Silviase/jgov_v1.4_full_ann huggingface dataset:
from borrowers. By setting a so-called reference rate, FISIM further splits the output into the outputs associated with lending service and deposit service. Then, the former (latter) is counted as intermediate input (final consumption) in the extended SNA framework. In this paper, we put an adjustment for the credit risk taken by banks to such an original measure proposed in FISIM, and study to what extent such a modification is important. This reflects the consideration pointed out in the recent literature that the original FISM mistakenly estimate bank output if we do not appropriately take into account the degree of risk taken by banks. We use such a modified version of FISIM concept to measure the output of 100 banks in Japan.

Note that for this empirical exercise, we implicitly assume some of the sample firms are facing financial friction. Due to such a friction potentially generated by information asymmetry between firms and outside financiers as well as insufficient internal fund held by firms, the firm's investment and/or financing choices could be distorted (e.g., under-investment, excess cash hoarding, and/or the heavy reliance on costly external finance etc.). $ ^{2} $ A number of studies have already pointed out the possibility that such distorted firm behaviors generate the internal allocative inefficiency, which eventually end up with the deterioration of TFP (e.g., Chari et al. 2005; Pratap and Urrutia 2010). Thus, we conjecture that firms keeping relations with the banks exhibiting relatively high efficiency, which could be the sign of superior screening and/or monitoring abilities, tend to be less suffering from such a financial friction. Based on this perspective, we construct the hypothesis that firms show higher performance when they hold the relations with the banks with higher efficiency.

As we will briefly survey in the following section, some of the existing studies
have already explored how the existence of "Mainbank" affects firm's financial availability and performance. $ ^{3} $ This paper intends to follow this strand of the study on the value of intimate loan relationships with explicitly considering the heterogeneity of those banks.

This paper is structured as follows. Section 2 briefly surveys the related literature. Before constructing our panel data, Section 3 reviews the discussion about bank output, which includes FISIM concept. Then, the data we use are described in Section 4. Section 5 constructs and tests the hypotheses relating the lender banks' efficiency to firm TFP. Section 6 concludes and presents future research questions.

### 2. Related literature

There are only a few studies that examine the relationship between bank activity and the performance of its client firms empirically by using the firm-bank match-level data. As one exception, Fukao et al. (2005) tests whether the intensity of lender banks' monitoring activity are correlated with the client firms' ROA. They introduce the ratio of examination officers at the lender bank's headquarter to the all employees of the bank (EOH ratio) as a proxy for monitoring intensity. Through the estimation based on the firm-level panel data containing this EOH ratio as well as other standard determinants of firms' ROA (e.g., leverage, size, loan dependence etc.), they discuss how the lender bank characteristics are correlated with the client firm performances. We basically apply the same empirical strategy to see the effect of the lender banks efficiency, which is originally measured in this paper, onto client firm's
TFP. $ ^{4} $ As another example for the empirical examination between bank efficiency and firm performance, Pratap and Urrutia (2010) studies the effects of unexpected interest rate shock on firm TFP through its intermediate goods procurement. They construct a dynamic two-sector model of a small open economy with a financial friction (i.e., cash-in-advance constraint) and quantitatively analyze the effects of currency crisis on the Mexican economy. The model shows how the interest rate shock induces the decline of TFP level of the economy through the malfunction of financial sector. Their results could be one illustration for TFP in the economy to be influenced through financial channel. We share the same view with these studies.

About the determinants of firm TFP, Syverson (2010) comprehensively surveys the recent empirical studies. He classifies six major internal drivers of firms TFP differences. $ ^{5} $ Those include (i) managerial practice and/or talent, (ii) higher-quality general labor and capital inputs, (iii) informational technology and R&D spending, (iv) learning-by-doing, (v) product innovation, and (vi) firm structure decision. While the tradition literature largely emphasizes the importance of R&D spending as a determinant of TFP, he points out "the mechanisms the R&D literature highlights are likely to often overlap with the effects of unmeasured innovative spending". He also states that "understanding how such intangible capital stocks are built and sustained would shed light on many productivity-related issues for this reason." We consider the financially constrained firm cannot optimize the procurement of intermediate inputs, and presume that the accumulation of one type of intangible assets - the relation with
efficient banks - could mitigate such a constraint. It is our main object in this paper to shed a new light on the role of the financial intermediation on firm TFP.

The importance of having a mainbank has been an issue in the extant banking study (Aoki and Patrick 1994; Aoki and Saxonhouse 2000). This strand of literature presumes that the existence of mainbank could potentially extend the firm's credit availability, which improves the firm's outcome. Alternatively, it is claimed that the existence of such a mainbank may send a good signal to other banks and this can also improve credit availability. Those studies, however, have not explicitly taken into account the heterogeneity of each mainbank. From the same reason as we are interested in the variation of firm performance, it would be natural to expect that the difference among banks exists. We take this point seriously and empirically study it. In this sense, it is our interest to follow and deepen the classic discussion associated with the role of mainbank.

### 3. Bank Output

Unlike the firms in manufacturing industries, measuring output for the business entities in service industries (e.g., banking) is not necessarily straightforward. As one example, Das and Ghosh (2006) categorizes the measurement of bank output into two groups. The first group characterizes banks as in the analogy of usual manufacturing companies. Such a “production approach” (e.g., Ferrier and Lovell 1990) considers, for example, the number of deposits and loans as output while the wage, rental price, and intermediation cost payments as input. $ ^{6} $ Those studies naively consider the size of bank's balance sheet as the measure of output. One criticism for
this approach is on their ignorance about the specific characteristics of banks as financial intermediations, which uses the mismatch between lending and borrowing to generate its profit. The second group called “intermediation approach” responds to such criticism and intends to characterize explicitly banks as intermediaries between depositors and firms. This group is further categorized into two individual approaches based on their perspectives about intermediation. $ ^{7} $ First, the “asset approach” treats the liability and other physical resources as the input of bank's production process while the asset as output. $ ^{8} $ By putting a distinction between the two sides of bank's balance sheet, they intend to capture the role of banks as intermediaries. One shortcoming of this approach is the inability to analyze the productivity difference coming from the choice of capital structure (i.e., the composites of the liabilities and equities). Second, “user-cost approach” simply focuses on the return from the financial assets minus its reference rate, which corresponds to the opportunity cost of the funds. Whenever the net return is positive (negative), the bank's output is considered as positive (negative). This framework shares a view with the standard FISIM approach. One technical difficulty common both in the user-cost approach and FISIM approach is the difficulty to have the consensus on the measurement for reference rate, especially on how risk should be taken into account for this measurement. $ ^{9} $ FISIM is measuring bank's output by computing the net interest profit. Then, they split the output into the ones associated with lending and deposit services by using a reference rate. Notably, the recent FISIM literature further takes into account the risk adjustment since the user cost of money should be essentially adjusted for risk. For example, Basu et al. (2008), and Colangelo and
Inklaar (2008) uses various market rate data to construct an appropriate risk-adjusted reference rate. As another example, Guarda and Rouabah (2007) employs a simple micro-econometrics model to structurally estimate the shadow price of loans. Note that the instable nature of the estimated shadow price is criticized from the practical consideration.

In principle, FISIM interprets bank's net interest profit, which stands for the loan interest receipts minus the deposit interest payments, as its output. As widely pointed out in the literature (e.g., Basu et al. 2008), however, such a notion is somewhat problematic. In fact, the output associated with bank's lending service should be ideally computed as the loan interest receipts minus the required market return for the borrower's funding in the hypothetical situation where information asymmetry does not exist. This ideal reference rate for lending service is conceptualized in the center diagram in Figure-1. $ ^{10} $ Imagine the case where a firm is planning to finance its capital investment. If there is no information asymmetry between the firms and outside financiers, the firm can freely borrow from the market. Due to the existence of information problem, however, the firm needs to rely on banks, which could potentially mitigate the problem, and hence deserve rents. This is the reason why we need to measure the output associated with lending service by subtracting the required market return, which is computed in the hypothetical environment with no information asymmetry, from the loan interest receipts. Unless we take into account this adjustment, we inevitably over-estimate bank outputs by mistakenly subtracting risk-free interest, which is essentially lower than the hypothetical required market
Unfortunately, we could not generally observe the hypothetical required market return corresponding to the case without information problem. Corresponding to this concern, Basu et al. (2008) refers to the most plausible market indexes for the bank assets as possible. Those indexes containing the return of MBS, CMBS, or ABS, however, are not necessarily available. In this paper, we rely on the information on the allowance for loan losses, which we can observe in bank's balance sheet. In order to proxy the credit risk taken by banks in ex-ante perspective, we use the average of the changes in the allowance for loan losses over the next three years from a given period where we attempt to measure bank output. We use this information to quantify the average of the realized losses in banks' financial statement. Note that the allowance for loan losses is the estimated losses out of the loan outstanding at each point. Thus, the average change in the allowance for loan losses could summarize credit risk associated with the loan asset from the ex-post perspective. If the hypothetical financial market works well and the competition in the market is high enough, the credit risk observed ex-post could work as a good proxy for the credit risk estimated ex-ante (i.e., at the timing of loan provision). If this is the case, the hypothetical required market return for the loan asset could be set to the rate covering exactly such an ex-post observed credit risk. This is one justification for using the data on the allowance for loan losses in order to adjust the credit risk. $ ^{12} $ (Figure-1 right diagram)
### 4. Data

We have two datasets, which store firm-level and bank-level data. First database is for bank characteristics provided by NEEDS Financial Quest. Second database is for the financial characteristics of firms, which is stored in Development Bank of Japan Corporate Financial Databank. After compiling the panel data for the efficiency of banks and its characteristics, we combine the data with the client firm's characteristics. For this purpose, we employ the loan relationship information between the listed firms and their lender banks over our sample period, which covers 1976 to 2005 fiscal years. $ ^{13} $ As a result of this operation, we end up with the large unbalanced panel data. Table-1 and Table-2 list the summary statistics and the correlation coefficients, respectively. $ ^{14} $

#### 4.1. Bank data

Our first dataset - NEEDS Financial Quest - stores bank's financial characteristics in the form of an unbalanced panel data. One remark is that the identification of each bank is based on the identity of each bank as of 2009 fiscal year. If a bank is merged with another bank before 2009, the recognized continuing bank at the timing of merger in the database is automatically treated as a survival one. $ ^{15} $
Before implementing the risk-adjustment to the original FISIM output briefed in the previous section, we process two steps. First, the gross output of bank j at the period t is measured by simply following FISIM concept (i.e., loan interest receipt minus deposit interest payment).

$$ Gross Output_{j,t}=Interest Receipt_{j,t}-Interest Payment_{j,t}\cdots(1) $$

where

Interest Receipt $ _{j,t} $ : Bank j's Interest Receipt during the period t

Interest Payment $ _{j,t} $ : Bank j's Interest Payment during the period t

This output measure in (1), however, are likely to be negative in many bank-year cases due to the mismatch of loan asset and deposit, which is a typical feature of Japanese banks. Corresponding to this problem, we adjust the deposit interest payment by multiplying the ratio of loan outstanding to deposit outstanding, and construct the so-called B/S Adjusted Output in (2).

$$ \mathcal{B}/S\;A d j u s t e d\;O u t p u t_{j,t}=I n t e r e s t\;R e c e i p t_{j,t}-I n t e r e s t\;P a y m e n t_{j,t}\times\frac{L o a n\;O u t s t a n d i n g_{j,t-1}}{D e p o s i t\;O u t s t a n d i n g_{j,t-1}}\cdots(2) $$

where

Loan Outstanding $ _{j,t-1} $ : Bank j's Loan Outstanding at the end of the period t - 1

Deposit Outstanding $ _{j,t-1} $ : Bank j's Deposit Outstanding at the end of the period t - 1
Through this modification, we virtually compute a net interest profit for the bank, which finances all of the existing loan assets by deposit. Note that as a cost of this operation, we are inevitably forced to exclude the quality of asset-liability management in each bank from our analysis, which could be potentially an interesting research object. $ ^{16} $ Finally, we subtract the average of the changes in the allowance of loan losses over the following three years to each point as in (3).

$$ \begin{aligned}&\left\{\begin{aligned}\\ &Risk Adjusted Output_{j,t}=B/S Adjusted Output_{j,t}\\&\quad-\sum_{\tau=1}^{3}\frac{\left(Allowance of Loan Losses_{j,t+\tau}-Allowance of Loan Losses_{j,t+\tau-1}\right)}{3}\cdots(3\\&where\\&\quad Allowance of Loan Losses_{j,t}:Bank j^{\prime}s Allowance of Loan Losses at the end of the period t\\ &\end{aligned}\right.\\ \end{aligned} $$

Then, our measure of bank efficiency is computed through dividing this final output measure by the operating cost as in (4). Figure-2 plots the panel data for the efficiency of banks in (4) over our sample period. We can immediately notice the large cross-sectional dispersion and the seemingly structural change in time-series direction.

$$ \left\{\begin{aligned}Bank Efficiency_{j,t}&=\frac{Risk Adjusted Output_{j,t}}{Operation Cost_{j,t}}\cdots(4)\\ \\ where\quad&Operating Cost_{j,t}:Bank j^{\prime}s Operation Cost over the period t\end{aligned}\right. $$

#### 4.2. Firm data

The firm characteristics are obtained from Development Bank of Japan
Financial Data Bank, which stores the loan amounts from each bank to each firm and each firm's financial characteristics. Since the loan relation data stored in DBJ database, which is essential for our analysis, is available only for 1982 to 1999, we complement this data set with Nikkei Needs Financial Quest. We merge the firm and bank data by using these two databases, and construct the unbalanced panel data from 1976 to 2009 fiscal years. In our data, the list of firms is fixed to the one being alive in 2009 fiscal year. We do this to exclude the demographic effect associated with the entry and exit of firms. For bank's side, however, the list of banks vary over time due to the merger and acquisition among banks. The identification of each bank is based on the identity of each bank as of 2009 fiscal year. This means if a bank is merged with another bank before 2009, the recognized continuing bank at the timing of merger is automatically treated as a survival one. As a result, the data set consists of 3,197 firms and at most 164 banks. We use all the non-financial firms belonging to all the sectors. $ ^{17} $

As the firm’s ex-post efficiency measure for period $ t + \tau (\tau = 3 \text{ and } 5) $ , we employ the TFP of each firm. We also implement the same exercise for $ \tau = 1, 2, $ and 4. The obtained implication is almost same unless noted explicitly. $ ^{18} $ This is provided in East Asian Listed Companies Database (EALC) 2009 compiled by Japan Center for Economic Research (JCER), Center for Economic Institutions (IER, Hitotsubashi University), Center for China and Asian Studies (CCAS, Nihon University), and Center for National Competitiveness (Seoul University). As detailed in Fukao et al. (2011), the TFP level of firm f, industry j in year t, $ TFP_{f,j,t} $ is calculated as follows in the case that
the data cover a period from t = 0 to T and $ t_{0} $ (0 < t < T) is the benchmark year. The estimation for each firm's TFP level is implemented as relative to the industry average TFP level. They use the multilateral TFP index method developed by Good et al. (1997).

$$ \begin{aligned}&LN\big(TFP_{f,j,t}\big)=\Big\{LN\big(Q_{f,j,t}\big)-\overline{LN\big(Q_{j,t}\big)}\Big\}-\sum_{i=1}^{n}\big(S_{f,i,j,t}+\overline{S_{i,j,t}}\big)\Big\{LN\big(X_{f,i,j,t}\big)-\overline{LN\big(X_{i,j,t}\big)}\Big\}\\ &\\ &for t=t_{0}\\ &\\ &LN\big(TFP_{f,j,t}\big)=\Big\{LN\big(Q_{f,j,t}\big)-\overline{LN\big(Q_{j,t}\big)}\Big\}-\frac{1}{2}\sum_{i=1}^{n}\big(S_{f,i,j,t}+\overline{S_{i,j,t}}\big)\Big\{LN\big(X_{f,i,j,t}\big)-\overline{LN\big(X_{i,j,t}\big)}\Big\}\\ &+\sum_{s=t_{0}+1}^{t}\Big\{\overline{LN\big(Q_{j,s}\big)}-\overline{LN\big(Q_{j,s-1}\big)}\Big\}-\sum_{s=t_{0}+1}^{t}\sum_{i=1}^{n}\frac{1}{2}\big(\overline{S_{i,j,s}}+\overline{S_{i,j,s-1}}\big)\Big\{\overline{LN\big(X_{i,j,s}\big)}-\overline{LN\big(X_{i,j,s-1}\big)}\Big\}\\ &\\ &for t>t_{0}\\ &\\ &LN\big(TFP_{f,j,t}\big)=\Big\{LN\big(Q_{f,j,t}\big)-\overline{LN\big(Q_{j,t}\big)}\Big\}-\frac{1}{2}\sum_{i=1}^{n}\big(S_{f,i,j,t}+\overline{S_{i,j,t}}\big)\Big\{LN\big(X_{f,i,j,t}\big)-\overline{LN\big(X_{i,j,t}\big)}\Big\}\\ &-\sum_{s=t+1}^{t_{0}}\Big\{\overline{LN\big(Q_{j,s}\big)}-\overline{LN\big(Q_{j,s-1}\big)}\Big\}+\sum_{s=t+1}^{t_{0}}\sum_{i=1}^{n}\frac{1}{2}\big(\overline{S_{i,j,s}}+\overline{S_{i,j,s-1}}\big)\Big\{\overline{LN\big(X_{i,j,s}\big)}-\overline{LN\big(X_{i,j,s-1}\big)}\Big\}\\ &\\ &for t
in year t, of all firms in industry j to which firm f belongs.

#### 4.3. Matching data

As a result of the matching between firms and banks, we have a large size firm-bank match-level unbalanced panel data. By using the information about the short-term and long-term loan outstanding for each match, we also compute the loan share of each lender banks out of the total loan for each listed company. We use this variable to measure the strength or depth of loan relations. The share information is important to connect our study to the classic study on the value of mainbank.

### 5. Empirical Analysis

In this section, we empirically examine the quantitative impact of lender bank's efficiency at t - 1 onto client firm's ex-post efficiency at $ t + \tau $ . Note that in order to see the correlation between lender bank's efficiency to client firm's TFP, we need to somehow summarize the potentially multiple lender banks' efficiency. For this purpose, we focus on the efficiency of top lenders. This reflects our consideration that the largest lender's characteristics exhibit the most important impact on its client firm's performance. $ ^{19} $ We also use a match between firm and its top lender as a group for our panel estimation. This means the group is changed when firms switch their top lender. This treatment allows us to partly control the endogeneity of matching between firms and their top lenders (see, for example, Fukao et al. 2005). $ ^{20} $
In the following subsection, we go over one theoretical illustration motivating our empirical study and construct our hypotheses. Note that no attempt is made to create any original theoretical model in this paper. We simply intend to set up a conceptual framework we refer to in our empirical study.

#### 5.1. Theoretical underpinnings and hypothesis formulation

As pointed out in Chari et al. (2005), some sort of financial friction could be one candidate generating the correlation between external shocks to firms and the firms' productivity. As one illustration, Pratap and Urrutia (2010) constructs a theoretical model explicitly containing cash-in-advance constraint and demonstrates how firm's investment choice is distorted and its TFP deteriorates due to the internal allocative inefficiency. $ ^{21} $ By further considering the long-strand of literature about bank's role as motoring and/or screening, which essentially aims at mitigating the financial friction originated from information problem, we construct the following hypothesis.

Hypothesis-1: The ex-post increment in firm's TFP and the ex-post level of firm's TFP are positively correlated with the lagged efficiency of lender banks.

For the test of this hypothesis, we consider the model $ (5).^{22} $ Note that in order to quantify the marginal effect of bank efficiency to their client firms' productivity, we need to model the firm's hypothetical performance in the absence of the bank. $ ^{23} $ For this