Does Credit Competition Affect Small-Firm Finance?

Does Credit Competition Affect Small-Firm Finance? TARA RICE and PHILIP E. STRAHA* ABSTRACT While relaxation of geographical restrictions on bank expansion permitted banking organizations to expand across state lines, it allowed states to erect barriers to branch expansion. These differences in states branching restrictions affect credit supply. In states more open to branching, small firms borrow at interest rates 80 to 100 basis points lower than firms operating in less open states. Firms in open states also are more likely to borrow from banks. Despite this evidence that interstate branch openness expands credit supply, we find no effect of variation in state restrictions on branching on the amount that small firms borrow. * Rice is from the Board of Governors, Division of Monetary Affairs, and Strahan is from Boston College, Wharton Financial Institutions Center and BER. The views expressed in this paper are solely those of the authors and should not be interpreted as reflecting the views of the Board of Governors or the staff of the Federal Reserve System. We thank Ron Borzekowski, Courtney Carter, Diana Hancock, Traci Mach and John Wolken for providing data and research support, and acknowledge the helpful comments of Robert DeYoung, Campbell Harvey, Mitchell Petersen, the associate editor, an anonymous referee, and seminar participants at the Federal Reserve Bank of Chicago, the Federal Reserve Board, the 2008 Federal Reserve Bank of Chicago Conference on Bank Structure and Competition, and the 2009 American Finance Association Meetings.

Relaxation of geographical restrictions on bank expansion was completed in 1997 when Federal legislation the Interstate Banking and Branching Efficiency Act (IBBEA) permitted banks and bank holding companies to expand across state lines. This legislation seemingly ended the era of geographical restrictions on bank expansion that date back to the 19th Century (Kroszner and Strahan (2007)). In 1994, while most states allowed out-of-state bank holding companies to own in-state banks (interstate banking), there were almost no interstate branches. IBBEA, which was passed in 1994, allowed both unrestricted interstate banking (in effect in 1995) and interstate branching (in effect in 1997). The allowance of interstate branching was the watershed event of IBBEA. The interstate branching provisions contained in IBBEA granted states the right to erect roadblocks to branch expansion, and some states took advantage by forbidding out-of-state banks from opening new branches or acquiring existing ones, by mandating age restrictions on bank branches that could be purchased, or by limiting the amount of total deposits any one bank could hold. State exercise of such powers restricted entry by large, national banks and distorted their means of entry. This recent history continued a long political struggle that had played out between large, expansion-minded banks (who have favored removing barriers to expansion) versus small, insulated banks (the typical beneficiaries of these regulatory constraints). In this paper, we use differences in regulatory barriers to interstate branching as an instrument to test how credit competition affects credit supply and, in turn, small-firm financial decisions. Our approach shares some similarities with the bank lending literature from macroeconomics. These studies exploit variation in the amount of credit available due to changes in monetary policy (e.g., Bernanke and Blinder (1988), Kashyap and Stein (2000)),

variation from exogenous shocks to bank capital (Peek and Rosengren (2001), Ashcraft (2005), Chava and Purananadam (2008)), or variation in bank liquidity (Paravisini (2007), Khwaja and Mian (2008), Loutskina and Strahan (2008)). Most authors find that declines in loan supply translate into less debt for non-financial firms, and early evidence suggests that the Financial Crisis of 2007-8 led to very sharp declines in new loan originations (Ivashina and Scharfstein (2008), Strahan (2009)). Our strategy differs because the instrument varies credit competition. We first tie changes in competition to credit supply conditions, and then tie these changes to firm capital structure choices. In our first tests, we show that in states where restrictions on out-of-state entry are tight, firms pay higher rates for loans than similar firms operating where restrictions are loose. The difference in rates translates into a savings of 80 to 100 basis points, comparing firms in states most open to branching with those in states least open. We also find that small firms are more likely to borrow from banks where branching is less restricted. Together these results indicate that more vigorous competition between banks expands the supply of capital from banks. Our findings are robust to inclusion of credit demand controls such as time-varying industry effects, local economic growth, the relative importance of small firms in the state, the importance of small banks in the state, as well as state fixed effects. We find some evidence that both interest group and economic growth help explain states choices about restrictions on branching, but these factors do not subsume our key result: credit competition leads to lower loan rates. We then test whether branching reform affects capital structure. Do the declines in borrowing rates from better competition translate into more borrowing and less binding credit constraints? To our surprise, we find no significant increase in any measure of the quantity of credit. More firms use bank debt in states open to interstate branching (consistent with greater

competition across banks), but this does not translate into more total borrowing, higher rates of credit approval, or changes of debt maturity. We find some evidence of an increase in the use of expensive trade credit where interstate branching is unfettered, suggesting that competition may actually increase credit rationing even as prices fall. These non-results help validate the identification assumption that our instrument is not correlated with credit demand (since quantity should rise with demand). Moreover, the result lines up with earlier instances of intrastate (as opposed to interstate) branching reform. Jayaratne and Strahan (1996) find that economic growth accelerated after reform because the banking system became more efficient and competitive. Their evidence points to better quality lending and lower loan prices (e.g. lower loan losses and lower rates of bank loans) after reform as the key to better growth performance, rather than an increase in credit demand. Similarly, Bertrand, Schoar and Thesmar (2007) find that banks in France improve loan quality by reducing credit to poorly performing firms when government subsidies were reduced following reform in 1985. Our study extends the earlier research on intrastate branching reform because the more recent deregulation allows us to use the Survey of Small Business Finance (SSBF), which includes data on borrowing costs, non-price loan terms, and measures of capital structure. By studying financial policy, we extend the new literature linking credit supply to capital structure. Several of these papers link leverage to credit ratings. For example, Faulkender and Petersen (2006) find that firms with a credit rating have higher leverage than those without; Kisgen (2006, 2008) finds that firms adjust leverage to maintain a good credit rating; and Sufi (2008) finds that firms receiving newly introduced bank-loan ratings increase leverage. While these studies suggest that credit supply affects leverage, they all face the challenge of separately identifying credit supply from credit demand. Zarutskie (2006) tests how interstate branching reform

affected capital structure, finding some evidence of declines in the use of debt for very young firms and increases for more seasoned firms. But, her data do not allow her to test how smallfirm loan pricing, non-price terms (collateral, guarantees and maturity) and loan denial rates change following reform. Thus, she is unable to trace out how the regulatory reform affected competitive conditions within the banking industry and how those changes affected credit constraints for borrowers. Lemmon and Roberts (2006) and Leary (2006) use natural experiments to trace out credit supply shocks, similar to our empirical strategy. Lemmon and Roberts find that debt issuance and investment fell for speculative-grade firms in the wake of the Drexel failure, suggesting that a negative supply shock reduced access to capital. However, they find no change in leverage ratios junk bond firm issuance of both debt and equity declined with the demise of Drexel, leaving leverage ratios unchanged. Leary exploits two shocks to the banking system during the 1960s first the introduction of CDs in the early 1960s (a positive supply shock) and second the 1966 Credit Crunch (a negative supply shock). He finds that firms substituted into (away from) bank debt following the positive (negative) supply shock, leading to increased leverage. 1 Our study differs from the existing literature in two important ways. First, we study small firms (the largest firm has 500 employees) rather than the large ones (e.g., Compustat firms). Introducing adverse selection or moral hazard problems, which likely matter most for small and private firms, may complicate how credit supply affects capital structure. If lenders restrict quantity under asymmetric information (e.g. Stiglitz and Weiss, 1981; Holmstrom and Tirole, 1997), then a lower cost of debt (from supply shocks) may not translate in higher leverage. Second, our instrument varies credit supply by changing the degree of competition

across lenders; much of the existing literature focuses on changes in banks access to funds. For example, Khwaja and Mian (2008) exploit bank runs following Pakistan s unexpected nuclear test in 1998; they show that firms borrowed less from banks experiencing greater runs and more from banks experiencing smaller runs. Paravisini (2007) finds that profitable lending expands following an infusion of liquidity by the Argentine government into banks. Both studies find big changes in the amount of credit, especially for small firms, while we find no change in quantity but a large change in price. Our study extends the existing literature by first demonstrating that branching indeed expands competition and credit supply (loan prices fall), and second showing that the expanded supply does not translate into more borrowing (loan terms do not loosen). Increased competition may worsen financial contracting problems at the same time that it forces down prices. Entry can worsen adverse selection (Marquez (2002)), while declines in market power may harm relationship formation (Petersen and Rajan (1995)). So, lower loan prices reduce borrowing costs but banks continue to limit credit to solve contracting problems, which may even worsen somewhat with greater competition. 2 As noted above, we find some (limited) evidence that credit rationing increases in states most open to interstate branching. In conjunction with the earlier literature, the results imply that the effects of credit supply on capital structure depend on what caused the expansion in supply. The remainder of the paper is structured as follows. In Section I, we discuss the relaxation of restrictions on bank expansion through interstate banking and branching, prior to and following IBBEA. In Section II, we present our empirical design, data and results, and in Section III we conclude.

I. RELAXATIO OF RESTRICTIOS O BAK EXPASIO A. Toward interstate banking and branching Restrictions on banks ability to expand geographically date back to colonial times (see Kroszner and Strahan (2007)). Federal legislation formalizing state authority to regulate in-state branching became law with adoption of the 1927 McFadden Act. Economides, Hubbard and Palia (1996) examine the political-economy behind passage of the McFadden Act and find results consistent with a triumph of the numerous small and poorly capitalized banks over the large and well-capitalized banks. Although there was some deregulation of branching restrictions in the 1930s, about twothirds of the states continued to enforce restrictions on in-state branching well into the 1970s. Only 12 states allowed unrestricted statewide branching in 1970, and another 16 states prohibited branching entirely. Between 1970 and 1994, however, 38 states eased their restrictions on branching. Kroszner and Strahan (1999) show that the timing of this state-level deregulation reflected the political clout of interest groups within financial services, particularly large-bank and small-bank interests. States dominated by well-capitalized large banks tended to reform branching restrictions early. The reform of restrictions on intrastate branching typically occurred in a two-step process. First, states permitted multi-bank holding companies (MBHCs) to convert subsidiary banks (existing or acquired) into branches. MBHCs could then expand geographically by acquiring banks and converting them into branches. Second, states began permitting de novo branching, whereby banks could open new branches anywhere within state borders. In addition to branching limitations, states also prohibited cross-state ownership of banks and bank branches. Following passage of the McFadden Act, banks had begun to undermine

state branching restrictions by building multi-bank holding companies with operations in many states. The Douglas Amendment to the 1956 Bank Holding Company (BHC) Act ended this practice by prohibiting a BHC from acquiring banks outside the state where it was headquartered unless the target bank s state permitted such acquisitions. Since all states chose to bar such transactions, the amendment effectively prevented interstate banking. The first step toward interstate banking came in 1978, when Maine passed a law allowing entry by out-of-state BHCs if, in return, banks from Maine were allowed to enter those states. 3 o state reciprocated, however, so the interstate deregulation process remained stalled until 1982, when Alaska and ew York passed laws similar to Maine s. Other states then followed suit, and state deregulation of interstate banking was nearly complete by 1992, by which time all states but Hawaii had passed similar laws. These state changes, however, did not permit banks to open branches across state lines. 4 The transition to full interstate banking was completed with passage of the Interstate Banking and Branching Efficiency Act of 1994 (IBBEA), which effectively permitted bank holding companies to enter other states without permission and to operate branches across state lines. B. Interstate branching after IBBEA Despite the passage of IBBEA, the struggle over bank expansion continued as some states exercised their authority under the new law to restrict or limit interstate branching. While IBBEA opened the door to nationwide branching, it allowed the states to have considerable influence in the manner in which it was implemented. States that opposed entry by out-of-state banks could use the provisions contained in IBBEA to erect barriers to some forms of out-ofstate entry, to raise the cost of entry, and to distort the means of entry. From the time of

enactment in 1994 until the branching "trigger date" of June 1, 1997, IBBEA allowed states to employ various means to erect these barriers. States could set regulations on interstate branching with regard to four important provisions: (1) the minimum age of the target institution, (2) de novo interstate branching, (3) acquisition of individual branches, and (4) statewide deposit cap. Although IBBEA expressly permits interstate branching through interstate bank mergers, IBBEA preserves state age laws with respect to such acquisitions. Under IBBEA, states are allowed to set their own minimum age requirements with respect to how long a bank must have been in existence prior to its acquisition in an interstate bank merger. The state law, however, cannot impose an age requirement of more than five years. If a newly established subsidiary office is located in a state which mandates a minimum age requirement, then the BHC has to wait to convert the subsidiary to a branch until the subsidiary has met the necessary age requirement. Many states set their age requirement at five years, but several states implemented a lower age requirement (3 years or less) or required no minimum age limit at all. While interstate branching done through an interstate bank merger (e.g. the purchase and conversion of an existing bank to a branch office) is now permitted in every state, de novo interstate branching is only permitted under IBBEA if a state expressly opts-in. A bank thus may only open a new interstate branch if state law expressly permits it to do so. A de novo branching rule subjects existing banks to more new competition by out-of-state institutions by making it easier for an entering bank to locate its branches in markets with the greatest demand for financial services. Without de novo branching, entry into a particular out-of-state market becomes more difficult, because it is only possible via an interstate whole-bank merger, and it also potentially distorts or limits the entering bank s choice of where to locate within the state.

IBBEA specifies a statewide deposit concentration limitation of 30% with respect to interstate mergers that constitute an initial entry of a bank into a state. IBBEA protects the right of each state to cap, by statute, regulation or order, the percentage of deposits in insured depository institutions in the state that is held or controlled by any single bank or bank holding company. A state is free to relax the concentration limitation to above 30% or to impose a deposit cap on an interstate bank merger transaction below 30% and with respect to initial entry. The obvious impact of such a statute would be to prevent a bank from entering into a larger interstate merger in such state. For example, if a state had set a deposit cap of 15%, a bank could not enter into an interstate merger transaction with any institution that held more than 15% of the deposits in that particular state. IBBEA dictates that an interstate merger transaction may involve the acquisition of a branch (or number of branches) of a bank without the acquisition of the entire bank, only if the state in which the branch is located permits such a purchase. Like de novo branching, states must explicitly opt-in to this provision. Permitting acquisition of individual branches lowers the cost of entry for interstate banks. Rather than being required to enter the market by buying an entire bank, a bank may instead pick and choose those interstate branches that it wants to acquire. We use these four state powers to build a simple index of interstate branching restrictions. The index is set to zero for states that are most open to out-of-state entry. We add one to the index when a state adds any of the four barriers just described. Specifically, we add one to the index: if a state imposes a minimum age on target institutions of interstate acquirers of 3 or more years; if a state does not permit de novo interstate branching; if a state does not permit the acquisition of individual branches by an out-of-state bank; and if a state imposes a deposit

cap less that 30%. So, the index ranges from zero to four. Table I describes in detail how each state chose to deal with the possibility of interstate branching following IBBEA. States such as Illinois (since 2004), Massachusetts, and Ohio have the most open stance toward interstate entry; states such as Arkansas, Colorado and Montana have the most restrictive stance toward interstate entry. Figure 1 lays out the deregulatory history with a simple time line. Intrastate branching began in the 1970s and was completed by the early 1990s; cross-state expansion through multibank holding companies began in the early 1980s grew steadily through the middle of the 1990s. Interstate branching (our focus here) began in earnest following passage of IBBEA in 1997 and expanded into the early 2000s. C. Latter-day branching restrictions continue to bind Interstate branching has made dramatic inroads in many states. By 2004, almost half of all branches in the United States were owned by banks with branch operations in more than one state. Yet, the actual degree of entry by interstate banks has been constrained in many states by state authority to erect barriers to entry. We have already described the tools that IBBEA gives states to reduce or distort the means by which banks may enter. Johnson and Rice (2008) build a dataset of the share of interstate branches as a percentage of total branches in each state-year from 1994 to 2005. They show states with greater restrictions in fact have fewer interstate branches as a share of total branches. A. Data II. EMPIRICAL DESIG, DATA & RESULTS

We combine data from the Survey of Small Business Finance (SSBF) with the state-level branching restrictions index defined above. The SSBF is a survey by the Federal Reserve of the financial condition of firms with fewer than 500 employees. 5 The survey was first conducted in 1987 and repeated in 1993, 1998 and 2003. It contains details on small businesses' income, expenses, assets, liabilities, characteristics of the firm and firm owners, in addition to characteristics of small businesses' financial relationships with financial service suppliers for a broad set of products and services. The sample is randomly drawn but stratified to ensure geographical representation across all regions of the United States. The SSBF also oversamples relatively large firms (conditional on having fewer than 500 workers). We can measure assets and liabilities, profits, firm age and the length of time firms have established relationships with banks and other lenders. We also know the location of firms, so we can control for local market conditions, and we can use the state of the firm to merge our branching restrictions variable to the dataset. The SSBF also asks about sources of debt. We use these survey responses to build an indicator equal to one for firms borrowing from banks. 6 Each survey contains a different sample of firms, so we cannot follow firms over time. Moreover, there are small differences in variable definitions, so we are somewhat constrained in the way we construct variables to make sure we have comparability in the results across time. Berger and Udell (1995) and Petersen and Rajan (1994) were the first to use these data from the 1987 survey. These two papers both find that banking relationships expand credit availability for small firms. Others authors have also used these data to study whether bank size affects credit allocation decisions (Cole (1998), Cole, Goldberg and White (2004), Berger, et al (2005)). Our paper is the first to use these data to test whether credit availability depends on openness to interstate branching. To test this notion, we focus on the data from the 1993, 1998

and 2003 surveys. The 1993 survey reflects credit conditions just before passage of IBBEA and thus represents a clean control group for our empirical design. To run a falsification test, we assign the 2003 branching index to the 1993 data. Since deregulation had not yet occurred, we should observe no relationship between the hypothetical branching restrictions and credit conditions in 1993. The 1993 survey represents a better control sample than 1987 because unobservable economic and technological factors are more similar to the post-interstate banking sample during the latter period compared to the earlier one. The last two surveys (1998 and 2003) occur after passage of IBBEA; thus, if interstate branching matters for credit supply, we ought to observe the state-imposed constraints affecting firms operating in different states in those years. These last two years can be thought of as the treatment group in our empirical design. We focus on small business data because bank credit supply matters most for firms without access to national and international equity and debt markets. Moreover, the survey contains enough detail for us to explore the price and non-price terms of loans as well as various measures of firms use of debt finance. B. Interest-rate regression We begin by testing how loan interest rates vary with interstate branching restrictions. Our interest-rate regressions have the following structure: Y i,j,t = β t Branching Restrictions j,t + Interest rate, lender, firm and market controls i,j,t + ε i,j,t for t=1993, 1998 & 2003, (1)

where i is an index across firms, j is an index across states, and t is an index across years. We estimate equation (1) separately for each of the three sample years (therefore allowing each coefficient to vary by year). We report weighted least squares coefficients, using the survey weights provided in the SSBF data that account for the disproportionate sampling of larger firms and unit non-response. The estimated effects of interstate branching restrictions in these WLS models are larger than those estimated with OLS (see Internet Appendix), although the statistical significance (or lack thereof) is consistent across either statistical approach. The dependent variable is the interest rate on the most recent loan. This variable is only available for about 40% of the firms in the sample. Branching Restrictions is our state branching restriction index, which varies between zero and four. ote that the branching restriction index does not vary across firms operating in the same state. Since there may be a common element to the regression error across all firms operating in the same state, we cluster by state in constructing our standard errors. We have also estimated robust standard errors without clustering. These standard errors are very close to those reported here, which suggests that the error term does not contain an important state-wide component. 7 C. Control variables Following Petersen and Rajan (1994), we include a large number of control variables in our reported specifications, including interest-rate variables observed during the month in which the loan was approved, borrower and lender characteristics, relationship characteristics, and market characteristics. For interest-rate controls, we include the prime rate, a corporate-bond default spread equal to the difference between the corporate bonds rated BAA and the yield on the ten-year government bonds, and a term-structure spread equal to the difference between the

yield on the same ten-year government bonds minus the three-month constant maturity Treasury bill yield. 8 We include an indicator if the lender is a bank and another indicator if the lender is a non-financial firm. For loan terms, we include an indicator for floating rate loans. As additional controls for market structure (beyond the branching restrictions), we include an indicator for urban markets, a measure of concentration in the local market, and the growth rate of local output during the five years prior to the survey year. The concentration measure equals the Herfindahl index (HHI) from deposits in the local market, which has been used for antitrust enforcement in bank mergers. Local output is measured by the average lagged per capita personal income growth rate during the preceding five years. Urban markets are defined as Metropolitan Statistical Areas (MSAs), rural markets are defined by county, and for consistency across the three survey years, we use the 2003 market definitions from the U.S. Census Department. For borrower control variables, we include firm size (log of assets) and firm age (in years), the lender s risk assessment of the borrower, an indicator for corporations, indicators for the 2-digit SIC code of the borrower (this adds upwards of 50 variables to the model), return on assets (net income/assets), the length of the relationship between the borrower and lender (in years), the number of information and non-information based services that come from the lender, the number of unique relationships the borrower has with all of its lenders, and an indicator equal to one if the borrower has a deposit account with the lender. Information services are defined as those services which the borrower may purchase from the lender that can be used by the lender to monitor the firm (such as cash management services or credit card processing). oninformation services are those services, also purchased by the borrower, that arguably do not give the lender additional information with which to monitor the borrower (Petersen and Rajan

(1994)). Finally, we include the credit risk rating of the borrower, which varies from one to five, with one indicating the safest type of borrower and five the riskiest. 9 This credit risk rating is derived from the Dun and Bradstreet credit score of the company and is available in all survey years. D. Summary Statistics Table II reports the summary statistics on the interest rate on the most recent loan, the branching restrictions index, and all of the control variables. The average interest rate on loans ranged from 8.5% in 1993, to 9.0% in 1998, to 5.8% in 2003. In each year, these rates exceed the prime interest rate because the sample contains small and risky firms. The variation over time reflects mainly the change in the overall level of interest rates, although the average spread over the prime rate does fall in 1998 relative to the other two survey years. Many of the variables are quite stable over time (e.g. borrower risk rating, market characteristics), although we see a spike in the frequency of fixed rate borrowing and loan maturity during the 1998 sample, probably because of the relatively flat term structure during that year. We do see the incidence of collateral decline across all three samples, from 72% of loans in 1993 to just 55% by 2003. Firm size and firm age both decline sharply between 1993 and 1998, and then both increase again in 2003. We also see a drop in the importance of banks as lenders in 1998, which again rebounds in 2003. These patterns likely reflect the large number of start-up firms entering the economy during the boom of the second half of the 1990s, along with the growth of nontraditional financiers such as venture capitalists during those years. This change in the structure of the economy also shows up in the average relationship length between firms and lenders,

which falls from 8.1 to 5.5 years between 1993 and 1998, and then increases to more than 10 years by 2003. While the sample size in 1998 is smaller than the other two years, the mean firm characteristics are similar other than firm size and age. E. Relaxing branching restrictions lowers loan prices Table III reports our benchmark regression result linking the interest rate paid on the most recent loan to branching restrictiveness and the other variables. Columns 1-6 report these regressions for 1993, 1998 and 2003. As noted earlier, 1993 represents the control or placebo group. We assign the 2003 branching index to each observation in the 1993 equation; if our identification strategy works, there should be no impact of this hypothetical index in that year, since before 1994 all states prohibited interstate branching. As shown in the table, the coefficient on branching restrictions is small and not statistically significant in 1993. Thus, there is no placebo effect of branching restrictions. This is important because if there were systematic differences in unobservable dimensions that are correlated with a state s stance toward banking, these would bias our results. In contrast to 1993, the coefficient on branching restrictions enters with a positive and statistically significant coefficient in both 1998 and 2003. The coefficients are similar in magnitude in the two years (0.21 and 0.25). The coefficient of 0.25 in 2003 suggests that in states completely open to branching, firms could borrow at rates about 100 basis points lower than they could in states with the most restrictions on interstate branching (4 * 0.25 = 1.0 percentage points in the borrowing rate). For the control variables, we find that size is consistently negatively related to loan interest rates, and borrower risk rating is consistently positively related to the rates, as one would

expect. Many of the other control variables are either insignificant or not stable across the three samples. This instability is notable for the relationship variables in particular, where we find that the relationship length enters negatively in 1998 but not in the other two survey years. 10 ext, we pool our three sample years to test whether the effects of branching restrictions changed significantly in reaction to passage of IBBEA. In the pooled model, we include both year and state fixed effects, so the coefficient on the branching restriction index is generated solely by within-state variation over time. In this case, we code the branching restriction index in 1993 equal to 4, its most restrictive value, for all states. Hence the variable does not change over time for states like Colorado, which imposed the tightest level of restrictions, while it falls from 4 to zero for states like Illinois, which were relatively open to interstate expansion. The pooled model allows us to control for trends by adding year effects, and also for persistent differences in states by incorporating state fixed effects, as follows: Y i,j,t = α t + γ j + βbranching Restrictions j,t + Interest rate, lender, firm and market controls i,j,t + ε i,j,t, (2) where α t are the year-specific fixed effects and γ j are the state fixed effects. By including these fixed effects, we have stripped out all of the cross-state variation and thus eliminated the possibility that the coefficient on branching restrictions is biased because it is correlated with some unmeasured state characteristic. This model by necessity constrains the coefficients to be constant over time. Also, because we only have variation in the branching variable across states and over time, but not across observations within the same state-year, we cluster the error at the state-year level in building standard errors.

Columns 7 & 8 of Table III report the pooled, fixed effects results of equation (2). Consistent with the year-by-year regressions, we find that the coefficient on branching restrictions is positive and statistically significant. The coefficient equals 0.22, which is roughly equal to the average of the effects from the 1998 and 2003 cross sections. According to this estimate, states that relaxed interstate branching restrictions the most enjoyed a decline in borrowing rates for small firms of about 88 basis points (0.22 * 4 = 88 basis points). F. What drives the branching index? Deregulation occurs through a political process shaped by interest group lobbying as well as ideological jockeying between constituents and legislators. Thus, one concern with our results is that the forces generating variation in a state s stance toward openness to interstate branching may be correlated with demand for credit in the state, or with other supply-side factors such as the relative bargaining power of interest groups within the financial services sector. For example, if states with strong growth opportunities are more likely to deregulate, then the branching index may be related to credit demand. Or, if states with strong allies of large banks are more likely to have openness to branching, then the results may have to do with the structure of banks in a state rather than with a change in competition. We offer three pieces of evidence to rule out these alternative explanations for our results. First, including the state fixed effects will strip out any persistent differences across states. For example, differences in the structure of industry, or differences in the relative bargaining power of large versus small banks will tend to be very persistent and thus will be taken out by the fixed effects. Second, we report the cross-state correlation between variables linked to the timing of past intrastate (opposed to interstate) deregulation and our branching

index and between lagged and contemporaneous state-level economic growth and our branching index. Third, we test whether controlling for these factors in the interest-rate model affects the year-by-year results. In choosing the set of factors possibly related to deregulation, we follow Kroszner and Strahan (1999), who show that the size of the insurance sector relative to banking, the size of small banks relative to large, the capital-asset ratio of small banks relative to large, and the fraction of small non-financial firms in the state are all related to the timing of intrastate branch reform. Kroszner and Strahan also find a limited role for ideology states controlled by Democrats were slower to deregulate intrastate restrictions, all else equal. For the relative size of insurance, we use the ratio of value added from insurance to value added from insurance plus banking; these data come from the Bureau of Economic Analysis. For small bank share, we compute the fraction of total assets in the state held by banks with assets below the state median, and we compute the size-weighted average difference in the capital-asset ratio of small banks minus large ones (again using the median asset size to define small ). Both of these are built from the Call Reports. We use fraction of total establishments with fewer than 20 employees in the state, as reported by the Census Bureau, as a proxy for the share of small non-financial firms (i.e. the share of bank dependent borrowers). To measure political ideology, we compute an indicator equal to one if the Governor is a Democrat, and we also build the fraction of State Legislators who register as Democrats, both taken from the Book of the States. We compute all of these as of 1993, the year before passage of IBBEA so that the variables themselves are not affected by restructuring that may occur as a result of deregulation. Table IV reports the correlation between our set of interest group, political and economic factors with the branching index. We report the correlations in both 1998 and 2003, using one

observation per state. With two exceptions, these correlations are small and not statistically significant. There is no correlation between the structure of the non-banking industry (share of small firms or the relative importance of insurance), or between relative power of Democrats at either the level of the legislature or the executive. However, consistent with Kroszner and Strahan (1999) we do find that states where small banks are more important choose to restrict out-of-state branching more than other states (i.e. the correlation is positive). We also find that states with rapidly growing economies between 1993 and 1997 years before interstate branching went into effect - tended to restrict branching more. 11 The correlation with economic growth contrasts with the evidence surrounding intrastate branching. In the early case, there was no correlation between past growth and deregulation, whereas there is a positive association between growth and deregulation after reform goes into effect (Jayaratne and Strahan, 1996). Thus, states with weak economies may have viewed increased banking competition as a means to improve growth prospects going forward. In fact, there is a significant negative correlation between the branching restriction index and the change in growth surrounding reform. But, crucially for us, there is no contemporaneous correlation across states between economic conditions and the branching index. Thus, there is little evidence that credit demand is higher in states that restricted branching than in those that did not during either 1998 or 2003. Moreover, lagged economic growth does not have a significant impact on loan rates in Table III, nor are the effects of the index on interest rates on rates changed if we drop economic conditions from our model. To complement our fixed effects analysis in ruling out alternative explanations, Table V re-estimates the year-by-year regressions after adding each of the potential state characteristics related to political or ideological factors just described. 12 We include, in turn, each of the

variables identified by Kroszner and Strahan. To make the table compact, we report only the coefficient on the branching index and the coefficient on the alternative state characteristic small bank share (Panel A), the share of insurance relative to insurance plus banking (Panel B), small firm share (Panel C), the difference between small and large banks capital-asset ratio (Panel D), an indicator for Democrat as Governor (Panel E), and the share of Democrats in the state legislature (Panel F). In all but one case, the new control variable is not significant. The fraction of small firms does enter the regression with a positive and significant coefficient in both 1998 and 2003, although not in 1993. However, the effect of the branching index remains positive and both economically and statistically significant when we add this variable to the model. G. Firms substitute toward bank finance when branching is unrestricted Declining prices on loans suggests greater competition and credit supply. The rate regressions, however, include only those firms that actually borrow during the survey year. The other standard implication of greater supply is an increase in quantity. We test this by asking whether more firms have bank debt in states with greater openness toward branching using all of the firms in the SSBF. The structure is the same as before, except we drop all of the loan and interest rate variables and include just firm and market characteristics as explanatory variables. Thus, we include the branching restrictions index along with the measure of market concentration and the urban dummy variable, and borrower characteristics (log of assets, age in years, return on assets, the risk rating, an indicator for corporations, and the 2-digit SIC indicators). We also incorporate a measure of bank relationships equal to the length of the firm s longest relationship with a lender. Since the dependent variable is qualitative - equal to one if a

firm borrows from a bank and 0 otherwise - we estimate a Probit model. We report the marginal effects rather than the Probit coefficients, which are difficult to interpret. Columns 1 & 2 of Table VI report the results, suggesting that firms are more likely to borrow from banks when barriers to interstate branching are low. The coefficient on the branching restrictions index of - 0.015 is significant at the 5% level. In the year-by-year models, we find a very small and statistically insignificant coefficient in 1993, and although they are not significant in the later years, the coefficients are larger and increase from -0.008 in 1998 to -0.016 in 2003. The pooled model is more powerful because we can include all of the data to identify the coefficient. Also, the fact that the coefficients in the cross sections are similar to the pooled model with state fixed effects provides strong evidence that we have not omitted an important state-specific variable in our regressions. The magnitude suggests that a firm s probability of borrowing from a bank is about 6 percentage points greater in the most open states relative to the least open states. 13 H. Relaxing branching does not relax credit constraints We have shown that credit supply conditions improve with relaxation of branching restrictions. Prices fall and more firms borrow from banks when barriers to entry are low. Does this increase in credit supply relax credit constraints for small firms? To address this issue, we look at three measures of credit access: total debt (measured as log of 1+total debt); a denial indicator equal to one if a firm was denied credit or was discouraged from applying for credit; and the percent of trade credit paid late. The total debt variable is an all-encompassing measure of how much the firm borrows, and allows us to include all firms. 14 The denial indicator is also available for the full sample. For late trade credit, Petersen and Rajan (1994) show that the imputed interest for firms paying late on trade credit annualizes to 44.6%, thus suggesting that

only credit-constrained firms would use this source of finance. For this variable, however, we include only firms that have some trade credit. We regress these three measures (log of total debt, denial of credit, late trade credit) on the same set of firm and market conditions. And, again, while we have estimated these models for 1993, 1998 and 2003 separately, we report just the pooled model with state and year fixed effects (year-by-year results are available in the Internet Appendix). As shown in Table VI (columns 3-8), we find no evidence that branching restrictions - the instrument for credit supply - increase access to credit. The branching variable is never close to statistically significant in the models for total debt or the probability of denial. If we estimate the model on the same sample as in the interest rate models, we find no significant effect of branching restrictions on total debt or debt denial. We do find a negative and significant coefficient in the trade credit regressions (columns 7 & 8), suggesting if anything that credit constraints are tighter in states more open to branching. While this may reflect greater control problems in more competitive markets, we hesitate to highlight this result because the effect is not statistically robust. For example, if we do not weight the model, the effect of branching restrictions on trade credit loses significance (see Internet Appendix). If price prices fall, why don t firms borrow more? As we have argued, small firms have chronic problems raising external finance for both adverse selection and moral hazard reasons. If credit rationing partially solves contracting problems, as suggested by classic models such as Stiglitz and Weiss (1982), then there is no reason to expect these constraints to loosen with more competition. As further support for this notion, in our last set of tests we ask whether non-price loan terms are looser in states more open to branch competition. For this analysis, we return to the sample of firms with detailed loan information (40% of the surveyed firms), and report three

contracting terms: an indicator for loans with collateral, an indicator for loans personally guaranteed by the firm s owner (meaning that the lender has some claim against non-business assets in default), and loan maturity (in years). Our regressors are the same as those included in the pricing regressions (Table III). As shown in Table VII, there is no evidence that non-price terms (collateral, guarantee, and maturity) loosen with interstate branch openness. In fact, if we estimate the model without the survey weights, we estimate a negative and significant effect of branching restrictions on collateral, meaning that, if anything, collateral is more common in states with open branching relative to other states (see Internet Appendix). Given this result, one might wonder whether the decline in interest rates occurs because of tighter collateral requirements on loans. This is not the case, as in robustness tests we find that adding collateral (as well as adding maturity) has almost no impact on the size or statistical significance of branching restrictions in the interest rate models reported earlier. The results suggest that more competition across banks improves loan pricing and encourages firms to substitute bank debt for other sources of debt. Firms do benefit from the lower prices, but mechanisms that banks and other lenders use to deal with adverse selection and control problems rationing (total borrowing, loan approval rates), collateral, guarantees, and maturity do not vary. Lenders continue to solve the adverse selection and moral hazard problems just as much after deregulation as before. By enhancing competition, branch openness limits banks ability to charge high prices but does not help them solve contracting problems for small firms. III. COCLUSIOS

Barriers to bank expansion, both within and across state lines, have slowly fallen over the past 30 years. Removing these barriers has led to an increase in competitive pressure on banks and greater credit supply. Despite these gains, however, some states continue to exploit their ability to erect barriers to competition from out of state. We use these differences in state openness to interstate branching as an instrument for variation in credit competition. In our first two main results, we find that the cost of credit is lower in states open to interstate branching and that firms shift toward banks. Banks recognize the state anticompetitive burden permitted by IBBEA, and some have pressed the regulatory agencies and Congress to streamline banking law. A section contained in the Financial Services Regulatory Relief Act as passed by the House in 2006 would have eliminated remaining interstate branching barriers. 15 According to Federal Reserve Vice Chairman Donald Kohn, the interstate branching provisions originally contained in the Act would remove the last obstacle to full interstate branching for banks and level the playing field between banks and thrifts. 16 Although the final bill did not contain these provisions, the issue is sure to be revisited in future bills and legislation. Despite the gains in credit supply, we find little variation in access to credit across states as the degree of branching restrictions loosens; if anything credit rationing increases. This nonresult helps validate our key identification assumption that branching reform does not reflect variation in credit demand, since higher demand would naturally be associated with more borrowing. Moreover, our results contrast with several recent studies of larger firms, where debt increases when credit supply expands. Small firms face greater constraints in their access to external finance than large, public firms, and most external finance that small banks can raise comes in the form of debt. Thus, the borrowing choices of small firms combine standard

tradeoffs e.g. taxes versus financial distress with an additional set of constraints on access imposed by lenders (or external financiers more generally). Our results suggest that even when competition improves and the cost of borrowing falls, constraints on small firms ability to raise external finance, and thus debt finance, continue to bind.

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1 Additionally, Kliger and Sarig (2000) and Tang (2006) exploit the increase in information content from Moody s 1982 ratings refinement (adding 1, 2 or 3 to the letter-grade ratings bins). Kliger and Sarig find that yields fell for firms near the top of the ratings bins, while Tang (2006) shows that this change reduced the cost of capital and led to more borrowing, higher leverage and greater investment. 2 Credit constraints in the smallest firms may be reflected in areas other than capital structure, such as in labor productivity (Garmaise (2008)) or leasing versus purchasing decisions (Eisfeldt and Rampini (2009)). 3 Entry in this case means the ability to purchase existing whole banks; entry via branching was still not permitted. 4 With the exception of eight states (Alaska, Massachusetts, ew York, Oregon, Rhode Island, evada, orth Carolina and Utah) which allowed limited interstate branching. Despite allowance of interstate branching in these states, however, it was not exercised except in a few cases prior to the passage of IBBEA in 1994. 5 For complete documentation of the SSBF, see http://federalreserve.gov/pubs/oss/oss3/ssbftoc.htm. 6 The SSBF makes it difficult to construct the share of total assets financed by bank debt because the balance sheet date is not the same as the date at which firms report their borrowing by type of lender. Hence, we use a simple indicator variable to test whether the quantity borrowed from banks increases as barriers to entry fall. 7 For example, the robust standard error without clustering is 0.078 in the 1998 regressions, 0.070 in the 2003 regression versus 0.07 with clustering (in both 1998 and 2003). We thank

Mitchell Petersen for suggesting that we compare the state-clustered standard errors with unclustered standard errors. 8 The rate on the most-recently approved loan is as of the month of the approval. We match this rate to the monthly observations on the interest-rate controls. 9 In the 2003 survey, the risk rating varies from 1 to 6, with 6 being least risky, while the ratings for the 1993 and 1998 surveys vary from 1 to 5, with 5 being the most risky. For comparability over time, we recalibrate the 2003 rating to lie between 1 and 5, with 5 being the most risky. 10 We have tested whether the average length of bank relationships differs as states open up to interstate branching but find no evidence that such relationships change. This may occur because most banks typically buy existing branches when they enter new markets, thus leaving relationships and relationship length unaffected. 11 These two effects also correlate significantly if we estimate a multiple regression. 12 The baseline models in Table 4 already controls for lagged economic growth, and the results do not change when we drop this variable. 13 We have also tested whether the substitution into bank debt is more pronounced for small and young firms. The sign of these interactions suggests that the effects are larger for the small and young, but neither effect enters the regression significantly. 14 We have also modeled the leverage ratio and found no effects of branching. Leverage is problematic in our sample of small firms due to a large number of outliers, so we focus here on total debt. 15 H.R. 3505 and S.2856. The House bill, passed in March 2006, contains a section (401) titled Easing the restrictions on interstate branching and mergers, which removes remaining restrictions on de novo interstate branching and prohibits branching by commercially owned

ILCs chartered after October 1, 2003. The Senate bill, passed in May 2006, contains no such section. 16 Testimony before the Committee of Banking, Housing and Urban Affairs, United States Senate in March 2006. The testimony is available online at www.federalreserve.gov/boarddocs/testimony/2006/20060301.

Vermont begins state-level moves to lower barriers to intrastate branching Figure 1: Timeline of Intra- and Interstate Branching Deregulation; 1970-2003 Maine becomes the first state to permit outof-state bank ownership, with reciprocity ew York joins Maine, beginning the interstate banking era Most states lower barriers to intrastate branching and interstate bank ownership (1982-1992) First SSBF (placebo) sample IBBEA passes, permitting cross-state branching starting in 1997 Interstate branching in effect for all states Second SSBF sample 1970 1978 1982 1992 1993 1994 1997 1998 2003 Third SSBF sample

Branching Restrictivness Index Table I State Interstate Branching Laws: 1994-2005 This tables lists the index of interstate branching restrictions, effective date of interstate branching regulation changes, and each of the four provisions: the minimum age of the institution for acquisition, allowance of de novo interstate branching, allowance of interstate branching by acquisition of a single branch or portions of an institution, and the statewide deposit cap on branch acquisitions. The index is set to zero for states that are most open to out-of-state entry. We add one to the index when a state adds any of the four barriers just described. Specifically, we add one to the index: if a state imposes a minimum age on target institutions of interstate acquirers of 3 or more years; if a state does not permit de novo interstate branching; if a state does not permit the acquisition of individual branches by an out-of-state bank; and if a state imposes a deposit cap less that 30%. The index ranges from zero to four. Minimum Age of Institution (Bank or Branch) for Acquisitions Allows de novo Interstate Branching Interstate Branching by Acquisition of Single Branch or Portions of an Institution State Effective Date Alabama 3 5/31/1997 5 years o o 30% Alaska 2 1/1/1994 3 years o Yes 50% Statewide Deposit Cap on Branch Acquisitions Arizona 2 8/31/2001 5 years o Yes 30% Arizona 3 9/1/1996 5 years o o 30% Arkansas 4 6/1/1997 5 years o o 25% California 3 9/28/1995 5 years o o 30% Colorado 4 6/1/1997 5 years o o 25% Connecticut 1 6/27/1995 5 years Yes Yes 30% Delaware 3 9/29/1995 5 years o o 30% DC 0 6/13/1996 o Yes Yes 30% Florida 3 6/1/1997 3 years o o 30% Georgia 3 5/10/2002 3 years o o 30% Georgia 3 6/1/1997 5 years o o 30% Hawaii 0 1/1/2001 o Yes Yes 30% Hawaii 3 6/1/1997 5 years o o 30% Idaho 3 9/29/1995 5 years o o one Illinois 0 8/20/2004 o Yes Yes 30% Illinois 3 6/1/1997 5 years o o 30% Indiana 1 7/1/1998 5 years Yes Yes 30% Indiana 0 6/1/1997 o Yes Yes 30% Iowa 4 4/4/1996 5 years o o 15% Kansas 4 9/29/1995 5 years o o 15% Kentucky 3 3/22/2004 o o o 15% Kentucky 3 3/17/2000 o o o 15% Kentucky 4 6/1/1997 5 years o o 15% Louisiana 3 6/1/1997 5 years o o 30% Maine 0 1/1/1997 o Yes Yes 30% Maryland 0 9/29/1995 o Yes Yes 30% Massachusetts 1 8/2/1996 3 years Yes Yes 30% Michigan 0 11/29/1995 o Yes Yes one Minnesota 3 6/1/1997 5 years o o 30% Mississippi 4 6/1/1997 5 years o o 25% Missouri 4 9/29/1995 5 years o o 13% Montana 4 10/1/2001 5 years o o 22% Montana 4 9/29/1995 /A /A /A Increases 1% per year from 18% to 22% ebraska 4 5/31/1997 5 years o o 14% evada 3 9/29/1995 5 years Limited Limited 30% ew Hampshire 0 1/1/2002 o Yes Yes 30% ew Hampshire 1 8/1/2000 5 years Yes Yes 30% ew Hampshire 4 6/1/1997 5 years o o 20% ew Jersey 1 4/17/1996 o o Yes 30% ew Mexico 3 6/1/1996 5 years o o 40% ew York 2 6/1/1997 5 years o Yes 30% orth Carolina 0 7/1/1995 o Yes Yes 30% orth Dakota 1 8/1/2003 o Yes Yes 25%

orth Dakota 3 5/31/1997 o o o 25% Ohio 0 5/21/1997 o Yes Yes 30% Oklahoma 1 5/17/2000 o Yes Yes 20% Oklahoma 4 5/31/1997 5 years o o 15% Oregon 3 7/1/1997 3 years o o 30% Pennsylvania 0 7/6/1995 o Yes Yes 30% Rhode Island 0 6/20/1995 o Yes Yes 30% South Carolina 3 7/1/1996 5 years o o 30% South Dakota 3 3/9/1996 5 years o o 30% Tennessee 1 3/17/2003 3 years Yes Yes 30% Tennessee 1 7/1/2001 5 years Yes Yes 30% Tennessee 2 5/1/1998 5 years o Yes 30% Tennessee 3 6/1/1997 5 years o o 30% Texas 2 9/1/1999 o Yes Yes 20% Texas 4 8/28/1995 /A /A /A 20% Utah 1 4/30/2001 5 years Yes Yes 30% Utah 2 6/1/1995 5 years o Yes 30% Vermont 0 1/1/2001 o Yes Yes 30% Vermont 2 5/30/1996 5 years o Yes 30% Virginia 0 9/29/1995 o Yes Yes 30% Washington 1 5/9/2005 5 years Yes Yes 30% Washington 3 6/6/1996 5 years o o 30% West Virginia 1 5/31/1997 o Yes Yes 25% Wisconsin 3 5/1/1996 5 years o o 30% Wyoming 3 5/31/1997 3 years o o 30% Source: Johnson and Rice (2008)

Table II Summary Statistics This table reports summary statistics from the 1993, 1998 and 2003 Surveys of Small Business Finance. The survey contains a random sample of firms with fewer than 500 employees from each year. Each sample is drawn independently, so we do not follow the same firms over time. The data on loan terms, interest rates, loan characteristics and lender characteristics reflect information from the most recent loan by the borrower; these data are available for about 40% of the sample. The other statistics are available for all of the respondents, but here we report the summary statistics for the smaller sample that are included in the interest rate regressions (Tables III and V). Market concentration equals the sum of squared share of deposits held by all banks in the borrower's local market, where market is defined as the Metropolitan Statistical Area (MSA) or county. 1993 1998 2003 Standard Standard Standard Mean Deviation Mean Deviation Mean Deviation Loan Terms Interest Rate on Most Recent Loan 8.47 2.21 9.04 2.37 5.79 2.68 Share with Collateral 0.72 0.45 0.63 0.48 0.55 0.50 Share Guaranteed 0.56 0.50 0.56 0.50 0.60 0.49 Loan Maturity (years) 3.28 4.38 4.57 5.49 3.76 4.95 Index of branching restrictions 4 is most, 0 least, restricted 1.99 1.35 2.43 1.41 2.05 1.34 Interest Rates Prime interest rate 6.00 0.00 8.35 0.27 4.13 0.12 Term structure (10 year government bond - 3 month t-bill) 2.84 0.36 0.35 0.14 2.94 0.39 Default premium (BAA - 10 year government bond) 4.91 0.42 2.31 0.35 2.75 0.29 Borrower characteristics Log of borrower assets 13.25 2.11 12.75 2.25 13.51 2.13 Indicator if borrower is a corporation 0.46 0.50 0.33 0.47 0.33 0.47 Borrower ROA 0.33 0.65 0.66 1.10 0.46 0.82 Borrower risk rating (1 is safest; 5 is riskiest) 2.94 1.16 3.00 1.14 2.74 1.18 Loan characteristics Indicator if loan is floating-rate 0.58 0.49 0.34 0.47 0.55 0.50 Lender characteristics Indicator if lender is a bank 0.88 0.33 0.77 0.42 0.87 0.34 Indicator if lender is a non-financial company 0.03 0.16 0.05 0.21 0.02 0.14 Indicator if loan is a line of credit renewal /A 0.46 0.50 Relationship variables Length of lender-borrower relationship (years) 8.07 8.34 5.48 7.09 10.37 10.56 Borrower age (years) 16.27 14.24 13.90 10.95 18.32 13.08 umber of information services from lender 0.26 0.44 0.17 0.37 0.39 0.49 umber of non-information services from lender 0.26 0.44 0.32 0.47 0.48 0.50 Indicator if borrower has a deposit with lender 0.72 0.45 0.53 0.50 0.75 0.43 umber of relationships 1.94 1.44 2.10 1.71 2.19 1.57 Market characteristics Local market concentration 0.16 0.09 0.17 0.09 0.17 0.10 Indicator if borrower is in an MSA 0.77 0.42 0.74 0.44 0.93 0.26 Average lagged 5-year growth rate in the local market 0.22 0.06 0.25 0.06 0.19 0.06

Table III Regression of Interest Rate on Most Recent Loan on State, Bank and Borrower Characteristics This table reports regressions of the interest rate on the most recent loan on state, bank and borrower characteristics. The regressions are weighted by the survey weights, which account for disproportionate sampling and unit nonresponse, and include a set of 2-digit SIC indicator variables to control for industry effects. Standard errors are clustered by state (state-year in pooled model). The pooled model includes both state and year fixed effects. In the 1993 sample, we assign the 2003 value for the branching index; in the pooled model, in contrast, we assign the value 4 to all states in 1993 since this is the most restrictive value that the variable takes. Market concentration equals the sum of squared share of deposits held by all banks in the borrower's local market, where market is defined as the Metropolitan Statistical Area (MSA) or county. Pooled, with State Fixed 1993 (Control Group) 1998 2003 Effects Coefficient T-stat Coefficient T-stat Coefficient T-stat Coefficient T-stat Index of branching restrictions (4 is most, 0 least, restricted) 0.01 0.15 0.21 3.01 0.25 3.62 0.22 3.84 Interest Rates Prime interest rate /A -2.70 3.05-1.12 0.35-0.84 2.23 Term structure (10 year government bond - 3 month t-bill) -0.39 0.28 0.25 0.27 0.43 0.72 0.02 0.11 Default premium (BAA - 10 year government bond) 0.11 0.10-1.68 2.95 0.63 0.77-0.09 0.66 Borrower characteristics Log of borrower assets -0.23 4.12-0.25 3.26-0.27 5.62-0.28 7.78 Indicator if borrower is a corporation -0.18 1.39-0.30 1.08-0.34 1.69-0.17 1.60 Borrower ROA -0.23 2.34 0.02 0.18 0.01 0.04-0.06 0.71 Borrower risk rating (1 is safest; 5 is riskiest) 0.14 2.17 0.08 1.00 0.24 2.39 0.19 3.19 Loan characteristics Indicator if loan is floating-rate -0.51 3.23-0.17 0.74-0.94 4.55-0.61 5.00 Lender characteristics Indicator if lender is a bank -0.26 0.47-0.66 1.29-1.13 2.33-0.75 2.48 Indicator if lender is a non-financial company -0.30 0.38-0.21 0.27-1.13 0.67-0.75 1.01 Relationship variables Length of lender-borrower relationship (years) 0.00 0.33-0.03 2.54 0.01 1.13 0.00 0.66 Borrower age (years) 0.00 0.29-0.01 1.19-0.02 2.51-0.01 2.05 umber of information services from lender -0.47 2.71-0.34 0.99 0.10 0.45-0.13 0.85 umber of non-information services from lender 0.07 0.39-0.14 0.45 0.10 0.53 0.00 0.04 Indicator if borrower has a deposit with lender -0.37 2.12 0.18 0.74 0.30 1.27 0.11 0.78 umber of relationships 0.09 1.36 0.07 1.02 0.06 0.89 0.04 0.94 Market characteristics Market concentration 0.39 0.40-1.59 1.11 0.78 0.83 0.42 0.58 Indicator if borrower is in an MSA 0.06 0.32-0.54 1.91-0.26 0.70-0.08 0.69 Average lagged 5-year growth rate -0.45 0.33 0.98 0.45-0.46 0.18 0.46 0.50 1,636 788 1,737 16.31% 20.06% 23.51% 4,161 31.65%

Table IV Correlation between Interest Group Factors, Political Factors and Economic Conditions with Index of Branching Restrictions For each variable described in the far-left column, this table reports its mean and its correlation with the branching restrictions index, where each statistic is built from 51 observations, one per state (including DC). With the exception of the economic growth variables, all state characteristics are measured as of 1993, the year before passage of the Interstate Banking and Branching Efficiency Act. Cross-state Correlation with Branching Index Mean 1998 Index 2003 Index Small bank share of all banking assets in state 0.059 0.49** 0.39** Relative size of insurance to banking plus insurance 0.443-0.16-0.22 Small firm share of the number of firms in the state 0.761 0.16 0.15 Capital ratio of small banks relative to large banks in state (%) 1.39-0.19-0.07 Indicator if Governor is Democrat 0.55 0.05-0.01 Share of state legislature that are Democrat 0.65 0.05 0.04 State annual personal income growth - 1993 to 1997 0.053 0.32** 0.33** State annual personal income growth - 1998 to 2002 0.051-0.07 **Denotes significance at the 1% level.

Table V Regression of Interest Rate on Most Recent Loan on State, Bank and Borrower Characteristics Controlling for possible determinants of the Branching Restrictions Index Each panel reports regression results that add one of the possible determinants of the branching index (from Table IV) to the interest rate models from Table III. These regressions include the same set of other variables, which we do not report to save space in presenting the results. The pooled model is not estimated because the state characteristics are not identified with state fixed effects. In the 1993 sample, we assign the 2003 value for the branching index; in the pooled model, in contrast, we assign the value 4 to all states in 1993 since this is the most restrictive value that the variable takes. Standard errors are clustered by state. 1993 (Control Group) 1998 2003 Coefficient T-stat Coefficient T-stat Coefficient T-stat Panel A: Index of branching restrictions (4 is most, 0 least, restricted) 0.016 0.23 0.250 3.64 0.266 3.50 Small bank share of all banking assets in state -0.011 0.43-0.049 1.46-0.036 1.00 1,636 16.32% 788 20.32% 1,737 23.60% Panel B: Index of branching restrictions (4 is most, 0 least, restricted) 0.010 0.15 0.210 2.93 0.244 3.75 Relative size of insurance to banking plus insurance 0.004 0.40-0.009 0.60-0.025 1.77 1,636 16.33% 788 20.14% 1,737 23.87% Panel C: Index of branching restrictions (4 is most, 0 least, restricted) 0.016 0.25 0.175 2.41 0.230 3.49 Small firm share of the number of firms in the state -0.058 1.59 0.139 2.04 0.138 2.47 1,636 16.46% 788 20.85% 1,737 24.10% Panel D: Index of branching restrictions (4 is most, 0 least, restricted) 0.010 0.15 0.217 2.87 0.254 3.65 Capital ratio of small banks relative to large banks in state (%) 0.002 0.36-0.008 0.38-0.011 1.28 1,636 16.31% 788 20.06% 1,737 23.56% Panel E: Index of branching restrictions (4 is most, 0 least, restricted) 0.010 0.16 0.207 2.80 0.266 3.74 Indicator if Governor is Democrat 0.015 0.09 0.137 0.55 0.315 1.22 1,636 16.31% 788 20.13% 1,737 23.72% Panel F: Index of branching restrictions (4 is most, 0 least, restricted) 0.003 0.04 0.262 3.89 0.263 3.98 Share of state legislature that are Democrat 0.076 0.42-0.474 1.55-0.115 0.56 1,636 16.33% 788 20.67% 1,737 23.53%

Table VI Regression of Log Total Debt, Probability of Denial and Late Trade Credit on State, Bank and Borrower Characteristics (Pooled Models) This table reports regressions of financial policies by small firms, weighted by the survey weights to account for disproportionate sampling and unit nonresponse, and includes a set of 2- digit SIC indicator variables to control for industry effects. Standard errors are clustered by state-year. A Probit model is estimated for the bank debt and denied/discouraged regressions. The marginal effects, rather than the Probit coefficients, are reported. Market concentration equals the sum of squared share of deposits held by all banks in the borrower's local market, where market is defined as the Metropolitan Statistical Area (MSA) or county. Standard errors are clustered by state-year. Indicator if firm was Indicator if firm has bank denied or discouraged Percent late payments on debt Log of total debt from applying for a loan trade credit Coefficient T-stat Coefficient T-stat Coefficient T-stat Coefficient T-stat Index of branching restrictions (4 is most, 0 least, restricted) -0.015 1.96 0.045 0.86-0.002 0.37-0.542 1.79 Borrower characteristics Log of borrower assets 0.043 9.73 0.556 14.89-0.023 7.39-0.063 0.29 Indicator if borrower is a corporation -0.041 2.35 0.132 1.04 0.014 1.23 0.890 1.08 Borrower ROA 0.008 1.34 0.139 2.74-0.024 4.89-0.355 0.91 Borrower risk rating (1 is safest; 5 is riskiest) 0.013 2.24-0.059 1.32 0.077 16.54 3.425 8.64 Relationship variables and Age Length of longest bank relationship -0.001 1.37-0.002 0.31-0.003 3.78 0.046 1.05 Borrower age (years) 0.000 0.09-0.015 3.02-0.003 5.21-0.043 1.50 umber of relationships 0.320 21.79 1.181 23.79 0.053 11.89 1.334 4.99 Market characteristics Market concentration 0.081 1.05 0.347 0.55-0.076 1.16-0.122 0.03 Indicator if borrower is in an MSA -0.079 4.38-0.326 2.16 0.029 1.81 1.129 1.46 Average lagged 5-year growth rate 0.172 1.60 2.020 2.27-0.043 0.59-1.075 0.24 11,945 11,957 34.04% 22.59% 11,939 8,236 12.48% 12.65%

Table VII Regression of on-price Loan Terms from Most Recent Loan on State, Bank and Borrower Characteristics (Pooled Models) This table reports regressions weighted by the survey weights to account for disproportionate sampling and unit nonresponse, and include a set of 2-digit SIC indicator variables to control for industry effects. Standard errors are clustered by state-year. A Probit model is estimated for the collateral and guarantee regressions. The marginal effects, rather than the Probit coefficients, are reported. Market concentration equals the sum of squared share of deposits held by all banks in the borrower's local market, where market is defined as the Metropolitan Statistical Area (MSA) or county. Standard errors are clustered by state-year. Indicator if loan has Indicator if loan is collateral guaranteed Loan maturity (years) Coefficient T-stat Coefficient T-stat Coefficient T-stat Index of branching restrictions (4 is most, 0 least, restricted) -0.011 0.87 0.002 0.15 0.198 1.38 Interest Rates Prime interest rate -0.029 0.31 0.095 1.12-0.123 0.17 Term structure (10 year government bond - 3 month t-bill) -0.005 0.13 0.000 0.00-0.464 1.36 Default premium (BAA - 10 year government bond) -0.066 1.76-0.035 1.11-0.442 1.47 Borrower characteristics Log of borrower assets 0.053 6.30 0.022 2.73 0.188 1.72 Indicator if borrower is a corporation -0.038 1.53 0.089 3.40-1.172 4.96 Borrower ROA -0.010 0.57-0.025 1.93-0.280 1.72 Borrower risk rating (1 is safest; 5 is riskiest) 0.024 2.62 0.024 2.37 0.104 0.81 Loan characteristics Indicator if loan is floating-rate -0.023 1.02 0.117 4.53 0.057 0.22 Lender characteristics Indicator if lender is a bank 0.052 1.21 0.092 2.03 0.146 0.25 Indicator if lender is a non-financial company -0.131 1.32 0.058 0.55-0.773 0.75 Relationship variables Length of lender-borrower relationship (years) -0.001 0.40-0.004 3.19-0.023 1.58 Borrower age (years) -0.001 0.89-0.002 1.70 0.005 0.37 umber of information services from lender 0.017 0.52-0.015 0.45-0.775 3.39 umber of non-information services from lender 0.014 0.40 0.023 0.98-0.755 3.17 Indicator if borrower has a deposit with lender -0.110 3.67 0.032 1.00-1.586 4.12 umber of relationships 0.009 1.17 0.039 4.41-0.285 2.90 Market characteristics Market concentration 0.077 0.64-0.062 0.49-0.935 0.75 Indicator if borrower is in an MSA 0.008 0.27 0.019 0.70-0.359 1.20 Average lagged 5-year growth rate -0.085 0.45 0.232 1.36-0.465 0.28 4,153 4,156 4,065 11.86% 10.02% 15.91%

Internet Appendix for Does Credit Competition affect Small-Firm Finance? This document describes all of the empirical results that are discussed in Does Credit Competition affect Small Business Finance? The document is designed to stand alone. Hence, we repeat the most of the data description and empirical methods sections from the original paper, and we also report the original tables from the paper (Tables I-VII, in the paper and in this Appendix). In addition, we describe an additional set of tables mentioned but not described in detail in the original paper (All tables in this Appendix ending with an A or B ). We make no attempt to interpret the results here; we only describe them. Data States that opposed entry by out-of-state banks could use the provisions contained in the Interstate Banking and Branching Efficiency Act (IBBEA) to erect barriers to some forms of outof-state entry, to raise the cost of entry, and to distort the means of entry. From the time of enactment in 1994 until the branching "trigger date" of June 1, 1997, IBBEA allowed states to employ various means to erect these barriers. States could set regulations on interstate branching with regard to four important provisions: (1) the minimum age of the target institution, (2) de novo interstate branching, (3) acquisition of individual branches, and (4) statewide deposit cap. We use the four state powers granted by IBBEA to build a simple index of interstate branching restrictions. The index is set to zero for states that are most open to out-of-state entry. We add one to the index when a state adds any of the four barriers just described. Specifically, we add one to the index: if a state imposes a minimum age on target institutions of interstate acquirers of 3 or more years; if a state does not permit de novo interstate branching; if a state does not permit the acquisition of individual branches by an out-of-state bank; and if a state imposes a deposit

cap less that 30%. So, the index ranges from zero to four. Figure 1A illustrates graphically the geographical distribution of the state branching index. The states with no restrictions (e.g., the index equals zero) are colored a light gray, those with moderate restrictions (e.g., the index equals to 1 or 2) are gray with a light pattern and those with the highest restrictions (e.g., the index equals 3 or 4) are dark gray. We combine data from the Survey of Small Business Finance (SSBF) with the state-level branching restrictions index defined above. The SSBF is a survey by the Federal Reserve of the financial condition of firms with fewer than 500 employees. The survey was first conducted in 1987 and repeated in 1993, 1998 and 2003. It contains details on small businesses' income, expenses, assets, liabilities, characteristics of the firm and firm owners, in addition to characteristics of small businesses' financial relationships with financial service suppliers for a broad set of products and services. The sample is randomly drawn but stratified to ensure geographical representation across all regions of the United States. The SSBF also oversamples relatively large firms (conditional on having fewer than 500 workers). We can measure assets and liabilities, profits, firm age and the length of time firms have established relationships with banks and other lenders. We also know the location of firms, so we can control for local market conditions, and we can use the state of the firm to merge our branching restrictions variable described in the original paper (Section II). The SSBF also asks about sources of debt. We use these survey responses to build an indicator equal to one for firms borrowing from banks. We use data from the 1993, 1998 and 2003 surveys. The 1993 survey reflects credit conditions just before passage of IBBEA and thus represents a clean control group for our

empirical design. To run a falsification test, we assign the 2003 branching index to the 1993 data. Since deregulation had not yet occurred, we should observe no relationship between the hypothetical branching restrictions and credit conditions in 1993. The last two surveys (1998 and 2003) occur after passage of IBBEA; thus, if interstate branching matters for credit supply, we ought to observe the state-imposed constraints affecting firms operating in different states in those years. These last two years can be thought of as the treatment group in our empirical design. Interest-rate regressions (Internet Appendix Tables III and IIIA) We begin by testing how loan interest rates vary with interstate branching restrictions. Our interest-rate regressions have the following structure: Y i,j,t = β t Branching Restrictions j,t + Interest rate, lender, firm and market controls i,j,t + ε i,j,t for t=1993, 1998 & 2003, (1) where i is an index across firms, j is an index across states, and t is an index across years. We estimate equation (1) separately for each of the three sample years (therefore allowing each coefficient to vary by year). In the original paper we report weighted least squares coefficients, using the survey weights provided in the SSBF data that account for the disproportionate sampling of larger firms and unit nonresponse. In this appendix we also report unweighted results for comparison. The dependent variable is the interest rate on the most recent loan. This variable is only available for about 40% of the firms in the sample. Branching Restrictions is our state branching

restriction index, which varies between zero and four. ote that the branching restriction index does not vary across firms operating in the same state. Since there may be a common element to the regression error across all firms operating in the same state, we cluster by state in constructing our standard errors. We include the following control variables: interest-rate variables observed during the month in which the loan was approved, borrower and lender characteristics, relationship characteristics, and market characteristics. For interest-rate controls, we include the prime rate, a corporate-bond default spread equal to the difference between the corporate bonds rated BAA and the yield on the ten-year government bonds, and a term-structure spread equal to the difference between the yield on the same ten-year government bonds minus the three-month constant maturity Treasury bill yield. We include an indicator if the lender is a bank and another indicator if the lender is a non-financial firm. For loan terms, we include an indicator for floating rate loans. As additional controls for market structure (beyond the branching restrictions), we include an indicator for urban markets, a measure of concentration in the local market, and the growth rate of local output during the five years prior to the survey year. The concentration measure equals the Herfindahl index (HHI) from deposits in the local market, which has been used for antitrust enforcement in bank mergers. Local output is measured by the average lagged per capita personal income growth rate during the preceding five years. Urban markets are defined as Metropolitan Statistical Areas (MSAs), rural markets are defined as Counties, and for consistency across the three survey years, we use the 2003 market definitions from the U.S. Census Department.

For borrower control variables, we include firm size (log of assets) and firm age (in years), the lender s risk assessment of the borrower, an indicator for corporations, indicators for the 2-digit SIC code of the borrower (this adds upwards of 50 variables to the model), return on assets (net income/assets), the length of the relationship between the borrower and lender (in years), the number of information and non-information based services that come from the lender, the number of unique relationships the borrower has with all of its lenders, and an indicator equal to one if the borrower has a deposit account with the lender. Information services are defined as those services which the borrower may purchase from the lender that can be used by the lender to monitor the firm (such as cash management services or credit card processing). oninformation services are those services, also purchased by the borrower, that arguably do not give the lender additional information with which to monitor the borrower (Petersen and Rajan, 1994). Finally, we include the credit risk rating of the borrower, which varies from one to five, with one indicating the safest type of borrower and five the riskiest. This credit risk rating is derived from the Dun and Bradstreet credit score of the company and is available in all survey years. Table II reports the summary statistics on the interest rate on the most recent loan, the branching restrictions index, and all of the control variables. Table III reports the weighted regression results from the original paper, and Table IIIA reports a parallel set of results that are not weighted. These regression results link the interest rate paid on the most recent loan to branching restrictiveness and the other variables. Columns 6 report these regressions for 1993, 1998 and 2003. As noted earlier, 1993 represents the control

or placebo group. We assign the 2003 branching index to each observation in the 1993 equation. ext, we pool our three sample years. In the pooled model, we include both year and state fixed effects, so the coefficient on the branching restriction index is generated solely by within-state variation over time. In this case, we code the branching restriction index in 1993 equal to 4, its most restrictive value, for all states. The pooled model allows us to control for trends by adding year effects, and also for persistent differences in states by incorporating state fixed effects, as follows: Y i,j,t = α t + γ j + βbranching Restrictions j,t + Interest rate, lender, firm and market controls i,j,t + ε i,j,t, (2) where α t are the year-specific fixed effects and γ j are the state fixed effects. Because we only have variation in the branching variable across states and over time, but not across observations within the same state-year, we cluster the error at the state-year level in building standard errors. Columns 7 & 8 of Table III report the pooled, fixed effects results of equation (2) using the survey weights (as in the original paper), and columns 7 & 8 of Table IIIA report the same models without using the survey weights. What drives the branching index? (Tables IV & V) Table IV reports the correlation between a set of interest group, political and economic factors with the branching index (as in the original paper). In choosing the set of factors possibly related to deregulation, we follow Kroszner and Strahan (1999), who show that the size of the insurance sector relative to banking, the size of small banks relative to large, the capital-asset ratio of small banks relative to large, and the fraction of small non-financial firms in the state are

all related to the timing of intrastate branch reform. Kroszner and Strahan also find a limited role for ideology states controlled by Democrats were slower to deregulate intrastate restrictions, all else equal. For the relative size of insurance, we use the ratio of value added from insurance to value added from insurance plus banking; these data come from the Bureau of Economic Analysis. For small bank share, we compute the fraction of total assets in the state held by banks with assets below the state median, and we compute the size-weighted average difference in the capital-asset ratio of small banks minus large ones (again using the median asset size to define small ). Both of these are built from the Call Reports. We use fraction of total establishments with fewer than 20 employees in the state, as reported by the Census Bureau, as a proxy for the share of small non-financial firms (i.e. the share of bank dependent borrowers). To measure political ideology, we compute an indicator equal to one if the Governor is a Democrat, and we also build the fraction of State Legislators who register as Democrats, both taken from the Book of the States. We compute all of these as of 1993, the year before passage of IBBEA so that the variables themselves are not affected by restructuring that may occur as a result of deregulation. Table IV re-estimates the year-by-year interest rate regressions after adding each of the potential state characteristics related to political or ideological factors just described weighted by the survey weights (as in the paper), and Table IVA reports the same models without weighting the regression. We include each of the variables identified by Kroszner and Strahan in turn. To make the table compact, we report only the coefficient on the branching index and the coefficient on the alternative state characteristic small bank share (Panel A), the share of insurance relative to insurance plus banking (Panel B), small firm share (Panel C), the difference between small

and large banks capital-asset ratio (Panel D), an indicator for Democrat as Governor (Panel E), and the share of Democrats in the state legislature (Panel F). Firm finance pooled models (Tables VI & VIA) Tables VI and VIA (columns 1& 2) report Probit regressions, where the dependent variable equals one if the firm borrows from a bank. The sample for these regressions includes all of the firms in the SSBF. Table VI results are weighted (as in the paper), and 6A is unweighted. The structure is the same as before, except we drop all of the loan and interest rate variables and include just firm and market characteristics as explanatory variables. Thus, we include the branching restrictions index along with the measure of market concentration and the urban dummy variable, and borrower characteristics (log of assets, age in years, return on assets, the risk rating, an indicator for corporations, and the 2-digit SIC indicators). We also incorporate a measure of bank relationships equal to the length of the firm s longest relationship with a lender. Since the dependent variable is qualitative - equal to one if a firm borrows from a bank and 0 otherwise - we estimate a Probit model. We report the marginal effects. Columns 4-8 of Tables VI (weighted) and VIA (unweighted) report regressions of total debt (measured as log of 1+total debt); a denial indicator equal to one if a firm was denied credit or was discouraged from applying for credit; and the percent of trade credit paid late. For this variable, however, we include only firms that have some trade credit. We regress these three measures (log of total debt, denial of credit, late trade credit) on the same set of firm and market conditions. All of these models are pooled, with both year and state fixed effects. Loan Terms - pooled models (Tables VII and VIIA)

Tables VII (weighted, as in the paper) and VIIA (unweighted) regress non-price loan terms on the branching index and control variables. For this analysis, we return to the sample of firms with detailed loan information (40% of the surveyed firms), and report three contracting terms: an indicator for loans with collateral, an indicator for loans personally guaranteed by the firm s owner (meaning that the lender has some claim against non-business assets in default), and loan maturity (in years). Our regressors are the same as those included in the pricing regressions (Table III). Firm finance year-by-year models (Tables VIIIA & VIIIB through XIA & XIB) Tables VIIIA (weighted) and VIIIB (unweighted) through XIA and XIB report the firm finance variables in the year-by-year models as an alternative to the pooled regressions reported in the original paper (Table VI). The A tables are weighted, and the B tables are unweighted. As in the earlier year-by-year approach, 1993 represents the control group (or the falsification test) in which we assign the branching index of 2003 to each state. In these tables we also introduce each of the political-economy variables in turn to rule out alternative explanations for the results. To preserve space, we report only the coefficient on the branching index and the political variable, although the regressions include the same set of control variables as in Tables VI and VIA. Loan terms year-by-year models (Tables XIIA & XIIB through XIVA & XIVB) Tables XIIA (weighted) and XIIB (unweighted) through XIVA and XIVB report the loan term variables in the year-by-year models as an alternative to the pooled regressions reported in the original paper (Table VII). As in the earlier year-by-year approach, 1993 represents the control group in which we assign the branching index of 2003 to each state. In these tables we

also introduce each of the political-economy variables to rule out alternative explanations for the results. To preserve space, we report only the coefficient on the branching index and the political variable, although the regressions include the same set of control variables as in Tables VII and VIIA.

Figure 1A: State Branching Restriction Index, 2005

Table II Summary Statistics 1993 1998 2003 Standard Standard Standard Mean Deviation Mean Deviation Mean Deviation Loan Terms Interest Rate on Most Recent Loan 8.47 2.21 9.04 2.37 5.79 2.68 Share with Collateral 0.72 0.45 0.63 0.48 0.55 0.50 Share Guaranteed 0.56 0.50 0.56 0.50 0.60 0.49 Loan Maturity (years) 3.28 4.38 4.57 5.49 3.76 4.95 Index of branching restrictions 4 is most, 0 least, restricted 1.99 1.35 2.43 1.41 2.05 1.34 Interest Rates Prime interest rate 6.00 0.00 8.35 0.27 4.13 0.12 Term structure (10 year government bond - 3 month t-bill) 2.84 0.36 0.35 0.14 2.94 0.39 Default premium (BAA - 10 year government bond) 4.91 0.42 2.31 0.35 2.75 0.29 Borrower characteristics Log of borrower assets 13.25 2.11 12.75 2.25 13.51 2.13 Borrower is a corporation? 0.46 0.50 0.33 0.47 0.33 0.47 Borrower ROA 0.33 0.65 0.66 1.10 0.46 0.82 Borrower risk rating 2.94 1.16 3.00 1.14 2.74 1.18 Loan characteristics Floating-rate loan? 0.58 0.49 0.34 0.47 0.55 0.50 Lender characteristics Lender is a bank? 0.88 0.33 0.77 0.42 0.87 0.34 Lender is a non-financial company? 0.03 0.16 0.05 0.21 0.02 0.14 Line of Credit Renewal? /A 0.46 0.50 Relationship variables Length of lender-borrower relationship (years) 8.07 8.34 5.48 7.09 10.37 10.56 Borrower age (years) 16.27 14.24 13.90 10.95 18.32 13.08 umber of information services from lender 0.26 0.44 0.17 0.37 0.39 0.49 umber of non-information services from lender 0.26 0.44 0.32 0.47 0.48 0.50 Borrower has deposit with lender? 0.72 0.45 0.53 0.50 0.75 0.43 umber of relationships 1.94 1.44 2.10 1.71 2.19 1.57 Market characteristics Market concentration (Local HHI) 0.16 0.09 0.17 0.09 0.17 0.10 MSA Indicator 0.77 0.42 0.74 0.44 0.93 0.26 5-Year Local Economic Growth Rate 0.22 0.06 0.25 0.06 0.19 0.06 Summary statistics are based on data from the 1993, 1998 and 2003 Surveys of Small Business Finance.

Table III Regression of Interest Rate on Most Recent Loan on State, Bank and Borrower Characteristics Pooled, with State Fixed 1993 (Control Group) 1998 2003 Effects Coefficient T-stat Coefficient T-stat Coefficient T-stat Coefficient T-stat Index of branching restrictions (4 is most, 0 least, restricted) 1 0.01 0.15 0.21 3.01 0.25 3.62 0.22 3.84 Interest Rates Prime interest rate /A -2.70 3.05-1.12 0.35-0.84 2.23 Term structure (10 year government bond - 3 month t-bill) -0.39 0.28 0.25 0.27 0.43 0.72 0.02 0.11 Default premium (BAA - 10 year government bond) 0.11 0.10-1.68 2.95 0.63 0.77-0.09 0.66 Borrower characteristics Log of borrower assets -0.23 4.12-0.25 3.26-0.27 5.62-0.28 7.78 Borrower is a corporation? -0.18 1.39-0.30 1.08-0.34 1.69-0.17 1.60 Borrower ROA -0.23 2.34 0.02 0.18 0.01 0.04-0.06 0.71 Borrower risk rating 0.14 2.17 0.08 1.00 0.24 2.39 0.19 3.19 Loan characteristics Floating-rate loan? -0.51 3.23-0.17 0.74-0.94 4.55-0.61 5.00 Lender characteristics Lender is a bank? -0.26 0.47-0.66 1.29-1.13 2.33-0.75 2.48 Lender is a non-financial company? -0.30 0.38-0.21 0.27-1.13 0.67-0.75 1.01 Relationship variables Length of lender-borrower relationship (years) 0.00 0.33-0.03 2.54 0.01 1.13 0.00 0.66 Borrower age (years) 0.00 0.29-0.01 1.19-0.02 2.51-0.01 2.05 umber of information services from lender -0.47 2.71-0.34 0.99 0.10 0.45-0.13 0.85 umber of non-information services from lender 0.07 0.39-0.14 0.45 0.10 0.53 0.00 0.04 Borrower has deposit with lender? -0.37 2.12 0.18 0.74 0.30 1.27 0.11 0.78 umber of relationships 0.09 1.36 0.07 1.02 0.06 0.89 0.04 0.94 Market characteristics Market concentration (Local HHI) 0.39 0.40-1.59 1.11 0.78 0.83 0.42 0.58 MSA Indicator 0.06 0.32-0.54 1.91-0.26 0.70-0.08 0.69 5-Year Local Economic Growth Rate -0.45 0.33 0.98 0.45-0.46 0.18 0.46 0.50 1,636 788 16.31% 20.06% 1,737 23.51% 4,161 31.65% All regressions are weighted by the survey weights, which account for disproportionate sampling and unit nonresponse, and include a set of 2-digit SIC indicator variables to control for industry effects. Standard errors are clustered by state (state-year in pooled model). The pooled model includes both state and year fixed effects. 1 In the 1993 sample, we assign the 2003 value for the branching index; in the pooled model, in contrast, we assign the value 4 to all states in 1993 since this is the most restrictive value that the variable takes.

Table IIIA Regression of Interest Rate on Most Recent Loan on State, Bank and Borrower Characteristics: Unweighted Regressions Pooled, with State Fixed 1993 (Control Group) 1998 2003 Effects Coefficient T-stat Coefficient T-stat Coefficient T-stat Coefficient T-stat Index of branching restrictions (4 is most, 0 least, restricted) 1 0.04 0.92 0.10 1.69 0.12 2.93 0.06 1.95 Interest Rates Prime interest rate /A -1.33 2.33-0.06 0.03-0.63 2.72 Term structure (10 year government bond - 3 month t-bill) -1.10 1.60-0.66 1.03 0.19 0.51-0.22 1.94 Default premium (BAA - 10 year government bond) 0.65 1.14-0.71 1.70 0.28 0.70-0.03 0.35 Borrower characteristics Log of borrower assets -0.25 7.73-0.31 4.93-0.30 9.44-0.30 13.41 Borrower is a corporation? -0.11 1.16 0.09 0.53-0.09 0.76-0.05 0.80 Borrower ROA -0.17 2.24-0.04 0.32-0.01 0.14-0.09 1.56 Borrower risk rating 0.13 4.34 0.12 1.83 0.12 2.05 0.14 4.62 Loan characteristics Floating-rate loan? -0.36 2.82 0.07 0.51-0.97 8.25-0.54 7.02 Lender characteristics Lender is a bank? -0.54 1.49-0.74 1.90-0.44 1.32-0.47 2.35 Lender is a non-financial company? -0.11 0.17-0.74 1.14 0.87 1.02-0.01 0.03 Relationship variables Length of lender-borrower relationship (years) 0.00 0.55-0.02 2.18 0.00 0.65 0.00 0.16 Borrower age (years) 0.00 0.51-0.02 1.97-0.02 3.37-0.01 2.28 umber of information services from lender -0.33 3.02-0.26 1.21 0.03 0.19-0.12 1.46 umber of non-information services from lender 0.18 1.53 0.09 0.41-0.01 0.09 0.05 0.62 Borrower has deposit with lender? -0.09 0.70 0.09 0.44 0.22 1.35 0.01 0.08 umber of relationships 0.06 1.83 0.09 1.78 0.03 0.86 0.05 1.86 Market characteristics Market concentration (Local HHI) 0.01 0.01-0.41 0.41 0.84 1.14 0.34 0.65 MSA Indicator 0.00 0.04-0.26 1.35 0.08 0.50 0.05 0.62 5-Year Local Economic Growth Rate -0.26 0.25 1.59 1.04-0.44 0.43 0.39 0.74 1,636 788 15.86% 19.19% 1,727 4,161 20.68% 38.27% All regressions include a set of 2-digit SIC indicator variables to control for industry effects. Standard errors are clustered by state (state-year in pooled model). The pooled model includes both state and year fixed effects. 1 In the 1993 sample, we assign the 2003 value for the branching index; in the pooled model, in contrast, we assign the value 4 to all states in 1993 since this is the most restrictive value that the variable takes.

Table IV Correlation between Interest Group Factors, Political Factors and Economic Conditions with Index of Branching Restrictions Cross-state Correlation with Branching Index Mean 1998 2003 Small bank share of all banking assets in state 0.059 0.49** 0.39** Relative size of insurance to banking plus insurance 0.443-0.16-0.22 Small firm share of the number of firms in the state 0.761 0.16 0.15 Capital ratio of small banks relative to large banks in state (%) 1.39-0.19-0.07 Indicator if Governor is Democrat 0.55 0.05-0.01 Share of state legislature that are Democrat 0.65 0.05 0.04 State annual personal income growth - 1993 to 1997 0.053 0.32** 0.33** State annual personal income growth - 1998 to 2002 0.051-0.07 These statistics are built from 51 observations, one per state (including DC). With the exception of the economic growth variables, all state characteristics are measured as of 1993, the year before passage of the Interstate Banking and Branching Efficiency Act. **Denotes significance at the 1% level.

Table V Regression of Interest Rate on Most Recent Loan on State, Bank and Borrower Characteristics Controlling for possible determinants of the Branching Index 1993 (Control Group) 1998 2003 Coefficient T-stat Coefficient T-stat Coefficient T-stat Panel A: Index of branching restrictions (4 is most, 0 least, restricted) 1 0.016 0.23 0.250 3.64 0.266 3.50 Small bank share of all banking assets in state -0.011 0.43-0.049 1.46-0.036 1.00 1,636 16.32% 788 20.32% 1,737 23.60% Panel B: Index of branching restrictions (4 is most, 0 least, restricted) 0.010 0.15 0.210 2.93 0.244 3.75 Relative size of insurance to banking plus insurance 0.004 0.40-0.009 0.60-0.025 1.77 1,636 16.33% 788 20.14% 1,737 23.87% Panel C: Index of branching restrictions (4 is most, 0 least, restricted) 0.016 0.25 0.175 2.41 0.230 3.49 Small firm share of the number of firms in the state -0.058 1.59 0.139 2.04 0.138 2.47 1,636 16.46% 788 20.85% 1,737 24.10% Panel D: Index of branching restrictions (4 is most, 0 least, restricted) 0.010 0.15 0.217 2.87 0.254 3.65 Capital ratio of small banks relative to large banks in state (%) 0.002 0.36-0.008 0.38-0.011 1.28 1,636 16.31% 788 20.06% 1,737 23.56% Panel E: Index of branching restrictions (4 is most, 0 least, restricted) 0.010 0.16 0.207 2.80 0.266 3.74 Indicator if Governor is Democrat 0.015 0.09 0.137 0.55 0.315 1.22 1,636 16.31% 788 20.13% 1,737 23.72% Panel F: Index of branching restrictions (4 is most, 0 least, restricted) 0.003 0.04 0.262 3.89 0.263 3.98 Share of state legislature that are Democrat 0.076 0.42-0.474 1.55-0.115 0.56 1,636 16.33% 788 20.67% 1,737 23.53% Each panel reports regression results that add one of the possible determinants of the branching index (from Table 4) to the interest rate models from Table 3. These regressions include the same set of other variables, which we do not report to save space in presenting the results. The pooled model is not estimated because the state characteristics are not identified with state fixed effects. 1 In the 1993 sample, we assign the 2003 value for the branching index; in the pooled model, in contrast, we assign the value 4 to all states in 1993 since this is the most restrictive value that the variable takes.

Table VA Regression of Interest Rate on Most Recent Loan on State, Bank and Borrower Characteristics Controlling for possible determinants of the Branching Index: Unweighted Regressions 1993 (Control Group) 1998 2003 Coefficient T-stat Coefficient T-stat Coefficient T-stat Panel A: Index of branching restrictions (4 is most, 0 least, restricted) 1 0.034 0.82 0.139 2.43 0.124 2.88 Small bank share of all banking assets in state 0.003 0.20-0.053 2.10-0.012 0.52 1,636 15.86% 788 20.76% 1,737 20.69% Panel B: Index of branching restrictions (4 is most, 0 least, restricted) 0.036 0.94 0.097 1.61 0.119 2.95 Relative size of insurance to banking plus insurance 0.002 0.26-0.005 0.40-0.011 1.35 1,636 15.86% 788 20.45% 1,737 20.77% Panel C: Index of branching restrictions (4 is most, 0 least, restricted) 0.035 0.87 0.075 1.18 0.111 2.94 Small firm share of the number of firms in the state 0.005 0.21 0.103 2.30 0.076 2.32 1,636 15.86% 788 20.98% 1,737 20.92% Panel D: Index of branching restrictions (4 is most, 0 least, restricted) 0.036 0.93 0.101 1.62 0.119 2.93 Capital ratio of small banks relative to large banks in state (%) -0.004 0.70-0.004 0.37-0.002 0.45 1,636 15.87% 788 19.84% 1,737 20.68% Panel E: Index of branching restrictions (4 is most, 0 least, restricted) 0.043 1.11 0.087 1.56 0.127 3.20 Indicator if Governor is Democrat 0.128 1.13 0.383 2.15 0.146 1.10 1,636 15.94% 788 21.02% 1,737 20.74% Panel F: Index of branching restrictions (4 is most, 0 least, restricted) 0.028 0.73 0.146 2.82 0.117 2.94 Share of state legislature that are Democrat 0.089 0.92-0.502 2.82 0.023 0.19 1,636 15.89% 788 21.23% 1,737 20.68% Each panel reports regression results that add one of the possible determinants of the branching index (from Table 4) to the interest rate models from Table 3. These regressions include the same set of other variables, which we do not report to save space in presenting the results. The pooled model is not estimated because the state characteristics are not identified with state fixed effects. 1 In the 1993 sample, we assign the 2003 value for the branching index; in the pooled model, in contrast, we assign the value 4 to all states in 1993 since this is the most restrictive value that the variable takes.

Table VI Pooled Model 1 if Bank Debt Log 1+Total Debt Denied / Discouraged Pct Late on TC Coefficient T-stat Coefficient T-stat Coefficient T-stat Coefficient T-stat Index of branching restrictions (4 is most, 0 least, restricted) -0.015 1.96 0.045 0.86-0.002 0.37-0.542 1.79 Borrower characteristics Log of borrower assets 0.043 9.73 0.556 14.89-0.023 7.39-0.063 0.29 Borrower is a corporation? -0.041 2.35 0.132 1.04 0.014 1.23 0.890 1.08 Borrower ROA 0.008 1.34 0.139 2.74-0.024 4.89-0.355 0.91 Borrower risk rating 0.013 2.24-0.059 1.32 0.077 16.54 3.425 8.64 Relationship variables and Age Length of longest bank relationship -0.001 1.37-0.002 0.31-0.003 3.78 0.046 1.05 Borrower age (years) 0.000 0.09-0.015 3.02-0.003 5.21-0.043 1.50 umber of relationships 0.320 21.79 1.181 23.79 0.053 11.89 1.334 4.99 Market characteristics Market concentration (Local HHI) 0.081 1.05 0.347 0.55-0.076 1.16-0.122 0.03 MSA Indicator -0.079 4.38-0.326 2.16 0.029 1.81 1.129 1.46 5-Year Local Economic Growth Rate 0.172 1.60 2.020 2.27-0.043 0.59-1.075 0.24 Regression of Log Total Debt, Probability of Denial and Late Trade Credit on State, Bank and Borrower Characteristics 11,945 11,957 11,939 8,236 34.04% All regressions are weighted by the survey weights, which account for disproportionate sampling and unit nonresponse, and include a set of 2-digit SIC indicator variables to control for industry effects. Standard errors are clustered by state-year. A Probit model is estimated for the bank debt and denied/discourage regressions. The marginal effects, rather than the Probit coefficients, are reported. 22.59% 12.48% 12.65%

Table VIA Regression of Log Total Debt, Probability of Denial and Late Trade Credit on State, Bank and Borrower Characteristics: Unweighted Results Pooled Model 1 if Bank Debt Log 1+Total Debt Denied / Discouraged Pct Late on TC Coefficient T-stat Coefficient T-stat Coefficient T-stat Coefficient T-stat Index of branching restrictions (4 is most, 0 least, restricted) -0.012 2.13 0.052 1.06 0.004 0.99-0.373 1.26 Borrower characteristics Log of borrower assets 0.041 10.97 0.841 18.80-0.032 14.81-0.217 1.55 Borrower is a corporation? -0.017 1.33 0.312 2.59 0.015 1.64 0.450 1.00 Borrower ROA -0.003 0.50 0.202 4.27-0.028 6.45-0.579 2.14 Borrower risk rating 0.006 1.12-0.041 0.87 0.074 18.27 3.375 9.71 Relationship variables and Age Length of longest bank relationship -0.002 2.25-0.012 2.11-0.002 4.49 0.008 0.29 Borrower age (years) 0.000 0.43-0.012 2.69-0.002 4.45-0.005 0.24 umber of relationships 0.278 19.48 0.978 20.86 0.045 15.52 1.051 5.92 Market characteristics Market concentration (Local HHI) 0.086 1.17-0.294 0.53-0.047 0.94 1.658 0.68 MSA Indicator -0.072 4.21-0.357 2.37 0.038 3.27 1.344 2.34 5-Year Local Economic Growth Rate 0.030 0.35 1.399 1.73-0.031 0.57-2.645 0.75 11,945 11,957 11,939 8,236 31.37% 29.69% 13.69% 13.64% All regressions include a set of 2-digit SIC indicator variables to control for industry effects. Standard errors are clustered by state-year. A Probit model is estimated for the bank debt and denied/discourage regressions. The marginal effects, rather than the Probit coefficients, are reported.

Table VII Regression of on-price Loan Terms from Most Recent Loan on State, Bank and Borrower Characteristics, Pooled Model with State and Year Fixed Effects Pooled Data: 1993, 1998 and 2003 Surveys Collateral? Loan Guarantee? Maturity (years) Coefficient T-stat Coefficient T-stat Coefficient T-stat Index of branching restrictions (4 is most, 0 least, restricted) -0.011 0.87 0.002 0.15 0.198 1.38 Interest Rates Prime interest rate -0.029 0.31 0.095 1.12-0.123 0.17 Term structure (10 year government bond - 3 month t-bill) -0.005 0.13 0.000 0.00-0.464 1.36 Default premium (BAA - 10 year government bond) -0.066 1.76-0.035 1.11-0.442 1.47 Borrower characteristics Log of borrower assets 0.053 6.30 0.022 2.73 0.188 1.72 Borrower is a corporation? -0.038 1.53 0.089 3.40-1.172 4.96 Borrower ROA -0.010 0.57-0.025 1.93-0.280 1.72 Borrower risk rating 0.024 2.62 0.024 2.37 0.104 0.81 Loan characteristics Floating-rate loan? -0.023 1.02 0.117 4.53 0.057 0.22 Lender characteristics Lender is a bank? 0.052 1.21 0.092 2.03 0.146 0.25 Lender is a non-financial company? -0.131 1.32 0.058 0.55-0.773 0.75 Relationship variables Length of lender-borrower relationship (years) -0.001 0.40-0.004 3.19-0.023 1.58 Borrower age (years) -0.001 0.89-0.002 1.70 0.005 0.37 umber of information services from lender 0.017 0.52-0.015 0.45-0.775 3.39 umber of non-information services from lender 0.014 0.40 0.023 0.98-0.755 3.17 Borrower has deposit with lender? -0.110 3.67 0.032 1.00-1.586 4.12 umber of relationships 0.009 1.17 0.039 4.41-0.285 2.90 Market characteristics Market concentration (Local HHI) 0.077 0.64-0.062 0.49-0.935 0.75 MSA Indicator 0.008 0.27 0.019 0.70-0.359 1.20 5-Year Local Economic Growth Rate -0.085 0.45 0.232 1.36-0.465 0.28 4,153 4,156 4,065 11.86% 10.02% 15.91% All regressions are weighted by the survey weights, which account for disproportionate sampling and unit nonresponse, and include a set of 2-digit SIC indicator variables to control for industry effects. Standard errors are clustered by state-year. A Probit model is estimated collateral and guarantee regressions. The marginal effects, rather than the Probit coefficients, are reported.

TableVIIA Regression of on-price Loan Terms from Most Recent Loan on State, Bank and Borrower Characteristics, Pooled Model with State and Year Fixed Effects: Unweighted Results Pooled Data: 1993, 1998 and 2003 Surveys Collateral? Loan Guarantee? Maturity (years) Coefficient T-stat Coefficient T-stat Coefficient T-stat Index of branching restrictions (4 is most, 0 least, restricted) -0.017 2.07-0.002 0.21-0.015 0.18 Interest Rates Prime interest rate -0.046 0.69 0.090 1.50-0.781 1.43 Term structure (10 year government bond - 3 month t-bill) -0.026 1.17 0.021 0.84-0.106 0.51 Default premium (BAA - 10 year government bond) -0.018 0.83-0.031 1.51-0.417 2.40 Borrower characteristics Log of borrower assets 0.040 7.20 0.000 0.00 0.082 1.52 Borrower is a corporation? 0.005 0.31 0.048 2.84-0.644 4.05 Borrower ROA -0.016 1.29-0.035 3.12-0.193 1.88 Borrower risk rating 0.024 4.20 0.027 4.07 0.040 0.58 Loan characteristics Floating-rate loan? -0.017 0.97 0.109 6.04-0.300 1.64 Lender characteristics Lender is a bank? -0.001 0.04 0.088 3.02 0.316 0.97 Lender is a non-financial company? -0.114 2.05 0.036 0.60 0.333 0.56 Relationship variables Length of lender-borrower relationship (years) -0.002 2.29-0.002 1.50-0.006 0.59 Borrower age (years) -0.001 1.19-0.003 4.47 0.003 0.49 umber of information services from lender 0.016 0.77-0.032 1.59-0.486 3.35 umber of non-information services from lender 0.031 1.48-0.011 0.56-0.538 3.43 Borrower has deposit with lender? -0.073 3.40 0.091 3.72-1.305 4.73 umber of relationships 0.019 3.29 0.036 6.46-0.134 2.40 Market characteristics Market concentration (Local HHI) 0.095 1.01 0.175 1.83-0.452 0.47 MSA Indicator 0.012 0.49 0.038 1.74-0.297 1.42 5-Year Local Economic Growth Rate 0.021 0.16 0.084 0.64 0.786 0.62 4,153 4,156 4,065 9.52% 7.30% 12.68% All regressions include a set of 2-digit SIC indicator variables to control for industry effects. Standard errors are clustered by state-year. A Probit model is estimated collateral and guarantee regressions. The marginal effects, rather than the Probit coefficients, are reported.

1993 (Control Group) 1998 2003 Coefficient T-stat Coefficient T-stat Coefficient T-stat Panel A: Index of branching restrictions (4 is most, 0 least, restricted) 1-0.004 0.58-0.019 3.34-0.018 1.51 Small bank share of all banking assets in state 0.013 5.33 0.015 3.83 0.005 0.83 4,506 11.41% 3,386 12.60% 4,031 9.80% Panel B: Index of branching restrictions (4 is most, 0 least, restricted) 0.002 0.31-0.007 0.98-0.016 1.31 Relative size of insurance to banking plus insurance 0.001 1.31 0.003 1.82 0.001 0.80 4,506 11.12% 3,386 12.30% 4,031 9.78% Panel C: Index of branching restrictions (4 is most, 0 least, restricted) 0.003 0.38-0.003 0.46-0.014 1.26 Small firm share of the number of firms in the state -0.006 1.60-0.018 3.26-0.008 1.04 4,506 11.13% 3,386 12.45% 4,031 9.82% Panel D: Index of branching restrictions (4 is most, 0 least, restricted) 0.002 0.26-0.006 0.72-0.017 1.39 Capital ratio of small banks relative to large banks in state (%) 0.002 2.71 0.003 3.29-0.001 0.98 4,506 11.13% 3,386 12.25% 4,031 9.77% Panel E: Index of branching restrictions (4 is most, 0 least, restricted) 0.003 0.42-0.008 0.98-0.017 1.40 Indicator if Governor is Democrat 0.020 1.07-0.006 0.21-0.009 0.26 4,506 11.13% 3,386 12.15% 4,031 9.76% Panel F: Index of branching restrictions (4 is most, 0 least, restricted) 0.001 0.12-0.009 1.17-0.016 1.38 Share of state legislature that are Democrat 0.008 0.49 0.009 0.28-0.003 0.11 4,506 3,386 4,031 11.10% 12.15% 9.75% Table VIIIA Probit Regressions of Whether Firm borrowers from a Bank on State, Bank and Borrower Characteristics Controlling for possible determinants of the Branching Index: Weighted Results Each panel reports regression results that add one of the possible determinants of the branching index (from Table 4) to the interest rate models from Table 3. These regressions include the same set of other variables, which we do not report to save space in presenting the results. The pooled model is not estimated because the state characteristics are not identified with state fixed effects. All of the regressions are weighted by the sample weights that account for disproportionate sampling and unit nonresponse. 1 In the 1993 sample, we assign the 2003 value for the branching index; in the pooled model, in contrast, we assign the value 4 to all states in 1993 since this is the most restrictive value that the variable takes.

1993 (Control Group) 1998 2003 Coefficient T-stat Coefficient T-stat Coefficient T-stat Panel A: Index of branching restrictions (4 is most, 0 least, restricted) 1-0.003 0.58-0.020 3.27-0.012 1.21 Small bank share of all banking assets in state 0.009 3.95 0.016 4.30 0.005 0.98 4,506 3,386 4,031 Panel B: Index of branching restrictions (4 is most, 0 least, restricted) 0.001 0.21-0.008 1.06-0.010 1.02 Relative size of insurance to banking plus insurance 0.001 0.86 0.003 1.72 0.001 0.75 4,506 3,386 4,031 Panel C: Index of branching restrictions (4 is most, 0 least, restricted) 0.003 0.48-0.005 0.71-0.008 0.88 Small firm share of the number of firms in the state -0.009 2.69-0.013 1.92-0.011 1.97 4,506 3,386 4,031 Panel D: Index of branching restrictions (4 is most, 0 least, restricted) 0.001 0.18-0.006 0.75-0.010 1.06 Capital ratio of small banks relative to large banks in state (%) 0.001 1.69 0.003 3.23-0.001 1.00 4,506 3,386 4,031 Panel E: Index of branching restrictions (4 is most, 0 least, restricted) 0.002 0.32-0.008 0.99-0.011 1.11 Indicator if Governor is Democrat 0.014 0.74-0.011 0.44-0.013 0.49 4,506 3,386 4,031 Panel F: Index of branching restrictions (4 is most, 0 least, restricted) -0.001 0.18-0.012 1.54-0.009 1.00 Share of state legislature that are Democrat 0.021 1.33 0.028 1.03-0.014 0.51 4,506 3,386 4,031 Table VIIIB Probit Regressions of Whether Firm borrowers from a Bank on State, Bank and Borrower Characteristics Controlling for possible determinants of the Branching Index: Unweighted Results 12.45% 15.08% 10.65% 12.31% 14.72% 10.63% 12.36% 14.76% 10.73% 12.31% 14.72% 10.62% 12.31% 14.63% 10.63% 12.32% 14.66% 10.63% Each panel reports regression results that add one of the possible determinants of the branching index (from Table 4) to the interest rate models from Table 3. These regressions include the same set of other variables, which we do not report to save space in presenting the results. The pooled model is not estimated because the state characteristics are not identified with state fixed effects. 1 In the 1993 sample, we assign the 2003 value for the branching index; in the pooled model, in contrast, we assign the value 4 to all states in 1993 since this is the most restrictive value that the variable takes.

1993 (Control Group) 1998 2003 Coefficient T-stat Coefficient T-stat Coefficient T-stat Panel A: Index of branching restrictions (4 is most, 0 least, restricted) 1-0.001 0.02 0.062 1.00 0.049 0.51 Small bank share of all banking assets in state 0.088 2.88 0.032 1.26 0.105 2.66 4,514 3,393 4,050 Panel B: Index of branching restrictions (4 is most, 0 least, restricted) 0.043 0.62 0.084 1.49 0.111 1.20 Relative size of insurance to banking plus insurance 0.008 0.85-0.001 0.13 0.030 1.75 4,514 3,393 4,050 Panel C: Index of branching restrictions (4 is most, 0 least, restricted) 0.045 0.63 0.094 1.63 0.132 1.39 Small firm share of the number of firms in the state -0.036 0.82-0.037 1.09-0.176 3.68 4,514 3,393 4,050 Panel D: Index of branching restrictions (4 is most, 0 least, restricted) 0.038 0.55 0.075 1.34 0.089 0.89 Capital ratio of small banks relative to large banks in state (%) 0.002 0.13-0.014 1.94 0.012 0.44 4,514 3,393 4,050 Panel E: Index of branching restrictions (4 is most, 0 least, restricted) 0.030 0.46 0.082 1.52 0.089 0.93 Indicator if Governor is Democrat -0.121 0.69 0.159 1.03 0.017 0.05 4,514 3,393 4,050 Panel F: Index of branching restrictions (4 is most, 0 least, restricted) 0.018 0.26 0.071 1.13 0.086 0.86 Share of state legislature that are Democrat 0.183 1.13 0.109 0.60 0.028 0.10 4,514 3,393 4,050 Table IXA Regressions of Log of 1+Total Debt on State, Bank and Borrower Characteristics Controlling for possible determinants of the Branching Index: Weighted Results 16.78% 8.47% 16.57% 8.43% 18.61% 16.57% 8.45% 16.55% 8.47% 18.40% 16.56% 8.46% 16.58% 8.44% 18.38% Each panel reports regression results that add one of the possible determinants of the branching index (from Table 4) to the interest rate models from Table 3. These regressions include the same set of other variables, which we do not report to save space in presenting the results. The pooled model is not estimated because the state characteristics are not identified with state fixed effects. All of the regressions are weighted by the sample weights that account for disproportionate sampling and unit nonresponse. 1 In the 1993 sample, we assign the 2003 value for the branching index; in the pooled model, in contrast, we assign the value 4 to all states in 1993 since this is the most restrictive value that the variable takes. 18.70% 18.80% 18.38%

1993 (Control Group) 1998 2003 Coefficient T-stat Coefficient T-stat Coefficient T-stat Panel A: Index of branching restrictions (4 is most, 0 least, restricted) 1-0.031 0.49 0.005 0.08-0.006 0.07 Small bank share of all banking assets in state 0.075 2.59 0.023 0.80 0.089 2.75 4,514 3,393 4,050 Panel B: Index of branching restrictions (4 is most, 0 least, restricted) 0.007 0.11 0.023 0.37 0.043 0.57 Relative size of insurance to banking plus insurance 0.006 0.76-0.017 1.74 0.029 2.17 4,514 3,393 4,050 Panel C: Index of branching restrictions (4 is most, 0 least, restricted) 0.010 0.15 0.030 0.47 0.057 0.73 Small firm share of the number of firms in the state -0.026 0.67-0.029 0.78-0.133 2.59 4,514 3,393 4,050 26.09% 12.30% 30.25% Panel D: Index of branching restrictions (4 is most, 0 least, restricted) 0.004 0.07 0.015 0.24 0.033 0.40 Capital ratio of small banks relative to large banks in state (%) 0.005 0.64-0.011 1.20 0.016 0.72 4,514 3,393 4,050 Panel E: Index of branching restrictions (4 is most, 0 least, restricted) 0.003 0.05 0.021 0.35 0.034 0.44 Indicator if Governor is Democrat -0.007 0.04 0.107 0.60 0.046 0.19 4,514 3,393 4,050 26.09% 12.30% 30.08% Table IXB Regressions of Log of 1+Total Debt on State, Bank and Borrower Characteristics Controlling for possible determinants of the Branching Index: Unweighted Results 26.19% 12.31% 30.26% 12.31% 30.11% Panel F: Index of branching restrictions (4 is most, 0 least, restricted) -0.005 0.08 0.034 0.54 0.038 0.50 Share of state legislature that are Democrat 0.087 0.55-0.096 0.52-0.079 0.34 4,514 26.09% 3,393 12.30% 4,050 30.08% 26.09% 26.09% 12.36% 30.24% Each panel reports regression results that add one of the possible determinants of the branching index (from Table 4) to the interest rate models from Table 3. These regressions include the same set of other variables, which we do not report to save space in presenting the results. The pooled model is not estimated because the state characteristics are not identified with state fixed effects. 1 In the 1993 sample, we assign the 2003 value for the branching index; in the pooled model, in contrast, we assign the value 4 to all states in 1993 since this is the most restrictive value that the variable takes.

1993 (Control Group) 1998 2003 Coefficient T-stat Coefficient T-stat Coefficient T-stat Panel A: Index of branching restrictions (4 is most, 0 least, restricted) 1-0.004 0.53 0.003 0.27-0.002 0.37 Small bank share of all banking assets in state -0.002 0.71-0.010 3.35-0.009 3.26 4,505 3,370 3,989 Panel B: Index of branching restrictions (4 is most, 0 least, restricted) -0.005 0.73-0.005 0.49-0.004 0.75 Relative size of insurance to banking plus insurance -0.001 0.44-0.001 0.77 0.001 0.11 4,505 3,370 3,989 9.30% 8.80% 12.46% Panel C: Index of branching restrictions (4 is most, 0 least, restricted) -0.007 0.97-0.008 0.77-0.006 1.05 Small firm share of the number of firms in the state 0.007 1.48 0.012 2.02 0.007 2.10 4,505 3,370 3,989 9.30% 9.00% 12.57% Panel D: Index of branching restrictions (4 is most, 0 least, restricted) -0.005 0.69-0.007 0.70-0.004 0.76 Capital ratio of small banks relative to large banks in state (%) -0.001 0.42-0.004 2.39-0.001 1.85 4,505 3,370 3,989 Panel E: Index of branching restrictions (4 is most, 0 least, restricted) -0.004 0.62-0.005 0.46-0.004 0.74 Indicator if Governor is Democrat 0.009 0.55 0.007 0.27 0.004 0.23 4,505 3,370 3,989 9.30% 8.80% 12.46% Panel F: Index of branching restrictions (4 is most, 0 least, restricted) -0.004 0.50-0.008 0.84-0.005 0.78 Share of state legislature that are Democrat -0.012 0.71 0.021 0.87 0.002 0.12 4,505 3,370 3,989 Table XA Probit Regression of whether Firm Denied a Loan on State, Bank and Borrower Characteristics Controlling for possible determinants of the Branching Index: Weighted Results 9.30% 9.20% 12.89% 9.30% 8.80% Each panel reports regression results that add one of the possible determinants of the branching index (from Table 4) to the interest rate models from Table 3. These regressions include the same set of other variables, which we do not report to save space in presenting the results. The pooled model is not estimated because the state characteristics are not identified with state fixed effects. All of the regressions are weighted by the sample weights that account for disproportionate sampling and unit nonresponse. 1 In the 1993 sample, we assign the 2003 value for the branching index; in the pooled model, in contrast, we assign the value 4 to all states in 1993 since this is the most restrictive value that the variable takes. 9.30% 9.00% 12.49% 12.46%

1993 (Control Group) 1998 2003 Coefficient T-stat Coefficient T-stat Coefficient T-stat Panel A: Index of branching restrictions (4 is most, 0 least, restricted) 1-0.003 0.40-0.001 0.08 0.003 0.68 Small bank share of all banking assets in state -0.005 1.48-0.007 2.70 4.350-0.01 4,505 3,370 3,989 10.32% 9.89% 11.94% Panel B: Index of branching restrictions (4 is most, 0 least, restricted) -0.005 0.75-0.006 0.73 0.001 0.01 Relative size of insurance to banking plus insurance -0.001 0.91 0.960 0.00-0.001 1.42 4,505 3,370 3,989 10.27% 9.74% 11.47% Panel C: Index of branching restrictions (4 is most, 0 least, restricted) -0.005 0.91-0.008 1.04-0.001 0.12 Small firm share of the number of firms in the state 0.004 0.84 0.011 2.27 0.005 1.68 4,505 3,370 3,989 10.29% 9.91% 11.49% Panel D: Index of branching restrictions (4 is most, 0 least, restricted) -0.004 0.71-0.008 1.01 0.001 0.06 Capital ratio of small banks relative to large banks in state (%) -0.001 1.14-0.004 2.85-0.001 2.79 4,505 3,370 3,989 Panel E: Index of branching restrictions (4 is most, 0 least, restricted) -0.003 0.54-0.006 0.74 0.001 0.01 Indicator if Governor is Democrat 0.015 1.03 0.013 0.60-0.011 0.78 4,505 3,370 3,989 Panel F: Index of branching restrictions (4 is most, 0 least, restricted) -0.004 0.64-0.007 1.07 0.001 0.08 Share of state legislature that are Democrat -0.001 0.08 0.012 0.57-0.001 0.05 4,505 3,370 3,989 Table XB Probit Regression of whether Firm Denied a Loan on State, Bank and Borrower Characteristics Controlling for possible determinants of the Branching Index: Unweighted Results 10.28% 10.28% 10.26% 9.91% 11.48% 9.72% 11.44% 9.72% 11.42% Each panel reports regression results that add one of the possible determinants of the branching index (from Table 4) to the interest rate models from Table 3. These regressions include the same set of other variables, which we do not report to save space in presenting the results. The pooled model is not estimated because the state characteristics are not identified with state fixed effects. 1 In the 1993 sample, we assign the 2003 value for the branching index; in the pooled model, in contrast, we assign the value 4 to all states in 1993 since this is the most restrictive value that the variable takes.

1993 (Control Group) 1998 2003 Coefficient T-stat Coefficient T-stat Coefficient T-stat Panel A: Index of branching restrictions (4 is most, 0 least, restricted) 1 0.011 0.47-1.250 2.57 0.358 0.76 Small bank share of all banking assets in state -0.021 2.13 0.240 1.19-0.232 0.96 3,085 2,299 2,852 Panel B: Index of branching restrictions (4 is most, 0 least, restricted) 0.003 0.12-1.067 2.47 0.334 0.70 Relative size of insurance to banking plus insurance 0.001 0.23-0.018 0.24 0.107 1.43 3,085 2,299 2,852 16.48% 8.24% 11.03% Panel C: Index of branching restrictions (4 is most, 0 least, restricted) -0.002 0.08-1.040 2.46 0.251 0.51 Small firm share of the number of firms in the state 0.018 1.15-0.118 0.55 0.062 0.17 3,085 2,299 2,852 Panel D: Index of branching restrictions (4 is most, 0 least, restricted) 0.002 0.08-1.055 2.48 0.267 0.56 Capital ratio of small banks relative to large banks in state (%) -0.002 0.31 0.024 0.30-0.009 0.10 3,085 2,299 2,852 16.48% 8.24% 10.91% Panel E: Index of branching restrictions (4 is most, 0 least, restricted) -0.003 0.13-1.077 2.45 0.238 0.52 Indicator if Governor is Democrat -0.082 1.31 0.527 0.38-1.088 0.82 3,085 2,299 2,852 Panel F: Index of branching restrictions (4 is most, 0 least, restricted) 0.011 0.44-0.88 1.78 0.163 0.35 Share of state legislature that are Democrat -0.082 1.30-1.467 0.95 1.252 0.90 3,085 2,299 2,852 16.54% 8.23% 10.96% Table XIA Regressions of Percent Trade Credit Paid Late on State, Bank and Borrower Characteristics Controlling for possible determinants of the Branching Index: Weighted Results 16.63% 8.30% 10.98% 16.53% 8.24% 10.91% 16.56% 8.24% 10.95% Each panel reports regression results that add one of the possible determinants of the branching index (from Table 4) to the interest rate models from Table 3. These regressions include the same set of other variables, which we do not report to save space in presenting the results. The pooled model is not estimated because the state characteristics are not identified with state fixed effects. All of the regressions are weighted by the sample weights that account for disproportionate sampling and unit nonresponse. 1 In the 1993 sample, we assign the 2003 value for the branching index; in the pooled model, in contrast, we assign the value 4 to all states in 1993 since this is the most restrictive value that the variable takes.

1993 (Control Group) 1998 2003 Coefficient T-stat Coefficient T-stat Coefficient T-stat Panel A: Index of branching restrictions (4 is most, 0 least, restricted) 1 0.008 0.41-1.229 2.61 0.315 0.76 Small bank share of all banking assets in state -0.021 2.35 0.345 1.83-0.217 1.32 3,085 2,299 2,852 18.79% 9.11% 12.33% Panel B: Index of branching restrictions (4 is most, 0 least, restricted) -0.001 0.05-0.982 2.34 0.238 0.60 Relative size of insurance to banking plus insurance -0.001 0.11 0.016 0.29 0.078 1.40 3,085 2,299 2,852 Panel C: Index of branching restrictions (4 is most, 0 least, restricted) -0.005 0.24-0.938 2.36 0.190 0.47 Small firm share of the number of firms in the state 0.017 1.31-0.097 0.56 0.140 0.50 3,085 2,299 2,852 18.69% 8.98% 12.28% Panel D: Index of branching restrictions (4 is most, 0 least, restricted) -0.001 0.05-0.974 2.37 0.219 0.55 Capital ratio of small banks relative to large banks in state (%) -0.002 0.83-0.024 0.35.-6 0.69 3,085 2,299 2,852 Panel E: Index of branching restrictions (4 is most, 0 least, restricted) -0.006 0.31-0.964 2.29 0.183 0.48 Indicator if Governor is Democrat -0.081 1.40 0.189 0.17-0.734 0.66 3,085 2,299 2,852 18.72% 8.98% 12.29% Panel F: Index of branching restrictions (4 is most, 0 least, restricted) -0.001 0.01-0.991 2.32 0.109 0.29 Share of state legislature that are Democrat -0.006 0.09 0.272 0.22 1.547 1.33 3,085 2,299 2,852 Table XIB Regressions of Percent Trade Credit Paid Late on State, Bank and Borrower Characteristics Controlling for possible determinants of the Branching Index: Unweighted Results 18.64% 18.65% 18.64% 8.98% 12.34% 8.98% 12.30% 8.98% 12.35% Each panel reports regression results that add one of the possible determinants of the branching index (from Table 4) to the interest rate models from Table 3. These regressions include the same set of other variables, which we do not report to save space in presenting the results. The pooled model is not estimated because the state characteristics are not identified with state fixed effects. 1 In the 1993 sample, we assign the 2003 value for the branching index; in the pooled model, in contrast, we assign the value 4 to all states in 1993 since this is the most restrictive value that the variable takes.

1993 (Control Group) 1998 2003 Coefficient T-stat Coefficient T-stat Coefficient T-stat Panel A: Index of branching restrictions (4 is most, 0 least, restricted) 1-0.001 0.04-0.009 0.62-0.003 0.22 Small bank share of all banking assets in state 0.026 4.58 0.015 1.61 0.018 2.98 1,619 748 1,727 Panel B: Index of branching restrictions (4 is most, 0 least, restricted) 0.013 0.89 0.004 0.24 0.004 0.29 Relative size of insurance to banking plus insurance 0.004 1.92 0.008 2.17 0.003 1.23 1,619 748 1,727 Panel C: Index of branching restrictions (4 is most, 0 least, restricted) 0.013 0.86 0.007 0.40 0.006 0.44 Small firm share of the number of firms in the state -0.001 0.09-0.015 0.94-0.016 1.40 1,619 748 1,727 Panel D: Index of branching restrictions (4 is most, 0 least, restricted) 0.013 0.84 0.002 0.10 0.003 0.25 Capital ratio of small banks relative to large banks in state (%) -0.001 0.39-0.002 1.07-0.001 0.54 1,619 748 1,727 Panel E: Index of branching restrictions (4 is most, 0 least, restricted) 0.013 0.83 0.004 0.28 0.004 0.29 Indicator if Governor is Democrat 0.002 0.06-0.056 1.01 0.018 0.44 1,619 11.72% 748 11.96% 1,727 9.96% Panel F: Index of branching restrictions (4 is most, 0 least, restricted) 0.010 0.62-0.006 0.38 0.003 0.22 Share of state legislature that are Democrat 0.041 1.05 0.085 1.75 0.001 0.02 1,619 748 1,727 Table XIIA Probit Regressions if Most-Recent Loan has Collateral on State, Bank and Borrower Characteristics Controlling for possible determinants of the Branching Index: Weighted Results 13.40% 12.15% 12.03% 12.72% 10.08% 11.72% 11.93% 11.74% 11.81% 9.96% 11.84% 12.10% 9.94% Each panel reports regression results that add one of the possible determinants of the branching index (from Table 4) to the interest rate models from Table 3. These regressions include the same set of other variables, which we do not report to save space in presenting the results. The pooled model is not estimated because the state characteristics are not identified with state fixed effects. All of the regressions are weighted by the sample weights that account for disproportionate sampling and unit nonresponse. 1 In the 1993 sample, we assign the 2003 value for the branching index; in the pooled model, in contrast, we assign the value 4 to all states in 1993 since this is the most restrictive value that the variable takes. 10.53% 10.16%

1993 (Control Group) 1998 2003 Coefficient T-stat Coefficient T-stat Coefficient T-stat Panel A: Index of branching restrictions (4 is most, 0 least, restricted) 1 0.008 0.98-0.019 1.79-0.004 0.40 Small bank share of all banking assets in state 0.014 3.34 0.013 1.77 0.019 3.67 1,619 748 1,727 Panel B: Index of branching restrictions (4 is most, 0 least, restricted) 0.016 2.03-0.009 0.77 0.004 0.32 Relative size of insurance to banking plus insurance 0.003 2.96 0.002 0.84 0.002 1.37 1,619 748 1,727 Panel C: Index of branching restrictions (4 is most, 0 least, restricted) 0.017 2.00-0.007 0.60 0.006 0.57 Small firm share of the number of firms in the state -0.008 1.49-0.008 1.08-0.020 2.21 1,619 748 1,727 Panel D: Index of branching restrictions (4 is most, 0 least, restricted) 0.016 1.83-0.010 0.86 0.004 0.34 Capital ratio of small banks relative to large banks in state (%) -0.001 0.09-0.001 0.80-0.002 1.37 1,619 748 1,727 9.49% 10.40% 6.96% Panel E: Index of branching restrictions (4 is most, 0 least, restricted) 0.015 1.67-0.009 0.76 0.005 0.45 Indicator if Governor is Democrat -0.015 0.61-0.011 0.29 0.027 0.82 1,619 748 1,727 Panel F: Index of branching restrictions (4 is most, 0 least, restricted) 0.013 1.51-0.013 1.09 0.004 0.31 Share of state legislature that are Democrat 0.029 1.27 0.037 1.12 0.001 0.01 1,619 748 1,727 Table XIIB Probit Regressions if Most-Recent Loan has Collateral on State, Bank and Borrower Characteristics Controlling for possible determinants of the Branching Index: Unweighted Results 10.06% 9.80% 9.56% 9.51% 9.56% 10.72% 7.67% 10.47% 7.00% 10.45% 7.25% 10.39% 6.95% 10.45% 6.90% Each panel reports regression results that add one of the possible determinants of the branching index (from Table 4) to the interest rate models from Table 3. These regressions include the same set of other variables, which we do not report to save space in presenting the results. The pooled model is not estimated because the state characteristics are not identified with state fixed effects. 1 In the 1993 sample, we assign the 2003 value for the branching index; in the pooled model, in contrast, we assign the value 4 to all states in 1993 since this is the most restrictive value that the variable takes.

1993 (Control Group) 1998 2003 Coefficient T-stat Coefficient T-stat Coefficient T-stat Panel A: Index of branching restrictions (4 is most, 0 least, restricted) 1 0.015 0.84-0.024 1.51 0.014 0.93 Small bank share of all banking assets in state 0.007 1.06-0.004 0.64 0.007 1.17 1,631 770 1,720 10.96% 14.89% 11.68% Panel B: Index of branching restrictions (4 is most, 0 least, restricted) 0.020 1.18-0.027 2.07 0.012 0.80 Relative size of insurance to banking plus insurance 0.001 0.40 0.001 0.32 0.001 0.44 1,631 770 1,720 10.86% 14.86% 11.60% Panel C: Index of branching restrictions (4 is most, 0 least, restricted) 0.019 1.14-0.033 2.66 0.013 0.86 Small firm share of the number of firms in the state 0.007 0.70 0.022 1.87-0.008 1.15 1,631 770 1,720 10.89% 15.19% 11.64% Table XIIIA Probit Regressions if Most-Recent Loan is Guaranteed on State, Bank and Borrower Characteristics Controlling for possible determinants of the Branching Index: Weighted Results Panel D: Index of branching restrictions (4 is most, 0 least, restricted) 0.019 1.18-0.025 1.82 0.011 0.75 Capital ratio of small banks relative to large banks in state (%) -0.001 0.07 0.004 2.44 0.002 0.95 1,631 10.85% 770 14.98% 1,720 11.61% Panel E: Index of branching restrictions (4 is most, 0 least, restricted) 0.020 1.23-0.030 2.50 0.011 0.75 Indicator if Governor is Democrat 0.015 0.36 0.095 2.75-0.002 0.05 1,631 10.86% 770 15.36% 1,720 0 Panel F: Index of branching restrictions (4 is most, 0 least, restricted) 0.011 0.76-0.028 1.90 0.013 0.77 Share of state legislature that are Democrat 0.097 2.55 0.001 0.02-0.013 0.30 1,631 11.33% 770 14.86% 1,720 11.59% Each panel reports regression results that add one of the possible determinants of the branching index (from Table 4) to the interest rate models from Table 3. These regressions include the same set of other variables, which we do not report to save space in presenting the results. The pooled model is not estimated because the state characteristics are not identified with state fixed effects. All of the regressions are weighted by the sample weights that account for disproportionate sampling and unit nonresponse. 1 In the 1993 sample, we assign the 2003 value for the branching index; in the pooled model, in contrast, we assign the value 4 to all states in 1993 since this is the most restrictive value that the variable takes.

1993 (Control Group) 1998 2003 Coefficient T-stat Coefficient T-stat Coefficient T-stat Panel A: Index of branching restrictions (4 is most, 0 least, restricted) 1 0.018 1.47 0.002 0.16 0.021 1.80 Small bank share of all banking assets in state 0.002 0.27-0.005 0.88-0.001 0.46 1,631 770 1,720 8.94% 6.36% Panel B: Index of branching restrictions (4 is most, 0 least, restricted) 0.019 1.46-0.002 0.21 0.020 1.87 Relative size of insurance to banking plus insurance -0.001 0.42-0.002 1.22-0.002 1.04 1,631 770 1,720 8.95% 10.88% 6.40% Panel C: Index of branching restrictions (4 is most, 0 least, restricted) 0.017 1.31-0.006 0.60 0.021 1.85 Small firm share of the number of firms in the state 0.010 1.20 0.014 1.14-0.002 0.33 1,631 770 1,720 9.01% 10.96% 6.36% Table XIIIB Probit Regressions if Most-Recent Loan is Guaranteed on State, Bank and Borrower Characteristics Controlling for possible determinants of the Branching Index: Unweighted Results Panel D: Index of branching restrictions (4 is most, 0 least, restricted) 0.019 1.47-0.003 0.25 0.021 1.91 Capital ratio of small banks relative to large banks in state (%) -0.001 0.80-0.001 0.36-0.002 1.29 1,631 8.96% 770 10.81% 1,720 6.41% Panel E: Index of branching restrictions (4 is most, 0 least, restricted) 0.020 1.44-0.004 0.40 0.020 1.92 Indicator if Governor is Democrat 0.016 0.47 0.110 1.90-0.002 0.09 1,631 8.95% 770 1,720 6.36% Panel F: Index of branching restrictions (4 is most, 0 least, restricted) 0.010 0.95-0.004 0.39 0.020 1.80 Share of state legislature that are Democrat 0.109 3.52 0.019 0.66 0.006 0.18 1,631 9.55% 770 10.83% 1,720 6.36% Each panel reports regression results that add one of the possible determinants of the branching index (from Table 4) to the interest rate models from Table 3. These regressions include the same set of other variables, which we do not report to save space in presenting the results. The pooled model is not estimated because the state characteristics are not identified with state fixed effects. 1 In the 1993 sample, we assign the 2003 value for the branching index; in the pooled model, in contrast, we assign the value 4 to all states in 1993 since this is the most restrictive value that the variable takes.

1993 (Control Group) 1998 2003 Coefficient T-stat Coefficient T-stat Coefficient T-stat Panel A: Index of branching restrictions (4 is most, 0 least, restricted) 1-0.053 0.51 0.090 0.49 0.005 0.03 Small bank share of all banking assets in state 0.039 0.68-0.119 0.99-1.070 1.82 1,636 788 1,641 Panel B: Index of branching restrictions (4 is most, 0 least, restricted) -0.032 0.32-0.005 0.03-0.036 0.23 Relative size of insurance to banking plus insurance -0.016 1.17 0.019 0.80 0.009 0.42 1,636 788 1,641 16.87% 17.52% 24.18% Panel C: Index of branching restrictions (4 is most, 0 least, restricted) -0.028 0.27-0.200 0.11-0.031 0.19 Small firm share of the number of firms in the state -0.023 0.39 0.055 0.35-0.048 0.42 1,636 788 1,641 Panel D: Index of branching restrictions (4 is most, 0 least, restricted) -0.031 0.29 0.028 0.15-0.043 0.27 Capital ratio of small banks relative to large banks in state (%) -0.008 0.58 0.050 0.69 0.030 1.74 1,636 788 1,641 16.81% 17.65% 24.27% Panel E: Index of branching restrictions (4 is most, 0 least, restricted) -0.049 0.45-0.006 0.03-0.046 0.29 Indicator if Governor is Democrat -0.344 1.36-0.012 0.02-0.192 0.39 1,636 788 1,641 16.91% 17.46% 24.19% Panel F: Index of branching restrictions (4 is most, 0 least, restricted) -0.017 0.16-0.066 0.32 0.020 0.13 Share of state legislature that are Democrat -0.145 0.52 0.549 1.00-0.662 1.26 1,636 788 1,641 Table XIVA Regressions if Most-Recent Loan Maturity on State, Bank and Borrower Characteristics Controlling for possible determinants of the Branching Index: Weighted Results 16.85% 17.73% 24.41% 16.80% 17.48% 24.18% Each panel reports regression results that add one of the possible determinants of the branching index (from Table 4) to the interest rate models from Table 3. These regressions include the same set of other variables, which we do not report to save space in presenting the results. The pooled model is not estimated because the state characteristics are not identified with state fixed effects. All of the regressions are weighted by the sample weights that account for disproportionate sampling and unit nonresponse. 1 In the 1993 sample, we assign the 2003 value for the branching index; in the pooled model, in contrast, we assign the value 4 to all states in 1993 since this is the most restrictive value that the variable takes. 24.39%

1993 (Control Group) 1998 2003 Coefficient T-stat Coefficient T-stat Coefficient T-stat Panel A: Index of branching restrictions (4 is most, 0 least, restricted) 1-0.041 0.54-0.027 0.20-0.131 1.25 Small bank share of all banking assets in state -0.029 0.56-0.080 1.00-0.082 2.23 1,636 788 1,641 15.96% 16.20% 15.07% Panel B: Index of branching restrictions (4 is most, 0 least, restricted) -0.052 0.76-0.090 0.72-0.169 1.75 Relative size of insurance to banking plus insurance -0.010 0.73-0.010 0.43 0.001 0.01 1,636 788 1,641 Panel C: Index of branching restrictions (4 is most, 0 least, restricted) -0.038 0.55-0.085 0.68-0.166 1.76 Small firm share of the number of firms in the state -0.069 1.22-0.024 0.18-0.025 0.24 1,636 16.01% 788 16.06% 1,641 14.86% Panel D: Index of branching restrictions (4 is most, 0 least, restricted) -0.051 0.75-0.077 0.60-0.171 1.83 Capital ratio of small banks relative to large banks in state (%) 0.019 3.19 0.018 0.47 0.027 1.82 1,636 788 1,641 Panel E: Index of branching restrictions (4 is most, 0 least, restricted) -0.048 0.66-0.097 0.80-0.185 1.84 Indicator if Governor is Democrat 0.055 0.32 0.261 0.67-0.313 1.07 1,636 788 1,641 15.95% 16.10% 14.94% Panel F: Index of branching restrictions (4 is most, 0 least, restricted) -0.045 0.62-0.126 0.86-0.164 1.70 Share of state legislature that are Democrat -0.063 0.33 0.360 0.69-0.069 0.24 1,636 788 1,641 Table XIVB Regressions if Most-Recent Loan Maturity on State, Bank and Borrower Characteristics Controlling for possible determinants of the Branching Index: Unweighted Results 15.97% 16.02% 15.95% 16.07% 14.85% 16.09% 15.00% 16.13% 14.86% Each panel reports regression results that add one of the possible determinants of the branching index (from Table 4) to the interest rate models from Table 3. These regressions include the same set of other variables, which we do not report to save space in presenting the results. The pooled model is not estimated because the state characteristics are not identified with state fixed effects. 1 In the 1993 sample, we assign the 2003 value for the branching index; in the pooled model, in contrast, we assign the value 4 to all states in 1993 since this is the most restrictive value that the variable takes.