’s total population. Across all specifications and whether in the cross-section (Panel A) or panel

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setting (Panel B), I find that my main results are robust to these alternative definitions, as the coefficient

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on the small bank variable is negative and significant.

7. Conclusion

Since its establishment, the Consumer Financial Protection Bureau (CFPB) has accepted complaints from Americans regarding the quality of financial services. This practice provides a rich set of data that allows researchers to gain a better understanding of what influences customer satisfaction in the banking and finance industry. This paper is the first to comprehensively describe the CFPB data across all product

8

This result is robust to a Heckman correction, which accounts for the selection bias that may be a result of dropping all the below-median population counties.

22

Journal Pre-proof types. Moreover, an additional contribution of this paper is that it examines the role of competition, regulation, and small banks in providing consumer satisfaction. As complaints with the CFPB have increased over time, it is also apparent that consumers in markets that have a greater share of small banks and markets that are less competitive are less likely to file complaints to the CFPB, indicating increased customer satisfaction.

For markets with a large

presence of small banks, it is likely the case that the small banks’ community-focus and customized

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service contribute to greater customer satisfaction. In markets with high concentration, it may instead be

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the case that customers leave the market and deal less with banks that have high market power, or that

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consumers don’t feel that filing a complaint against these institutions will make a big enough difference. These findings add to the extant literature on small bank finance, and build upon earlier findings

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that show the benefits of small banks to small businesses and consumers (e.g., Brevoort and Wolken

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(2008); Berger, Cerqueiro, and Penas (2015); and Berger, Bouwman, and Kim (2017)). Notably, they make sense with respect to the Chicago Booth/Kellogg School Financial Trust Index Survey which

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discusses the fact that small banks are trusted more by consumers relative to large national banks.

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Moreover, results related to competition suggest that banks engage in a race to the bottom, thus

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sacrificing customer service along the way (Petersen and Rajan, 1995). The findings I present have clear policy implications. Regulators may wish to consider consumer outcomes as it relates to large banks’ services and customer relations. This implication is especially important considering the number of bank mergers over time and the increasing size of financial institutions. Additionally, regulators may also consider the regulatory burden that complaints place on small banks, as additional time and resource costs can redirect the focus of small banks away from their local customers. The CFPB data opens a path for new research, as it is yet unclear what specific types of banks are likely to be the subject of a complaint, and how a complaint impacts a bank’s performance and business

23

Journal Pre-proof model. Additionally, this research path is noteworthy to policy makers, as it may guide the creation of

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new policy that is focused on enhancing the experience of customers in the U.S. banking system.

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Journal Pre-proof Table 1 – Variable Definitions and Summary Statistics This table presents variable definitions and summary statistics for key variables used in the analysis below. The sample period is 2011-2017. Panel A provides variable definitions, and Panel B provides summary statistics. Panel A: Variable Definitions Dependent Variables: Complaints/10,000 Residents

Definition

Source

Number of Consumer Financial Protection Bureau (CFPB) complaints filed per 10,000 residents of a county.

CFPB Complaints: https://www.consumerfinance.gov/dataresearch/consumer-complaints/

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County Population: https://factfinder.census.gov/faces/tableservices /jsf/pages/productview.xhtml?src=bkmk https://www.consumerfinance.gov/dataresearch/consumer-complaints/

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Number of CFPB complaints filed per 10,000 residents, separated by product types.

Key Independent Variables: Definition

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Category Complaints/10,000 Residents

Source

Competitiveness of the county’s banking system, calculated using bank deposits.

FDIC Summary of Deposits

Small Bank Share

The share of small banks (less than $1 billion in assets) in a county. Calculated using bank assets.

FDIC Summary of Deposits

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Herfindahl-Hirschmann Index (HHI)

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Control Variables:

Definition

Source

Noninterest expense less amortization of intangible assets as a percent of net interest income plus noninterest income. This ratio measures the proportion of net operating revenues that are absorbed by overhead expenses, so that a lower value indicates greater efficiency. Calculated as the weighted average among banks in a county.

FDIC Statistics on Depository Institutions (SDI)

Tier 1 (core) capital as a percent of risk-weighted assets. Calculated as the weighted average among banks in a county.

FDIC Statistics on Depository Institutions (SDI)

Loans/Assets

Total loans in a county normalized by total bank assets in a county. For multi-county banks, loans and assets in a county are proportionally allocated by the share of the bank’s deposits located in a given county.

FDIC Statistics on Depository Institutions (SDI)

Noncurrent Loans/Assets

Total noncurrent loans in a county normalized by total bank assets in a county. For multi-county banks, loans and assets in a county are proportionally allocated by the share of the bank’s deposits located in a given county. This fraction is very small, so I scale by 1,000,000 so as to have sensible coefficients.

FDIC Statistics on Depository Institutions (SDI)

Capital Adequacy

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Bank Efficiency

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Journal Pre-proof County Demographic Variables: Definition

Source

County Minority Population

Percentage of nonwhite residents in a county.

2011 American Community Survey

County Poverty

Percentage of residents in a county who live below the poverty line.

2011 American Community Survey

County Education Level

Percentage of residents in a county who have not attained a high school diploma.

2011 American Community Survey

Panel B: Summary Statistics

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Std. Dev. 8.91 1.18 1.36 3.49 2.19 0.31 3.28 0.42 0.56 0.44 0.21 0.34 1.10 24.27 4.04 0.08 6.55 0.16 0.07 0.05

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Mean 10.92 1.07 1.30 2.35 2.18 0.11 2.98 0.26 0.40 0.28 0.31 0.53 1.29 67.59 14.47 0.03 1.22 0.16 0.16 0.12

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N 3,111 3,111 3,111 3,111 3,111 3,111 3,111 3,111 3,111 3,111 3,111 3,111 3,111 3,111 3,111 3,111 3,111 3,111 3,111 3,111

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Variable Comp./Capita Checking Comp./Capita Credit Card Comp./Capita Credit Report Comp./Capita Debt Comp./Capita Money Transfer Comp./Capita Mortgage Comp./Capita Payday Loan Comp./Capita Student Loan Comp./Capita Vehicle Loan Comp./Capita HHI Small Bank Share RSI Bank Efficiency Capital Adequacy Loans/Assets Noncurrent Loans/Assets (x1,000,000) County Minority Pop. County Poverty County Education

Min 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.05 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01

Max 110.51 13.68 13.78 79.19 45.52 11.81 67.09 5.58 8.37 6.02 1.00 1.00 3.00 827.98 101.36 0.77 153.49 0.89 0.54 0.43

30

Journal Pre-proof Table 2 – CFPB Complaints by Product Class and Year: This table presents a summary of complaints received by the Consumer Financial Protection Bureau (CFPB) from 2011-2017. I sort complaints by the underlying product type and provide figures by year. I organize the complaints according to the April 2017 product category update published by the CFPB. This update rearranges product categories and streamlines the reporting into fewer groups. The sample period runs from 2011-2017. Reporting to the CFPB began in late-2011.

Product Checking or Savings Account Credit Card or Prepaid Card Credit Reporting, Credit Repair Services, or Other Personal Consumer Reports Debt Collection Money Transfer, Virtual Currency, or Money Service Mortgage Payday Loan, Title Loan, or Personal Loan Student Loan Vehicle Loan or Lease Total

Number Percent of Total 98,961 10.54% 107,996 11.50%

2011 0 1,260

2012 12,212 15,353

2013 13,388 13,105

2014 14,662 14,270

2015 17,140 18,927

2016 21,848 22,152

2017 19,711 22,929

14,380

29,254

34,306

44,121

89,999

o r p

f o

213,933

22.78%

0

1,873

178,412 10,013 246,149 19,680 39,893 24,131

19.00% 1.07% 26.21% 2.10% 4.25% 2.57%

0 0 1,276 0 0 0

0 0 38,109 617 2,840 1,369

11,069 559 49,401 967 3,005 2,344

39,147 1,311 42,962 3,639 4,283 3,524

39,749 2,062 42,351 4,514 4,501 4,959

40,478 2,164 41,469 5,191 8,087 5,978

47,969 3,917 30,581 4,752 17,177 5,957

2,536

72,373

108,218

153,052

168,509

191,488

242,992

939,168

n r u

l a

r P

e

100.00%

Jo

31

Journal Pre-proof Table 3 – CFPB Complaints by Subproduct: This table provides a breakdown of complaints filed to the CFPB grouped by subproduct. Within each category of complaints, users have an option to apply a complaint to a certain subproduct within the category. The sample period is 2011-2017, and complaints to the CFPB began in late-2011. Number of Complaints % of Category Total 98,961 625 0.63% 3,780 3.82% 68,605 69.33% 19,896 20.10% 6,055 6.12% 107,996 101,098 93.61% 2,290 2.12% 469 0.43% 593 0.55% 2,859 2.65% 191 0.18% 459 0.43% 37 0.03% 213,933 372 0.17% 212,242 99.21% 1,319 0.62% 178,412 4,782 2.68% 34,539 19.36% 3,079 1.73% 36,137 20.25% 26,616 14.92% 5,776 3.24% 3,465 1.94% 55,296 30.99% 8,722 4.89% 10,013 326 3.26% 411 4.10% 3,457 34.53% 96 0.96% 3,428 34.24% 1,002 10.01% 202 2.02% 93 0.93% 166 1.66% 832 8.31%

Jo

ur

na

lP

re

-p

ro

of

Product Checking or Savings Account Cashing a Check Without an Account (Discontinued, April 2017) Certificate of Deposit (CD) Checking Account Other Banking Product or Service Savings Account Credit Card or Prepaid Card General-purpose Credit Card or Charge Card General-Purpose Prepaid Card Gift Card Government Benefit Card Student Prepaid Card Other Special Purpose Card (Discontinued, April 2017) Payroll Card Transit Card (Discontinued, April 2017) Credit Reporting, Credit Repair Services, or Other Personal Consumer Reports Credit Repair Services Credit Reporting Other Personal Consumer Reports Debt Collection Auto Debt Credit Card Debt Federal Student Loan Debt I don't know Medical Debt Mortgage Debt Private Student Loan Debt Other Debt Payday Loan Debt Money Transfer, Virtual Currency, or Money Service Check Cashing Service Debt Settlement Domestic (US) Money Transfer Foreign Currency Exchange International Money Transfer Mobile or Digital Wallet Money Order Refund Anticipation Check Traveler's Check or Cashier's Check Virtual Currency

32

Journal Pre-proof Table 3 – CFPB Complaints by Subproduct, Continued Mortgage Conventional Home Mortgage FHA Mortgage Home Equity Loan or Line of Credit (HELOC) Other Type of Mortgage Reverse Mortgage VA Mortgage Payday Loan, Title Loan, or Personal Loan Installment Loan Pawn Loan Payday Loan Personal Line of Credit Title Loan Student Loan Federal Student Loan Private Student Loan Vehicle Loan or Lease Lease Loan

246,149 106,326 27,101 12,917 91,432 2,409 5,964

43.20% 11.01% 5.25% 37.14% 0.98% 2.42%

9,837 107 6,488 2,415 833

49.98% 0.54% 32.97% 12.27% 4.23%

14,721 25,172

36.90% 63.10%

3,248 20,883

13.46% 86.54%

19,680

of

39,893

Jo

ur

na

lP

re

-p

ro

24,131

33

Journal Pre-proof Table 4 – CFPB Complaints by State: This table presents a summary of complaints to the CFPB by state, both in total and per capita. The sample period is 2011-2017, and complaints to the CFPB began in late-2011.

na

ur

of

Tot. Complaints/100,000 214.34 146.29 324.01 145.51 356.56 316.41 305.45 529.55 880.31 474.90 482.43 220.54 194.06 275.04 165.98 128.32 164.10 157.63 230.58 222.76 497.16 270.30 232.12 197.16 161.73 209.67 152.01 157.31 426.88 302.78 411.36 216.11 326.47 291.32 139.91 247.18 163.38 275.46 257.89 272.00 281.71 154.76 237.80 299.25 189.99 226.45 363.59 285.74 128.49 176.17 153.83 -

re

-p

ro

Total Complaints 10,245 1,039 20,711 4,243 132,833 15,913 10,917 4,755 5,297 89,287 46,736 3,000 3,042 35,290 10,762 3,909 4,682 6,840 10,453 2,959 28,704 17,698 22,942 10,457 4,799 12,557 1,504 2,873 11,528 3,986 36,166 4,450 63,264 27,779 941 28,516 6,129 10,553 32,758 2,863 13,030 1,260 15,091 75,248 5,251 1,417 29,091 19,215 2,381 10,019 867 3,576 9,342

lP

2010 Census Population 4,779,736 710,231 6,392,017 2,915,918 37,253,956 5,029,196 3,574,097 897,934 601,723 18,801,310 9,687,653 1,360,301 1,567,582 12,830,632 6,483,802 3,046,355 2,853,118 4,339,367 4,533,372 1,328,361 5,773,552 6,547,629 9,883,640 5,303,925 2,967,297 5,988,927 989,415 1,826,341 2,700,551 1,316,470 8,791,894 2,059,179 19,378,102 9,535,483 672,591 11,536,504 3,751,351 3,831,074 12,702,379 1,052,567 4,625,364 814,180 6,346,105 25,145,561 2,763,885 625,741 8,001,024 6,724,540 1,852,994 5,686,986 563,626 -

Jo

State Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware District of Columbia Florida Georgia Hawaii Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming Other (e.g. Guam, Puerto Rico) Not Provided

Per Capita Rank 32 47 11 48 9 12 13 2 1 5 4 30 35 20 38 51 39 42 27 29 3 22 26 34 41 33 46 43 6 14 7 31 10 16 49 24 40 19 23 21 18 44 25 15 36 28 8 17 50 37 45 -

34

Journal Pre-proof Table 5 – Other Characteristics of CFPB Complaints: This table presents sorted CFPB complaint data along additional characteristics. Panel A tabulates the number of complaints that received a timely response from the financial institution in question. Panel B tabulates the number of complaints received by military servicemembers and/or senior citizens. Finally, Panel C tabulates the number of times that a consumer disputed the response provided by the financial institution in question. This option was discontinued in April 2017.

Panel A: Proportion of Timely Responses from Financial Institutions

of

Yes No

Total Percentage 2011 2012 2013 2014 2015 2016 2017 912,906 97.20% 2,285 69,865 106,948 149,410 163,652 184,798 235,948 26,262 2.80% 251 2,508 1,270 2,642 4,857 6,690 7,044

-p

Total Percentage of total 2011 2012 66,681 7.10% 261 4,598 53,589 5.71% 91 2,194 10,448 1.11% 28 509

2013 8,537 4,281 1,122

2014 2015 2016 2017 12,809 14,639 16,056 9,781 7,947 9,123 9,934 20,019 1,809 2,167 2,328 2,485

re

Older American Servicemember Older American + Servicemember

ro

Panel B: Special Designations

ur

na

Total Percentage of total 2011 2012 2013 2014 2015 2016 2017 148,378 15.80% 573 16,427 22,594 29,711 34,283 34,788 10,002 620,170 66.03% 1,963 55,946 85,624 123,341 134,226 156,700 62,370 170,620 18.17% 0 0 0 0 0 0 170,620

Jo

Yes No N/A

lP

Panel C: Consumer Disputes to Financial Institution Responses (Discontinued in April 2017)

35

Journal Pre-proof

Panel A: Cross-Section Regressions (1)

na

ur

Loans/Assets

-2.561***

(-4.203)

(-3.926)

-1.800***

-1.636**

(-2.692)

(-2.411)

-0.00594

-0.00358

-0.00436

(-1.643)

(-1.051)

(-1.267)

lP

Small Bank Share

Capital Adequacy

(3)

-2.734***

re

HHI

Bank Efficiency

(2)

Complaints per 10,000

-p

Dependent Variable

ro

of

Table 6 – Complaints and County Banking Characteristics: This table presents OLS regression results. The dependent variable is Complaints per 10,000. In Panel A, this variable measures the total number of CFPB complaints filed per 10,000 residents of a county from 2012-2017 and in Panels B and C it measures the number of complaints filed per 10,000 residents for each county-year observation. The key independent variables are HHI and Small Bank Share. These variables respectively measure the county-level Herfindahl-Hirschmann Index based on deposits and the county-level small-bank share based on assets in either the cross-section (Panel A) or the time-series (Panels B and C). County-level banking controls include the county average Bank Efficiency Ratio and Capital Adequacy, and the aggregate Loans/Assets and Noncurrent Loans/Assets. County-level demographic controls include County Education, County Minority Population, and County Poverty. These variables represent the percentage of county residents who hold at least a bachelor’s degree, the percentage of nonwhite county residents, and the percentage of county residents who are below the poverty line, respectively. All dependent variables in Panel A are measured as of 2011 and all dependent variables in Panels B and C are lagged one year. ***, **, and * represent significance at the 1%, 5%, and 10% levels, respectively.

-0.0255

-0.0269

(-0.754)

(-0.842)

-1.365

-1.584

0.0313

(-0.809)

(-1.022)

(0.0222)

-0.0244

-0.0225

-0.0222

(-0.904)

(-0.904)

(-0.881)

0.235***

0.233***

0.223***

(10.39)

(10.58)

(10.29)

9.570***

9.422***

9.484***

(4.914)

(4.967)

(4.999)

-0.125***

-0.138***

-0.131***

(-2.915)

(-3.237)

(-3.020)

Observations

2,970

2,970

2,970

Adjusted R-squared

0.539

0.538

0.540

Fixed Effects

MSA

MSA

MSA

Clustering

MSA

MSA

MSA

Jo

Noncurrent Loans/Assets

-0.0455 (-1.457)

County Education County Minority Population County Poverty

36

Journal Pre-proof Panel B: Panel Regressions (1)

(2)

(3)

Dependent Variable Small Bank Share t-1

(5)

(6)

Complaints per 10,000 -0.366**

-0.558**

-0.326**

-0.490**

(-3.360)

(-3.853)

(-3.051)

(-3.593)

HHIt-1 Bank Efficiency t-1

(4)

-0.454**

-0.640**

-0.398**

-0.529**

(-3.507)

(-3.836)

(-3.208)

(-3.502)

-4.10e-05

0.000862

0.00101

(-0.0275)

(0.635)

(0.731)

-0.00448

0.000736

-7.79e-05

(-1.112)

(0.323)

(-0.0229)

f o

0.00114

0.00141

0.000160

(0.807)

(1.001)

(0.116)

0.000354

-0.000650

-0.00195

(0.152)

(-0.180)

(-0.702)

-0.156

-0.133

-0.164

o r p -0.169

-0.0732

-0.0201

(-1.175)

(-0.923)

(-1.111)

(-0.907)

(-0.759)

(-0.197)

0.00233

0.00838

0.00157

0.00753

0.00173

0.00740

(0.732)

(1.830)

(0.553)

(1.989)

(0.590)

(1.857)

0.0323***

0.0430***

0.0334***

0.0459***

0.0308***

0.0408***

(7.170)

(8.447)

(7.213)

(8.597)

(6.803)

2.209***

1.627**

2.303***

1.613**

2.237***

(4.131)

(3.279)

(4.134)

(3.299)

(4.141)

-0.0456***

-0.0198**

-0.0433**

-0.0213**

-0.0448***

(-3.351)

(-4.334)

(-3.050)

(-4.018)

(-3.263)

(-4.176)

Observations

18,641

18,641

18,641

18,641

18,641

18,641

Adjusted R-squared

0.288

0.257

0.288

0.256

0.289

0.259

Capital Adequacy t-1 Loans/Assets t-1 Noncurrent Loans/Assets t-1 County Education t-1

n r u

(8.667) County Minority Population t-1

1.580** (3.252)

County Poverty t-1

Jo

-0.0216**

l a

e

r P

Fixed Effects

MSA, Year State, Year, State x Year MSA, Year State, Year, State x Year MSA, Year State, Year, State x Year

Clustering

MSA, Year

MSA, Year

MSA, Year

MSA, Year

MSA, Year

MSA, Year

37

Journal Pre-proof Panel C: Panel Regressions with Urban Dummy (1)

(2)

-0.833*** (-6.051)

(3)

(5)

(6)

-0.396**

-0.811***

-0.357**

(-2.946)

(-6.004)

(-2.707)

-0.458**

-0.376*

-0.396**

(-2.520)

(-2.895)

0.00284

0.000827

(1.575)

(0.599)

-0.00125

-0.000520

Dependent Variable Small Bank Share t-1

Complaints per 10,000

HHIt-1 Bank Efficiency t-1 Capital Adequacy t-1 Loans/Assets t-1 Noncurrent Loans/Assets t-1 County Education t-1 County Minority Population t-1

(4)

-0.477** (-3.184)

(-3.186)

0.00312

0.00111

0.00118

7.67e-05

(1.675)

(0.777)

(0.739)

(0.0531)

-0.00189

-0.000971

-0.0105*

(-0.689)

(-0.287)

(-2.049)

-0.239

-0.134

-0.513

(-1.708)

(-0.965)

(-1.645)

o r p

0.00701

0.00658

0.00495

(1.625)

(1.766)

0.0379***

0.0354***

(7.208)

(6.189)

2.105**

rn

(3.738)

l a

1.976**

(-1.023)

e

r P (1.563)

-0.00364

f o

(-0.463)

(-0.163)

-0.156

-0.150

-0.0490

(-0.954)

(-1.367)

(-0.453)

0.00589

0.00619

0.00597

(1.855)

(1.585)

(1.775)

0.0443***

0.0370***

0.0373***

0.0342***

(8.188)

(5.969)

(7.037)

(6.144)

2.342***

2.032**

2.150**

2.013**

(3.817)

(4.152)

(3.849)

(3.835)

(3.863)

-0.0388***

-0.0216*

-0.0369**

-0.0253**

-0.0386***

(-4.064)

(-2.371)

(-3.912)

(-2.820)

(-4.047)

0.627***

0.462***

0.749***

0.481***

0.585***

0.429**

(4.459)

(4.086)

(5.064)

(4.300)

(4.375)

(3.924)

Observations

18,641

18,641

18,641

18,641

18,641

18,641

Adjusted R-squared

0.203

0.262

0.193

0.262

0.204

0.263

Year

State, Year, State x Year

Year

State, Year, State x Year

Year

State, Year, State x Year

MSA, Year

MSA, Year

MSA, Year

MSA, Year

MSA, Year

MSA, Year

County Poverty t-1

u o

-0.0256** (-2.815)

Urban Dummy

Fixed Effects Clustering

J

38

Journal Pre-proof

ro

of

Table 7 – Complaints Sorted by Small Bank Share and HHI: This table presents OLS regression results. The dependent variable is Complaints per 10,000, which measures the total number of CFPB complaints filed per 10,000 residents of a county from 2012-2017. Panel A presents sorted summary statistics and Panel B reports regression results. In Panel B, Regressions (1) and (2) are sorted by aboveor below-median county HHI and regressions (3) and (4) are sorted by above- or below-median county Small Bank Share. The key independent variables are HHI and Small Bank Share. These variables respectively measure the county-level Herfindahl-Hirschmann Index based on deposits and the countylevel small-bank share based on assets. County-level banking controls include the county average Bank Efficiency Ratio and Capital Adequacy, and the aggregate Loans/Assets and Noncurrent Loans/Assets. County-level demographic controls include County Education, County Minority Population, and County Poverty. These variables represent the percentage of county residents who hold at least a bachelor’s degree, the percentage of nonwhite county residents, and the percentage of county residents who are below the poverty line, respectively. All dependent variables are measured as of 2011. MSA fixed effects are included in all regressions, and all standard errors are clustered by MSA. ***, **, and * represent significance at the 1%, 5%, and 10% levels, respectively. Panel A: Summary Statistics

7.62 14.23

6.58 9.68

-p

Std. Dev. Complaints/10,000 8.01 9.11

re

High HHI Low HHI

lP

High Small Bank Share Low Small Bank Share

Jo

Small Bank Share

ur

Dependent Variable

na

Panel B: Regression Results

HHI

Mean Complaints/10,000 8.5 13.35

(1)

(2)

(3)

(4)

Complaints per 10,000

High HHI

Low HHI

-1.243

-2.018**

(-1.601)

(-2.176)

High SB Share Low SB Share

-2.457***

-3.811***

(-3.022)

(-3.184)

County Banking Controls

Yes

Yes

Yes

Yes

County Demographic Controls

Yes

Yes

Yes

Yes

Observations

1,488

1,393

1,506

1,406

Adjusted R-squared

0.303

0.690

0.203

0.630

Fixed Effects

MSA

MSA

MSA

MSA

Clustering

MSA

MSA

MSA

MSA

39

Journal Pre-proof

Panel A: County Minority Population (2)

(3)

(5)

(6)

High Nonwhite

Low Nonwhite

-3.899**

-2.487***

Complaints per 10,000 High Nonwhite

Low Nonwhite

-4.116**

-2.635***

(-2.413)

(-3.082)

Yes

Yes

County Demographic Controls

No

No

900 0.556

Fixed Effects

MSA

Clustering

MSA

-1.740**

-2.270*

-1.585**

(-1.917)

(-2.460)

(-1.811)

(-2.272)

Yes

Yes

Yes

Yes

No

No

No

No

(-2.434)

(-2.821)

996

900

996

900

996

0.235

0.554

0.233

0.558

0.238

MSA

MSA

MSA

MSA

MSA

MSA

MSA

MSA

MSA

MSA

Jo

ur

na

Adjusted R-squared

lP

County Banking Controls

Low Nonwhite

-2.514*

re

Small Bank Share

High Nonwhite

-p

HHI

Observations

(4)

ro

(1) Dependent Variable:

of

Table 8 – Complaints Sorted by County Demographics: This table presents OLS regression results. The dependent variable is Complaints per 10,000, which measures the total number of CFPB complaints filed per 10,000 residents of a county from 2012-2017. Panel A sorts counties by high or low percentages of minority residents. Panel B sorts counties by a high or low percentage of the population below the poverty line. Panel C sorts counties by the percentage of residents who have or have not completed a high school education. The key independent variables are HHI and Small Bank Share. These variables respectively measure the county-level Herfindahl-Hirschmann Index based on deposits and the countylevel small-bank share based on assets. County-level banking controls include the county average Bank Efficiency Ratio and Capital Adequacy, and the aggregate Loans/Assets and Noncurrent Loans/Assets. All dependent variables are measured as of 2011. MSA fixed effects are included in all regressions, and all standard errors are clustered by MSA. ***, **, and * represent significance at the 1%, 5%, and 10% levels, respectively.

40

Journal Pre-proof

Table 8 – Complaints Sorted by County Demographics, Continued Panel B: County Poverty (1)

(2)

(3)

Dependent Variable:

High Poverty

Low Poverty -4.592***

-4.604***

-2.361***

(-2.842)

(-2.783)

(-2.744)

(-2.771)

-3.871**

-1.108

-3.862***

(-1.851)

(-2.601)

(-1.562)

(-2.683)

Yes

Yes

Yes

No

No

Yes

Yes

Yes

No

No

No

0.548

Fixed Effects

MSA

MSA

Clustering

MSA

MSA

Panel C: County Education Level

High Education -4.616***

Low Education

County Banking Controls

949

967

0.548

0.385

0.553

MSA

MSA

MSA

MSA

MSA

MSA

MSA

MSA

(4)

(5)

(6)

High Education

Low Education

-4.087***

-2.089**

(-2.907)

(-2.198)

(3)

Complaints per 10,000 High Education

Low Education

(-2.291) -4.152***

-0.833

-3.783***

-0.672

(-3.219)

(-0.974)

(-2.856)

(-0.764)

Yes

Yes

Yes

Yes

Yes

Yes

No

No

No

No

No

No

Jo

County Demographic Controls

ur

(-3.406) Small Bank Share

967

0.381

-2.169**

na

HHI

(2)

lP

(1) Dependent Variable:

949

No

ro

967

0.384

-p

949

Adjusted R-squared

re

Observations

Low Poverty

-1.342*

County Demographic Controls

Clustering

High Poverty

-2.543***

County Banking Controls

Fixed Effects

(6)

Low Poverty

Small Bank Share

Adjusted R-squared

(5)

High Poverty

of

HHI

Observations

(4)

Complaints per 10,000

924

974

924

974

924

974

0.546

0.228

0.547

0.226

0.552

0.228

MSA

MSA

MSA

MSA

MSA

MSA

MSA

MSA

MSA

MSA

MSA

MSA

41

Journal Pre-proof Table 9 – Regressions by Complaint Type : This table presents OLS regression results. Panel A presents cross-section regressions and Panel B presents panel regressions. The dependent variables are Complaints per 10,000, sorted by the complaint’s product type. The key independent variables are HHI and Small Bank Share. These variables respectively measure the county-level Herfindahl-Hirschmann Index based on deposits and the county-level small-bank share based on assets. County-level banking controls and demographic controls are included as in Table 6. In Panel A. all dependent variables are measured as of 2011, and in Panel B, all dependent variables are lagged one year. ***, **, and * represent significance at the 1%, 5%, and 10% levels, respectively. Panel A: Cross-Section Regressions Dependent Variable - (Complaint Types/10,000): Small Bank Share HHI

(1)

(2)

(3)

(4)

(5)

Checking or Savings Acct.

Credit or Prepaid Card

Credit Reporting

Debt Collection

Money Transfer or Virtual Money

-0.170*

-0.141

0.0537

-0.606**

-0.0263

(-1.702)

(-1.241)

(0.178)

(-2.532)

(-1.227)

-0.206

-0.713**

-0.476*

0.00338

(-1.279)

(-2.168)

(-1.883)

(0.0836)

County Banking Controls

Yes

Yes

Yes

Yes

County Demographic Controls

Yes

Yes

Yes

Observations

2,970

2,970

2,970

Adjusted R-squared

0.403

0.255

0.163

Fixed Effects

MSA

MSA

MSA

Clustering

MSA

MSA

MSA

Dependent Variable - (Complaint Types/10,000): Small Bank Share

Mortgage

Payday Loan or Title Loan

Student Loan

Vehicle Loan

-0.505*

-0.0819**

-0.0840

-0.0753

(-1.880)

(-2.041)

(-1.616)

(-1.047)

-0.552*

-0.165**

-0.226***

-0.0629

(-2.168)

(-3.058)

(-0.978)

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes 2,970

2,970

2,970

2,970

2,970

-0.037

0.566

0.040

0.180

0.090

MSA

MSA

MSA

MSA

MSA

MSA

MSA

MSA

MSA

MSA

MSA

MSA

(3)

(4)

(6)

(8)

(9)

Student Loan

Vehicle Loan

a n

(1) Checking or Savings Acct.

(2) Credit or Prepaid Card

Credit Reporting

Debt Collection

(5) Money Transfer or Virtual Money

Mortgage

(7) Payday Loan or Title Loan

-0.0354**

-0.0171

-0.0512

-0.107*

-0.000843

-0.0802*

-0.0163*

-0.00723

-0.0113

(-0.962)

(-1.361)

(-2.384)

(-0.143)

(-2.347)

(-2.194)

(-0.860)

(-0.975)

(-3.041) HHI

Jo

(9)

0.183

r u

Panel B: Panel Regressions

2,970

(8)

(-1.956)

re

P l Yes

(7)

ro

-p

-0.163 (-1.330)

f o

(6)

-0.0302

-0.105

-0.0881***

-0.000234

-0.0832

-0.0251

-0.0372

-0.00901

(-0.919)

-0.0198

(-1.354)

(-1.482)

(-9.320)

(-0.142)

(-1.217)

(-1.810)

(-1.296)

(-0.790)

County Banking Controls

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

County Demographic Controls

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Observations

18,641

18,641

18,641

18,641

18,641

18,641

18,641

18,641

18,641

Adjusted R-squared

0.127

0.067

0.073

0.140

-0.007

0.319

0.019

0.049

0.016

Fixed Effects

MSA, Year

MSA, Year

MSA, Year

MSA, Year

MSA, Year

MSA, Year

MSA, Year

MSA, Year

MSA, Year

Clustering

MSA, Year

MSA, Year

MSA, Year

MSA, Year

MSA, Year

MSA, Year

MSA, Year

MSA, Year

MSA, Year

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Journal Pre-proof Table 10 – Robustness – Dropping Counties with No Complaints or Counties with Low Population: This table presents OLS regression results. The dependent variable is Complaints per 10,000, which measures the total number of CFPB complaints filed per 10,000 residents of a county from 2012-2017. The key independent variables are HHI, and Small Bank Share. These variables respectively measure the county-level Herfindahl-Hirschmann Index based on deposits and the county-level small-bank share based on assets. County-level banking controls and demographic controls are included as in Table 6. All dependent variables are measured as of 2011. Regressions (1)-(3) drop any county that has no complaints during the sample period, and regressions (4)-(6) drop any county that is below the median population. MSA fixed effects are included in all regressions, and all standard errors are clustered by MSA. ***, **, and * represent significance at the 1%, 5%, and 10% levels, respectively. (2)

(3)

Dropping Counties with No Complaints -0.0730 (-0.0975) -1.749***

0.0397

-0.0730

(0.0536)

(-0.0975)

-1.751***

Yes

County Demographic Controls

Yes

Observations

2,805 0.558

(-2.716)

(-2.711)

(-2.716)

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

2,805

1,402

1,402

1,402

2,805 0.560

0.559

0.714

0.717

0.717

MSA

MSA

MSA

MSA

MSA

MSA

MSA

MSA

MSA

MSA

MSA

MSA

Jo

ur

na

Adjusted R-squared

(0.0536)

Yes

lP

County Banking Controls

re

(-2.711)

0.0397 -1.749*** -1.751***

-p

Small Bank Share

Clustering

(6)

Complaints per 10,000

HHI

Fixed Effects

(5)

Dropping Below Median Population Counties

ro

Dependent Variable:

(4)

of

(1)

43

Journal Pre-proof Table 11 – Robustness – Alternative Bank Sizes: This table presents OLS regression results. The dependent variable is Complaints per 10,000. In Panel A, this variable measures the total number of CFPB complaints filed per 10,000 residents of a county from 2012-2017 and in Panel B it measures the number of complaints filed per 10,000 residents for each county-year observation. In this table, the key independent variables measure small bank presence in a different way. In regressions (1) and (2) a small bank is defined as one that has total assets less than $3 billion and in regressions (3) and (4) a small bank is defined as one that has total assets less than $5 billion. In regressions (5) and (6) I proxy for small bank presence by normalizing the number of small bank branches in a county by the total number of bank branches in the county. Finally, in regressions (7) and (8), I proxy for small bank access by normalizing the number of small bank branches by a county’s total population. ***, **, and * represent significance at the 1%, 5%, and 10% levels, respectively. Panel A: Cross-Section Regressions (1)

(2)

(3)

-5.218***

-4.824***

(-5.983)

(-5.757)

Small Bank Share (<$5 Billion)

p e

r P -5.469*** (-6.174)

Small Bank Branches/Total Branches

l a

Small Bank Branches/County Population

County Banking Controls

Fixed Effects Clustering

(6)

(7)

(8)

-2,770***

-2,576***

(-3.538)

(-3.618)

J

-5.060*** (-5.919)

-3.885***

-3.573***

(-4.087)

(-3.852)

rn

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

3,110 0.475

3,110 0.478

3,110 0.475

3,110 0.478

3,110 0.468

3,110 0.472

3,110 0.464

3,110 0.469

MSA

MSA

MSA

MSA

MSA

MSA

MSA

MSA

MSA

MSA

MSA

MSA

MSA

MSA

MSA

MSA

u o

County Demographic Controls

Adjusted R-squared

(5)

Complaints per 10,000

Small Bank Share (<$3 Billion)

Observations

ro

(4)

Dependent Variable:

f o

Yes

44

Journal Pre-proof Table 11 – Robustness – Alternative Bank Sizes, Continued. Panel B: Panel Regressions (1)

(2)

(3)

(4)

Dependent Variable: Small Bank Share (<$3 Billion)

(5)

(6)

(7)

(8)

-298.4**

-469.4**

(-3.476)

(-3.918)

Complaints per 10,000 -0.519**

-0.785**

(-3.381)

(-3.917)

Small Bank Share (<$5 Billion)

-0.589***

-0.871***

(-4.438)

(-4.672)

Small Bank Branches/Total Branches

-0.420**

f o

ro

(-3.212) Small Bank Branches/County Population

County Banking Controls

Yes

Yes

Yes

County Demographic Controls

Yes

Yes

Yes

Observations

18,641

18,641

18,641

Adjusted R-squared

0.289

0.260

l a 0.290

p e

-0.686** (-3.889)

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

18,641

18,641

18,641

18,641

18,641

0.261

0.289

0.258

0.288

0.255

r P

Fixed Effects

MSA, Year State, Year, State x Year MSA, Year State, Year, State x Year MSA, Year State, Year, State x Year MSA, Year State, Year, State x Year

Clustering

MSA, Year

MSA, Year

n r u

MSA, Year

MSA, Year

MSA, Year

MSA, Year

MSA, Year

MSA, Year

Jo

45

Journal Pre-proof Figure 1 – Sample CFPB Complaint: This figure shows one example of a complaint registered in the CFPB’s complaint database. This complaint relates to a mortgage that a consumer has with U.S. Bancorp. Within this complaint, the consumer provides information on their geographic location, the product type, and a narrative to describe the problem in detail.

f o

l a

o r p

r P

e

n r u

Jo

46

Journal Pre-proof

Jo

ur

na

lP

re

-p

ro

of

Figure 2 – CFPB Complaints by State: This figure presents a visualization of complaints to the CFPB per 100,000 residents by state. The sample period is 2011-2017, and complaints to the CFPB began in late-2011.

47