Ogura “the objective function of government controlled banks in a financial crisis”, j banking and finance (2018)

Accepted Manuscript The Objective Function of Government-Controlled Banks in a Financial Crisis Yoshiaki Ogura PII: DOI: Reference: S0378-4266(18)30022-0 10.1016/j.jbankfin.2018.01.015 JBF 5291 To appear in: Journal of Banking and Finance Received date: Revised date: Accepted date: 21 October 2016 15 January 2018 27 January 2018 Please cite this article as: Yoshiaki Ogura, The Objective Function of Government-Controlled Banks in a Financial Crisis, Journal of Banking and Finance (2018), doi: 10.1016/j.jbankfin.2018.01.015 This is a PDF file of an unedited manuscript that has been accepted for publication As a service to our customers we are providing this early version of the manuscript The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain ACCEPTED MANUSCRIPT Highlights • GCBs increased lending to SMEs with a weaker main-bank relationship in the financial crisis AC CE PT ED M AN US CR IP T • This is consistent with the welfare maximization by GCBs rather than the profit maximization ACCEPTED MANUSCRIPT Abstract CR IP T The Objective Function of Government-Controlled Banks in a Financial Crisis AN US We present evidence that government-controlled banks (GCBs) significantly increased their lending to small and medium-sized enterprises (SMEs) whose main bank was a large bank in the 2008–09 financial crisis Further analyses show that the weak relationship between large banks and SMEs is a major cause for this phenomenon The mixed Cournot oligopoly model with relationship banking, where profit-maximizing private banks and a welfare-maximizing GCB coexist, shows that this finding is consistent with the welfare maximization by a GCB rather than its own profit maximization AC CE PT ED M JEL Classification: G21; H44 Keywords: government-controlled banks, mixed oligopoly, relationship banking, small business financing ACCEPTED MANUSCRIPT Introduction The existing literature on government-controlled banks (GCBs) has presented mixed judgments on the banks’ contribution to economic efficiency The seminal empirical study by La Porta et al (2002) shows international evidence of the underperformance of GCBs Several subsequent studies CR IP T show evidence that such inefficiency mainly comes from the political constraint or the political capture (e.g., Sapienza, 2004; Din¸c, 2005),1 and that a privatization significantly improves the efficiency (Bertrand et al., 2007) On the other hand, recent studies show evidence of the benefits of GCBs, such as mitigating the credit constraint against SMEs (Behr et al., 2013; Lin et al., 2014; Sekino and Watanabe, 2014) and the less procyclicality of their lending (Micco and Panizza, AN US 2006; Brei and Schclarek, 2013; Cull and Per´ıa, 2013; Coleman and Feler, 2015; Behr et al., 2017),2 especially in countries with good governance (Bertay et al., 2015) Moreover, macroeconomic analyses theoretically predict the possibility of welfare improvement via counter-cyclical policy lending to firms in a model with a financial friction (e.g., Gertler and Karadi, 2011; Martin and M Ventura, 2016) However, it remains an open empirical question whether the lending behavior of GCBs improves welfare ED This interesting and important issue boils down to the question of which is the actual objective function of GCBs among various alternatives, such as their own profits, the social welfare, or some PT other political interests To figure out an empirical strategy to detect their objective function, first we applied a mixed Cournot oligopoly model (Fraja and Delbono, 1989; Ide and Hayashi, 1992; CE Matsumura, 1998) to the loan market for a firm The standard mixed-oligopoly model assumes a public firm, which maximizes the social welfare, and multiple profit-maximizing private firms We introduce an additional twist of the asymmetry among profit-maximizing private banks to AC take into account relationship banking, which is a widely accepted phenomenon in small business financing (for the list of the existing studies, see, e.g., Degryse et al., 2009) Namely, we assume a credit market with a GCB, a main bank providing a differentiated service based on its information More recently, Pereira and Maia-Filho (2015) find a slower transmission of the monetary policy to the interest rates of GCBs Illueca et al (2014) and Iannotta et al (2013) provide evidence of excess risk-taking by governmentcontrolled banks Brei and Schclarek (2015) theoretically explain that this phenomenon is due to the differences between private banks and public banks in terms of the objective functions and the funding sources ACCEPTED MANUSCRIPT advantage, and another private bank without such an advantage We consider two cases; first, the case where the GCB maximizes the social welfare, which is defined by the sum of the profits of all banks and the surplus for the borrowing firm; second, the case where the GCB is a profit maximizer, like a private non-main bank We find that, in response to the increased loan demand, the welfare-maximizing GCB increases its lending more for firms CR IP T with a weak relationship with its main bank, in the sense that the extent of differentiation of the main bank is lower and that the main-bank loan demand is more price-elastic This is because the GCB is less willing to interrupt a lending relationship between a firm and its main bank if it provides a differentiated service that is more valuable for the firm and contributes more to the AN US social welfare In contrast, a profit-maximizing GCB never adjusts its lending according to the strength of the main-bank relationship This result suggests that we can detect whether a GCB is a profit maximizer or a welfare maximizer by examining whether it increases lending more to firms with a weaker main-bank relationship in response to a surge in loan demand The microdata provided by the Small and Medium Enterprise Unit of the Japan Finance Cor- M poration (JFC), one of the major GCBs for SMEs, enables us to conduct this empirical test The dataset contains information on the annual financial statements and other basic characteristics of ED each past and current borrower, as well as outstanding loan amounts from the SME Unit of JFC and private banks up to the four largest lenders The most desirable feature of the data is that it PT contains the identifier of these private banks so that we can match the bank information We focus on the dataset from 2007 to 2011 before and after the 2008-09 financial crisis severely CE affected the Japanese economy through the sharp reduction of exports to the USA and Europe in the accounting period ending in 2009 The benefits of using the Japanese dataset are threefold First, AC the financial crisis was an exogenous shock to Japanese banking and industrial sectors The banking sector was barely affected by the shock, while the shock had a deep impact on the performance and financing behavior of the industrial sectors, especially the exporting sectors Second, we observed a clear surge in the demand for bank loans in the accounting period ending in 2009 (for typical Japanese firms, the end of the accounting year is in March) The survey of large banks conducted by the Bank of Japan clearly shows this (Figure 1) This is because of the temporary shutdown of the commercial paper and bond markets (Uchino, 2013) and the precautionary motivations in ACCEPTED MANUSCRIPT response to the disastrous drop in corporate earnings in the exporting sector, as shown in Section These points ensure the theoretical assumption of the exogenous loan demand shift for our empirical hypothesis Third, the dataset enables us to construct a three-way panel dataset by firm, year, and lender This three-way panel data enables us to fully control for the unobservable time-varying firm characteristics, such as the magnitude of a demand shock and other credit characteristics by CR IP T introducing the firm-by-year cross fixed effect In the context of this study, both the intensity of a main-bank relationship and the lending attitude of GCBs are correlated with unobservable firm characteristics The estimated correlation between the lending attitude of GCBs and the intensity of main-bank relationship can be biased due to these unobservables, if we cannot control AN US for them perfectly The firm-by-year cross fixed effect minimize this problem and provide a clear identification, as proposed by Gan (2007) and subsequent studies From the regression using the three-way panel data to control for the firm-by-year cross fixed effect, we find that the GCBs increased their lending to SMEs whose main bank is a large bank, which operates nationwide and internationally, in the crisis period of two years after September M 2008, while they decreased lending for other SMEs On the other hand, we also find that a main bank decreased lending in the crisis period if it is a large bank, whereas it increased if it is a regional ED bank Thus, GCBs filled in the loan supply shortage of large main banks Since we control for unobservable time-varying firm characteristics as mentioned above, this PT result is less likely to be driven by unobservable firm characteristics, such that a large main bank obtains negative private information and reduces lending to an SME while a GCB without it keep CE lending, or that a GCB increases its lending because of positive private information while a main bank without it reduces lending AC Further analyses with more explicit relationship measures, such as the main-bank loan or deposit share, a dummy variable indicating that a firm switched main banks before the crisis, or the number of lenders before the crisis, show that this result is driven by the weak relationship between large banks and SMEs, which has been recognized in the existing literature (e.g., Cole et al., 2004; Berger et al., 2005; Uchida et al., 2008; Ogura and Uchida, 2014) This is consistent with the welfare-maximizing behavior in the above theoretical prediction The remaining part of this paper is organized as follows We describe the source of our dataset in ACCEPTED MANUSCRIPT Section The financial condition and financing behavior of Japanese SMEs in the 2008-09 financial crisis are described in Sections and A theoretical model to derive an empirical strategy to detect the objective function of the GCB is presented in Section The hypothesis for the statistical test, the data description, the specification for the estimation, and the result of the test are presented CR IP T in Section Section presents the conclusion and the limitation of our analysis Data The dataset for this study is the internal credit information on borrowers at the Small and Medium Enterprise (SME) Unit of the Japan Finance Corporation (JFC) JFC is a 100% government- AN US owned and government-controlled lending institution that provides subsidized long-term loans to SMEs; microcorporations including start-up firms and farmers; and individuals It also provides reinsurance for the public guarantee system for SME loans JFC does not take deposits and is financed mostly by borrowing from the Japanese government and partially by issuing bonds with or without government guarantees It has a nationwide branch network of 152 branches (March M 2009) The SME Unit is the business unit focusing on loans to SMEs The total outstanding loan amount of this unit was about 5.2 trillion JPY in March 2009 The asset size is close to that of ED larger regional banks The unit was called the Japan Finance Corporation for Small and Medium Enterprise (JASME) before its merger with other units in October 2008 PT The internal credit information of the SME unit includes the annual financial statement information and other basic characteristics of each borrowing firm, such as the industrial classification, CE the year of establishment, and the location of the JFC branch that transacts with the firm, as well as the internal credit rating The most notable feature of the dataset is that it contains the AC outstanding loan amount provided by JFC and other private and government-owned institutions The names of lenders can be identified for the largest four lenders to match the financial and other information of each lender JFC identifies a main bank of each firm based on information such as deposit share and loan share This information enables us to examine what types of firms became more dependent on GCB lending in the crisis and evaluate the economic efficiency of GCB lending We use the observations from calendar years 2007 to 2011, from right before the outbreak of the crisis to several years after The dataset covers not only firms with a current positive amount of loan ACCEPTED MANUSCRIPT outstanding from JFC but also those without this for several years before starting a transaction or after closing a transaction with JFC The number of firm-year observations is 230,587 From the original sample, we drop firms whose main bank is a JA bank (agricultural cooperative), a JF marine bank (fishery cooperative), a GCB (739 observations), or a Shinkumi bank (3,298 observations), which is a smaller credit cooperative, since the data of their characteristics are not fully available CR IP T We drop 14,454 observations whose borrow/asset is greater than one to avoid the effect of firms under a bankruptcy procedure in all of the estimations Finally, we drop 64,514 observations for which any item required for the preliminary regression in Section is not available The remaining 147,582 observations are the baseline sample for our analysis AN US The industrial composition of the borrowers at the SME Unit of JFC tilted more toward the manufacturing sector than did the population, which was measured by the 2009 Economic Census (Table 1) Table shows the descriptive statistics of the variables to be used for the regressions later The definition of each variable is listed in Table The median of the main-bank share of loans is about 38% and that of deposits are about 66% The median of the loan share of GCBs is M about 35%, somewhat lower than that of the main bank The number of lenders other than JFC is three on average The minimum is one, i.e., each firm has a relationship with at least one bank ED other than JFC This is because JFC does not provide checking, savings, or settlement services The median of the asset size is 780 million JPY The Credit Risk Database (CRD), which is closer PT to the population of the SMEs with access to the loan market, indicates that the median asset size was 85 million JPY in 2003 (Table 1.4 on p.21 in Shikano, 2008) Thus, our dataset focuses on CE larger firms among the SMEs More than 70% of our sample firms chose regional banks3 as their main bank (Table 4) Regional AC banks operate within a single or a couple of adjacent prefectures The remaining 30% chose large banks, which have a nationwide branch network and operate nationwide or internationally.4 Large banks have features clearly different from those of other types of banks First, the main-bank shares Regional banks include both the member banks of the Regional Banks Association of Japan and the Second Association of Regional Banks, and cooperative banks, such as Shinkin and Shinkumi banks The asset size of the banks in the two regional bank associations ranges from 0.2 to 11.6 trillion JPY as of March 2009 The asset size of cooperative banks is smaller, and ranges from 0.004 to 3.9 trillion JPY as of March 2009 Large banks include city banks (Mitsubishi UFJ, Sumitomo-Mitsui, Mizuho, Mizuho Corporate, Risona, Saitama Risona, Shinsei, and Aozora) and trust banks (Mitsubishi UFJ Trust, Mizuho Trust, Chuo-Mitsui Trust, and Sumitomo Trust) The asset size ranges from 6.1 to 149 trillion JPY as of March 2009 ACCEPTED MANUSCRIPT of deposits and loans are significantly lower when the main bank is a large bank than otherwise (Panels (a) and (b), Figure 2) Second, firms switch their main banks more frequently when their main bank is a large bank than otherwise (Table 5) The probability that a firm had switched main banks from the previous year is higher by at least 1% for larger banks The difference was at a maximum in 2010, the later stage of the financial crisis Third, the ratio of SME loans over CR IP T total loans of large banks is significantly lower than that for other types of banks (Panel (c), Figure 2).5 The difference is about 10-17% The gap significantly widened in 2009, in the midst of the crisis, and has remained wide since then, as large banks decreased the SME ratio considerably, while regional banks slightly increased the SME ratio In contrast to the decline of large banks in AN US SME lending, the share of the GCBs for firms whose main bank was a large bank kept increasing in 2008 (Panel (d), Figure 2) These figures and table suggest that large banks maintain a weaker relationship with SMEs than regional banks do, even if they are recognized as a main bank by the firm or other lenders Table shows a comparison of the characteristics of those firms whose main bank was a large M bank and others in the crisis period of two years from September 2008 The main-bank share of loans and deposits decreased significantly more for firms whose main bank was a large bank, and ED the loan share of GCBs increased more for them In terms of creditworthiness, the firms whose main bank is a large bank had assets twice as large as the other firms The JFC credit rating for PT them was significantly higher than that for others, whereas the damage to the credit rating, sales, interest coverage ratio (∆credit rating, ∆ln(sales), and ∆int.cover) were more severe for the former CE group of firms This is because the weight of exporters such as the manufacturing sector is larger for the clientele of large banks than that of regional banks or cooperative banks In short, those AC whose main bank was a large bank were larger and more creditworthy, but they were affected more severely by the temporary shock of the global financial crisis Cooperative banks are allowed to lend to individuals and SMEs only by regulation ACCEPTED MANUSCRIPT Corporate Finance of Japanese SMEs in the 2008-09 Financial Crisis 3.1 Loan demand increased sharply in 2009 The dataset shows that the 2008-09 financial crisis severely affected the Japanese SME loan market CR IP T with a short lag through the plummeting export to the USA and Europe Panel (a) of Figure is the plot of the sector average of the ratio of EBITDA over total assets, which is calculated from the microdata provided by JFC and is normalized to 100 in 2007 for all sectors Clearly, the earnings of Japanese SME exporters in the electronics, transportation equipment (including auto makers and their suppliers), and other manufacturing sectors dropped by more than 50% from 2008 to AN US 2009 These exporters increased their cash holdings in response to this serious crisis, probably with a precautionary motivation (Panel (b) in Figure 3) despite the fact that the cash flow from their usual operation had dramatically contracted The increased cash holdings were mainly financed by bank loans as is indicated by the sharp increase in the ratio of loans over assets in the export 3.2 M sectors (Panel (c) in Figure 3) Banks responded differently by type ED The response of each individual bank varies by bank type Figure shows the average annual change in loans from each lender to each firm The values are normalized by the total asset of each PT firm in the previous year GCBs for SMEs including all units of JFC and the Shoko Chukin Bank,6 another GCB for SMEs, increased their lending sharply in 2009 and kept increasing it until 2011 as CE a result of the reinforcement of the safety-net lending for SMEs by the government through these GCBs Regional banks also increased their lending in 2009 but decreased it in 2010 In contrast, AC large banks never increased their lending even in 2009, although the speed of reduction slowed in 2009 This stark contrast between regional banks and large banks is likely to stem from the fact that the relationships of a large bank with SMEs are weaker than those of a regional bank, as shown The JFC SME Unit accounts for about 70%, JFC other units account for about 10%, and the Shoko Chukin Bank accounts for about 20% of observations in each year in our sample (Table 8) The influence of the government is somewhat smaller for the Shoko Chukin Bank than for JFC The government holds 46.46% (March 2009) of the share of the Shoko Chukin Bank, and the remainders are widely held by various private entities, including financial institutions The bank is mostly financed by deposits and bank debentures (the latter is until 2012) Its board includes several members sent from the government It has a nationwide branch network of 93 branches The amount of outstanding loans is 9.2 trillion JPY, which is larger than that for the SME Unit of JFC (March 2009) CR IP T ACCEPTED MANUSCRIPT Figure 5: Average Risk-Adjusted Capital Ratio (%, End of March) Regionalbank 12.9 12.7 10.4 10.2 12.8 11.8 10.1 16.0 14.4 13.4 13.7 10.8 11.0 PT ED M 13.3 13.0 Shinkinbanks AN US Largebanks 2007 2008 2009 2010 2011 AC CE Source: Author’s calculation from the financial statements of individual institutions The financial statement information is collected from Nikkei NEEDS and is augmented by the database on the Japanese Bankers Association website for those who are not publicly traded 33 ACCEPTED MANUSCRIPT Figure 6: Estimated Average Annual ∆loan/asset (%) Note: Author’s calculation from the regression (1) in Table 10 The values are calculated by adding the mean of ∆loan/asset of regional banks in all the sample period (0.141), the base category in the regression, to each of the values listed in the columns of est of (i)–(iv), Table 11 Pre-crisis: January 2007–August 2008 Crisis: September 2008–August 2010 Post-crisis: September 2010–December 2011 Crisis PostͲcrisis 0.47 0.40 0.26 0.19 0.20 0.00 Ͳ0.20 Ͳ0.02 Ͳ0.12 Ͳ0.60 Ͳ0.64 Ͳ0.80 Ͳ0.94 Ͳ0.90 M Ͳ1.20 0.16 AN US Ͳ0.18 Ͳ0.40 Ͳ1.00 CR IP T PreͲcrisis 0.60 ED GCBsforSMEs(MBislarge) GCBsforSMEs(MBisregional) mainbank(regional) AC CE PT mainbank(large) 34 Ͳ0.17 Ͳ0.29 CR IP T ACCEPTED MANUSCRIPT Figure 7: Average Funding Cost (%) 2001 ED M AN US 1.8 1.6 1.4 1.2 0.8 0.6 0.4 0.2 2003 2005 2007 2011 2013 regionalbank PT largebank 2009 regionalbankII cooperativebanks AC CE Source: Japanese Bankers Association and Zenkoku Shinyo Kinko Gaikyo (Shinkin Central Bank) for cooperative banks 35 ACCEPTED MANUSCRIPT Table 1: Number of Sample SMEs by Year and Sector 2008 304 2,480 1,867 892 532 12,139 2,273 2,369 2,821 4,342 450 30,469 2009 325 2,343 1,812 843 523 11,560 2,213 2,256 2,770 4,193 432 29,270 2010 441 2,281 1,752 852 542 11,099 1,995 2,160 2,850 4,206 429 28,607 2011 502 2,205 1,771 849 538 10,764 1,834 2,107 2,869 4,194 426 28,059 AN US 2007 297 2,627 1,923 890 535 12,486 2,297 2,452 2,737 4,466 467 31,177 AC CE PT ED M communication construction logistics manufacturing (electronics) manufacturing (trans equip.) manufacturing (other) real estate retail service wholesale others total CR IP T (Note) The top row indicates the year at the end of each accounting period The Economic Census column is based on the number of companies (excluding sole proprietorships) in the 2009 Economic Census for Business Frame (Kiso Chosa), Statistics Bureau, Ministry of Internal Affairs and Communications, Japan Three sectors in the manufacturing sector are calculated by the author based on this statistic The other parts are from Panel (3), Table 1, on page 285 of the Statistical Appendix of the 2012 White Paper on Small and Medium Enterprises in Japan 36 Total 1,869 11,936 9,125 4,326 2,670 58,048 10,612 11,344 14,047 21,401 2,204 147,582 (Share) 1.3% 8.1% 6.2% 2.9% 1.8% 39.3% 7.2% 7.7% 9.5% 14.5% 1.5% 100.0% Economic Census 46,747 2.8% 331,079 20.0% 56,444 3.4% 21,776 1.3% 11,381 0.7% 241,873 14.6% 182,060 11.0% 279,626 16.9% 295,077 17.8% 189,621 11.4% 2,583 0.2% 1,658,267 100.0% ACCEPTED MANUSCRIPT Table 2: Variable Definition Definition ∆loan/asset Annual change in the amount of outstanding loan from a bank to a firm in each year, norm large bank Dummy variable, which equals one if the lender is a large bank or zero otherwise Large ban GCB for SME Dummy variable, which equals one if the lender is a government-controlled bank (GCB) for GCB Dummy variable, which equals one if the lender is a GCB not for SMEs (e.g., the Developm other institutions Dummy variable, which equals one if the lender is an institution classified into other miscell CR IP T Variable Pre-crisis period dummy, which equals one in the period from January 2007 to August 2008 crisis Crisis period dummy, which equals one in the period from September 2008 to August 2010 post-crisis Post-crisis period dummy, which equals one in the period from September 2010 to Decemb GCB for SME share Share of loans from GCBs for SMEs at each firm at the end of the accounting year ending MB loan share Main bank share of loans at the end of the accounting year ending in each calendar year M MB deposit share Main bank share of deposits at the end of the accounting year ending in each calendar year MB switch A dummy variable, which is equal to one if a firm switched main banks in the pre-crisis per AC CE PT ED M AN US pre-crisis 37 ACCEPTED MANUSCRIPT Table 2: (cont.) Variable Definition ln(GCB for SME share) Logit-transformed GCB for SME share, i.e., ln(GCB for SME share /(1 - GCB for SME ln(MB loan share) Logit-transformed main bank loan share, i.e., ln(MB loan share /(1 - MB loan share)) (Total loan outstanding) / (total asset) of each firm at the end of the accounting year CR IP T borrow/asset A dummy variable, which equals if the main bank is a large bank, or zero otherwise NMB large A dummy variable, which equals if the main bank is not a large bank, but a non-main MB capital ratio Risk-adjusted capital adequacy ratio minus the regulatory minimum requirement for th MB ROA (Net profit after tax) / (total asset) × 100 of the main bank (%) as of March in the yea MB NPL ratio (Non-performing loan) / (total loan) × 100 of the main bank (%) as of March in the y #lenders The number of lenders which are private banks (max 4) Maximum in the pre-crisis per main bank A dummy variable, which equals if the lender is the main bank, or zero otherwise credit rating Internal rating of the SME unit of the Japan Finance Corporation (1: least creditworth AN US MB large Annual change of credit rating ∆ln(sales) Annual difference of ln(total sales (mil JPY)) of a firm ln(asset) Natural logarithm of the total asset of a firm (mil JPY) ln(firm age) Natural logarism of the years after the start up of a firm M ∆credit rating EBITDA/sales of a firm EBITDA equals (operating profit) plus (depreciation) AC CE PT ED profitability 38 ACCEPTED MANUSCRIPT Table 2: (cont.) tangibility int.cover Definition (Land + building + construction in progress + other tangible assets)/total assets of a firm CR IP T Variable EBITDA/(interest costs) of a firm Natural logarithm of (EBITDA/(interest costs)+1) EBITDA is replaced with zero if it is negative liquid.short The liquidity shortage of a firm defined by - (operating cash flow[t] - operating cash flow[t-1]) + ca AN US ln(int.cover) Mean S.D Min p10 Med p90 Max 147,576 144,053 147,582 147,582 147,582 147,582 147,582 147,582 147,582 147,582 147,582 147,582 147,582 147,582 147,582 147,582 147,582 0.39 0.63 0.41 7.08 0.22 3.76 0.54 1560.0 51 -0.10 -0.03 0.07 0.45 12.53 -0.14 0.25 0.29 0.29 3.36 0.52 2.24 0.24 3004.6 32 1.46 0.24 0.33 0.24 66.61 0.19 0.00 0.00 0.00 0.56 -6.37 0.49 0.00 0.1 1 -10 -6.25 -69.00 0.00 -1.00 0.00 0.19 0.08 4.11 -0.13 1.67 0.20 189.8 19 -2 -0.24 -0.01 0.14 -0.37 0.38 0.66 0.35 6.65 0.31 3.25 0.56 781.0 48 10 -0.01 0.05 0.43 3.85 -0.12 0.73 1.00 0.90 10.28 0.59 6.37 0.84 3458.7 86 12 0.18 0.19 0.79 21.86 0.05 1.00 1.00 1.00 63.76 2.54 24.90 1.00 225251.4 1003 12 11 4.86 1.09 1.00 7927.33 1.00 AC CE PT MB loan share MB deposit share GCB for SMEs share MB capital ratio MB ROA MB NPL ratio borrow/asset #lenders firm asset age credit rating ∆credit rating ∆ln(sales) profitability tangibility int.cover liquid.short N ED Variable M Table 3: Descriptive Statistics 39 ACCEPTED MANUSCRIPT Table 4: Number of Sample Firms by Year and Main Bank Type (Note) Year is the year at the end of the accounting year of each firm Regional banks include regional banks and cooperative institutions; Shinkin banks Regional banks 22,882 22,513 21,635 20,931 20,401 108,362 Large banks 8,295 7,956 7,635 7,676 7,658 39,220 Total 31,177 30,469 29,270 28,607 28,059 147,582 CR IP T Year 2007 2008 2009 2010 2011 Total Table 5: Frequency of Switching Main Banks AN US (Note) The sample firms with information about their main bank in the previous year are used among those in the dataset used for the regression (1) in Table Type of main bank in the previous year 2009 2010 i no switch ii switch i no switch ii switch i no switch ii switch i no switch ii switch i no switch ii switch 22,288 478 21,941 464 20,927 588 20,263 503 19,853 297 8,105 278 7,770 280 7,430 301 7,480 323 7,453 188 2.1% 2.1% 2.8% 2.5% 1.5% AC CE PT 2011 (b) Large banks #obs (ratio of ii) M 2008 (a) Regional banks #obs (ratio of ii) ED 2007 From the previous year 40 3.4% 3.6% 4.1% 4.3% 2.5% CR IP T ACCEPTED MANUSCRIPT Table 6: Difference in the impact of the global financial crisis by main bank type ED AC CE a large bank Med *** 0.000 ** 0.000 *** 0.000 ** 0.001 *** 0.000 *** 1174.5 52.00 *** 10.000 *** 0.000 *** -0.064 -0.003 0.041 *** 0.000 *** -0.008 *** -0.126 *** *** ** M (i) Main bank is N Mean 14,891 -1.607 14,894 -0.497 15,283 1.853 15,383 0.012 15,380 0.011 15,383 2246.8 15,383 54.41 15,383 9.705 15,383 -0.137 15,383 -0.103 15,383 -0.010 15,383 0.066 15,383 0.004 15,333 -0.155 15,383 -0.142 15,383 0.042 15,383 0.423 15,383 0.020 PT Variable ∆MB loan share ∆MB deposit share ∆GCB for SME share ∆borrow/asset ∆#lenders firm asset age credit rating ∆credit rating ∆ln(sales) profitability ∆profitability ∆tangibility ∆ln(int.cover) liquid.short Electronics dummy Trans equip dummy Other mfg dummy AN US All descriptive statistics are calculated from the information in the crisis period from Sept 2008 to Aug 2010 ∆ indicates the difference from the previous accounting year *, **, and *** on the right of mean indicates that the mean difference between the subsamples (i) and (ii) is statstitically significant at a %, 5%, and 10% significance level, respectively (two-sided t-test with the common variance between the subsamples) *, **, and *** on the right of median indicates that the median difference between the subsamples (i) and (ii) is statstitically significant at a %, 5%, and 10% significance level, respectively (one-sided Wilcoxson rank sum test) 41 *** *** *** *** *** *** *** *** *** ** *** (ii) Otherwise N Mean 41,636 -0.624 41,516 0.002 42,984 0.823 43,194 0.009 43,175 -0.015 43,194 1276.1 43,194 50.58 43,194 9.104 43,194 -0.101 43,194 -0.081 43,194 -0.006 43,194 0.063 43,194 0.002 43,072 -0.096 43,194 -0.124 43,194 0.025 43,194 0.382 43,194 0.017 Med 0.000 0.000 0.000 0.000 0.000 667.9 46.00 10.000 0.000 -0.050 -0.002 0.043 -0.001 0.000 -0.107 0.000 0.000 0.000 ACCEPTED MANUSCRIPT Table 7: Share Regression (Note) Dependent variables are indicated at the top of each row The coefficients are estimated by the firm fixed-effect model Standard errors are estimated by the firm cluster robust standard errors The estimated constant term and the coefficients of the interaction terms of the sector dummies (communication, logistics, manufacturing(electronics), manufacturing (transportation equipment), manufacturing (other), real estate, retail, service, wholesale, and others) and the year dummies are omitted from the table *, **, and *** indicate that the estimated coefficient is different from zero at a 10%, 5%, and 1% statistical significance level, respectively (two-sided) S.E 0.154 0.034 0.050 0.051 0.070 0.007 0.015 0.009 0.023 0.008 0.005 0.034 0.077 0.216 0.035 0.196 0.015 0.037 *** *** ** *** * ** * ** *** *** *** *** *** Coef -0.0056 -0.0042 -0.0044 0.0087 0.0182 0.0000 -0.0027 -0.0009 0.0037 -0.0124 0.0058 -0.0188 0.0396 -0.0802 0.0050 0.2225 -0.0265 0.0165 yes yes 147,582 40,052 0.207 0.252 0.249 M AN US Coef -1.565 -0.108 -0.124 0.146 0.128 -0.016 -0.027 -0.025 -0.218 -0.031 0.040 0.095 0.902 0.015 0.048 0.463 -0.178 -0.044 yes yes 147,576 40,051 0.017 0.094 0.082 (3) borrow/asset CR IP T (2) ln(MB loan share) AC CE PT Ind var MB large MB large × crisis MB large × post-crisis crisis post-crisis MB capital ratio MB ROA MB NPL ratio #lenders credit rating ∆credit rating ∆ln(sales) ln(asset) ln(firm age) profitability tangibility ln(int.cover) liquid.short Firm fixed effect year × industry fe N #groups R-sq: within between overall (1) ln(GCB for SME share) Coef S.E 0.283 0.096 *** 0.068 0.029 ** 0.104 0.043 ** -0.139 0.042 *** -0.051 0.061 0.010 0.005 * 0.032 0.013 ** 0.007 0.007 -0.039 0.021 * 0.032 0.007 *** -0.040 0.004 *** -0.074 0.031 ** -0.875 0.073 *** -0.075 0.192 -0.036 0.027 -0.350 0.183 * 0.168 0.014 *** 0.047 0.034 yes yes 147,582 40,052 0.012 0.135 0.118 ED Dep Var 42 ** *** S.E 0.0032 0.0011 0.0016 0.0018 0.0025 0.0002 0.0005 0.0003 0.0007 0.0003 0.0002 0.0013 0.0038 0.0077 0.0020 0.0087 0.0005 0.0014 * *** *** *** *** *** *** *** *** *** *** *** *** ** *** *** *** ACCEPTED MANUSCRIPT Table 8: Number of Observations by Bank Type and Year Large banks Regional banks GCBs for SMEs JFC (SME unit) JFC (other units) Shoko Chukin Bank Other GCBs Other institutions Total 2008 33,467 75,297 60,022 40,587 7,945 11,490 530 2,233 171,549 2009 33,322 75,075 59,156 40,304 7,229 11,623 521 2,204 170,278 2010 32,125 72,187 57,120 38,659 6,652 11,809 500 1,923 163,855 2011 30,834 69,789 55,233 37,237 6,078 11,918 473 1,627 157,956 Total 161,516 366,334 290,795 196,819 36,010 57,966 2,532 10,304 831,481 (%) (19.4) (44.1) (35.0) (23.7) (4.3) (7.0) (0.3) (1.2) (100.0) CR IP T 2007 31,768 73,986 59,264 40,032 8,106 11,126 508 2,317 167,843 Variable N Mean S.D Min P10 Med P90 Max 831,481 -0.021 4.350 -17.289 -4.016 -0.061 4.452 27.259 AC CE PT ED M ∆loan/asset AN US Table 9: Descriptive Statistics of Dependent Variable 43 ACCEPTED MANUSCRIPT Table 10: Main Result GCB for SME×post-crisis AC CE PT GCB for SME ×Relation GCB for SME×crisis × Relation GCB for SME×post-crisis × Relation GCB for SME ×NMB large GCB for SME×crisis ×NMB large GCB for SME×post-crisis ×NMB large large bank large bank×crisis large bank×post-crisis *** *** Coef (S.E.) -1.745 (0.036) 0.971 (0.051) 1.327 (0.059) 1.687 (0.067) -0.437 (0.094) -0.549 (0.111) -0.055 (0.043) 0.618 (0.060) 0.549 (0.068) -0.328 (0.029) -0.280 (0.038) -0.118 (0.041) M GCB for SME×crisis Coef (S.E.) -1.042 (0.023) 0.717 (0.033) 1.059 (0.037) -0.043 (0.042) 0.491 (0.059) 0.359 (0.067) -0.051 (0.045) 0.797 (0.063) 0.630 (0.071) -0.340 (0.031) -0.076 (0.041) -0.051 (0.044) *** ED Ind Var GCB for SME (2) Relation= MB loan share *** *** *** *** *** * (3) Relation= MB deposit share AN US (1) Relation= MB large CR IP T (Note) Dependent variable: ∆loan/asset The coefficients are estimated by the firm×year cross fixed effect model The numbers in parentheses are the firm×year cluster robust standard errors The estimated constant term, and the coefficients of the dummy variables for other government banks and other financial institutions and their interaction term with crisis-phase dummies are omitted from the table *, **, and *** indicate that the estimated coefficient is different from zero at a 10%, 5%, and 1% statistical significance level, respectively (two-sided) The base cell is regional banks MB (main bank) large is a dummy variable, which is equal to one if the main bank at the end of the accounting year ending in 2007 is a large bank MB loan or deposit shares is as of the end of accounting year ending in 2007 MB switch is a dummy variable to indicate whether a firm switched main banks in the pre-crisis period #lenders is the number of identified lenders except for GCBs at the end of the accounting year ending in 2007 *** *** *** *** *** *** *** *** *** *** *** Coef (S.E.) -1.112 (0.043) 1.186 (0.059) 1.401 (0.069) 0.127 (0.062) -0.553 (0.086) -0.439 (0.099) -0.071 (0.043) 0.618 (0.060) 0.552 (0.068) -0.375 (0.029) -0.101 (0.038) 0.010 (0.041) (continue to the next page) 44 *** *** *** ** *** *** *** *** *** *** (4) Relation= MB switch Coef (S.E.) -1.072 (0.020) 0.845 (0.029) 1.167 (0.032) -0.233 (0.125) 0.490 (0.165) 0.545 (0.183) -0.037 (0.043) 0.651 (0.059) 0.535 (0.067) -0.382 (0.028) -0.093 (0.037) -0.007 (0.040) *** *** *** * *** *** *** *** *** *** (5) Relation= #lenders Coef (S.E.) -1.094 (0.034) -0.199 (0.049) 0.617 (0.055) 0.023 (0.014) 0.537 (0.020) 0.301 (0.023) -0.114 (0.046) 0.181 (0.063) 0.250 (0.071) -0.386 (0.028) -0.094 (0.037) -0.005 (0.040) *** *** *** *** *** ** *** *** *** ** ACCEPTED MANUSCRIPT Table 10: (cont.) main bank×post-crisis main bank×large bank main bank×large bank ×crisis main bank×large bank ×post-crisis main bank×Relation yes 831,481 196,819 51,238 0.0076 0.0031 0.0058 *** -1.491 (0.042) 2.987 (0.058) 2.227 (0.065) *** *** *** -0.705 (0.058) 0.919 (0.079) 0.540 (0.086) *** *** *** 2.922 (0.100) -6.392 (0.136) -5.380 (0.151) yes 781,085 183,092 44,561 0.0176 0.0016 0.0113 *** *** *** AC CE PT main bank×Relation ×crisis main bank×Relation ×post-crisis Firm×Year fixed effect N #groups #firms R2 : within between overall *** (4) Coef (S.E.) *** *** -0.433 (0.025) 0.394 (0.034) 0.077 (0.038) *** AN US main bank×crisis -0.261 (0.030) 0.313 (0.042) -0.050 (0.046) -0.185 (0.061) 0.312 (0.081) 0.403 (0.089) M main bank (3) Coef (S.E.) ED Ind Var (2) Coef (S.E.) 45 0.692 (0.089) -0.995 (0.121) -0.917 (0.133) yes 776,274 181,955 44,235 0.0077 0.0015 0.0051 (5) Coef (S.E.) *** *** -0.133 (0.044) 0.019 (0.063) -0.129 (0.070) CR IP T (1) Coef (S.E.) *** *** *** 4.400 (0.206) -0.890 (0.262) -1.671 (0.276) yes 831,481 196,819 51,238 0.0122 0.0088 0.0108 ** *** *** *** -0.084 (0.019) 0.152 (0.026) 0.074 (0.029) yes 831,481 196,819 51,238 0.0101 0.0026 0.0066 *** * *** *** ** CR IP T ACCEPTED MANUSCRIPT Table 11: Estimated increase in lending of each type of bank relative to average regional banks est s.e -1.085 0.037 *** 0.124 0.036 *** 0.332 0.046 *** (iii) Large main bank est s.e -0.786 0.051 *** -0.161 0.054 *** -0.432 0.062 *** (v) Difference (iii)-(iv) est s.e -0.524 0.057 *** -0.213 0.062 *** -0.121 0.072 * est -1.042 -0.325 0.017 M (ii) GCB for SMEs if MB is regional PT Pre-crisis Crisis Post-crisis (i) GCB for SMEs if MB is large ED Pre-crisis Crisis Post-crisis AN US Note: Note: Marginal effects relative to average regional banks and their standard errors estimated by Spec (1) in Table 10 are listed We assume N M B large = in all calculations Panel (i) indicates the sum of coefficients of GCB for SMEs, its interaction terms with the crisis-phase dummies (crisis, post-crisis), and MB large (ii) indicates the sum of coefficients of GCB for SMEs, and its interaction terms with the crisis-phase dummies (iii) indicates the sum of coefficients of large bank, and its interaction terms with the main bank and the crisis-phase dummies (iv) indicates the coefficient of the interaction terms of the main bank and the crisis-phase dummies (v) indicates the difference of (iii) from (iv) ***: p < 0.01, ** p < 0.05 (two-sided t-test for H0 : effect is zero) AC CE Pre-crisis Crisis Post-crisis 46 s.e 0.023 *** 0.023 *** 0.029 (iv) Regional main bank est s.e -0.261 0.030 *** 0.052 0.028 * -0.311 0.034 *** ACCEPTED MANUSCRIPT Table 12: Additional Regressions GCB for SME× crisis GCB for SME×post-crisis AC CE PT ED GCB for SME× × MB large GCB for SME×crisis × MB large GCB for SME×post-crisis × MB large GCB for SME ×MB capital ratio GCB for SME× × NMB large GCB for SME×crisis × NMB large GCB for SME×post-crisis × NMB large Firm×Year fixed effect Bank×Year fixed effect N #groups #firms R2 : within between overall Coef (S.E.) *** *** *** M GCB for SME Coef (S.E.) -1.214 (0.035) 0.765 (0.035) 1.070 (0.039) 0.015 (0.043) 0.403 (0.060) 0.239 (0.070) 0.019 (0.004) -0.056 (0.049) 0.819 (0.067) 0.676 (0.076) yes no 766,729 181,293 48,145 0.0077 0.0030 0.0058 (2) bank×yr fe (3) complete panel AN US (1) MB capital ratio CR IP T (Note) Dependent variable: ∆loan/asset MB capital ratio in Column (1) is the difference of the risk-adjusted capital ratio from the regulatory requirement in each year Column (2) is the estimation including the firm-year and bankyear fixed effect All unidentified lenders are treated as a virtual bank in controling for bank-year fixed effects The coefficients in the other columns are estimated by the firm-year fixed effect model The numbers in parentheses in all columns are the firm-year cluster robust standard errors The estimated constant term and the coefficient of large bank, other GCB, other institution, main bank, and their interaction terms with crisis-phase dummies are included in the model but are omitted from the table *, **, and *** indicate that the estimated coefficient is different from zero at a 10%, 5%, and 1% statistical significance level, respectively (two-sided) The base cell is regional banks Column (3) is estimated by the sample firms who kept borrowing from a GCB for SMEs in every year from 2007 to 2011 Column (4) is estimated by the other part of the sample *** *** 0.203 (0.038) 0.476 (0.051) 0.071 (0.061) *** *** Coef (S.E.) -1.041 (0.030) 0.720 (0.042) 0.863 (0.045) 0.012 (0.053) 0.352 (0.073) 0.252 (0.080) *** *** *** *** *** (4) incomplete panel Coef (S.E.) -1.041 (0.037) 0.713 (0.054) 1.402 (0.066) -0.142 (0.068) 0.727 (0.098) 0.694 (0.123) *** *** *** ** *** *** *** *** *** 0.076 (0.043) 0.800 (0.058) 0.412 (0.067) yes yes 830,206 196,819 51,238 0.0017 0.0030 0.0017 47 * *** *** 0.003 (0.056) 0.629 (0.077) 0.554 (0.083) yes no 529,611 122,722 26,026 0.0069 0.0025 0.0051 *** *** -0.166 (0.079) 1.170 (0.112) 1.091 (0.139) yes no 301,870 74,100 25,212 0.0102 0.0048 0.0080 ** *** *** ... Regional banks include both the member banks of the Regional Banks Association of Japan and the Second Association of Regional Banks, and cooperative banks, such as Shinkin and Shinkumi banks The asset... “Brazilian Retail Banking and the 2008 Financial Crisis: Were the Government- Controlled Banks That Important?,” Journal of Banking and Finance 37, 2210-2215 27 ACCEPTED MANUSCRIPT Sapienza, P.,... lending and Governmental Involve- M ment in Banks, ” Journal of Banking and Finance 77, 64-77 ED Berger, A. , N Miller, M Petersen, R Rajan and J Stein, (2005) “Does Function Follow Organizational