Bank Consolidation and Soft Information Acquisition in Small Business Lending

1. Bank Consolidation and Soft Information Acquisition in Small Business Lending Conference on Mergers and Acquisitions of Financial Institutions November 30, 2007 Yoshiaki Ogura, Hitotsubashi University Hirofumi Uchida, Wakayama University Downloadable from the RIETI homepage: http://www.rieti.go.jp/en/publications/summary/07050017.html

2. Motivation Surge of bank consolidations in Japan to cope with the financial crisis in the late 1990s. • Major banks • Community banks (Shinkin)

3. Literature • Impact on bank performance or its market value. • Impact on deposit/credit market performance. • Impact on bank/borrower relationship. • Existing evidence finds temporal negative impact. • Consolidations decrease small business lending (Berger et al 1998, Peek et al 1998). • Lending relationships with acquired banks are more likely to be terminated (Focarelli et al 2002, Sapienza 2002). • In the long-run, these adverse effects are compensated by rivals (Berger et al 1998, Bonaccorsi-di-Patti et al 2007). • However, it is empirically less clear what mechanism generates such impacts.

4. Our Empirical Question • Did bank consolidations hinder bankers’ production of customer-specific soft information? Or, did they damage the information asset accumulated before consolidations? • Soft information: Information hard to be communicated in a verifiable manner even within an organization. e.g. entrepreneur’s enthusiasm and competence. ⇐ Our dataset answers, “YES.”

5. Our Empirical Question • What mechanism can explain such impacts? • Bank complexity hypothesis (Stein 2002). • Cost-cut hypothesis. • Competition Hypothesis (Boot and Thakor 2000, Hauswald and Marquez 2006).

6. Outline • Background Theory • Data • Univariate Analysis • Multivariate Analysis • Relative Significance of Competing Hypotheses • Conclusion

7. Background Theory 1 Bank-Complexity Hypothesis Bank Merger • Larger discrepancy between information-collecting divisions and decision authority, and communication failure among different corporate cultures. • It becomes harder to transmit soft information to the decision authority. • Soft information is less likely to be utilized in decision making. • Information collecting divisions are less willing to collect soft information since it is no good for their carrier (Stein 2002). • Soft information↓.

8. Background Theory 2 Cost-cut Hypothesis Bank Merger • Personnel cut/relocation to realize the synergy. • This could diminish the production capacity for soft information. • Soft information↓.

9. Background Theory 3 Competition Hypothesis Bank Merger • Number of competing banks decreases. • Existing customers are less likely to be poached by a rival bank. • Customer-specific information is more valuable. • Banks are more willing to invest in the acquisition of customer-specific information (Boot and Thakor 2000, Hauswald and Marquez 2006). • Soft information↑.

10. Data • Firm-level micro dataset: The Management Survey of Corporate Finance Issues in the Kansai Area (RIETI, Japan, June 2005). • Random sampling by employee-size category and prefecture. 2,041 SMEs responded (22.4%). • We used 987 samples of those information is fully available. • Consolidation information: Nikkin Shiryo Nen’po. • Bank data: Nikkei NEEDS Bank File, Japanese Bankers’ Association website, Financial Statements of Shinkin Banks (Kin’yu Tosho Consultant Sha).

11. Key variables • Measure of Firm-Specific Soft Information Q35 in the survey: “Please evaluate your main bank by 1 (very poor) to 5 (very good) grades with respect to: • knowledge about your company, • knowledge about the managers and owners, • knowledge about the industry of your company, • knowledge about your local community, • knowledge about the market of your company, • frequency of contacts by lending officers. • Six indicators. • The first principal component of them, SOFTINFO.

12. Key variables • Indicator of Bank Consolidation • Merger dummy: equal to 1 if MB experienced at least one merger in April 2001-June 2005. • Existing studies find the effect of consolidation vanishes within 3 years after a merger (e.g. Rhodes 1998). • BHC dummy: equal to 1 if MB was acquired by a bank holding company or other financial institutions in April 2001-June 2005. • Potential problem: we cannot trace back the name of MB before a merger.

13. Soft Information at non-BHC banks Soft information of BHC banks Soft information at non-merged banks Soft information at merged banks Univariate analysis 1 Table 2. Descriptive statistics of soft information measures Statistically significant ＝ ＞

14. Univariate analysis 2 Table 3. Difference in soft information by bank size and merger significant ＜ ＝ significant ＜ • Small banks: regional banks and Shinkin banks. • Large banks: major banks.

15. Multivariate analysis • Regression analysis with control variables SOFTINFOi = β0+ β1*Mergeri + β2*BHCi + β3*(control variablesi) + εi • i : the index of responding companies • control variables: listed in Table 4. • Also ordered-probit with regard to each of 6 indicators. • Ifβ1 < 0, then the bank-complexity hypothesis and/or the cost-cut hypothesis have significant explanatory power. • Ifβ1> 0, then the competition hypothesis does.

16. Multivariate analysis Table 5. Effects of mergers and BHCs on soft information Merger and BHC decrease SOFTINFO. Small banks are more affected. Small banks have more SOFTINFO.

17. Multivariate analysis • Bank-complexity hypothesis and/or cost-cut hypothesis are stronger candidates. • Remaining question • Which of bank-complexity or cost-cut is more significant? • Hint: The difference in the merger impacts by bank size.

18. Bank-complexity vs Cost-cut • Complexity-increment upon merger • Merged banks: weighted average of the increasing rate from the size of each pre-merger banks to that of the post-merger bank (Eq. 2). • Size: total asset, loan, # of bankers, # of branches. • Non-merged banks: average annual increasing rate of the size of non-merged banks in the sample period (Eq. 3).

19. Bank-complexity vs Cost-cut • Cost-cut upon merger • Merged banks: Increasing rates of costs, between the sum of pre-merger banks and the post-merger bank two years later (Eq 4). • Cost: # of branches, # of bankers, overhead & personnel costs, ordinary costs. • Non-merged banks: (Average annual increasing rate of the cost of non-merged banks) * 3(Eq 5).

20. Bank-complexity vs Cost-cut Univariate Analysis Table 7. Panel A. Complexity measures by size and merger > > The magnitude of complexity-increment upon merger does not differ by bank size.

21. Bank-complexity vs Cost-cutUnivariate Analysis Table 7. Panel B. Cost-cut measures by size and merger = = The magnitude of post-merger cost-cut does not differ by bank size.

22. Bank-complexity vs Cost-cutRegression Analysis Table 8. Impacts of the complexity increment and cost-cut on SOFTINFO Replace Merger dummy in the (3) specification of the previous regression with complexity-increment or cost-cut measures. Coef. of complexity-increment Coef. of cost-cut Complexity affects only small banks

23. Summary of Our Findings • Merger ⇒ Soft information of small banks↓. No impacts to large banks (Tables 3, 5, and 6). • Complexity↑ upon merger has more significant impact on small banks (Table 8). • The magnitudes of cost-cut and complexity-increment upon merger do not vary with bank size (Table 7). • Small banks collect more soft information than large banks, irrespective of merger (Table 3). ⇒ The bank-complexity hypothesis has more significant explanatory power.

24. Soft information Soft information = f(Compexity) Small banks (before merger) Merger Large banks (before merger) Merger 0 Complexity Summary of Our Findings

25. Policy Implication • Promotion of bank consolidations • Improves stability, • Improves cost efficiency. • A proviso against this prescription; • Information production is deterred, and/or, • Accumulated information are damaged, at least, temporarily. • Need more general theoretical/empirical analyses taking into account both of them.