Abstract
We construct an index measure that quantitatively describes the monitoring activities of Japanese banks. Using micro data on Japanese banks and borrower firms, we examine the effects of bank monitoring on the profitability of borrower firms. We find significant positive effects in the periods 1986–1991 and 1992–1996, although there is no significant effect in the period 1981–1985. We also examine how banks’ monitoring affects borrowers. The results show that the positive effects of banks’ monitoring on borrowers’ profitability are mostly caused by screening effects, not performance-improving effects.
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Notes
According to a survey conducted by a trade journal (Kinyu Zaisei Jijou, 1991, April 1, pp. 24–29), the loan limit amount at branches’ discretion is on average 91.5 million yen for regional banks and 81.2 million yen for second-tier regional banks. Unfortunately, we do not have any comparable information for city banks and trust banks.
While linear interpolation gives an interpolation formula with a zero second derivative in the interior of each interval, cubic spline interpolation gives an interpolation formula that is smooth in the first derivative, and continuous in the second derivative, both within an interval and at its boundaries (Press et al. 1992).
The estimated value is nearly identical to the actual value whenever the latter is available.
The above procedure is not enough to interpolate all missing data, and there are some banks in some periods of which the EOH ratio is still missing. In preparing Fig. 1 we simply ignored them and took the average of only positive values of this ratio.
More evidence is needed to obtain a definitive answer on this issue. We will not address it in this paper.
These results are available upon request.
In this sense, the words “main-bank” and “non-main bank” have no special meaning, and thus we do not deal with the issue of the effectiveness or non-effectiveness of the “main bank system” associated with the keiretsu industry groups.
A note on the theoretical foundation of this empirical model is available from the authors on request.
We exclude those firms where two or more banks are the largest lender at the same time for the technical reason that we cannot determine which of them is the main bank. Such cases exist but they are practically unimportant.
If this has a statistically significant negative sign, this would suggest a “congestion effect”, i.e., that the effectiveness of monitoring activities may be compromised if there is a large number of projects that the officers have to examine. If instead we have a statistically significant positive sign, this would be consistent with “economies of scale” in loan-project examining activities.
The counterpart of r i,j,t is not included because of data availability problems mentioned earlier.
Inclusion of those merged banks does not affect the estimation of the bank effects on the firm’s profitability since we estimate the bank effect in the separated periods and within each period they do not experience the merger. Note that for the Saitama Bank, we do not have the EOH ratio for the period 1987–91 and cannot estimate the bank effect. Although there were some other mergers among the regional banks and second-tier regional banks, those smaller merged banks are not included in our sample because either most of them did not play the role of main bank in our data or we do not have data on the EOH ratio.
This data set is based on firms’ financial reports and annual reports. Thus, when a company collapses or goes bankrupt, the data of that firm are not included after they are de-listed – a potential source of sample selection bias. However, in our sample period, corporate failures were rather rare: there were only twenty among all the firms listed on the first and second sections according to the Tokyo Shoko Research, Zenkoku Tosan Hakusho.
These results are available from the authors upon request. One may still want to point out that there may still be some sample selection biases. However, there is no sign of systematic bias in non-reporting of the number of loan-project examining officers, which implies such sample-selection biases, if they exist, are rather negligible.
A remark on the relationship between the profitability and the borrowing rate: in the case of “screening effects”, even banks with advanced screening ability will lend funds to relatively bad firms, if they can expect higher interest payments. In the case of “performance-improving effects”, firms’ profitability depends not only on lenders’ monitoring intensity but also on firms’ own productivity level. And firms with higher productivity can borrow funds at lower interest rates in the lending market. Therefore, in both cases the profitability of the borrowing firm is a decreasing function of the interest rate of the lending contract in equilibrium.
Using panel data of workers, they investigated why wages in high tech industries were higher. They found that the positive correlation between wages and technological change in industries is significantly weakened when they controlled for individual unobserved effects. Based on their regression results of a two-stage double-fixed-effect model, they concluded that we can explain the observed higher wages in high-tech industries mainly by a sorting of workers among industries and not by the effect of technological change on workers’ productivity.
These results are available from the authors upon request.
It should be noted here that we assume away any possible endogeneity of firm–bank matching.
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Acknowledgements
The authors are grateful for the comments by Joe Peek, Robert M. Stern, Gary R. Saxonhouse, Takeo Hoshi and other participants at the October 2004 conference in Ann Arbor.
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Fukao, K., Nishimura, K.G., Sui, QY. et al. Japanese Banks’ monitoring activities and the performance of borrower firms: 1981–1996. IEEP 2, 337–362 (2005). https://doi.org/10.1007/s10368-005-0040-2
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DOI: https://doi.org/10.1007/s10368-005-0040-2