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Bank Consolidation and Soft Information Acquisition in Small Business Lending

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Abstract

We empirically examine the impact of bank consolidation on bank acquisition of soft information about borrowers. Using a dataset of small business financing, we find that mergers of small banks have a negative impact on soft information acquisition, whereas mergers of large banks have no impact. We also find some evidence that an increase in organizational complexity upon a merger, rather than a post-merger cost-cut, is likely to cause a negative and significant impact on soft information acquisition by small banks. These findings are consistent with the organizational theory that predicts a comparative advantage of simple and flat organizations in acquiring and processing soft information.

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Notes

  1. Berger, Demsetz, and Strahan (1999), Amel, Barnes, Panetta, and Salleo (2004), and DeYoung, Evanoff, and Molyneux (2009) provide a literature review.

  2. Uchida, Udell, and Yamori (2012) also investigate the determinants of soft information production by banks. However, their focus is on the role of loan officers in producing soft information, and they do not take into account the effect of bank consolidation.

  3. See, for example, Berger and Udell (2002).

  4. Boot and Thakor (2000) also show (in their Theorem 3) that banks are more likely to provide relationship lending for a larger portion of borrowers as the number of rivals increases, given the level of upfront investment in relationship lending. This is because relationship lending can shield their existing customers from poachers. We do not, however, focus on this effect in the present paper, since our dataset does not enable us to capture the number of borrowers with whom the banks try to maintain strong relationships. Rather, our focus is on how much soft information a bank maintains regarding each existing borrower.

  5. Uchida et al. (2008b, 2012) use the same survey to investigate the use of lending technologies and the role of loan officers.

  6. The Kansai area is the second-largest metropolitan area in Japan and the business center of Western Japan. This area consists of six prefectures. Among them, the target firms were chosen from Osaka, Hyogo, and Kyoto Prefectures, including those located in three major cities, Osaka, Kobe, and Kyoto, in their respective prefectures. Osaka is the third-largest city in Japan.

  7. We eliminated these newest firms from our sample since it is likely that the amount of soft information accumulated by main banks about them may not be sufficiently large to have been affected by bank consolidation.

  8. The scoring coefficients of variables Q1–Q6 with respect to this first principal component are all positive (respectively, 0.434, 0.419, 0.433, 0.363, 0.413, and 0.383).

  9. We also conducted our analysis by using alternative measures: a simple average of the six indices, and the first principal component of the responses to Q1, Q2, and Q6, which are more closely related to firm-specific information. The results of the univariate and the multivariate analyses with these variables do not qualitatively differ from the results presented in the next two sections. These results are available from the authors upon request.

  10. Member firms of Shinkin banks have 300 or fewer employees or capital of 900 million yen or less.

  11. Note that long-term credit banks have not existed in Japan since 2006.

  12. The average total assets of each institution type in our dataset as of March 2005 are 48,059 billion JPY for major banks (city, long-term credit, and trust banks), 2,716 billion JPY for regional banks, and 918 billion JPY for Shinkin banks.

  13. We also conducted the analysis by changing the starting point of the window period from April 2000 to April 2003, but the result did not differ qualitatively. The result is available from the authors upon request.

  14. Some banks experienced multiple mergers during the window period: 2 city, 1 trust, and 1 Shinkin banks.

  15. See Table 4 for more detailed definitions of these variables and their descriptive statistics.

  16. Some empirical evidence in support of this anecdotal evidence is reported in Uchida, Udell, and Watanabe. (2008).

  17. Another noteworthy result is that the non-performing loan ratio of a main bank has a negative and significant coefficient in all the specifications. This result suggests the possibility that the accumulation of non-performing loans prevents banks from actively producing soft information about borrowers, although a more careful examination of the causality between bad loans and soft information acquisition is needed.

  18. If we change the definition of the BHC dummy to include banks that have undergone a merger as well as the establishment of a bank holding company, the effect of a merger becomes less significant.

  19. We are very grateful for the referee’s insightful suggestion on this point.

  20. To be more precise, for each variable X (= asset size, loan size, number of bankers, or number of branches), we calculate the following measure:

    $$ {{{\left( {X\,of\,the\,post-merger\,bank\,at\,the\,end\,of\,years} \right)}} \left/ {{\left( {weighted\,average\,of\,X\,of\,pre-merger\,banks\,at\,the\,end\,of\,year\,s-1} \right)}} \right.}-1, $$

    where s is the year in which the merger took place.

  21. We calculate the following measure.

    $$ \frac{1}{5}\sum\limits_{t=2001}^{2005 } {\left[ {{{{\left( {X\,at\,the\,end\,of\,year\,t} \right)}} \left/ {{\left( {X\,at\,the\,end\,of\,year\,t-1} \right)}} \right.}-1} \right].} $$

  22. That is, the cost-cut measure of variable X (=the number of branches, the number of bankers, overhead and personnel expenses, or ordinary expenses) is:

    $$ {{{\left( {X\,of\,the\,post-merger\,bank\,at\,the\,end\,of\,year\,s+2} \right)}} \left/ {{\left( {X\,summed\,over\,all\,the\,pre-merger\,banks\,at\,the\,end\,of\,year\,s-1} \right)}} \right.}-1. $$

  23. To be precise, the measure is defined as follows:

    \( 3\cdot \frac{1}{5}\sum\limits_{t=2001}^{2005 } {\left[ {\left\{ {{{{\left( {X\,at\,the\,end\,of\,year\,t} \right)}} \left/ {{\left( {X\,at\,the\,end\,of\,year\,t-1} \right)}} \right.}-1} \right\}} \right]} . \)

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Authors and Affiliations

  1. School of Political Science and Economics, Waseda University, 1-6-1 Nishi Waseda, Shinjuku-ku, Tokyo, 169-8050, Japan

    Yoshiaki Ogura

  2. Graduate School of Business Administration, Kobe University, 2-1 Rokkodai-Cho Nada-ku, Kobe, 657-8510, Japan

    Hirofumi Uchida

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  1. Yoshiaki Ogura
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  2. Hirofumi Uchida
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Correspondence to Yoshiaki Ogura.

Additional information

This paper is a result of the research performed at the Industrial and Financial Structure Workshop at the Research Institute of Economy, Trade, and Industry (RIETI) in Japan. This paper was presented at the Contract Theory Workshop, the Conference on Mergers and Acquisitions of Financial Institutions (hosted by the Federal Deposit Insurance Corporation (FDIC), the Federal Reserve Bank of Chicago, and Journal of Financial Services Research), Kwansei Gakuin University, the Japan Society of Monetary Economics, the Japanese Economic Association, Chuo University, and the RIETI. We are grateful to Ken’ichi Amaya, Munetomo Ando, Ken Cyree, Kohei Daido, Kozo Harimaya, Yuji Honjo, Hideshi Ito, Shinsuke Kambe, Kenji Kutsuna, Toshihiro Okada, Eiji Okuyama, Tetsuya Shinkai, Atsushi Tanaka, and Larry Wall for their insightful comments. We also gratefully acknowledge the financial support by the Daiginkyo Forum of the Osaka Bankers’ Association. This study is partly supported by a Grant-in-Aid for Scientific Research, Japan Society for the Promotion of Science (Subject No.18730215 and No.21330076).

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Ogura, Y., Uchida, H. Bank Consolidation and Soft Information Acquisition in Small Business Lending. J Financ Serv Res 45, 173–200 (2014). https://doi.org/10.1007/s10693-013-0163-5

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