The full text on this page is automatically extracted from the file linked above and may contain errors and inconsistencies.
Federal Reserve Bank of Chicago Finance as a Barrier to Entry: Bank Competition and Industry Structure in Local U.S. Markets Nicola Cetorelli and Philip E. Strahan WP 2004-04 FINANCE AS A BARRIER TO ENTRY: BANK COMPETITION AND INDUSTRY STRUCTURE IN LOCAL U.S. MARKETS* Nicola Cetorelli Federal Reserve Bank of Chicago, Wharton Financial Institutions Center & University of California, Davis & Philip E. Strahan Boston College, Wharton Financial Institutions Center & NBER November 10, 2003 Abstract This paper tests how competition in local U.S. banking markets affects the market structure of non-financial sectors. Theory offers competing hypotheses about how competition ought to influence firm entry and access to bank credit by mature firms. The empirical evidence, however, strongly supports the idea that in markets with concentrated banking, potential entrants face greater difficulty gaining access to credit than in markets where banking is more competitive. JEL Classification Codes: L2, G2, G3 * The views expressed herein are those of the authors and not necessarily those of the Federal Reserve Bank of Chicago or the Federal Reserve System. We thank seminar participants at Boston College, the Federal Reserve Banks of Chicago and New York, The Ente Einaudi of Rome, Italy and Wesleyan University. Page 1 of 42 I. Introduction Economic research has focused intensely in recent years on the role played by financial markets for real economic activity. Based on ideas tracing back at least to Schumpeter (1912), and inspired by the early contributions of Goldsmith (1969), Gurley and Shaw (1955), and McKinnon (1973), the work of King and Levine (1993 a,b), Demirguc-Kunt and Maksimovic (1998), Levine and Zervos (1998), Rajan and Zingales (1998), Levine, Loayza and Beck (2000), among others, has provided robust empirical evidence that broader, deeper financial markets are strongly associated, causally, with better prospects for future economic growth. Having established this basic finding, the research effort is now focused on the analysis of the mechanisms through which finance affects real economic activity. What are the specific characteristics of financial markets that seem to affect firms and industries in non-financial sectors of production? For example, does it matter whether banks are privately or government owned (La Porta, Lopez-de-Silanes and Shleifer, 2001), or whether there is higher or lower protection for financial contracts (Levine, 1999), or whether banks are in a more or less competitive environment (Jayaratne and Strahan, 1996, Cetorelli and Gambera, 2001)? And, what specific characteristics of firms and industries are especially affected by finance so that it eventually translates into higher economic activity? This paper contributes to this line of research by investigating the role of well-defined characteristics of banking markets on equally well-defined industry characteristics in production sectors. More precisely, we investigate the impact of bank concentration and bank deregulation on measures of industry structure in non-financial sectors. We ask whether concentration of Page 2 of 42 market power in banking has an effect on the number of firms in a given sector, on the average size of existing firms in a sector, and on the overall firm size distribution within a sector. Using data on U.S. local markets for banking and non-financial sectors, we find that more vigorous banking competition – that is, lower concentration and looser restrictions on geographical expansion -- is associated with more firms in operation and with a smaller average firm size. In fact, the whole firm-size distribution shifts toward the origin as our measures of banking competition increase. Because we exploit data at the industry level, we are able to control for alternative (omitted) variables that may drive market structure both within and outside banking by exploiting differential reliance on bank finance across industrial sectors. Whether bank competition is “good or bad” for economic activity has been and continues to be a lively topic of research and policy analysis.1 In addition to the conventional argument that concentration of market power in banking means lower equilibrium amounts of credit, it has also been claimed that banking market power is actually needed for banks in order to establish valuable lending relationships.2 Hence, whether more or less competition in banking is socially desirable is still under discussion. This paper thus contributes to expand our understanding of the economic role of bank concentration and competition. The number of competitors in a sector, the average firm size and the composition between small and large firms are all important factors having a bearing on conduct and market performance. They are therefore important determinants of the sector’s capital accumulation and 1 A conference titled “Bank Competition: Good or Bad?” was organized in 2000 by The Wharton School and the Center for Financial Studies at Frankfurt University. More recently, in 2003, two conferences on the role of bank concentration and competition have been organized by the World Bank and by the Cleveland Fed-JMCB. 2 There is also a heated debate (outside the scope of this paper) on the potential effect of banking market structure on systemic risk and overall financial fragility. See, e.g. Hellman, Murdock, and Stiglitz (2000), Beck, Demirguc-Kunt and Levine (2003) and Boyd, De Nicolo’ and Smith (2003). Page 3 of 42 growth and consequently of the sector’s contribution to the overall level of economic activity. Various related streams of literature have focused on determinants of product market competition (e.g., Brander and Lewis (1986), Chevalier (1995), Kovenock and Phillips (1995, 1997), Maksimovic (1988)), on firm size (e.g., Kumar, Rajan and Zingales (2001), Campbell and Hopenhayn (2002)) and on firm-size distribution and more general industry dynamics (e.g., Lucas (1978), Jovanovic, (1982), Evans (1987), Hopenhayn (1992)). This paper relates to these parallel lines of research and makes a contribution bridging them together. Our evidence is consistent with that documented in several recent papers focusing on banking concentration and competition policies across countries. Cetorelli (2001) provides evidence of larger average firm size in countries with more concentrated banking. Along similar lines, Cetorelli (2003a) finds that enhanced bank competition following passage of the Second European Banking Directive brought a reduction in average firm size. Matching data on job creation and destruction in US manufacturing sectors with banking data across US markets, Cetorelli (2003b) shows that more bank concentration implies less entry and thriving of younger firms and also delayed exit of older firms. Again based on cross-country data, Beck, DemirgucKunt and Maksimovic (2003) find that higher bank concentration is associated with more financing obstacles, especially for smaller firms. In contrast, Bonaccorsi and Dell’Arriccia (forthcoming) find that concentration in banking reduces entry rates for Italian firms in industries with relatively opaque assets (i.e. few intangible assets) relative to entry in industries with less asset opacity. Our study is an important addition to this literature because we are able to measure banking structure at the local level rather than at the country level. Thus, our data offer a distinct Page 4 of 42 advantage because much of the research on bank market power suggests that the relevant geographical market for banking services, especially for small firms or potential entrepreneurs, is local (see, for example, Berger, Demsetz and Strahan, 1999). Moreover, this is the first paper that explores not only how average firm size responds to banking competition, but how the whole size distribution responds. By doing so, we are able to test more directly whether more or less bank competition is beneficial for all firms in a sector, or whether instead the effect may be different for firms in distinct size classes. In the remainder of the paper we first flesh out the theoretical links between banking concentration and industrial structure in order to motivate our empirical tests (Section II). In Section III, we present the data set and the main variables used in the analysis. Section IV documents the empirical results, and Section V concludes. II. Estimation Strategy How does bank competition affect the market structure of non-financial industries? As pointed out in Cetorelli (2001), several countervailing forces are potentially in play. The first force emphasizes that lending to opaque firms requires the bank and the borrower to forge a long-term relationship. Information gained over the course of time by the bank can be used to make value-enhancing credit decisions (i.e. expand credit to “winners” and restrict credit to “losers”). Banks can sustain the cost of starting a relationship with unknown, risky entrepreneurs, however, only if market power allows them to recoup the cost at later stages if Page 5 of 42 such entrants turn out to be successful (Petersen and Rajan, 1995).3 To the extent that it forecloses the opportunity to extract profits from successful relationships, vigorous competition may mitigate banks’ willingness to invest in relationships at all. This force, applied to our case, suggests that banks with market power should guarantee more industry entry than competitive banks would. Consequently, and ceteris paribus, one should expect to find more firms in an industry, a lower average firm size, and a larger prevalence of small rather than large firms where banks have more market power. Two countervailing forces suggest that market power may both dampen entry and reduce the relative importance of smaller firms. First, bank market power may reduce credit availability generally. This standard channel, whereby increased concentration in banking leads to less credit supply and higher loan prices, justifies antitrust enforcement. While less credit hurts all firms, smaller firms and potential entrants are likely to be more reliant on bank credit than larger and better established firms. Thus, these smaller firms may be harmed more by reduced credit supply than larger firms. In addition to this standard channel, banks with market power may tend to favor their established borrowers over new borrowers. The value of a bank’s current lending relationships will depend on the incumbent borrowers’ future profitability, which in turn depends on prospective entry and growth of new competitors. A bank’s incentive to support the profitability of its older clients could thus restrain its willingness to extend credit to potential industry entrants (or emerging small firms). In recent papers, Spagnolo (2000) and Cestone and White (forthcoming) have presented theoretical frameworks in which existing lending relationships do 3 Another solution is for the lender to hold an equity, or equity-like, claim against the establishment, as is commonly Page 6 of 42 indeed affect the behavior of lenders vis-à-vis potential new borrowers.4 The less competitive the conditions in the credit market, the lower the incentive for lenders to finance newcomers. Hence, banking concentration (as well as regulatory impediments to competition) can represent a form of financial barrier to entry in product markets. Banking market power may lead to fewer firms, a larger average firm size, and a higher proportion of large firms in markets where banks have more market power. These ideas suggest that bank concentration and competition play a key role in determining industrial structure, particularly if banks choose to privilege their older clients rather than potential new entrants in the same industries, or the other way around. Indeed, this latter conjecture is in large measure about competition for funding between industry incumbents and newer entrants. Consequently, it may be the case that bank competition is good for some firms but it is bad for other firms within the same sector. The difficulty in empirical implementation is that there may be common factors that drive the structure of both banking and industrial sectors that are difficult to measure and thus control in a regression. For industry-specific technological reasons, however, sectors differ in their dependence on external sources of finance (Rajan and Zingales, 1998). Our empirical strategy exploits these differences. Firms in sectors more dependent on bank finance ought to be affected more by variation (both across time and across states) in banking competition. Related to the specifics of our regressions, we emphasize an interaction term between bank dependence observed in the venture capital industry. 4 Also related are the contributions of Battacharya and Chiesa (1995) and Helmann and Da Rin (2002). Page 7 of 42 (described below) and several measures of banking competition. That is, we estimate models fitting into the following general structure: Ys,j,t = β (Bank Dependence)j(Banking Competition)s,t + Control Variables + Fixed Effects + es,j,t (1), where the unit of observation varies across states (s), industries(j) and time(t). In estimating equation (1), we use several measures of industry structure (Ys,j,t) and several measures of banking competition (described below). Moreover, because our dataset varies across three dimensions, we are able to control for local demand conditions (by controlling for state-specific, time-varying fixed effects), as well as industry-specific technological trends (by including industry-specific, time-varying fixed effects). By exploiting differences in bank dependence across industries, we can also effectively control for trends in structure that are specific to the local area (and common to both banks and industrial sectors) by focusing on the interaction effect (β).5 This identification strategy minimizes the risk that our results will be driven by either reverse causality (changes in industry structure driving changes in banking structure), or by an omitted factor (one that drives both banking structure and the structure of non-financial businesses). Finally, because we have data over a long span of time, we can exploit policy innovations that relaxed restrictions on banks’ ability to expand both within and across state lines that occurred during our sample period (again, described below). III. Variable Definitions and Data Sources 5 This identification strategy was first advocated by Rajan and Zingales (1998) in linking predetermined measures of financial depth to cross-country growth rates. Specifically, they emphasize that because financial depth has a greater correlation with future growth for financially dependent sectors than for other sectors, the correlation reflects a causal chain running from finance to growth rather than the other way around. Page 8 of 42 Our panel data starts in 1977, the beginning of the period of dramatic state-level deregulation. We end the sample in 1994, when deregulation of restrictions on banks’ ability to expand across local markets was completed with passage of the Riegle-Neal Interstate Banking and Branching Efficiency Act. After 1994, it becomes increasingly less plausible to view markets in banking as local, both because of the completion of deregulation and because the advent of new technologies in bank lending began allowing banks to lend to borrowers not physically close to their bank. For example, Petersen and Rajan (2002) show that banks during the 1990s are much more likely to lend over long geographic distances than they were in the 1970s. Also, banks began branching across state lines in 1995, making it impossible to construct measures of bank size and banking productivity at the state level after that point.6 Constructing an Instrument for Bank Dependence As we discussed in the previous section, the effects of banking competition on industry structure ought to depend on the relative bank dependence of firms in an industry. The trick empirically is to construct a measure of bank dependence that reflects demand for bank finance, rather than one that confounds demand-side effects with variation in the availability of credit supply from banks.7 We construct an instrument for bank dependence using information from Compustat, and justify that choice by documenting its high correlation with small firms’ actual use of bank (and other intermediary) funds. Table 1 reports three measures of external financial dependence. The first two are based 6 For example, NationsBank consolidated banks from several other states into its primary North Carolina bank (NationBank NC N.A.), leading to an increase of this bank’s assets from $31 billion in 1994 to $79 billion in 1995. 7 Variation in bank credit supply introduces noise (measurement error) into the actual use by small firms of bank finance. Moreover, the extent of that noise will be greater for firms in industries that are more bank dependent. Page 9 of 42 on Compustat firms, and the third comes from the 1998 Survey of Small Business Finance. Our procedure for the first two measures follows closely the one described in Rajan and Zingales (1998). Our key identifying assumption, as in Rajan and Zingales, is that the use of finance by Compustat firms will allow us to observe their demand for external funds. These firms are large and well established, with access to well-developed U.S. securities markets. Hence, there is little danger that observed financial policy will be skewed by constraints on the supply side. We begin by taking all of the Compustat firms between 1980 and 1997 and define any firm that was on Compustat for more than 10 years as “mature”, and any firm in Compustat for 10 years or less as “young”. Next, we sum across all years each firm’s total capital expenditures (Compustat item #128) minus cash flow from operations (item #110) net of changes in inventories, account receivable and accounts payable.8 This sum equals the total external funds needed to finance the firm’s investments. If the total is negative, it means the firm had free cash flow available for disbursement to shareholders or to pay down debt; otherwise, the firm needed to raise additional capital to finance its investment. We then divide this free cash flow figure by total capital expenditure. After constructing this ratio for each firm, we use the median value for all firms in each 2-digit SIC category.9 Panel A of Table 1 reports this measure of external financial dependence for both the mature and the young Compustat firms. The figures show, as expected, that the mature firms have a lower need for external finance than the younger firms. For example, the median value for the mature firms is exactly zero, compared to 0.41 for young firms. Looking across sectors, 8 These items are only defined for cash flow statements with codes 1, 2 or 3. For format code 7, we use the sum of items #123, 125, 126, 106, 213 and 217. Page 10 of 42 we find that leather and leather products, tobacco manufactures and apparel have the lowest need for external funds as mature firms, whereas electronic equipment and instruments and related products exhibit the highest need for external finance. In contrast, all of the “young” firms are using external funds. For each 2-digit SIC manufacturing sector, we also construct the share of small firms’ assets financed with debt (loans, capital leases and lines of credit) from financial institutions (“loans”) for the median firm (Table 1, column 3). These loans are supplied mainly by commercial banks (70% of the surveyed firms use banks for credit), but they also include some funds from other depository institutions (thrifts, credit unions) as well as unregulated finance companies (Bitler, Robb and Wolken, 2001). This variable represents the actual use of bank finance by small firms, as a share of their balance sheet.10 The data for the loans-to-asset ratio are taken from the 1998 Survey of Small Business Finance. This survey was conducted by the Federal Reserve and covers a sample of 3,561 small firms with fewer than 500 employees. The sample was designed to be nationally representative, but was structured to ensure representation across firm-size categories, location, and race of the owner. Panel B of Table 1 reports the correlation between the three measures of financial dependence. External financial dependence across industries for mature Compustat firms exhibits a high correlation with the loans-to-assets ratios for small firms (ρ=0.51). This high correlation suggests that financial dependence for mature Compustat firms makes a powerful instrument for small firms’ demand for bank credit. In contrast, there is no correlation between 9 Rajan and Zingales also use a somewhat shorter sample from Compustat to construct their external dependence measures. Page 11 of 42 the use of external funds by young Compustat firms and the loan-assets ratio for small businesses. These young firms have recently gone public, presumably because they have a (temporarily) high demand for funds due to their strong growth and investment opportunities. Thus, the measured “external dependence” for the young firms will not reflect financing demands, either of typical start-ups and other small firms, or of well-established and stable firms. In our main set of specifications, we use an indicator for external dependence for the mature Compustat firms (equal to one if the use of external funds positive) as our instrument for an industry’s bank dependence. For robustness, we report results using observed financial dependence for mature Compustat firms (rather than a zero-one indicator variable) and results using the actual loans-to-assets ratio for small firms. Competition in the Local Banking Market We focus on several measures of competition in the local banking industry. Our first two measures exploit policy innovations. Restrictions on bank expansion across geographical boundaries in the United States date back to the nineteenth century. Although there was some deregulation of branching restrictions in the 1930s, most states either prohibited branching altogether (the “unit banking” states) or limited branching until the 1970s, when only twelve states allowed unrestricted statewide branching. Between 1970 and 1994, however, 38 states deregulated their restrictions on branching (see Jayaratne and Strahan, 1996, Kroszner and Strahan, 1999 and Stiroh and Strahan, forthcoming). In addition to facing restrictions on in-state branching, the Douglas Amendment to the 1956 Bank Holding Company Act prohibited a bank holding company from acquiring banks 10 The Survey of Small Business Finance only reports data for a single year, hence balance sheet measures are more Page 12 of 42 outside the state where it was headquartered unless the target bank’s state permitted such acquisitions. Since no state allowed such transactions in 1956, the amendment effectively barred interstate banking organizations. Starting in the earlier 1980s, many states began to enter regional or national reciprocal arrangements whereby their banks could be bought by any other state in the arrangement. This history presents us with a convenient way to test how industry structure in non-financial sectors has been affected by the increased competition (real and potential) in banking that followed state-level deregulation.11 We capture the effects of each type of deregulation by including an indicator equal to one after a state permits branching by means of merger and acquisition within its borders, and another indicator equal to one after a state permits interstate banking (that is, after a state allows bank holding companies in other states to buy their banks).12 The two types of deregulation are somewhat distinct in their effects. Deregulation of restrictions on branching reduces entry barriers into new markets and also enhances the corporate takeover market by making it easier for banks to gain control over other bank’s assets. With full branching deregulation, a bank may enter a new market, either by buying existing branches or by opening new branches. Also, the cost of acquiring another bank is reduced because an acquiring bank may merge the target bank’s operation into its existing franchise. By reducing entry barriers, branching deregulation constrains banks’ ability to exploit market power. Interstate banking deregulation, however, only affects who can own bank assets. Prior to deregulation, only bank holding companies representative of financial policy than flow-based measures using capital expenditures and gross cash flow. 11 Deregulation of restrictions on bank expansion, both within and across states, has been shown to improve bank efficiency, to enhance corporate control, and to limit market power. See Jayaratne and Strahan (1998). Page 13 of 42 located within a state may buy banks chartered in that state, while, after deregulation, bank holding companies operating in other states may do so. In addition to looking at changes in competition induced by deregulation of the industry, we also include a direct measure of local market concentration, equal to the deposit HerfindahlHirschmann Index (HHI). The HHI is calculated as the deposit-weighted average of the HHI indexes of the Metropolitan Statistical Areas (MSAs) in a state/year. The Herfindahl index for each local market is defined as the sum of squared market shares, where market shares are based on branch-level deposit data from the FDIC’s Summary of Deposits dataset.13 So, for example, if a bank owned 10 branches within an MSA, this bank’s market share would equal the sum of all of its deposits in those 10 branches, divided by the total deposits held in by all bank branches within that market. For a market with a single bank owning all of the branches, the HHI would equal one, whereas in a perfectly atomistic market the HHI would approach zero. Industrial Structure Establishment counts and number of workers per establishment are available at a disaggregated level on an annual basis from the County Business Patterns, which is an annual survey by the Census Bureau. These data provide the best way to consider industry structure over a long span of time at a disaggregated level. Moving to a more finely disaggregated level, either by industry SIC code or by locality, creates substantial difficulties with missing values, so 12 Most states first permit banks to branch by buying existing branches in new markets or by purchasing whole banks and then creating branches out of the purchased bank’s offices. Then, states typically open up their markets to unrestricted branching in which banks may open new branches anywhere in the state. 13 The deposit HHI is the standard tool used in antitrust oversight of bank mergers. Local markets (usually MSAs or non-MSA counties) with HHI below 1800 are deemed to be served by enough banks to assume that conditions are competitive. For localities with HHI above 1800, antitrust concerns by the Federal Reserve and the Department of Justice are sometimes raised. See Berger, Demsetz and Strahan (1999) for an overview of bank mergers and antitrust policy. Page 14 of 42 we have decided to focus on the 2-digit level of aggregation by industry and the state level for geography. We focus on just industries within the manufacturing sector. From this data set, we compute the total number of establishments in an industry/state/year (in logs), and the average establishment size (log of workers per establishment). As shown in Table 2, there are, on average, 0.07 establishments per capita, and the average establishment has 69 workers. Before moving forward, it is worth noting that our data are based on employment at establishments rather than at firms. An establishment is an economic unit where production occurs, such as a plant, a factory, or a restaurant that employs people. So, there is some measurement error in our dependent variable induced by the fact that large firms often own many establishments. Nevertheless, we think that the number of new establishments ought to be highly correlated with the economic quantity that we are trying to observe. Early research has shown, for example that the rate of creation of new businesses is correlated with the share of new establishments in a local economy (Black & Strahan, 2002). The existence of a close correlation between the number of establishments and the number of firms is also documented in Cetorelli (2001) for a cross-section of countries. To characterize the whole distribution of establishment sizes, we construct the share of establishments in an industry, state and year in each of four categories: establishments with fewer than five employees, establishments with fewer than 20 employees, establishments with fewer than 100 employees and establishments with fewer than 250 employees. Unconditionally, 31 percent of establishments are in the smallest size category, while 94 percent of establishments have fewer than 250 establishments (Table 2). Chart 1 presents a picture of the cumulative distribution of establishments across these Page 15 of 42 four size categories, where each probability is constructed using the simple mean across industries, states and time during a “pre-deregulatory” period (1977-82) and a “postderegulatory” period (1990-94).14 The figure shows a clear migration of the size distribution toward the smaller firms, with a five percentage point increase in the share of establishments with fewer than five employees (29% to 34%), a five percentage point increase in the share of establishments with fewer than 20 employees (60% to 65%), a three point increase in the share with fewer than 100 employees (85% to 88%), and a two point increase for establishments with under 250 employees. Given the regulatory changes that enhanced competition during the 1980s, the movement of the distribution is, on its face, consistent with the idea that more vigorous banking competition helps the prospects of small firms and potential entrants. At the same time, some of the long-run trends are likely to be the result, in part, of common factors (e.g. technology) affecting market structure in both banking and non-financial sectors. Recognizing the importance of these other factors, our identification strategy is to emphasize the differential effects of bank competition across bank dependent and non-dependent sectors. By differencing out the effect of bank competition in the non-dependent sectors, we can construct a simple “difference-in-differences” estimate of the effect of changes in bank competition on industry structure. Table 3 illustrates the intuition behind this strategy using the raw data for average establishment size and for a single slice the cumulative size distribution (the fraction of establishments with less than 100 employees).15 We have averaged the data across four clusters, by sectors with high and low financial dependence, and either by markets/years 14 State-level moves toward removal of restrictions on branching and interstate banking were largely completed by 1990, although completion of the deregulation occurred in 1994, with passage of the Interstate Banking and Branching Efficiency Act. Page 16 of 42 with high and low bank concentration or with tight and relaxed bank regulation. Starting with the first panel, rows one and two show that establishment size is higher among sectors with high external dependence. Similarly, comparing columns one and two, size is higher in un-concentrated markets. As mentioned above, these observed patterns may be the result, respectively, of confounding industry or market factors. To understand the role of bank competition, consistent with the theoretical priors illustrated earlier, we observe that in concentrated banking markets the average establishment has 30 more employees in highdependence sectors than in low-dependence ones. This difference goes down to 21 employees in un-concentrated markets. Hence, the data suggests that going from concentrated to unconcentrated banking markets, the difference in establishment size between high- and lowdependence sectors falls by about 33 percent (9/30). Similarly, the data in the second panel suggests that when moving from a regulated to a deregulated period, the difference in establishment size between high- and low-dependence sectors goes down by 12 employees, or approximately by 35 percent. Both mean differences are statistically significant at the one percent level of confidence. Moving to the size distribution data, the third panel shows that the difference in the share of establishments with fewer than 100 employees between high- and low-dependence sectors increases by 3.2 percentage points, i.e. there is a relatively larger mass of smaller firms in high dependence sectors in un-concentrated banking markets. The 3.2 percentage point increase is about 60% of the original difference. The data in the fourth panel give no particular indication of a differential effect of banking deregulation on the establishment size distribution, since the 15 For brevity we do not report data on number of establishments in this table. Page 17 of 42 calculated mean difference is small and not statistically significant. These simple mean comparisons illustrate the identification strategy and give a first indication consistent with the conjecture that in the absence of competition banks may tend to favor incumbents over potential new entrants in product markets. IV. Regression Results In presenting our regressions, we report three sets of results for each dependent variable. The first set includes a state fixed effects (fixed across time and industry), and time-varying industry effects (i.e. a dummy variable for each industry in each year, or 18 indicators per industry). In these first specifications (column 1), we can identify the direct effect of the measures of banking competition because these variables have both time variation and state variation. In the second specification, we include industry fixed effects (fixed across time and state) and time-varying state effects (i.e. 18 indicators per state), and in the third specification we allow both the state and industry effects to vary across time, thereby absorbing state-specific annual shocks and industry-specific annual shocks. In the latter two specifications, we can not identify the direct effects of competition, since these do not vary across industries. Recall that our identification strategy, however, is to focus on differences in the effects of banking on industry structure for financial dependent sectors relative to less dependent sectors. Thus, in all cases, the coefficients of interest are those in which the competition variables are interacted with the external dependence indicator. Control variables Page 18 of 42 At the same time that banking has become more competitive, we have seen a marked consolidation of the industry. A large number of research articles in recent years have raised the concern that this decline in small banks may be having adverse consequences for small borrowers.16 The theoretical motivation for this notion is that bank lending to some borrowers depends on “soft” information; that is, information that can not be verified or communicated credibly by the holder of that information to others. For example, an individual loan officer’s personal knowledge of a borrower’s character or her propensity to pay back a loan despite opportunities to shift costs onto the lender would be difficult to verify absent making the same investment in knowing the borrower’s character made by the original loan officer. Stein (2002) argues that smaller organizations can compensate workers more efficiently than large ones for investing in such “soft” information, thereby giving smaller banks a comparative advantage in making loans to small firms or to potential entrepreneurs. If scale economies in other aspects of banking (e.g. collection of deposits, back office functions, etc.) are sufficiently important (and scope economies tie these other functions to lending), then reductions in the presence of small banks motivated by such scale economies could reduce the supply of capital to small and new firms. 16 Although it is clear that small banks lend more of their assets to small establishments, the evidence that small banks lend better or more efficiently to small establishments is mixed. For example, some papers find that lending to small business increases when small banks are acquired, suggesting the increased scale increases a banks willingness to lend, while others find declines in lending following mergers. See Keeton (1996,1997), Peek and Rosengren (1996,1998), Strahan and Weston (1998), Craig and Santos (1997), Kolari and Zardkoohi (1997a,b), Zardkoohi and Kolari (1997), Berger, Saunders, Scalise, and Udell (1998), Sapienza (1998), Berger, Demsetz and Strahan (1999). More recently, studies have explored whether in small-bank-dominated markets small establishments have better or worse access to credit. Again, results are mixed. See Jayaratne and Wolken (1999), Black and Strahan (2002), and Berger, Miller, Petersen, Rajan, and Stein (2003) report evidence that large banks are more likely to interact with small borrowers in impersonal ways (e.g. via the telephone and mail). Page 19 of 42 We have also seen trends in banking toward greater use of information technologies to make lending decisions. As banks increasingly substitute automated information technologies (e.g. credit scores) for the judgment of human loan officers, firms which rely on less quantifiable, “soft” information may find credit more difficult to attain. Again, since small and new firms are least likely to be able to certify their quality with a long track record of audited financial statements, the advent of information technology in banking could be harmful to them, even if they reduce costs on average. To control for the effects of consolidation, we include the share of assets held by banks with assets under $100 million in the state in our regressions. For information technology, we construct a measure of bank labor productivity by simply dividing the real value of total operating income for all banks in a state-year (a broad-based measure of output that incorporates off-balance sheet activity) by total full-time equivalent employees at all banks in the state-year. In states where more output is produced by banks per worker, we infer that banks take more advantage of information technologies such as credit scoring models. In addition, we control for the overall share of employment in the state coming from the industry in question. This variable controls for possible differences across states and time in the importance of the industry to the overall state economy that may stem from exogenous factors such as, for example, the availability of natural resources. In our first set of specifications we control for these factors, although when we include state by year fixed effects, these coefficients are not identified. Table 4 reports results for the log of the number of establishments. We find a positive effect of interstate banking reform, with a coefficient suggesting that opening up the banking Page 20 of 42 industry to out-of-state entry leads to a 12 percent increase in the number of establishments (columns 1 and 3; the effect in column 2 is about 8 percent). This large increase suggests that there is significant entry of new firms after banking deregulation, which is consistent with Black and Strahan (2002), who find that the number of new incorporations in a state increases by 6 to 11 percent following interstate banking reform. The effect of banking concentration is also large, both statistically (t>4) and economically: a one standard deviation decrease in concentration comes with a 3 percent increase in the number of establishments. This effect is also similar in magnitude to the one reported for new incorporations in Black and Strahan (2002). For control variables, we find more establishments in industries with a larger share of total state employment. In the first specification, we also find that the number of establishments falls with the market share of small banks (consistent with Black and Strahan, 2002) and rises with personal income growth. We do not find, however, any statistically significant effect of bank labor productivity on the total number of establishments. These last three coefficients lose identification in the models with time-varying industry effects because we have no variation across industry: all industries in a given market face the same banking structure, the same level of bank productivity and the same local economic conditions. Table 5 reports a similar set of specifications with the log of average establishment size (employees per establishment) as the dependent variable. These results are consistent with those obtained for number of establishments, suggesting that average size falls as banking becomes more competitive. Following branching deregulation, establishment size falls slightly (by 1.8 percent), although the coefficient is not statistically significant. Following interstate banking Page 21 of 42 deregulation, establishment size falls more, by 5 to 10 percent, and the decline is statistically significant at the one percent level. A standard-deviation decrease in concentration is associated with a decrease in establishment size of a little more than 3 percent. Control variables suggest that average establishment size increases with an industry’s total share of employment and with economic growth in the state. Bank labor productivity appears negatively correlated with establishment size. Next, we report our results focusing on banking competition and the distribution of establishment size in Tables 6-9. Each table corresponds to the share of establishments in a given size bin. For example, Table 6 reports the logit of the share of establishments with fewer than 5 employees, Table 7 the share with fewer than 20 employees, etc.17 The results suggest, broadly, that increases in banking competition lead to increases in the importance of small firms across all size categories. That is, the size distribution of establishments shifts to the left (toward small establishments) as the banking industry becomes increasingly competitive. The results are therefore consistent with the notion that market power in banking creates a barrier to entry (or expansion) of relatively small firms. In contrast to the average size results where interstate banking reform was very important, competition’s effect on share of establishments in each size category is most statistically robust for the deposit-based HHI, our measure of local-market banking concentration. For example, we estimate a negative and significant coefficient on the interaction between the HHI with the external dependence indicator across all four size categories and 17 Since the logit (log of the odds) is undefined at 0 or 1, we replace the share with 0.005; for cases where the share was 1, we replace the share with 0.995. These changes do not affect our conclusions. The statistical significance of our results remain when we use actual shares (rather than logits). Page 22 of 42 across all three specifications of the fixed effects. The coefficient estimate is also stable across all three approaches to modeling the fixed effects. The results for the banking deregulation indicators, while consistent with the idea that banking competition helps small firms, are much less robust statistically than the results on concentration. We estimate a positive effect of deregulation of restrictions on bank branching (interacted with external dependence) across all models and for all four size categories, but the coefficient never achieves statistical significance at the five percent level. For deregulation of interstate banking, we find a small negative effect for the smallest size category (under 5 employees) and a small positive effect for the next largest category (under 20). For establishments with fewer than 100 or fewer than 250 employees, however, we estimate a statistically significant positive coefficient on the interstate banking interaction variable, suggesting that in states open to entry by out of state banks, there is a greater share of establishments in the small (but not too small) category. This effect loses statistical significance, however, when we leave out the industry-year fixed effects. To understand the economic magnitude of these effects, we have also estimated the models using the share of establishments in the various size bins, rather than the logit, which is harder to interpret due to its non-linearity. According to these results (not reported), for industries with above-median use of external finance, the share of establishments with fewer than 20 employees is 0.9 percentage points higher after branching deregulation than before, and 0.7 percentage points higher after interstate banking. For concentration, a one standard deviation decline in banking concentration would raise the share of establishments with under 20 employees by 0.7 percentage points (again, focusing on the interaction; that is, the effect pertains Page 23 of 42 to industries with greater-than-average using of external funds). For establishments with fewer than 100 employees, interstate banking reform increases their share by 1.6 percentage points, and a standard deviation decline in concentration increases their share by about 1.1 percentage points. So, a “competitive” banking market – one with interstate banking and low banking concentration – would have something like 2.7 percent more establishments with fewer than 100 employees, relative to an average market with about 86 percent of establishments in this category (the unconditional mean). This effect of competition seems particularly large relative to the increase in the share of establishments over the whole period of just three percentage points, from 85% in the early years to 88% in the later years (recall Chart 1). As anticipated earlier, for robustness we have also run regressions using observed financial dependence for mature Compustat firms (rather than the zero-one indicator variable) and using the actual loans-to-assets ratio for small firms. We present results for the most stringent model specification only, the one with state-year and industry-year fixed effects. As indicated in Table 10 and 11, all the results presented in the paper are strongly confirmed using these two alternative measures of external financial dependence. As a final test, we considered whether regulatory change has altered the effects of banking concentration on the firm-size distribution. Black and Strahan (2002) argue that after states opened up their local banking markets to outside entry, the effects of concentration ought to have been mitigated by the threat of entry. And, in fact, they find that the effect of concentration on the rate of creation of new incorporations does fall significantly with deregulation. We have run similar tests using our measure of the size distribution and the number of establishments. Specifically, we add variables interacting the deregulation variables Page 24 of 42 with our measure of bank concentration. In these regressions, we do estimate a generally positive coefficient (six out of eight) on these interaction terms, suggesting that the effects of concentration on the size distribution may be attenuated by regulatory reform, although the coefficients are not statistically significant (not reported). V. Conclusions We have found that banking competition in local U.S. markets has been associated with a greater number of establishments, a smaller average establishment size and a greater share of small establishments across the whole size distribution. Theory does not paint a clear picture about how competition in banking ought to affect the firm-size distribution, but the empirical work does. Comparing industry structure across local markets within the U.S., or comparing structure across a large number of countries (both developed and developing), one reaches the same conclusion. Our empirical evidence is consistent with the idea that banks with market power erect an important financial barrier to entry to the detrimant of the entrepreneurial sector of the economy, perhaps in part to protect the profitability of their existing borrowers. The evidence thus indicates that banking competition has a significant impact on important structural characteristics of sectors of production. Moreover, it indicates that such impact is not uniform across firms, but rather that depending on the degree of banking competition some firms will benefits while others will lose. This is an important insight updating the conventional wisdom that banking competition is either good or bad overall. The policy implications associated with this issue are especially relevant. Banking market structure is a traditional policy variable whose control regulators across countries and over time Page 25 of 42 often attempt to influence, although sometimes in conflicting ways. For example, in the United States bank mergers have sometimes been altered to avoid excessive concentration in local markets. At the same time, however, until the 1980s many states protected their banks from competition through branching and interstate banking restrictions. Similar restraints on competition have been common elsewhere; for example, many countries continue to protect their banks from foreign entry. One can well understand why political forces lead to tight restraints on banking competition if both incumbent banks and incumbent firms benefit from the restraints. In fact, Rajan and Zingales (2003) use historical evidence to argue very broadly that incumbent firms often fought hardest to prevent financial openness, sometimes leading to long-term declines in a country’s growth prospects. The good news is that many of the political, legal and regulatory barriers to bank expansion and competition have been dismantled. State-level restrictions were removed in the 1970s and 1980s in the United States, the E.U. is now open to cross-border banking, and foreign banks have made great inroads into Latin America. Our results suggest that these changes will help small and entrepreneurial firms gain access to credit from banks. It seems reasonable to suppose (or at least to hope) that lower financial barriers to entry and greater entrpreneurship will lead to faster growth. Page 26 of 42 References Bhattacharya, Sudipto and Gabriella Chiesa, 1995, “Proprietary Information, Financial Intermediation, and Research Incentives”, Journal of Financial Intermediation, October 1995. Beck, Thorsten, Asli Demirguc-Kunt and Vojislav Maksimovic, 2003, “Bank Competition, Financing Obstacles and Access to Credit,” mimeo, January 2003. Berger, Allen N., Rebecca S. Demsetz and Philip E. Strahan, 1999, “The Consolidation of the Financial Services Industry: Cause, Consequences and Implications for the Future,” Journal of Banking and Finance 23, 135-94. Berger, Allen N., Nathan H, Miller, Mitchell A. Petersen, Raghuram G. Rajan and Jeremy C. Stein, 2003, “Does Function Follow Organizational Form? Evidence from the Lending Practices of Large and Small Banks,” Mimeo, March 2003. Berger, Allen N., Anthony A. Saunders, Joseph K. Scalise, and Gregory F. Udell, 1998, “The Effects of Bank Mergers and Acquisitions on Small Business Lending,” Journal of Financial Economics 50. Bitler, Marianne P., Alicia M. Robb, and John D. Wolken, 2001, “Financial Services Used by Small Businesses: Evidence from the 1998 Survey of Small Business Finance,” Federal Reserve Bulletin, 183-205. Black, Sandra E. and Philip E. Strahan, 2002, “Entrepreneurship and Bank Credit Availability,” Journal of Finance 57(6), 2807-33. Bonaccorsi di Patti, Emilia and Giovanni Dell’Ariccia, forthcoming, “Bank Competition and Firm Creation,” Journal of Money, Credit and Banking. Boyd, John H., Gianni De Nicolo, and Bruce Smith, 2003, “Crisis in Competitive versus Monopolistic Banking Systems,” mimeo, March 10, 2003 Brander, James and Tracy Lewis, “Oligopoly and Financial Structure: The Limited Liability Effect”, The American Economic Review, Vol. 76, No. 5. (Dec., 1986), pp. 956-970 Campbell, Jeffrey R., and Hugo A. Hopenhayn, 2002, “Market Size Matters,” mimeo. Cetorelli, Nicola and Michele Gambera, 2001, “Bank Structure, Financial Dependence and Growth: International Evidence from Industria Data,” Journal of Finance. Cetorelli, Nicola, 2001, “Does Bank Concentration Lead to Concentration in Industrial Sectors?” Federal Reserve Bank of Chicago WP# 01-01, April 2001. Page 27 of 42 Cetorelli, Nicola, 2003a, “Real Effects of Bank Concentration and Competition in Europe” Cetorelli, Nicola, 2003b, “Life-Cycle Dynamics in Industrial Sectors. The Role of Banking Market Structure”, Quarterly Review, Federal Reserve Bank of St. Louis, Forthcoming Cestone, Giacinta and Lucy White, forthcoming, “Anti-Competitive Financial Contracting: The Design of Financial Claims,” Journal of Finance. Chevalier, Judith, 1995, “Capital Structure and Product Market Competition: Empirical Evidence from the Supermarket Industry,” American Economic Review. Craig, Ben R. and Joao A.C. dos Santos, 1997, “Banking Consolidation: Impact on Small Business Lending,” working paper, Federal Reserve Bank of Cleveland. Demirguc-Kunt, Asli and Vojislav Maksimovic, 1998, “Law, Finance and Firm Growth,” Journal of Finance 53, 2107-37. Evans, David S., 1987, “The Relationship Between Firm Growth, Size, and Age: Estimates for 100 Manufacturing Industries (in Competition in the Long Run)”, Journal of Industrial Economics, Vol. 35, No. 4, pp. 567-581. Goldsmith, Raymond, Financial Structure and Development, (New Haven: Yale University Press, 1969). Gurley, John G. and Edward S. Shaw, 1955, “Financial Aspects of Economic Development,” American Economic Review, 45, 515-38. Hellmann, Thomas and Marco Da Rin, 2002 , “Banks as Catalysts for Industrialization,” Journal of Financial Intermediation, 11, 366-397. Hellmann, Thomas, Kevin Murdock and Joseph Stiglitz, 2000, “Liberalization, Moral Hazard in Banking and Prudential Regulation: Are Capital Requirements enough?” American Economic Review, 2000, 90(1), 147-165. Hopenhayn, Hugo A., 1992, “Entry, Exit, and Firm Dynamics in Long Run Equilibrium,” Econometrica, Vol. 60, No. 5. (Sep., 1992), pp. 1127-1150. Jayaratne, Jith and Philip E. Strahan, 1996, “The Finance-Growth Nexus: Evidence from Bank Branch Deregulation,” Quarterly Journal of Economics 101, 639-70. Jayaratne, Jith and Philip E. Strahan, 1998, “Entry Restrictions, Industry Evolution and Dynamic Efficiency: Evidence from Commercial Banking,” Journal of Law and Economics 41, 239-74. Page 28 of 42 Jayaratne, Jith and John D. Wolken, 1999, “How Important are Small Banks to Small Business Lending? New Evidence from a Survey of Small Businesses,” Journal of Banking and Finance 23, 427-58. Jovanovic, Boyan, 1982, “Selection and Evolution of Industry,” Econometrica, Vol. 50, No. 3., pp. 649-670. Keeton, William R., “Do Bank Mergers Reduce Lending to Businesses and Farmers? New Evidence from Tenth District States,” 1996, Federal Reserve Bank of Kansas City, Economic Review 81 (3), 63-75. Keeton, William R., 1997, “The Effects of Mergers on Farm and Business Lending at Small Banks: New Evidence from Tenth District States,” working paper, Federal Reserve Bank of Kansas City. King, Robert and Ross Levine, 1993a, "Finance, Entrepreneurship and Growth," Journal of Monetary Economics, 32, 513-542. King, Robert and Ross Levine, 1993b, "Finance and Growth: Schumpeter Might Be Right," Quarterly Journal of Economics, 108, 717-738. Kolari, J., and A. Zardkoohi, 1997a, “The Impact of Structural Change in the Banking Industry on Small Business Lending,” Report to the Small Business Administration. Kolari, J., and A. Zardkoohi, A., 1997b, “Bank Acquisitions and Small Business Lending,” working paper, Texas A&M University. Kovenock and Phillips, 1995, “Capital Structure and Product Market Rivalry: How do we Reconcile Theory and Evidence?” American Economic Review 85, 408-408. Kroszner, Randall S. and Philip E. Strahan, 1999, “What Drives Deregulation? Economics and Politics of the Relaxation of Bank Branching Restrictions,” Quarterly Journal of Economics 114(4), 1437-67. Kumar, Krishna, Raghuram G. Rajan and Luigi Zingales, 2001, “What Determines Firm Size?” NBER Working paper no. 7208. Laporta, Rafael, Florencio Lopez-de-Silanes and Andrei Shleifer, 2001, “Government Ownership of Banks,” Journal of Finance. Levine, Ross, 1999, “Law, Finance and Economic Growth”, Journal of Financial Intermediation. Page 29 of 42 Levine Ross, Norman Loayza, and Thorsten Beck, 2000, “Financial intermediation and growth: Causality and Causes,” Journal Of Monetary Economics 46(1), 31-77. Levine, Ross and Sara Zervos, 1998, “Stock Markets, Banks and Economic Growth,” American Economic Review 88(3), 537-58. Lucas, Robert E., 1978, “On the Size Distribution of Firms, Bell Journal of Economics 9(2), 50823. Maksimovic, Vojislav, 1988, “Capital Structure in a Repeated Oligoploy,” Rand Journal of Economics, 389-407. McKinnon, Ronald, Money and Capital in Economic Development, (Washington, DC: Brookings Institution, 1973). Peek, Joe and Eric S. Rosengren, 1996, “Small Business Credit Availability: How Important is the Size of the Lender?” In Financial System Design: The Case for Universal Banking, edited by A. Saunders and I. Walter (Burr Ridge, IL, Irwin Publishing), 628-55. Peek, Joe and Eric S. Rosengren, 1998, “Bank Consolidation and Small Business Lending: It=s not Just Bank Size that Matters,” Journal of Banking and Finance 22. Petersen, Mitchell A. and Raghuram G. Rajan, 1994, “The Benefits of Firm-Creditor Relationships: Evidence from Small Business Data,” Journal of Finance 49, 3-37. Petersen, Mitchell A. and Raghuram G. Rajan, 1995, “The Effect of Credit Market Competition on Lending Relationships,” Quarterly Journal of Economics 110, 407-43. Petersen, Mitchell A. and Raghuram G. Rajan, 2001, “Does Distance Still Matter? The Informatation Revolution in Small Business Lending,” mimeo, March 2001. Rajan, Raghuram G. and Luigi Zingales, 1998, “Financial Dependence and Growth,” American Economic Review 88, 559-87. Rajan, Raghuram G. and Luigi Zingales, 2003, Saving Capitalism from the Capitalists, Crown Business Division of Random House. Sapienza, Paola, 1998, “The Effects of Banking Mergers on Loan Contracts,” Northwestern University working paper. Schumpeter, Joseph A. 1912. Theorie der Wirtschaftlichen Entwicklung. Leipzig: Dunker & Humblot, [The Theory of Economic Development, translated by Redvers Opie. Cambridge, MA: Harvard University Press, 1934.] Page 30 of 42 Stiroh, Kevin and Philip E. Strahan, forthcoming, “Competitive Dynamics of Deregulation: Evidence from U.S. Banking,” Journal of Money, Credit and Banking. Spagnolo, Giancarlo, 2000, “Debt as a (Credible) Collusive Device”, SSE Ec.&Fin. W.P. No. 243. Strahan, Philip E. and James P. Weston, 1998, “Small Business Lending and the Changing Structure of the Banking Industry,” Journal of Banking and Finance 22, 821-45. Zardkoohi, A. and J. Kolari, 1997, “The Effect of Structural Changes in the U.S. Banking Industry on Small Business Lending,” working paper, Texas A&M University. Page 31 of 42 Share of Establishments CHART 1 CDF for Establishments 100% 85% 88% 90% 93% 95% 80% 70% 60% 60% 65% 50% 40% 30% 29% 34% 20% Under 5 Under 20 Under 100 Number of Employees 1977-1982 1990-1994 Under 250 Table 1 External Financial Dependence for Manufacturing Sectors External External Dependence Median Dependence for Young Loans/Assets for Mature for 1998 SSBF Compustat Compustat Panel A: Medians by Industry Sector Firms Firms Firms (2-digit SIC) Leather and leather products (31) -0.96 0.14 0.04 Tobacco manufactures (21) -0.92 0.94 N/A Apparel and other textiles (23) -0.61 0.72 0.13 Food and kindred products (20) -0.24 0.48 0.12 Fabricated metal products (34) -0.24 0.35 0.27 Furniture and fixtures (25) -0.23 0.22 0.36 Stone, clay, glass, and concrete products (32) -0.20 0.40 0.31 Miscellaneous manufacturing (39) -0.20 0.91 0.28 Printing and publishing (27) -0.07 0.16 0.33 Instruments and related products (38) -0.04 1.82 0.29 Transportation equipment (37) 0.01 0.35 0.06 Industrial machinery and equipment (35) 0.01 0.97 0.21 Primary metal industries (33) 0.03 0.34 0.31 Lumber and wood products (24) 0.04 0.42 0.49 Rubber and plastic products (30) 0.04 0.23 0.30 Paper and allied products (26) 0.06 0.21 0.37 Petroleum and coal products (29) 0.09 0.68 0.60 Textile mill products (22) 0.10 0.23 0.47 Electrical and electronic equipment (36) 0.22 0.83 0.14 Chemicals and allied products (28) 0.28 3.04 0.33 0 0.41 0.30 Median Panel B: Correlation Matrix External Dependence, Mature Firms External Dependence, Young Firms Loans/Assets Small Firms External Dependence, Mature Firms 1 External Dependence, Young Firms 0.24 1 Loans/Assets Small Firms 0.51 0.03 1 External dependence equals the proportion of capital expenditures financed with external funds. A negative value indicates that firms have free cash flow, whereas a positive value indicates firms must issue debt or equity to finance their investment. The figures represent the median value for Compustat firms in each industry sector over the 1980 to 1997 period. Mature firms are those that have been on Compustat for 10 years or more; all other firms are defined as young. The rows are sorted by the external finance measure for mature firms, which is our baseline measure of an industry’s long-term financing needs. Page 32 of 42 Table 2 Summary Statistics Establishments Per Capita Mean 0.07 Standard Deviation 0.09 Share of Establishments with < 5 Employees 0.31 0.16 Share of Establishments with <20 Employees 0.62 0.19 Share of Establishments with <100 Employees 0.86 0.13 Share of Establishments with <250 Employees 0.94 0.08 Average Establishment Size (# of Employees) 69 83 HHI (Sum of squared local-market deposit share) 0.19 0.07 Post Branching Deregulation Indicator 0.60 - Post Interstate Banking Deregulation Indicator 0.45 - Small Bank Share (“Small” means < $100 million in assets, 1994 $s) 0.14 0.13 $221,000 74,000 Operating Income / FTE (1994 $s) Page 33 of 42 Table 3 Comparison of Means for Competitive and Non-competitive Banking Markets Share of Establishments with Under 100 Employees Un-concentrated Banking Markets High External Dependence 0.848 Low External Dependence 0.874 Difference = -0.026 High External Dependence Low External Dependence Difference = Deregulated Banking Markets 0.852 0.897 -0.045 Average Size (Employees per Establishment) Un-concentrated Banking Markets High External Dependence 82 Low External Dependence 61 Difference = 21 Deregulated Banking Markets 74 52 22 Concentrated Banking Markets 0.863 0.921 -0.058 Difference-in-Differences = Regulated Banking Markets 0.831 0.872 -0.041 Difference-in-Differences = Concentrated Banking Markets 71 41 30 Difference-in-Differences = Difference -0.015 -0.047 0.032** Difference 0.021 0.025 -0.004 Difference 11 20 -9** Regulated Banking Markets Difference High External Dependence 90 -16 Low External Dependence 56 -4 Difference= 34 Difference-in-Differences = -12** Concentrated markets have an HHI in the first quartile of the distribution (below 1400); unconcentrated markets have an HHI in the top quartile (above 2400). Regulated markets allow neither branching nor interstate banking; deregulated markets permit both. * significant at 5%; ** significant at 1%, based on a simple t-test. Page 34 of 42 Table 4 Regression of Number of Establishments on Banking Competition Measures Log of Establishments per capita Post-Branching Indicator 0.018 (1.06) Post-Interstate Banking Indicator -0.082 (3.59)** Post-Branching * Ext. Dep. Indicator, Mature firms (1 if > median) -0.006 -0.007 -0.006 (0.34) (0.36) (0.30) Post-Interstate Banking * Ext. Dep. Indicator, Mature firms 0.120 0.081 0.122 (4.16)** (4.40)** (4.14)** Local-Market HHI 0.153 (0.97) Local-Market HHI * Ext. Dep. Indicator, Mature firms -0.459 -0.460 -0.457 (4.10)** (4.05)** (4.03)** Industry share of employment 7.648 7.666 7.647 (95.75)** (94.98)** (94.44)** Market Share of Bank Assets in Small Banks -0.308 (2.62)** Bank Productivity: Real Operating Income per Banking FTE -0.042 (1.51) Personal Income Growth 0.583 (3.72)** Observations 15127 15127 15127 R-squared 0.54 0.88 0.89 State Industry Industry* Fixed Effects Industry* State* Year Year Year State*Year Absolute value of t statistics in parentheses * significant at 5%; ** significant at 1% Page 35 of 42 Table 5 Regression of Average Establishment Size on Banking Competition Measures Log of Average Establishment Size Post-Branching Indicator 0.015 (0.74) Post-Interstate Banking Indicator 0.072 (2.66)** Post-Branching * Ext. Dep. Indicator, Mature firms (1 if > median) -0.018 -0.010 -0.018 (0.79) (0.42) (0.77) Post-Interstate Banking * Ext. Dep. Indicator, Mature firms -0.106 -0.045 -0.105 (3.06)** (2.04)* (2.98)** Local-Market HHI -0.748 (3.96)** Local-Market HHI * Ext. Dep. Indicator, Mature firms 0.940 0.926 0.934 (7.05)** (6.85)** (6.88)** Industry share of employment 6.672 6.696 6.672 (70.04)** (69.75)** (68.83)** Market Share of Bank Assets in Small Banks -0.155 (1.10) Bank Productivity: Real Operating Income per Banking FTE -0.081 (2.47)* Personal Income Growth 0.546 (2.92)** Observations 15127 15127 15127 R-squared 0.51 0.55 0.56 State Industry Industry* Fixed Effects Industry* State* Year Year Year State*Year Absolute value of t statistics in parentheses * significant at 5%; ** significant at 1% Page 36 of 42 Table 6 Regression of Share of Small Establishments on Banking Competition Measures Logit of: Share of Establishments <5 employees Post-Branching Indicator -0.038 (1.05) Post-Interstate Banking Indicator -0.075 (1.56) Post-Branching * Ext. Dep. Indicator, Mature firms (1 if > median) 0.064 0.056 0.063 (1.60) (1.41) (1.57) Post-Interstate Banking * Ext. Dep. Indicator, Mature firms -0.051 -0.056 -0.052 (0.84) (1.44) (0.85) Local-Market HHI 0.984 (2.95)** Local-Market HHI * Ext. Dep. Indicator, Mature firms -0.934 -0.910 -0.929 (3.97)** (3.84)** (3.91)** Industry share of employment -3.287 -3.334 -3.283 (19.56)** (19.82)** (19.36)** Market Share of Bank Assets in Small Banks 0.088 (0.35) Bank Productivity: Real Operating Income per Banking FTE -0.009 (0.16) Personal Income Growth -1.262 (3.82)** Observations 15127 15127 15127 R-squared 0.14 0.19 0.21 State Industry Fixed Effects Industry* State* Industry*Year Year Year State*Year Absolute value of t statistics in parentheses * significant at 5%; ** significant at 1% Page 37 of 42 Table 7 Regression of Share of Small Establishments on Banking Competition Measures Logit of: Share of Establishments <20 employees Post-Branching Indicator -0.044 (1.21) Post-Interstate Banking Indicator -0.099 (2.03)* Post-Branching * Ext. Dep. Indicator, Mature firms (1 if > median) 0.071 0.053 0.070 (1.76) (1.32) (1.70) Post-Interstate Banking * Ext. Dep. Indicator, Mature firms 0.064 -0.063 0.064 (1.03) (1.62) (1.02) Local-Market HHI 0.847 (2.51)* Local-Market HHI * Ext. Dep. Indicator, Mature firms -0.974 -0.947 -0.963 (4.08)** (3.93)** (3.98)** Industry share of employment -5.548 -5.579 -5.542 (32.55)** (32.62)** (32.05)** Market Share of Bank Assets in Small Banks 0.357 (1.42) Bank Productivity: Real Operating Income per Banking FTE 0.071 (1.21) Personal Income Growth -1.118 (3.34)** Observations 15127 15127 15127 R-squared 0.30 0.24 0.26 State Industry* Fixed Effects Industry* Industry Year Year State*Year State*Year Absolute value of t statistics in parentheses * significant at 5%; ** significant at 1% Page 38 of 42 Table 8 Regression of Share of Small Establishments on Banking Competition Measures Logit of: Share of Establishments <100 employees Post-Branching Indicator -0.054 (1.41) Post-Interstate Banking Indicator -0.167 (3.31)** Post-Branching * Ext. Dep. Indicator, Mature firms (1 if > median) 0.043 0.013 0.042 (1.01) (0.30) (0.97) Post-Interstate Banking * Ext. Dep. Indicator, Mature firms 0.268 0.008 0.267 (4.17)** (0.19) (4.07)** Local-Market HHI 1.047 (2.97)** Local-Market HHI * Ext. Dep. Indicator, Mature firms -1.744 -1.721 -1.736 (7.02)** (6.84)** (6.86)** Industry share of employment -7.684 -7.729 -7.684 (43.28)** (43.23)** (42.53)** Market Share of Bank Assets in Small Banks -0.122 (0.47) Bank Productivity: Real Operating Income per Banking FTE 0.109 (1.78) Personal Income Growth -0.733 (2.10)* Observations 15127 15127 15127 R-squared 0.38 0.33 0.35 State Industry* Fixed Effects Industry* Industry Year Year State*Year State*Year Absolute value of t statistics in parentheses * significant at 5%; ** significant at 1% Page 39 of 42 Table 9 Regression of Share of Small Establishments on Banking Competition Measures Logit of: Share of Establishments <250 employees Post-Branching Indicator 0.008 (0.23) Post-Interstate Banking Indicator -0.080 (1.72) Post-Branching * Ext. Dep. Indicator, Mature firms (1 if > median) 0.014 -0.003 0.014 (0.36) (0.08) (0.36) Post-Interstate Banking * Ext. Dep. Indicator, Mature firms 0.163 0.047 0.161 (2.75)** (1.25) (2.66)** Local-Market HHI 0.878 (2.70)** Local-Market HHI * Ext. Dep. Indicator, Mature firms -1.506 -1.494 -1.501 (6.56)** (6.44)** (6.42)** Industry share of employment -7.998 -7.993 -7.997 (48.73)** (48.48)** (47.87)** Market Share of Bank Assets in Small Banks 0.023 (0.10) Bank Productivity: Real Operating Income per Banking FTE 0.103 (1.82) Personal Income Growth -0.655 (2.03)* Observations 15127 15127 15127 R-squared 0.41 0.42 0.43 Industry* Fixed Effects State Industry Year Industry*Year State*Year State*Year Absolute value of t statistics in parentheses * significant at 5%; ** significant at 1% Page 40 of 42 Table 10 Bank Dependence Interaction using Level of External Financial Dependence for Mature Compustat Firms Post-Branching * Ext. Dep. Post-Int. Banking * Ext. Dep. Local-Market HHI * Ext. Dep. Industry share of employment Observations Fixed Effects R-squared Log of Est. per capita Log of Average Est. Size Share of Establishments <5 employees Share of Establishments <20 employees Share of Est. <100 employees Share of Establishments <250 employees -0.025 (0.81) 0.039 (0.77) -1.245 (6.56)** 7.652 (94.57)** 15127 0.120 (3.22)** -0.263 (4.41)** 2.377 (10.49)** 6.675 (69.11)** 15127 -0.040 (0.61) 0.182 (1.74) -2.221 (5.59)** -3.300 (19.48)** 15127 -0.075 (1.12) 0.278 (2.61)** -4.502 (11.15)** -5.569 (32.36)** 15127 -0.152 (2.18)* 0.522 (4.70)** -4.830 (11.43)** -7.688 (42.72)** 15127 -0.358 (5.55)** 0.604 (5.86)** -2.253 (5.76)** -7.986 (47.91)** 15127 State * Year Industry * Year 0.89 0.56 0.21 Absolute value of t statistics in parentheses * significant at 5%; ** significant at 1% Page 41 of 42 0.26 0.35 0.43 Table 11 Bank Dependence Interaction using Small Firm Loans/Asset Ratio Post-Branching * Loans/Assets Post-Int. Banking * Loans/Assets Local-Market HHI * Loans/Assets Industry share of employment Observations Fixed Effects Log of Est. per capita -0.095 (1.49) 0.152 (1.58) -1.104 (2.96)** 7.581 (101.56)** 14717 R-squared 0.86 Absolute value of t statistics in parentheses Log of Average Est. Size -0.145 (1.96) -0.904 (8.04)** 3.490 (8.02)** 6.497 (74.54)** 14717 0.62 Share of Share of Establishments Establishments <5 employees <20 employees -0.073 -0.164 (0.73) (1.60) 0.431 0.446 (2.86)** (2.86)** -4.288 -2.521 (7.35)** (4.17)** -3.113 -5.116 (26.65)** (42.32)** 14717 14717 State * Year Industry * Year 0.35 0.41 * significant at 5%; ** significant at 1% Page 42 of 42 Share of Est. <100 employees 0.249 (2.19)* 1.171 (6.79)** -2.434 (3.64)** -6.728 (50.28)** 14717 Share of Establishments <250 employees 0.011 (0.11) 1.250 (7.70)** -3.105 (4.94)** -6.535 (51.97)** 14717 0.48 0.52 Working Paper Series A series of research studies on regional economic issues relating to the Seventh Federal Reserve District, and on financial and economic topics. Does Bank Concentration Lead to Concentration in Industrial Sectors? Nicola Cetorelli WP-01-01 On the Fiscal Implications of Twin Crises Craig Burnside, Martin Eichenbaum and Sergio Rebelo WP-01-02 Sub-Debt Yield Spreads as Bank Risk Measures Douglas D. Evanoff and Larry D. Wall WP-01-03 Productivity Growth in the 1990s: Technology, Utilization, or Adjustment? Susanto Basu, John G. Fernald and Matthew D. Shapiro WP-01-04 Do Regulators Search for the Quiet Life? The Relationship Between Regulators and The Regulated in Banking Richard J. Rosen Learning-by-Doing, Scale Efficiencies, and Financial Performance at Internet-Only Banks Robert DeYoung The Role of Real Wages, Productivity, and Fiscal Policy in Germany’s Great Depression 1928-37 Jonas D. M. Fisher and Andreas Hornstein WP-01-05 WP-01-06 WP-01-07 Nominal Rigidities and the Dynamic Effects of a Shock to Monetary Policy Lawrence J. Christiano, Martin Eichenbaum and Charles L. Evans WP-01-08 Outsourcing Business Service and the Scope of Local Markets Yukako Ono WP-01-09 The Effect of Market Size Structure on Competition: The Case of Small Business Lending Allen N. Berger, Richard J. Rosen and Gregory F. Udell WP-01-10 Deregulation, the Internet, and the Competitive Viability of Large Banks and Community Banks Robert DeYoung and William C. Hunter WP-01-11 Price Ceilings as Focal Points for Tacit Collusion: Evidence from Credit Cards Christopher R. Knittel and Victor Stango WP-01-12 Gaps and Triangles Bernardino Adão, Isabel Correia and Pedro Teles WP-01-13 A Real Explanation for Heterogeneous Investment Dynamics Jonas D.M. Fisher WP-01-14 Recovering Risk Aversion from Options Robert R. Bliss and Nikolaos Panigirtzoglou WP-01-15 Economic Determinants of the Nominal Treasury Yield Curve Charles L. Evans and David Marshall WP-01-16 Working Paper Series (continued) 1 Price Level Uniformity in a Random Matching Model with Perfectly Patient Traders Edward J. Green and Ruilin Zhou WP-01-17 Earnings Mobility in the US: A New Look at Intergenerational Inequality Bhashkar Mazumder The Effects of Health Insurance and Self-Insurance on Retirement Behavior Eric French and John Bailey Jones WP-01-18 The Effect of Part-Time Work on Wages: Evidence from the Social Security Rules Daniel Aaronson and Eric French WP-01-20 Antidumping Policy Under Imperfect Competition Meredith A. Crowley WP-01-21 WP-01-19 Is the United States an Optimum Currency Area? An Empirical Analysis of Regional Business Cycles Michael A. Kouparitsas WP-01-22 A Note on the Estimation of Linear Regression Models with Heteroskedastic Measurement Errors Daniel G. Sullivan WP-01-23 The Mis-Measurement of Permanent Earnings: New Evidence from Social Security Earnings Data Bhashkar Mazumder WP-01-24 Pricing IPOs of Mutual Thrift Conversions: The Joint Effect of Regulation and Market Discipline Elijah Brewer III, Douglas D. Evanoff and Jacky So WP-01-25 Opportunity Cost and Prudentiality: An Analysis of Collateral Decisions in Bilateral and Multilateral Settings Herbert L. Baer, Virginia G. France and James T. Moser WP-01-26 Outsourcing Business Services and the Role of Central Administrative Offices Yukako Ono WP-02-01 Strategic Responses to Regulatory Threat in the Credit Card Market* Victor Stango WP-02-02 The Optimal Mix of Taxes on Money, Consumption and Income Fiorella De Fiore and Pedro Teles WP-02-03 Expectation Traps and Monetary Policy Stefania Albanesi, V. V. Chari and Lawrence J. Christiano WP-02-04 Monetary Policy in a Financial Crisis Lawrence J. Christiano, Christopher Gust and Jorge Roldos WP-02-05 Regulatory Incentives and Consolidation: The Case of Commercial Bank Mergers and the Community Reinvestment Act Raphael Bostic, Hamid Mehran, Anna Paulson and Marc Saidenberg WP-02-06 Working Paper Series (continued) Technological Progress and the Geographic Expansion of the Banking Industry Allen N. Berger and Robert DeYoung WP-02-07 2 Choosing the Right Parents: Changes in the Intergenerational Transmission of Inequality Between 1980 and the Early 1990s David I. Levine and Bhashkar Mazumder WP-02-08 The Immediacy Implications of Exchange Organization James T. Moser WP-02-09 Maternal Employment and Overweight Children Patricia M. Anderson, Kristin F. Butcher and Phillip B. Levine WP-02-10 The Costs and Benefits of Moral Suasion: Evidence from the Rescue of Long-Term Capital Management Craig Furfine WP-02-11 On the Cyclical Behavior of Employment, Unemployment and Labor Force Participation Marcelo Veracierto WP-02-12 Do Safeguard Tariffs and Antidumping Duties Open or Close Technology Gaps? Meredith A. Crowley WP-02-13 Technology Shocks Matter Jonas D. M. Fisher WP-02-14 Money as a Mechanism in a Bewley Economy Edward J. Green and Ruilin Zhou WP-02-15 Optimal Fiscal and Monetary Policy: Equivalence Results Isabel Correia, Juan Pablo Nicolini and Pedro Teles WP-02-16 Real Exchange Rate Fluctuations and the Dynamics of Retail Trade Industries on the U.S.-Canada Border Jeffrey R. Campbell and Beverly Lapham WP-02-17 Bank Procyclicality, Credit Crunches, and Asymmetric Monetary Policy Effects: A Unifying Model Robert R. Bliss and George G. Kaufman WP-02-18 Location of Headquarter Growth During the 90s Thomas H. Klier WP-02-19 The Value of Banking Relationships During a Financial Crisis: Evidence from Failures of Japanese Banks Elijah Brewer III, Hesna Genay, William Curt Hunter and George G. Kaufman WP-02-20 On the Distribution and Dynamics of Health Costs Eric French and John Bailey Jones WP-02-21 The Effects of Progressive Taxation on Labor Supply when Hours and Wages are Jointly Determined Daniel Aaronson and Eric French WP-02-22 3 Working Paper Series (continued) Inter-industry Contagion and the Competitive Effects of Financial Distress Announcements: Evidence from Commercial Banks and Life Insurance Companies Elijah Brewer III and William E. Jackson III WP-02-23 State-Contingent Bank Regulation With Unobserved Action and Unobserved Characteristics David A. Marshall and Edward Simpson Prescott WP-02-24 Local Market Consolidation and Bank Productive Efficiency Douglas D. Evanoff and Evren Örs WP-02-25 Life-Cycle Dynamics in Industrial Sectors. The Role of Banking Market Structure Nicola Cetorelli WP-02-26 Private School Location and Neighborhood Characteristics Lisa Barrow WP-02-27 Teachers and Student Achievement in the Chicago Public High Schools Daniel Aaronson, Lisa Barrow and William Sander WP-02-28 The Crime of 1873: Back to the Scene François R. Velde WP-02-29 Trade Structure, Industrial Structure, and International Business Cycles Marianne Baxter and Michael A. Kouparitsas WP-02-30 Estimating the Returns to Community College Schooling for Displaced Workers Louis Jacobson, Robert LaLonde and Daniel G. Sullivan WP-02-31 A Proposal for Efficiently Resolving Out-of-the-Money Swap Positions at Large Insolvent Banks George G. Kaufman WP-03-01 Depositor Liquidity and Loss-Sharing in Bank Failure Resolutions George G. Kaufman WP-03-02 Subordinated Debt and Prompt Corrective Regulatory Action Douglas D. Evanoff and Larry D. Wall WP-03-03 When is Inter-Transaction Time Informative? Craig Furfine WP-03-04 Tenure Choice with Location Selection: The Case of Hispanic Neighborhoods in Chicago Maude Toussaint-Comeau and Sherrie L.W. Rhine WP-03-05 Distinguishing Limited Commitment from Moral Hazard in Models of Growth with Inequality* Anna L. Paulson and Robert Townsend WP-03-06 Resolving Large Complex Financial Organizations Robert R. Bliss WP-03-07 4 Working Paper Series (continued) The Case of the Missing Productivity Growth: Or, Does information technology explain why productivity accelerated in the United States but not the United Kingdom? Susanto Basu, John G. Fernald, Nicholas Oulton and Sylaja Srinivasan WP-03-08 Inside-Outside Money Competition Ramon Marimon, Juan Pablo Nicolini and Pedro Teles WP-03-09 The Importance of Check-Cashing Businesses to the Unbanked: Racial/Ethnic Differences William H. Greene, Sherrie L.W. Rhine and Maude Toussaint-Comeau WP-03-10 A Structural Empirical Model of Firm Growth, Learning, and Survival Jaap H. Abbring and Jeffrey R. Campbell WP-03-11 Market Size Matters Jeffrey R. Campbell and Hugo A. Hopenhayn WP-03-12 The Cost of Business Cycles under Endogenous Growth Gadi Barlevy WP-03-13 The Past, Present, and Probable Future for Community Banks Robert DeYoung, William C. Hunter and Gregory F. Udell WP-03-14 Measuring Productivity Growth in Asia: Do Market Imperfections Matter? John Fernald and Brent Neiman WP-03-15 Revised Estimates of Intergenerational Income Mobility in the United States Bhashkar Mazumder WP-03-16 Product Market Evidence on the Employment Effects of the Minimum Wage Daniel Aaronson and Eric French WP-03-17 Estimating Models of On-the-Job Search using Record Statistics Gadi Barlevy WP-03-18 Banking Market Conditions and Deposit Interest Rates Richard J. Rosen WP-03-19 Creating a National State Rainy Day Fund: A Modest Proposal to Improve Future State Fiscal Performance Richard Mattoon WP-03-20 Managerial Incentive and Financial Contagion Sujit Chakravorti, Anna Llyina and Subir Lall WP-03-21 Women and the Phillips Curve: Do Women’s and Men’s Labor Market Outcomes Differentially Affect Real Wage Growth and Inflation? Katharine Anderson, Lisa Barrow and Kristin F. Butcher WP-03-22 Evaluating the Calvo Model of Sticky Prices Martin Eichenbaum and Jonas D.M. Fisher WP-03-23 5 Working Paper Series (continued) The Growing Importance of Family and Community: An Analysis of Changes in the Sibling Correlation in Earnings Bhashkar Mazumder and David I. Levine WP-03-24 Should We Teach Old Dogs New Tricks? The Impact of Community College Retraining on Older Displaced Workers Louis Jacobson, Robert J. LaLonde and Daniel Sullivan WP-03-25 Trade Deflection and Trade Depression Chad P. Brown and Meredith A. Crowley WP-03-26 China and Emerging Asia: Comrades or Competitors? Alan G. Ahearne, John G. Fernald, Prakash Loungani and John W. Schindler WP-03-27 International Business Cycles Under Fixed and Flexible Exchange Rate Regimes Michael A. Kouparitsas WP-03-28 Firing Costs and Business Cycle Fluctuations Marcelo Veracierto WP-03-29 Spatial Organization of Firms Yukako Ono WP-03-30 Government Equity and Money: John Law’s System in 1720 France François R. Velde WP-03-31 Deregulation and the Relationship Between Bank CEO Compensation and Risk-Taking Elijah Brewer III, William Curt Hunter and William E. Jackson III WP-03-32 Compatibility and Pricing with Indirect Network Effects: Evidence from ATMs Christopher R. Knittel and Victor Stango WP-03-33 Self-Employment as an Alternative to Unemployment Ellen R. Rissman WP-03-34 Where the Headquarters are – Evidence from Large Public Companies 1990-2000 Tyler Diacon and Thomas H. Klier WP-03-35 Standing Facilities and Interbank Borrowing: Evidence from the Federal Reserve’s New Discount Window Craig Furfine WP-04-01 Netting, Financial Contracts, and Banks: The Economic Implications William J. Bergman, Robert R. Bliss, Christian A. Johnson and George G. Kaufman WP-04-02 Real Effects of Bank Competition Nicola Cetorelli WP-04-03 Finance as a Barrier To Entry: Bank Competition and Industry Structure in Local U.S. Markets? Nicola Cetorelli and Philip E. Strahan WP-04-04 6 7
@FedFRASER