# Presentation: 531323

## 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](http://www.rieti.go.jp/en/publications/summary/07050017.html)_

**Notes:**
Thank you for the opportunity to present our paper. This research is a joint project with Prof Uchida. In this paper, we are empirically examining the impact of bank consolidation on information production by using Japanese dataset.   

## Motivation

- Surge of bank consolidations in Japan to cope with the financial crisis in the late 1990s.
-  Major banks
-  Community banks (S*hinkin*)

**Notes:**
As is well-known,  we observed the surge of bank consolidations all over the world since the late 1980s.  The surge hit Japan as well in the late 1990s and the early 2000s as a response to the financial crisis. As a result, the number of banks, in particular, major banks and community small banks, which is called Shinkin in Japanese, decreased  from 600 in total to some 400, as you can see from this figure.  

## 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.

**Notes:**
In response to this global phenomenon, many researchers has conducted a number of researches about its impact on bank performance or its market value, and its impact on deposit or credit market performance, as we listed up in a footnote of the paper. 

Some of them provides evidence regarding the impact of bank consolidations on lending relationship. First, some researches find that consolidations decrease small business lending temporarily. Some find that lending relationships with acquired banks are more likely to be terminated than those with acquiring banks.  But, these negative impacts are compensated by rival banks in the long-run, say three years after a merger. 

Although these evidence clarifies the direction of the impact of bank consolidations on lending relationship, still it is less clear about what mechanism generates this direction of impacts. 

We guess that soft information  at the hand of merging banks is the key factor of this impact. 

## 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.”

**Notes:**
So, first, we ask the following question to our dataset; 
“Did bank consolidations hinder bankers’ production of customer-specific soft information?”, or “did they damage the information asset accumulated before consolidations?”.

 Here, we use the term, soft information. By this word, we mean the information that is hard to be communicated in a verifiable manner even within an organization. Proprietary information could be more popular term in the literature.

As we will see soon, our dataset answers to this question, YES. 

## 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).

**Notes:**
Second, we ask what mechanism could explain such impacts on soft information. The existing theory predicts three mechanisms. The first one is the  bank complexity hypothesis, which is presented by Stein 2002. The second one is the cost-cut hypothesis. The third one is the  competition hypothesis, which was predicted by Boot and Thakor, or Hauswald and Marquez. Our detailed data analysis shows that the first one is the most potential hypothesis among them. 

## Outline

- Background Theory
- Data
- Univariate Analysis
- Multivariate Analysis
- Relative Significance of Competing Hypotheses
- Conclusion

**Notes:**
In the rest of the presentation, I will talk about the hypotheses derived from existing  theories in more detail. After that, I will introduce our dataset, and talk about the univariate analysis and the multivariate regression analysis with respect to soft information . After that, I will talk about the analysis to identify the explanatory power of each of competing theory. 

## 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****↓**.

**Notes:**
Now, let’s start with theory. 

We named the first hypothesis, “Bank-complexity hypothesis.” This hypothesis is an application of the famous theory proposed by Stein 2002. 

This theory goes as follows. 

As a result of bank merger, the discrepancy between information-collecting divisions and decision authority becomes wider. Even communication failure would occur between different corporate culture of merging banks. By these obstacles , 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 become less willing to collect soft information since it is no good for their carrier. Thus, the increased complexity of organizational decision process resulting from bank consolidation discourages the production and maintenance of  soft information. 

## Background Theory 2 Cost-cut Hypothesis

- Personnel cut/relocation to realize the synergy.
- This could diminish the production capacity for soft information.
**Soft information****↓.**

**Background Theory 2 *****Cost-cut Hypothesis***

**Notes:**
The second one is the cost-cut hypothesis. The personnel cut or relocation to realize the post-merger synergy can diminish the production capacity for soft information. This hypothesis also predicts that bank consolidation decreases soft information. 

## Background Theory 3 Competition Hypothesis

- 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****↑**.

**Background Theory 3 *****Competition Hypothesis***

**Notes:**
The third one is the competition hypothesis. 

Bank merger decreases the number of competing banks. Consequently, existing customers are less likely to be pouched away by a rival bank, and so customer-specific information becomes more valuable. Thus, banks become more willing to invest in the acquisition of customer-specific information. That means soft information would increase. This competition hypothesis is presented by Boot and Thakor 2000, and Hauswald and Marquez 2006. 

In what follows, we will examine which of these theory has the most significant explanatory power in our dataset. 

## Data

**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).

**Notes:**
Now, dataset. 

The main part of our dataset comes from the firm-level micro dataset from the management survey of corporate finance issues in the Kansai area, which was conducted by Research Institute of Economy, Trade and Industry in Japan in June 2005. Kansai area is the economic center in the western Japan and the second largest megalopolis. 

Survey targets are randomly chosen by employee-size category and prefecture.  Among them, 2041 small and medium sized enterprises responded. Response rate was 22.4%.....

Among them, we used 987 samples whose information is fully available, for our statistical inference.  

Bank consolidation information was collected from Nikkin Shiryo Nenpo. Bank financial data is collected from Nikkei NEEDS, Japanese Bankers’ Association online database, and Financial Statements of Shinkin Banks. 

## Key variables

**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***.

**Notes:**
To measure firm-specific soft information at the hand of main bank, we made use of each firm’s answer to the following question. 
 The question asks each firm, “Please evaluate your main bank by 1 (very poor) to 5 (very good) grades with respect to, the knowledge about you company, knowledge about the .... 

First, we use these 6 indicators as measures of firm-specific soft information. Second, we compiled these 6 indicators into one variable SOFTINFO.  To put more precisely, SOFTINFO is the first principal component of these 6 indicators. 

## Key variables

**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.

**Notes:**
As for the indicator of bank consolidation, we made the merger dummy, which is equal to 1 if the main bank experienced at least one merger in April 2001-June 2005. June 2005 is the survey date. Many empirical studies find that the effect of consolidation vanishes within three years after a merger.  Therefore, we set the length of window, some 4 years. 

We also made BHC dummy, which is equal to 1 if main bank was acquired by a bank holding company, or other financial institutions in the same period.  

A potential problem is that we cannot trace back the name of MB before a merger. This problem limits what we can derive from our dataset. 

Now, let’s move on to statistical inference. 

## Univariate analysis 1

|  | BHC=0 |  |  |  |  |  | BHC=1 |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Variables | No. of Obs. | Mean |  | S.D. | Min. | Max. | No. of Obs. | Mean |  | S.D. | Min. | Max. |
| Response to Q1 | 897 | 4.025 |  | 0.815 | 1 | 5 | 90 | 3.933 |  | 0.859 | 1 | 5 |
| Response to Q2 | 897 | 3.951 |  | 0.861 | 1 | 5 | 90 | 3.878 |  | 0.885 | 1 | 5 |
| Response to Q3 | 897 | 3.425 |  | 0.790 | 1 | 5 | 90 | 3.344 |  | 0.796 | 1 | 5 |
| Response to Q4 | 897 | 3.428 |  | 0.819 | 1 | 5 | 90 | 3.111 | *** | 0.756 | 1 | 5 |
| Response to Q5 | 897 | 3.262 |  | 0.780 | 1 | 5 | 90 | 3.156 |  | 0.733 | 1 | 5 |
| Response to Q6 | 897 | 3.541 |  | 0.889 | 1 | 5 | 90 | 3.578 |  | 0.821 | 1 | 5 |
| SOFTINFO | 897 | 0.125 |  | 1.782 | -7.465 | 4.198 | 90 | -0.182 |  | 1.897 | -7.465 | 4.198 |

**>**

**=**

|  | Merger=0 |  |  |  |  |  | Merger=1 |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Variables | No. of Obs. | Mean |  | S.D. | Min. | Max. | No. of Obs. | Mean |  | S.D. | Min. | Max. |
| Response to Q1 | 394 | 4.109 |  | 0.782 | 1 | 5 | 593 | 3.954 | ** | 0.838 | 1 | 5 |
| Response to Q2 | 394 | 4.076 |  | 0.800 | 1 | 5 | 593 | 3.857 | *** | 0.892 | 1 | 5 |
| Response to Q3 | 394 | 3.467 |  | 0.810 | 1 | 5 | 593 | 3.384 |  | 0.776 | 1 | 5 |
| Response to Q4 | 394 | 3.548 |  | 0.873 | 1 | 5 | 593 | 3.300 | *** | 0.765 | 1 | 5 |
| Response to Q5 | 394 | 3.287 |  | 0.776 | 1 | 5 | 593 | 3.229 |  | 0.776 | 1 | 5 |
| Response to Q6 | 394 | 3.668 |  | 0.846 | 1 | 5 | 593 | 3.462 | *** | 0.898 | 1 | 5 |
| SOFTINFO | 394 | 0.370 |  | 1.778 | -7.465 | 4.198 | 593 | -0.085 | *** | 1.783 | -7.465 | 4.198 |

**Univariate analysis 1**

**Statistically significant**

- Table 2.  Descriptive statistics of soft information measures

**Notes:**
The upper panel shows the mean grades to each question and the mean of SOFTINFO after sorting by Merger dummy. This panel shows that the mean grades and SOFTINFO of merged banks are lower than those of non-merged banks, and this difference is statistically significant at 1% or 5% level.  This tendency is consistent with the bank-complexity hypothesis, and the cost-cut hypothesis. 

In contrast, such differences are not clearly observed with respect to the BHC dummy.  

## Univariate analysis 2

| Mean of SOFTINFO (s.d.) | # of obs. |   | Mean of SOFTINFO(s.d.) | # of obs. |   | t-test H0: |(1)-(2)|=0p-value |
| --- | --- | --- | --- | --- | --- | --- |
| (1) Large banks |  |  | (2) Small banks |  |  |  |
| -0.065 | 653 |  | 0.415 | 334 |  | 0.000 |
| (0.069) |  |  | (0.100) |  |  | *** |
|  |  |  |  |  |  |  |
| A. Large banks only |  |  |  |  |  |  |
| (1) Merged banks |  |  | (2) Non-merged banks |  |  |  |
| -0.057 | 569 |  | -0.125 | 84 |  | 0.738 |
| (0.074) |  |  | (0.186) |  |  |  |
|  |  |  |  |  |  |  |
| B. Small banks only |  |  |  |  |  |  |
| (1) Merged banks |  |  | (2) Non-merged banks |  |  |  |
| -0.748 | 24 |  | 0.505 | 310 |  | 0.001 |
| (0.445) |   |   | (0.101) |   |   | *** |

**Univariate analysis 2**

**<**

**significant**

**=**

**<**

**significant**

- Table 3.  Difference in soft information by bank size and merger

- Small banks: regional banks and *Shinkin* banks.
- Large banks: major banks.

**Notes:**
The first row of this panel shows the difference of SOFTINFO by bank size. We split the dataset by institution type of main banks. If the main bank is a major bank that operates nationwide, then the sample is classified into large banks. If the main bank is a regional bank or a Shinkin bank, then the sample is classified into small banks. Clearly, SOFTINFO of larger banks is lower than that of smaller banks. The difference is statistically significant at 1% level. 

In the second row, we compare SOFTINFO of merged large banks and that of non-merged large banks.  The difference is negligible. 

However, the difference between merged banks and non-merged banks is significant with respect to small banks. SOFTINFO of small merged banks is significantly smaller than that of small non-merged banks. 

These descriptive statistics supports the bank-complexity hypothesis or the cost-cut hypothesis. 

## Multivariate analysis

**Multivariate analysis**

- Regression analysis with control variables
- 	SOFTINFOi = *β**0** *+ *β**1**Mergeri + *β**2**BHCi
- 					+ *β**3**(control variablesi) + *ε**i*
-  *i *: the index of responding companies
- control variables: listed in Table 4.
- Also ordered-probit with regard to each of 6 indicators.
- If ***β******1 ***< 0, then **the bank-complexity hypothesis **and/or **the cost-cut hypothesis **have significant explanatory power**.**
- If ***β******1**** *> 0, then **the competition hypothesis does**.

**Notes:**
Now, let’s move on to the regression analysis. 
First, we regressed SOFTINFO on the merger dummy, BHC dummy,  and other control variables listed in Table 4. We also regressed each of the six indicators by ordered-probit. 
The coefficient that we are the most interested in is beta 1, the coefficient of the merger dummy. If this coefficient is negative and significant, then the bank-complexity hypothesis and/or the cost-cut hypothesis have significant explanatory power. If the coefficient is positive, then it means that the competition hypothesis has significant explantory power.  

## Multivariate analysis

| Regional bank (0,1) | 0.415 |  |  |  | 0.740 |  |
| --- | --- | --- | --- | --- | --- | --- |
|  | (0.420) |  |  |  | (0.625) |  |
| Shinkin bank (0,1) | 1.178 | * |  |  | 1.570 | * |
|  | (0.655) |  |  |  | (0.895) |  |

| Independent variables | (1) |   | (2) |   | (3) |   |
| --- | --- | --- | --- | --- | --- | --- |
| Merger (0,1) | -0.893 | ** | -0.868 | *** |  |  |
|  | (0.391) |  | (0.284) |  |  |  |
| BHC (0,1) | -0.703 | * | -0.758 | ** |  |  |
|  | (0.422) |  | (0.354) |  |  |  |
| Merger*major bank (0,1) |   |  |  |  | -0.450 |  |
|  |   |  |  |  | (0.588) |  |
| Merger*regional bank (0,1) |   |  |  |  | -1.239 | *** |
|  |   |  |  |  | (0.462) |  |
| Merger*Shinkin bank (0,1) |   |  |  |  | -1.461 | ** |
|  |   |  |  |  | (0.597) |  |
| BHC*major bank (0,1) |   |  |  |  | -0.401 |  |
|  |   |  |  |  | (0.628) |  |
| BHC*regional bank (0,1) |   |  |  |  | -0.027 |  |
|  |   |  |  |  | (0.784) |  |

**Small banks are more affected.**

**Merger and BHC decrease SOFTINFO.**

**Small banks have more SOFTINFO.**

- Table 5.  Effects of mergers and BHCs on soft information

**Multivariate analysis**

**Notes:**
The regression results show, first, Merger and BHC, especially merger,  have negative impacts on soft information. This effect works especially against small banks, such as regional banks and community banks. Second, the result shows that, these smaller banks have more soft information,  irrespective of mergers. 

## 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.

**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.

**Multivariate analysis**

**Notes:**
Thus, we found that, mergers have negative impacts on soft information at small banks, while they do not have any impacts on large banks. We also found that large banks do not collect soft information, even before mergers. These are consistent with the bank-complexity hypothesis and/or the cost-cut hypothesis. 

A remaining question is, which of these hypothesis is more significant.  In examining this point, the difference in the merger impacts by bank size is useful as we will see soon. 

## Bank-complexity vs Cost-cut

**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).

**Notes:**
In order to examine Bank-Complexity vs Const-cut question, we constructed two measures. 

The first one is the measure for the increment of complexity by merger.  The definition is given by equation 2 in the paper. The measure is defined as follows. The weighted average of the increasing rate from the size of each pre-merger banks to that of the post-merger bank. Here, size is measured by total asset, loan, number of bankers, and number of branches. As for the control group, namely non-merged banks, we just used average increasing rate of size during the sample period. 

## Bank-complexity vs Cost-cut

**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).

**Notes:**
The second one is the measure for the magnitude of cost-cut. 

The definition is given by Equation4 in the paper. The measure is defined as the increasing rates of costs, which is calculated between the sum of  pre-merger banks and the post-merger bank two years later. 

The cost is measured by # of branches, # of bankers, overhead & personnel costs, and ordinary costs.

For the control category, non-merged banks, we use the average increasing rate of  the cost of non-merged banks. 

## Bank-complexity vs Cost-cut Univariate Analysis

| Variables | [A-1 All banks] |  |  |  | [A-2 Large banks] |  |  |  | [A-3 Small banks]
(Regional, Shinkin) |  |  |  | [A-4 Difference in means] |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
|  | Merged |  | Non-merged |  | Merged |  | Non-merged |  | Merged |  | Non-merged |  | Merged vs. Non-merged(all banks, P-value) | Merged vs. Non-merged (large only, P-value) | Merged vs. Non-merged (small only,P-value) | Large vs. Small  (merged only, P-value) |
|  | mean
(s.d.) | # of
obs. | mean
(s.d.) | # of obs. | mean
(s.d.) | # of obs. | mean
(s.d.) | # of obs. | mean
(s.d.) | # of obs. | mean
(s.d.) | # of obs. |  |  |  |  |
| Asset  | 1.059  | 10 | 0.011  | 43 | 0.884  | 5 | -0.010  | 2 | 1.234  | 5 | 0.012  | 41 | 0.000  | 0.072  | 0.007  | 0.664  |
|  | (0.843) |  | (0.033) |  | (0.782) |  | (0.062) |  | (0.955) |  | (0.032) |  | *** | * | *** |  |
| Loan | 0.979  | 10 | -0.005  | 43 | 0.753  | 5 | -0.040  | 2 | 1.206  | 5 | -0.003  | 41 | 0.000  | 0.046  | 0.009  | 0.537  |
|  | (0.799) |  | (0.030) |  | (0.594) |  | (0.050) |  | (0.978) |  | (0.028) |  | *** | ** | *** |  |
| Number of bankers  | 1.112  | 8 | -0.032  | 43 | 1.010  | 4 | -0.006  | 2 | 1.213  | 4 | -0.033  | 41 | 0.000  | 0.018  | 0.021  | 0.800  |
|  | (0.757) |  | (0.037) |  | (0.506) |  | (0.011) |  | (1.026) |  | (0.038) |  | *** | ** | ** |  |
| Number of branches | 1.033  | 8 | -0.013  | 43 | 0.937  | 4 | -0.008  | 2 | 1.128  | 4 | -0.013  | 41 | 0.000  | 0.005  | 0.006  | 0.740  |
|  | (0.563) |   | (0.035) |   | (0.320) |   | (0.013) |   | (0.783) |   | (0.036) |   | *** | *** | *** |   |

**>**

**>**

**The magnitude of complexity-increment  upon merger does not differ by bank size.**

- Table 7.  Panel A. Complexity measures by size and merger

**Notes:**
First, we examined whether the magnitude of complexity increments is larger against small banks.  As you can see from this table, mergers increase the complexity measures of both large banks and small banks. The difference in its magnitude is insignificant as you can see from the last column. 

## Bank-complexity vs Cost-cutUnivariate Analysis

| Variables | [B-1 All banks] |  |  |  | [B-2 Large banks] |  |  |  | [B-3 Small banks]
(Regional, Shinkin) |  |  |  | [B-4 Difference in means] |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
|  | Merged |  | Non-merged |  | Merged |  | Non-merged |  | Merged |  | Non-merged |  | Merged vs. Non-merged(all banks, P-value) | Merged vs. Non-merged (large only, P-value) | Merged vs. Non-merged (small only,P-value) | Large vs. Small  (merged only, P-value) |
|  | mean
(s.d.) | # of obs. | mean
(s.d.) | # of obs. | mean
(s.d.) | # of obs. | mean
(s.d.) | # of obs. | mean
(s.d.) | # of obs. | mean
(s.d.) | # of obs. |  |  |  |  |
| Number of bankers  | -0.260  | 6 | -0.096  | 43 | -0.270  | 4 | -0.017  | 2 | -0.240  | 2 | -0.100  | 41 | 0.004  | 0.031  | 0.043  | 0.784  |
|  | (0.090) |  | (0.112) |  | (0.107) |  | (0.034) |  | (0.069) |  | (0.113) |  | *** | ** | ** |  |
| Number of branches | -0.178  | 6 | -0.039  | 43 | -0.178  | 4 | -0.025  | 2 | -0.178  | 2 | -0.040  | 41 | 0.023  | 0.101  | 0.348  | 1.000  |
|  | (0.106) |  | (0.105) |  | (0.088) |  | (0.040) |  | (0.183) |  | (0.107) |  | ** |   |   |  |
| Overhead & personnel expenses | -0.180  | 7 | -0.056  | 43 | -0.180  | 5 | 0.038  | 2 | -0.181  | 2 | -0.060  | 41 | 0.074  | 0.117  | 0.353  | 0.994  |
|  | (0.144) |  | (0.090) |  | (0.157) |  | (0.064) |  | (0.162) |  | (0.089) |  | * |   |   |  |
| Ordinary expenses | -0.435  | 7 | -0.134  | 43 | -0.488  | 5 | -0.339  | 2 | -0.305  | 2 | -0.124  | 41 | 0.039  | 0.633  | 0.536  | 0.640  |
|  | (0.273) |   | (0.253) |   | (0.262) |   | (0.246) |   | (0.355) |   | (0.252) |   | ** |   |   |   |

**Bank-complexity vs Cost-cut****Univariate Analysis**

**=**

**=**

**The magnitude of post-merger cost-cut does not differ by bank size.**

- Table 7.  Panel B. Cost-cut measures by size and merger

**Notes:**
Second, we looked into the difference in the magnitudes of cost-cut by bank size. As you can see from the last second and third columns, mergers do not necessarily significant cost-cut both in large banks and in small banks. Again, the difference of cost-cut by bank size is insignificant. 

Thus, it turns out that the difference in the impacts on soft information by bank size does not come from the difference in the magnitudes of cost-cut or complexity-increment. 

## Bank-complexity vs Cost-cutRegression Analysis

| Asset  | 0.082  |  |  |  |
| --- | --- | --- | --- | --- |
|  | (0.127) |  |  |  |
| Asset * Regional bank dummy | -1.314  | ** |  |  |
|   | (0.550) |  |  |  |
| Asset * Shinkin bank dummy | -0.567  |  |  |  |
|  | (0.387) |  |  |  |
| Loan |   |  | 0.107  |  |
|  |   |  | (0.151) |  |
| Loan * Regional bank dummy |   |  | -1.267  | ** |
|  |   |  | (0.502) |  |
| Loan * Shinkin bank dummy |   |  | -0.551  |  |
|  |   |  | (0.386) |  |

**Bank-complexity vs Cost-cut****Regression Analysis**

| Number of bankers  | 0.031  |  |  |  |
| --- | --- | --- | --- | --- |
|  | (0.207) |  |  |  |
| Number of bankers  | -2.152  |  |  |  |
|    * Regional bank dummy | (3.993) |  |  |  |
| Number of bankers  | -0.517  |  |  |  |
|    * Shinkin bank dummy | (0.414) |  |  |  |
| Number of branches |  |  | 0.036  |  |
|  |  |  | (0.233) |  |
| Number of branches |  |  | -1.533  |  |
|    * Regional bank dummy |  |  | (3.135) |  |
| Number of branches |  |  | -0.609  |  |
|    * Shinkin bank dummy |   |   | (0.484) |   |

- 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

**Notes:**
Now, we tried another regression of SOFTINFO.  This time, we used complexity measure, or cost-cut measure as explanatory variables, instead of the merger dummy. When we use the complexity dummy, the coefficients show a similar tendency as in the previous regression. The complexity increment affects negatively against softinfo of small banks. 

In contrast, cost-cut measures do not have any significant coefficients. 

## Summary of Our Findings

**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.

**Notes:**
To wrap up our findings,  
First, bank mergers decreases soft information of small banks, while it doesn’t have any impacts on large banks. 
Second, we have found that the increase in complexity upon merger has more significant impact on small banks than cost-cuts as we have already seen in the previous slide. 
Third, we found that the magnitudes of cost-cut and complexity-increment upon merger do not vary with bank size. 
Lastly, we found that small banks collect more soft information than large banks, irrespective of merger. 

All in all, we conclude that the bank-complexity hypothesis has more significant explanatory power. 
(That’s it. Thank you.)

These result can be summarized into the following picture. 

## Summary of Our Findings

**Summary of Our Findings**

**Notes:**
The horizontal axis is complexity, the vertical axis is SOFTINFO. SOFTINFO is a function decreasing in complexity, and it has non-negative value. 

Before merger, small banks are located around here, large banks are located around here. Merger increases the complexity of both sizes of banks. But, the increment in complexity has significant impacts only against small banks. And, it does not have any impacts on large banks, which did not collect soft information even before merger. 

## Policy Implication

**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.

**Notes:**
The result suggests a couple of policy implications. 

The promotion of bank consolidations is a popular policy, especially in a financial recession, since it is believed that it improves stability of the banking sector, and improve the cost efficiency of the sector by realizing the synergy effect. But, our result suggests that it has also a dark side. By bank consolidations, information production is deterred and/or accumulated information could be damaged at least temporarily. 

To evaluate the overall cost and benefits, we need more general and extensive theoretical and empirical analysis.

That’s it. Thank you!