Abstract
This study investigates the effects of bank mergers on bank–firm relationships. Bank merger announcements are shown to increase the probability of bank–firm relationship termination. This finding is robust both to nonlinear and linear probability model analysis and to adjustments for censoring in the data. Those firms that find their bank relationship terminated following a bank merger experience a significant reduction in lending from their former main bank. Healthy firms are able to compensate with loans from other banks. However, unhealthy zombie firms that experience a loss of their main bank relationship experience a larger drop in credit supply to begin with and are unable to compensate for the loss with loans from other banks, so total borrowing declines precipitously. These findings provide evidence of hold-up costs and the balance sheet problem in banking and suggest that bank mergers may alleviate those sub-optimal outcomes, improving capital allocation.
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Notes
There are almost no relationship terminations without switching to a new bank, what Degryse et al. (2011) call “drop**”.
Aoki, Patrick, & Sheard (1995) report that “according to a survey of some 110,000 companies with annual sales of 1 billion yen or more…. almost every Japanese company has what it calls a main bank relationship” (p. 5). In our sample of 4,971 firms listed on the first and second section of the Tokyo Stock Exchange or other regional exchanges in Japan, all but one declare a main bank at some point in our sample of the 29 years from 1984-2012 and all but 12 firms declare a main bank in every year of the sample.
Evergreening refers to the practice in banking of reviving a loan on the verge of default by granting further loans to the same firm. It is this practice which enables unviable firms to remain solvent, creating zombie firms.
Note that zombie firms are a sub-sample of the credit-constrained firms.
The formation of Mizuho Financial Group, Sumitomo-Mitsui Banking Corporation (SMBC), Mitsubishi-UFJ Financial Group, as well as one internal merger within SMBC.
A logit model, assuming a cumulative distribution function of the logistic distribution, was estimated as well. Results did not differ qualitatively from those reported below.
This form of the proportional hazard model, which allows for multiple terminations, follows existing banking literature such as Karceski et al. (2005) in assuming that the random variable follows a Weibull distribution.
Namely, if the assumed form for how the expectation of the outcome depends upon the covariates is correct.
In the analysis to follow, the R-squared for the linear probability model is in fact higher than the pseudo-R-squared for the nonlinear probit model. The linear model still has the uncomfortable possibility of implied probabilities less than 0 or greater than 1. However, in this case less than 2% of the expected termination variables fall outside the 0–1 range.
As explained above in the summary statistics, the firms in this sample are large, publicly listed firms who may receive bank loans from more than on bank and have access to other sources of financing. However, each firm has one declared “main bank”. It is this main bank–client firm relationship that is identified by the bank–firm fixed effects. See section 2 for a discussion of the role of the main bank.
Note that in the preferred specification above, which includes the \({\text{Firm}}_{i} x{\text{Year}}_{t}\) interaction term and \({\text{Bank}}_{b}\), bank fixed effects, the individual covariates \({\text{Merged}}_{b}\) and \({\text{Post}}_{t}\) are subsumed into the year and bank fixed effects, respectively. A simpler specification similar to equation (8), which includes levels for the individual covariates as well and the interaction term, was also estimated as a baseline specification and parameter estimates on the main variables of interest were not qualitatively different. This specification is favored because including the \({\text{Firm}}_{i} x{\text{Year}}_{t}\) interaction term enables controlling for demand side variation to isolate the supply of credit from bank to firms.
Note that the inclusion of bank–firm and time fixed effects subsumes the individual covariates \({Merged}_{b}\) and \(Pos{t}_{t}\). A simpler specification similar to equation (8) above, which includes levels for the individual covariates as well and the interaction term, was also estimated as a baseline specification and parameter estimates on the main variables of interest were not qualitatively different.
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This work was supported by the Social Science Research Council and Japan Foundation Center for Global Partnership Abe Fellowship
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Montgomery, H. Should I Stay or Should I go Now? The Effect of Bank Mergers on Bank–Firm Relationships in Japan. Eastern Econ J 48, 390–417 (2022). https://doi.org/10.1057/s41302-022-00210-5
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DOI: https://doi.org/10.1057/s41302-022-00210-5