The Changing Role of Small Banks. in Small Business Lending

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1 The Changing Role of Small Banks in Small Business Lending Lamont Black Micha l Kowalik January 2016 Abstract This paper studies how competition from large banks affects small banks lending to small businesses. We model small banks as having a greater ability to monitor their borrowers while large banks have a lower cost of lending. In equilibrium, an increase in large bank competition makes small banks especially valuable to borrowers of intermediate quality. We then analyze bank data by loan size and find results consistent with this prediction. Small, single-market banks increase the share of their small business loans in the intermediate size category ($250,000 to $1 million) following large bank market entry. Results are stronger at smaller distances and robust to instrumenting for large bank entry. These findings suggest that small banks will continue to serve the intermediate segment of the small business loan market even as large bank competition increases. Keywords: commercial banking, competition, small business lending. JEL: G21, G28. We would like to thank Jose Berrospide, Matthieu Chavaz, Ralf Meisenzahl, Ali Ozdagli, Joe Peek, Christina Wang and seminar participants at the Bank of England and the Federal Reserve Bank of Boston for useful comments as well as Andy Barton and Laura Fried for excellent research assistance. The views expressed herein are those of the authors and do not necessarily represent those of the Federal Reserve Bank of Boston or the Federal Reserve System. DePaul University, 1 E Jackson Blvd, Chicago, IL 60601, tel.: , lblack6@depaul.edu Federal Reserve Bank of Boston, 600 Atlantic Ave, Boston, MA 02120, tel.: , michal.kowalik@bos.frb.org 1

2 1 Introduction In recent years, small banks in the U.S. have been exposed to increasing competition from large banks for small business borrowers. This increased competition has been facilitated by low cost lending technologies and improvements in information sharing, which have eroded informational advantages of small banks. Because small banks are still significant lenders to U.S. small businesses, an important question arises as to how competition from large banks will affect the type of lending provided by small banks. This paper studies these questions with a simple model of bank competition and an empirical analysis of bank data. In our theoretical model, small and large banks compete for entrepreneurs who differ in their productivity. The entrepreneurs productivity is observable to both small and large banks. However, the small and large banks differ along two dimensions. The advantage of small banks is their proximity to entrepreneurs, which allows them to better evaluate the risk of projects undertaken by entrepreneurs. 1 The disadvantage of small banks is their higher cost of lending (e.g., screening and monitoring costs). Large banks have a lower cost of lending, but cannot observe risk taken by their borrowers. Using our model, we study the effect of competition from large banks on the composition of small banks lending. Despite the cheaper loans offered by large banks, small banks attract entrepreneurs of intermediate productivity in equilibrium. These entrepreneurs borrow from small banks because small banks evaluate their projects and offer tailored loan rates. In contrast, high-productivity and low-productivity entrepreneurs choose the low-cost large banks. High-productivity entrepreneurs realize that small banks ability to monitor projects does not add much to the value of their projects, so they choose cheaper loans from the large banks. Low-quality entrepreneurs prefer to undertake riskier projects using funds from any bank that will lend to them, so they choose cheaper loans from the large banks as well. Therefore, our theoretical model yields the following prediction: an increase in competition from large banks results in small banks concentrating in 1 This builds on early theoretical literature on competition between informed and uninformed lenders (Sharpe (1990), Rajan (1992), and von Thadden (2004)), which examines the emergence of relationship lending when a lending bank obtains an informational advantage about its borrower over competing lenders. The model closest to ours is Diamond (1991), who studies borrower s choice between a monitoring and an arm-length lender in a model of reputation building. 2

3 lending to intermediate-productivity entrepreneurs and reducing their share of lending to highand low-productivity entrepreneurs. We test the above prediction of our model using Call Report data for small, single-market urban banks from 1994 to To analyze the changes in the nature of small banks small business lending, we study the shares of commercial and industrial (C&I) loans of different sizes in banks C&I loan portfolios. Although the data provides information on loan size rather than borrowers quality, the loan size categories can be used as a proxy for entrepreneurs productivity (as predicted by our theoretical model). In particular, we look at changes in these loan portfolio shares following changes in large bank competition. Consistent with the model, we find that small, single-market banks increase the share of their portfolio in loans between $250,000 and $1 million when additional large banks enter the small bank s market. Loans less than $250,000 are unchanged and the share of loans greater than $1 million declines. Additional analysis shows that the results are even stronger when large banks enter at a closer proximity (smaller distance) to a small bank. The empirical analysis also addresses the endogeneity of large bank entry. Following Goetz, Laeven, and Levine (2013), we instrument for large bank entry using interstate branch deregulation. The results point to a significant relationship between interstate branch deregulation and large bank entry, as expected. With this instrument, we re-estimate the response of small, single-market banks. The main result an increase in the share of C&I loans between $250,000 and $1 million is robust. The paper is organized as follows. Section 2 provides a basic theoretical model of large and small bank competition and develops theoretical predictions. Section 3 describes the data and descriptive statistics for the empirical analysis. Section 4 lays out the empirical methodology and results. Section 5 concludes. 3

4 2 Model of bank competition There are 3 dates, t = 1, 2, 3. The economy is populated by an entrepreneur and a number of banks. 2 The entrepreneur is risk-neutral and owns a project, but has no means to finance it. The exact payoff structure of the entrepreneur s project will be determined by her productivity and her risk choice (described later). To finance the project, the entrepreneur can borrow from a number of banks. There are two types, small and large banks, with at least two banks of each type. This ensures perfect competition not only between different but also the same type of banks. Small and large banks observe the entrepreneur s productivity, but otherwise they differ from each other in the following way. First, the small banks observe the entrepreneur s risk choice unlike the large banks. Second, the large banks have a lower marginal cost of lending than the small banks, ρ L > ρ S. These assumptions capture the idea that the small banks possess better information about their borrowers than the large banks, but acquiring this information is expensive. We model this idea by assuming that the small banks loan officers acquire this superior information by monitoring their borrowers actions. This monitoring is costly because the small banks have to pay their loan officers. Large banks, who do not employ such loan officers, have a lower cost of lending, but cannot acquire this additional information and, therefore, are subject to the moral hazard. 3 The timing is as follows. At date 1 each bank offers a loan contract (y; r) consisting of a loan size y and a loan rate r to an entrepreneur. The small banks can condition their contracts on the entrepreneur s risk choice at t=2. The entrepreneur chooses an offer from one bank and undertakes a project. At date 2 the entrepreneur can shift the risk of her project if she borrowed from the large bank, and chooses the project s risk corresponding to the contract she chose from the small bank at t=1. At date 3 the project returns are realized. The entrepreneur repays ry if the realized project return is high enough, and defaults otherwise. We model risk-shifting at date 2 as the entrepreneur s choice between a safe and risky project. 2 The model is a simplified and modified version of Kowalik (2014). 3 Because this is not focus of the paper we do not model the reasons behind small and large banks technology choices. Such a choice might follow from two reasons. First, the large banks are better able to diversify idiosyncratic risks and therefore they do not have to rely heavily on additional monitoring of entrepreneurs. Second, information acquired by the loan officer might be of soft nature and therefore not easily transferable within a large organization (Stein et al). 4

5 The safe project pays a return A log y (A > 1) with certainty, where A is a parameter describing the entrepreneur s productivity. The risky project pays δa log y with probability p (0; 1), and 0 otherwise. The risky project represents a change in the entrepreneur s business strategy, which makes the entrepreneur s business riskier but more profitable in case of success, δ > 1. In addition, the moral hazard problem arises, because the choice of the risky project leads in expected terms to a less profitable business, pδ < 1. We solve the model by deriving the small banks offers first. Because the small banks observe the borrower s risk choice at t=2, they offer two loan contracts, each conditioned on the risk the entrepreneur takes at t=2. Because the safe and risky projects returns only differ in p and δ, we can simply find the small banks optimal offer for the risky project first, and then find the optimal offer for the safe project by setting p = δ = 1. Because the banks are competitive, they offer loan contracts that maximize the entrepreneur s expected payoff at t=3 as long as the banks themselves break even. Formally, for the risky project the small banks offer y and r such that the entrepreneur s expected payoff at t=3 from the risky project is maximized, max y,r p (δa log y ry), s.t. the small banks participation constraint, pry ρ S y, or r ρ S. Deriving the entrepreneur s p expected payoff with respect to y and solving the emerging first order condition for y delivers that the optimal loan amount is y = δa r. Because the entrepreneur s expected payoff decreases in the loan rate r, the optimal loan rate is the one for which the small banks break even, r = ρ S. Hence, p A the small banks offer the entrepreneur a contract ρ S ; ρ S for the safe project and a contract for the risky project. pδa ρ S ; ρ S p The competitive large banks offers for a project of given risk differ from the offers of the small banks only due to a different cost of lending (we need to substitute ρs with ρ L ). However, the large banks do not observe the risk the entrepreneur takes at t=2. This implies that they can only offer only one contract. Moreover, when offering the contract corresponding to the safe project, the large have to make sure that the entrepreneur does not shift risk under this contract at t=2. A Hence, the large banks will offer the entrepreneur a contract ρ L ; ρ L as long as the entrepreneur 5

6 has no incentive to shift risk under this loan contract. Otherwise, the large banks will offer the pδa entrepreneur the contract ρ L ; ρ L. Formally, the entrepreneur does not shift risk under the p A contract ρ L ; ρ L if her payoff from the safe project is not lower than her payoff from the risky project under this contract: A log A ( A ρ L p δa log A ) A ρ L ρ L ρ L ρ L ρ L or A ρ L e 1 p 1 pδ AL. Hence, the large banks offer an entrepreneur with observed productivity A < A L the contract pδa ρ L ; ρ L A, whereas they offer an etnrepreneur with A A p L the contract ρ L ; ρ L. Now we can study the entrepreneur s choice of the offers. An entrepreneur with a given productivity A gets the following offers. The large banks offer her the loan contract A ρ L ; ρ L if her pδa productivity A A L or the contract ρ L ; ρ L if her productivity A < A p L. The small banks offer A the entrepreneur, regardless of her productivity A, two contracts: ρ S ; ρ S if she takes the safer project and if she takes the riskier project. Hence, the entrepreneur with productivity pδa ρ S ; ρ S p A chooses the contract that delivers her the highest payoff. The entrepreneur with productivity A A L chooses an offer from a large bank and takes a safer project. As shown above such an entrepreneur does not want to shift risk at t=2 and prefers the safe project when borrowing from a large bank. The loan offer from a large bank delivers higher payoff than the small banks offer for the safe project, because the small banks have a higher cost of lending and therefore their offered loan rates are higher and the offered loan amounts lower than those of the large banks. The entrepreneur with productivity A < A L faces a more complex trade-off. On the one hand, borrowing from a small bank is more costly than from a large bank, because the small banks have a higher cost of lending. On the other hand, when borrowing from a large bank, the entrepreneur has to take the risky project to compensate for the high loan rate that a large bank offers to protect itself against the moral hazard. When borrowing from a small bank, the entrepreneur prefers to 6

7 take the safe project, because the safe project delivers higher payoff than the risky project. To find out, which offer the entrepreneur takes, we compare her payoff from borrowing from a small bank and taking the safer project with the payoff from borrowing from a large bank and undertaking the risky project: A log A ( A ρ S p δa log pδa ρ L ρ S ρ S ρ L p ) pδa ρ L or pδ 1+ A e 1 pδ (log(pδ) log(ρ L))+ 1 1 pδ log(ρs) A S Hence, an entrepreneur with productivity A [A S ; A L ) takes an offer from a small bank and the safe project, and the entrepreneur with productivity A < A S chooses the large bank s offer and the risky project. 4 This result highlights the advantage of small banks superior information for the borrower. Because the small banks observe risk, the entrepreneur can choose the more profitable safe project. The safe project is not optimal when borrowing from the large bank, which protects itself from the moral hazard. For an entrepreneur with relatively high productivity the possibility to undertake the safe project is so valuable that she is willing to borrow from a small bank, whose lending cost is higher than the large banks cost. All other entrepreneurs A < A S prefer to take an offer from a large bank and run a riskier project, because their low productivity is making the safer project less profitable for them in the first place. The model equilibrium yields the following proposition. Proposition: High and low productivity entrepreneurs choose to borrow from large banks, which means that large banks concentrate on the largest and smallest loans. Intermediate productivity borrowers borrow from small banks, which focus on the intermediate-sized loans. 4 We assume here that the interval [A S ; A L ) is not empty, which holds when the small banks lending cost is too high. 7

8 3 Empirical hypothesis, data, and descriptive statistics 3.1 Hypothesis The theoretical model developed in the previous section allows us to formulate our main empirical hypothesis. Hypothesis: After a large bank enters a new market, the small banks in this market increase the concentration of their C&I lending in the middle-sized loans and decrease in the smallest and largest. We use the size of C&I loans as a proxy for the borrowers quality, because we cannot observe the borrowers quality in the data available to us. There is a compelling reason for using loan size as a proxy for borrowers quality: it is intuitive that a bank will be ready to lend a higher amount to a borrower whose quality is higher. 3.2 Data and descriptive statistics To test the hypothesis, we use data on bank balance sheets from 1994 to The Call Report data contains information on banks small business lending in every June 30 filing. Banks report the number and dollar amount of loans with small loan amounts. Specifically, we start with the following categories in the data: the dollar amount of a bank s loans with an amount less than $100,000, loans with an amount more than $100,000 but less than $250,000, and loans more than $250,000 but less than $1 million. We combine the smallest two loan categories (to create loans less than $250,000) and construct a category of loans greater than $1 million. 5 Although large banks lend across many markets, some small banks lend in specific markets. In particular, we focus on the small banks (less than $1 billion in assets) that are likely lending in a single urban market. The FDIC s Summary of Deposit (SoD) data contains information on bank deposits by market. The SoD data measures every insured bank s deposits in rural and urban markets as of June 30th each year. We use this data for two key purposes. First, we focus on single-market banks in 5 This is the residual of total C&I loans less the small business loan categories. 8

9 metropolitan statistical areas (MSAs). We define single-market banks as banks that only have deposits in a single metropolitan market. For these banks, we assume that their small business lending is done in the same market where they collect deposits. Second, we use the data to measure changes in large bank competition. Large banks are measured as banks with more than $10 billion in assets. There are a number of ways that competition from large banks could be measured. We begin with large bank entry as measured by the entrance of a large bank into a metropolitan area. Using the SoD, we identify large bank entry as an increase in the number of large banks collecting deposits in the market. This entry could either be from acquisition (non-organic) or new branches (organic). We use organic entry as our proxy of an increase in large bank competition. The reason is that this proxy captures more closely an increase of competition. An acquisition of a local bank does not necessarily increase a competitive pressure, since the acquired bank has been already competing with other small banks in the market, and the acquiring large bank might not change the acquired bank s lending technology, which is the main channel through which the large bank competition operates in our model. Tables 1 and 2 show descriptive statistics for small, single-market, urban banks with entry of large banks and without entry, respectively. These tables show information on our bank and market controls as well. The bank controls include the small bank s size (assets), leverage, depositsto-assets ratio, and loans-to-asset ratio. The market controls include unemployment rate and the number of individuals in the labor force. 4 Empirical methodology and results In a panel regression on the annual data, we analyze a sample of small, single-market, urban banks from 1994 to Each bank s share of C&I loans in the four loan categories (less than $100,000, between $100,000 and $250,000, between $250,000 and $1 million, and greater than $1 million) are regressed on a set of bank and market controls as well as our key explanatory variable of large bank competition. All regressions include bank and year fixed effects. 9

10 Formally, we test our hypothesis using the following equation: Loanshare it = α + β Largebankcompetition it + γx imt + µ i + t + ε imt, where Loan share is the share of a given loan size range in bank i s C&I portfolio in period t, Large bank competition is our proxy of competition from large banks that a bank i faces in period t and equal to the number of entering large banks into the market of bank i, µ i and t are bank and year fixed effects, and X imt are control variables for bank i and market m in period t. We also cluster the standard errors at the market level to control for potential correlation in errors between banks in a given market. Table 3 shows the results for the basic regression without market controls. Our key finding is that the share of a small bank s loans in the intermediate small business loan category (between $250,000 and $1 million) significantly increases with an increase in the number of entering large banks. In contrast, the share of loans in the largest category (greater than $1 million) significantly decreases with an increase in the number of entering large banks. The share of loans in the smallest category (less than $250,000) is unchanged. Table 4 shows that the results are robust to including market controls. Table 5 adds to our results by using distance as an alternative measure of large bank entry. Rather than simply using a large bank entry dummy, we measure the intensity of competition as the minimum distance of an entering large bank branch to one of a small bank s branches. These results support the baseline results by showing that entry with a smaller distance results in a larger increase in small banks share of lending in the intermediate small business loan category (between $250,000 and $1 million). The main concern with our results is that large bank entry is not random and could reflect changes in local demand for small business loans. To ease the concerns about this source of potential endogeneity of the large bank entry, we pursue an IV strategy similar to the one proposed in Goetz, Laeven and Levine (2013). As an instrument for the large bank entry, we use the years and yearssquared since interstate branching deregulation for the state. The interstate branch deregulation is a good instrument for large bank entry for two reasons. First, we can expect that the interstate 10

11 deregulation would lead to an increased entry by large banks into states that allowed for interstate branching sooner. Second, we can expect that the interstate branching should have nothing to do with the composition of the C&I portfolio of small banks. Panel A of Table 6 presents the results from the IV first-stage regressions. The regressions are market-level because our proxy for large bank competition, the number of entering large banks, measures entry at the market level. As expected the years and years-squared since the interstate deregulation are strongly correlated with the number of entering large banks. The negative impact is intuitive because we should expect that most of the entry activity would occur in the first years after the deregulation. Panel B of Table 6 presents the IV second stage-regressions (based on the second specification of our first stage). The results have the expected signs. The significance of the estimated coefficient on large bank entry drops slightly, but the results are still significant for our main focus category of loans, which is the 250K-1M category. This implies that the baseline results are robust despite the endogeneity of large bank entry. 5 Conclusion The future of community banks in the United States is uncertain. Our results have several implications for the changing role of small banks. First, small banks will likely be viable competitors despite increased competition from large banks. 6 Second, competitive pressures from large banks will render some traditional lending relationships unsustainable. Our results suggest that the small banks, usually understood as relationship lenders, should see a substantial change in small borrowers. Third, credit (and risk) distribution across the banking system should change with large banks attracting the high- and low-quality borrowers and small banks attracting borrowers of intermediate quality. Although the future of small banks remains somewhat uncertain, our results suggest that small banks will continue to serve some small business borrowers despite increased competition from 6 The necessary condition for the small banks to be competitive is that their cost of lending does not diverge significantly from the cost of lending of the large banks. 11

12 large banks. It will likely not be the smallest borrowers or the largest borrowers. Small banks will likely serve borrowers of intermediate size and quality. These borrowers benefit most from the proximity of small banks and the ability of small banks to tailor loans to specific credit needs. Our future research will continue to analyze this changing role of small banks in the U.S. financial system. 12

13 6 References Berger, A., and L. Black Bank Size, Lending Technologies, and Small Business Finance. Journal of Banking and Finance 35, Berger, A., W. Goulding and T. Rice Do small businesses still prefer community banks?. working paper. DeYoung, R., W. Hunter, and G. Udell The Past, Present, and Probable Future for Community Banks, Journal of Financial Services Research, vol. 25(2), Diamond, D Monitoring and Reputation: The Choice between Bank Loans and Directly Placed Debt. Journal of Political Economy, pp Gerling, K, M. Kowalik, and H. Schumacher Entrepreneurial Risk Choice and Credit Market Equilibria, Federal Reserve Bank of Kansas City Research Working Paper Goetz, M., L. Laeven, and R. Levine, Identifying the Valuation Effects and Agency Costs of Corporate Diversification: Evidence from the Geographic Diversification of U.S. Banks, Review of Financial Studies, 26, p Rajan, R Insiders and Outsiders: The Choice between Informed and Arm s-length Debt. Journal of Finance, 47(4), pp Sharpe, S Asymmetric Information, Bank Lending, and Implicit Contracts: A Stylized Model of Customer Relationships, Journal of Finance, vol. 45, no. 4, pp von Thadden, E.-L Asymmetric Information, Bank Lending, and Implicit Contracts: The Winner s Curse, Finance Research Letters 1, 2004,

14 Table 1: Small single-market urban banks with organic entry by large banks Descriptive statistics for banks/markets that experienced large bank entry between 1994 and The dummy for organic entry by a large bank is the establishment of a branch by a new large bank in a small, single-market bank s market. All statistics are derived from the June Call Report data and MSA economic data. VARIABLES N mean sd min max Large bank entry dummy 1, Assets 1, , ,184 5, ,782 Leverage ratio 1, Deposits-to-assets ratio 1, Loans-to-assets ratio 1, C&I-to-total-loans ratio 1, e Share of C&I smaller than 250K 1, Share of C&I between 250K and 1M 1, Share of C&I bigger than 1M 1, Unemployment rate 1, Labor force 1, e e+06 34, e+06 14

15 Table 2: Small single-market urban banks with no organic entry by large banks Descriptive statistics for banks/markets that did not experience large bank entry between 1994 and The dummy for organic entry by a large bank is the establishment of a branch by a new large bank in a small, single-market bank s market. All statistics are derived from the June Call Report data and MSA economic data. VARIABLES N mean sd min max Large bank entry dummy 36, Assets 36, , ,255 2, ,519 Leverage ratio 30, Deposits-to-assets ratio 36, Loans-to-assets ratio 36, C&I-to-total-loans ratio 36, Share of C&I smaller than 250K 36, Share of C&I between 250K and 1M 36, Share of C&I bigger than 1M 36, Unemployment rate 34, Labor force 34, e e e+06 15

16 Table 3: Change in small business loan portfolios following large bank entry A panel regression on small, single-market, urban banks from 1994 to The dependent variable is the share of a given loan size range in the C&I portfolio of bank i in period t. Column 1 is the size share less than $100,000; column 2 is the size share more than $100,000 but less than $250,000; column 3 is the size share more than $250,000 but less than $1 million; and column 4 is the size share more than $1 million. Robust standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1. (1) (2) (3) (4) VARIABLES <100K K 250K-1M >1M Large bank entry ** * ( ) ( ) ( ) ( ) Log of assets *** *** *** *** ( ) ( ) ( ) ( ) Loans-to-assets ratio *** *** *** *** (0.0168) (0.0135) (0.0187) (0.0153) Deposits-to-assets ratio 0.121*** *** (0.0288) (0.0216) (0.0316) (0.0297) Leverage ratio (0.0270) (0.0193) (0.0235) (0.0244) Constant 0.524*** 0.553*** 0.434*** *** (0.0645) (0.0491) (0.0657) (0.0601) Bank FE Yes Yes Yes Yes Year FE Yes Yes Yes Yes Observations 31,873 31,873 31,873 31,873 Number of banks 5,294 5,294 5,294 5,294 R-squared

17 Table 4: Change in small business loan portfolio following large bank entry (with market controls) A panel regression on small, single-market, urban banks from 1994 to The dependent variable is the share of a given loan size range in the C&I portfolio of bank i in period t. Column 1 is the size share less than $100,000; column 2 is the size share more than $100,000 but less than $250,000; column 3 is the size share more than $250,000 but less than $1 million; and column 4 is the size share more than $1 million. Robust standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1. (1) (2) (3) (4) VARIABLES <100K K 250K-1M >1M Large bank entry * ** ( ) ( ) ( ) ( ) Log of assets *** *** * *** ( ) ( ) ( ) ( ) Loans-to-assets ratio *** ** *** *** (0.0184) (0.0143) (0.0208) (0.0179) Deposits-to-assets ratio *** (0.0298) (0.0213) (0.0383) (0.0382) Leverage ratio *** *** (0.0686) (0.0517) (0.0777) (0.0706) Number of large banks in market ** *** ** ( ) ( ) ( ) ( ) Population 7.22e e e e-09 (9.41e-09) (7.28e-09) (9.94e-09) (9.42e-09) Growth of unemployment rate ( ) ( ) ( ) ( ) Growth of population (0.110) (0.0832) (0.118) (0.117) Growth of market rent * ** (0.0212) (0.0162) (0.0242) (0.0234) Constant 0.698*** 0.698*** 0.405*** *** (0.0822) (0.0590) (0.0915) (0.0895) Bank FE Yes Yes Yes Yes Year FE Yes Yes Yes Yes Observations 28,992 28,992 28,992 28,992 Number of banks 4,818 4,818 4,818 4,818 R-squared

18 Table 5: Distance as a measure of large bank competition This table uses distance as a measure of the intensity of large bank competition. Distance is the minimum distance of an entering large bank branch to one of a small bank s branches. The dependent variable is the share of a given loan size range in the C&I portfolio of bank i in period t. Column 1 is the size share less than $100,000; column 2 is the size share more than $100,000 but less than $250,000; column 3 is the size share more than $250,000 but less than $1 million; and column 4 is the size share more than $1 million. Robust standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1. (1) (2) (3) (4) VARIABLES <100K K 250K-1M >1M Minimum distance of large bank entry e ** * ( ) ( ) ( ) ( ) Number of large banks in market 9.82e (0.0101) ( ) ( ) ( ) Log of assets ** (0.0389) (0.0288) (0.0349) (0.0342) Loans-to-assets ratio (0.102) (0.0676) (0.106) (0.123) Deposits-to-assets ratio (0.174) (0.105) (0.165) (0.206) Leverage ratio (0.451) (0.300) (0.398) (0.507) Population -2.15e e e-07* -1.13e-07 (6.51e-08) (4.98e-08) (7.68e-08) (8.06e-08) Growth rate of unemployment rate (0.169) (0.110) (0.159) (0.198) Growth rate of population * 8.819** (2.395) (1.889) (2.862) (3.987) Growth rate of market rent (0.167) (0.183) (0.193) (0.270) Constant (0.742) (0.478) (0.719) (0.673) Bank FE Yes Yes Yes Yes Year FE Yes Yes Yes Yes Observations 1,340 1,340 1,340 1,340 Number of rssdid 1,020 1,020 1,020 1,020 R-squared

19 Table 6 (Panel A): First-stage of IV regression The number of entering large banks is regressed on the years since the interstate branching deregulation in column 1 and also on the years-squared in column 2. Robust standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1. VARIABLES (1) (2) Years since inter-dereg ** *** (0.0291) (0.0311) Years since inter-dereg - sq ** ( ) Unemployment rate 2.273*** 2.229*** (0.708) (0.708) Labor force 4.38e e-08 (1.95e-07) (1.95e-07) Constant ** (0.216) (0.223) Market fixed effects Yes Yes Year fixed effects Yes Yes Observations 4,693 4,693 R-squared

20 Table 6 (Panel B): Second-stage of IV regression Column 1 is the share of a bank s C&I loans with loan size less than $250,000; column 2 is the share of a bank s C&I loans with loan size more than $250,000 but less than $1 million; and column 3 is the share of a bank s C&I loans with loan size more than $1 million. Robust standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1. VARIABLES (<250K) (250K-1M) (>1M) No. of entering large banks ** (0.0463) (0.0441) (0.0366) Log of assets *** *** *** ( ) ( ) ( ) Loans-to-assets ratio *** *** *** (0.0195) (0.0190) (0.0155) Deposits-to-assets ratio 0.137*** *** (0.0335) (0.0327) (0.0309) Leverage ratio (0.0301) (0.0247) (0.0263) Unemployment rate (0.241) (0.219) (0.194) Labor force -1.54e e e-08** (2.64e-08) (2.39e-08) (1.98e-08) Constant 1.071*** 0.485*** *** (0.0820) (0.0750) (0.0675) Bank fixed effects Yes Yes Yes Year fixed effects Yes Yes Yes Observations 30,158 30,157 30,155 R-squared