Discussion of “Bank Consolidation and Soft Information Acquisition in Small Business Lending”

1. Discussion of “Bank Consolidation and Soft Information Acquisition in Small Business Lending” Discussant Ken B. Cyree Frank R. Day/Mississippi Bankers Chair of Banking University of Mississippi

2. I. Assumptions based on findings in the extant literature on small business lending…(Berger and Udell, Scott, etc.). • A. Soft information is used by small banks to lend to small firms. • Small banks are able to efficiently use soft information due to less organizational complexity. • If less soft information is produced, there will be less small business lending. • Mergers and acquisitions create more complex and larger banks, with less ability to produce and efficiently use soft information, and hence there will be less small business lending. • Note that the authors do not test for lending, but for soft information.

3. Empirical Findings • Firms who had a merger at their main bank believe there is less “soft information” about their firm. • B. Complexity reduces soft information but cost cutting does not. Size does not influence the complexity effect. • When there is no merger, small banks are likely to get more soft information. • Findings are consistent with small banks making more small business loans due to using more soft information. • Supports Scott (2004), Berger and Udell (2002), Petersen (2002), and Stein (2002).

4. III. Discussion • For the perfect experiment, the data would be gathered before and after a merger from the same firm. • The survey is a one-time survey in June 2005. Banks are coded as involved in a takeover if a merger occurs from April 2001 to June 2005. • It would seem that the response would be much different for mergers that just occurred versus one four years earlier. The authors should add a variable for time since the merger.

5. The majority of the mergers in Japan (78%) targeted Shinkin banks, yet only 29% of banks in the sample are Shinkin banks. • Is there a selection bias? If so, it could be that Shinkin banks are the most likely targets acquired by Large/Regional banks who do not use soft information. • If larger banks with more “transactions based” lending models are more likely to be acquirers and Shinkin banks, does small business lending decline or just soft information production? Hold up costs? • In order to eliminate this concern, I suggest a two-stage selection model using a Heckman (1979) correction. The dependent variable should be one if a merger. Explanatory variables should include a Shinkin indicator.

6. C. Do Shinkin banks have a different focus because they are “cooperative” organizations? If these mutuals are taken over by a large bank, it would be expected that personal knowledge would fall. Mutuals likely have different objective functions, which is likely to include non-profit motives. Might soft information be more important for non-profit motives? • D. What is the economic rationale for the order of the ordered logit model? • Since these are categories that do not have a natural order, a multinomial logit is likely the best choice. • E. The “complexity measure” is really a growth measure. Is growth synonymous with complexity?

7. F. If the main question to ask is whether or not mergers reduce small business lending due to lower soft information production, why not estimate: • The underlying reasons for doing this test are (1) To check on the initial assumptions that soft information is important for small business lending, and 2) If the survey generated measures of soft information actually used by lenders. • SOFTINFO can be an average of the survey questions, or the principle components. The SHINKIN indicator is to control for whether or not there is a structural reason these banks make more small business loans.

8. F. If soft information is not related to lending volume or prices, then it does not matter if it is more or less. • G. Test robustness using each question as in Scott (2002) Table 6? • Conclusion of discussion • A. Soft information should first be related to (small firm) lending. • You cannot say mergers cause less soft information, rather banks that merge have customers who perceive less company knowledge or personal attention from the bank. • Must solve selection bias issue

9. Be careful about causality. • Although it is important that results appear to be consistent with reduced information for firms that merged. • B. Overall the paper studies an important topic and has a unique dataset that can help answer one piece of the puzzle about the effects of mergers on small bank lending.