Silviase/jgov_v1.4_full_ann huggingface dataset:
purpose, we simply add the factor accounting for bank efficiency to the otherwise basic formulation employed in the extant empirical literature. Except for a few recent studies (e.g., Fukao et al. 2005; Fukuda et al. 2009, Goto 2010; Amiti and Weinstein 2010), we know little about the quantitative impact of continuously measured bank efficiency/performance/soundness onto their client firms.

$$ \begin{aligned}&\left\{\begin{aligned}\\ &Y_{i,t+\tau}=\beta_{0}+\beta_{1}\times BANKEFFIC_{i,t-1}+\gamma\times X_{i,t-1}+\alpha_{i}+\epsilon_{i,t+\tau}\cdots(5)\\&\quad where\\&Y_{i,t+\tau}=\Delta L N\big(TFP_{i,t+\tau}\big)\equiv L N\big(TFP_{i,t+\tau}\big)-L N\big(TFP_{i,t}\big)\quad or\quad L N\big(TFP_{i,t+\tau}\big)\\&\quad\\&X_{i,t-1}:Firm^{\prime}s R\&D Intensity,Firm^{\prime}s Leverage,size,ROA,Bank Dependency,Bank^{\prime}size,etc.\\ &\end{aligned}\right.\\ \end{aligned} $$

The model we employ above is an extension of the model for TFP determination employed in a number of extant studies (e.g., Griliches 1998; Kwon 2007). We choose this to provide additional empirical findings directly comparable with the extant studies. In this equation, i, t, and $ \tau $ denote the indexes for the pair of firm and its top lender, the current period, and how many periods we wait for setting the ex-post productivity measure (i.e., TFP), respectively. One crucial variable BANKEFFIC $ _{i,t-1} $ stores the efficiency of the top lender for firm i at the period t-1. $ X_{i,t-1} $ stores the vector of the lagged control variables containing, for example, firm's R&D intensity (i.e., R&D expenditure divided by the total sales), leverage, ROA, and so on. $ ^{24} $ Finally, $ \alpha_{i} $ and $ \epsilon_{i,t+\tau} $ stand for the individual fixed-effect and the error term in our panel estimation. $ ^{25} $ Note that the individual effect $ \alpha_{i} $ is measured for the pair of each firm and top lender.
since the group of our panel data is a match of a firm and its top lender. This means that the group for the panel estimation is changed when a firm switches its top lender.

The long strand of mainbank literature has been categorizing lender banks into two types (i.e., main or sub-main) in a discrete fashion by focusing on the tightness of relations. Then, they study the impact of the discrete characteristic onto firm performance, financial availability, and so on. It is our interest to follow this strand of classic literature on loan relationships as well as featuring the continuously measured bank efficiency. The second hypothesis we test aims at incorporating both the impact of bank efficiency and the depth of loan relations.

Hypothesis-2: 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 when the loan share is high enough.

This hypothesis also corresponds to our conjecture that banks need high enough loan share to exhibit its efficiency. The necessity of the high loan share reflects, for example, the fixed cost associated with the monitoring/screening activities. In order to test this hypothesis, we consider the following model in (6). $ ^{26} $

$$ \begin{aligned}&\left\{\begin{array}{l}Y_{i,t+\tau}=\beta_{0}+\left(\beta_{1}+\beta_{2}\times LOANSHARE_{i,t-1}\right)\times BANKEFFIC_{i,t-1}+\gamma\times X_{i,t-1}+\alpha_{i}+\epsilon_{i,t+\tau}\cdots(6)\\\\ where\\Y_{i,t+\tau}=\Delta L N\big(TFP_{i,t+\tau}\big)\equiv L N\big(TFP_{i,t+\tau}\big)-L N\big(TFP_{i,t}\big)\quad or\quad L N\big(TFP_{i,t+\tau}\big)\\\\X_{i,t-1}:Firm^{\prime}s R\&D Intensity,Firm^{\prime}s Leverage,size,ROA,Bank Dependency,Bank^{\prime}size,etc.\end{array}\right.\end{aligned} $$
We check if $ \left(\beta_{1}+\beta_{2}\times\mathrm{LOANSHARE}_{i,t-1}\right) $ jointly exhibits positive sign or not. We expect such a positive sign under the presumption that the efficient banks keeping high enough loan shares to firms could contribute to the improvement of firm's productivity.

#### 5.2. Estimation results

The main estimation results are summarized in Table-3 and -4. In both tables, the first column shows the results of (5) and (6) for all samples and $ Y_{i,t+\tau} = \Delta \mathrm{LN}\left(\mathrm{TFP}_{i,t+\tau}\right) $ . The second and third columns store the results for high leverage samples (i.e., higher than sample median) for $ Y_{i,t+\tau} = \Delta \mathrm{LN}\left(\mathrm{TFP}_{i,t+\tau}\right) $ and $ Y_{i,t+\tau} = \mathrm{LN}\left(\mathrm{TFP}_{i,t+\tau}\right) $ . Considering the fact that our data store limited number of R&D intensity, the fourth column shows the results based on high leverage samples for $ Y_{i,t+\tau} = \Delta \mathrm{LN}\left(\mathrm{TFP}_{i,t+\tau}\right) $ with omitting the firm's R&D intensity from the covariates. Table-3 and -4 correspond to the estimations based on $ \tau = 3 $ and 5, respectively.

First, we confirm that Hypothesis-1 is basically rejected for all the cases in Table-3. Although the second and third columns of Table-4, which are based on high leverage samples, provide the consistent results with the conjecture in Hypothesis-1 (i.e., positive coefficient), it is safe to conclude that Hypothesis-1 is not robustly supported when we alter $ \tau $ . Second, on the other hand, Hypothesis-2 is largely supported for the samples with high leverage (i.e., second, third, and fourth columns in Table-3 and -4). A number of results provide $ \beta_{2} > 0 $ and $ \beta_{1} < 0 $ , which implies that the bank efficiency leads to high firm TFP when the loan share is high enough. Some of the results also
give $ \beta_{2}>0 $ with insignificant $ \beta_{1} $ , which means that the efficiency of banks works as a complement to the deep loan relations for improving client firm's productivity. The result supporting Hypothesis-2 are kept for the larger samples which omit firm's R&D intensity from the covariates. Such an empirical property is also found both for the cases of $ Y_{i,t+\tau}=\Delta\mathrm{LN}\big(\mathrm{TFP}_{i,t+\tau}\big) $ and $ Y_{i,t+\tau}=\mathrm{LN}\big(\mathrm{TFP}_{i,t+\tau}\big) $ .

These results imply several important links between bank efficiency and firm performance. First, the contribution of banks to the improvement in firm TFP is somewhat conditional on the characteristics of firms. For the firms with low leverage, which generally means the larger room for borrowing, the efficiency of their lender banks do not matter. This is natural if we consider the potential channel determining firm TFP illustrated in Pratap and Urrutia (2010), which features the wedge originated from financial friction. Those firms could easily fulfill their financial needs and do not necessarily encounter the allocative inefficiency. Second, the contribution of banks are also related to the characteristics of matches. Notably, the high efficiency of lender banks are not sufficient to improve the firm TFP. The deep relation represented by high loan share is sufficient to exhibit the value of their high efficiency. This is consistent with the views on the mainbank notion and/or the fixed cost story associated with screening/monitoring activities.

Table-5 shows the results of the same estimation with employing the bank efficiency measure based on the FISIM output without adjusting risk. Contrary to the results in Table-3 and -4, almost all the coefficients associated with bank efficiency or its interaction with the loan share do not exhibit significant signs. This implies that the credit risk adjustment we introduce plays a key role to establish the correlation.
hypothesized in this paper, which provides one justification to the criticism against the naïve FISIM concept without risk adjustment. This issue is discussed in a few recent studies, for example, Inklaar et al. (2008). As far as we know, this paper is the first one to study the quantitative meaning of the risk-adjustment through the correlation between the bank efficiency and client firm TFP.

#### 5.3. Several omitted issues

The estimation results presented in the previous section potentially missed several important dimensions. First, we must have needed to focus not only on the whole risk-adjusted FISM but also the risk-adjusted FISIM associated with lending services. There are two technical issues toward this direction. On one hand, it is necessary to set the risk-free rate for each year to compute the FISIM output associated with deposit service. Since we do not have the exact information about the maturity of deposits, we need to rely on some proxy for this variable. Moreover, it is not easy to split the total operational cost into the ones associated with lending and deposit services. In order to focus on the risk-adjusted FISIM associated with lending services and go over the same empirical exercise in this paper, we need to overcome those technical difficulties. Second, the choice of our measure for bank efficiency is another point to be discussed. The current measure is a simple ratio of risk-adjusted bank profit to the operation cost. We could expect that the profits are affected, for example, by time-varying mark-up rates. Although we could potentially check if our results are robust to the variation in market-level mark-up by splitting the sample into the early and late periods, it could be insufficient. What we really need to measure is the one corresponding to TFP in the
standard productivity literature. In this regard, we should consult on the recent studies about the TFP measurement in medical industry (e.g., Castelli et al. 2010). We leave the empirical investigation on this issue to our future research question.

As another important point, the fixed-effect model selected in this paper has a beneficial feature to partly control the endogeneity in the matching process between firms and top lenders. This reflects the presumption that the estimated fixed-effect parameters account for the unobservable match-specific heterogeneity, which is potentially correlated with the determinants of matching. $ ^{27} $ This issue also leads to the discussion about causality between bank efficiency and client firm's performance. The appropriate usage of instrument variables would be another possibility to tackle this problem. We leave these issues to our future research.

### 6. Concluding Remarks

This paper features one measure for bank efficiency and studies its impact on the ex-post TFP growth and level of their client firms. By using the panel-data for the bank efficiency with the wide varieties of bank and firm characteristics as well as the loan relationship information, we empirically establish that the bank efficiency measure has statistically significant interactions with the firm performance measure when the firms are highly levered and the top lender's loan share is high enough. The empirical results also imply the complementarity between bank efficiency and the depth of loan relationships for improving firms' TFP. These imply that it is necessary to expand the discussion for the determinants of firm performance to the characteristics of the parties having relationships with the firm. In this perspective, we believe this paper also
contributes to the recently accumulated researches on the economics of relation.

To conclude, we list several future research questions. First, in order to establish the validity of our bank efficiency measure, we could implement the cross-county comparison of bank efficiency measure as well as the other efficiency measures of banks (e.g., based on DEA method). Second, the technical issues mentioned in the previous section need to be taken seriously (e.g., focusing on the lending FISIM output etc.). We believe all of these extensions provide further guides for better understanding of the bank efficiency as well as economic implication of firm-bank relations.
Figure-1: FISIM Concept and Risk-Adjusted Bank Output

Note-1: The above figure conceptualizes the method we use in this paper. In this paper, we are not using the adjusted value of borrower service and value of depositor service separately. The sum of the two values is used as our output measure. Note-2: The figure shows the case where D>A. We need to add some number to the deposit interest payments instead of subtracting.
Figure-2: Measured Bank Efficiency (Bank-Level)
Table-1: Summary Statistics

Variable Name	Def	Obs	Mean	Std. Dev.	Min	Max
LN_TFP	Natural log of firm TFP measured by the deviation from the industry average	9642	-2.1401	0.7483	-4.2987	0.3254
LN_TFP (t+3) - LN_TFP (t)	Growth in firm TFP measured by the deviation from the industry average, in 3 periods from t	15156	0.0249	0.0832	-1.0579	1.1569
LN_TFP (t+5) - LN_TFP (t)	Growth in firm TFP measured by the deviation from the industry average, in 5 periods from t	12600	0.0357	0.0961	-0.8195	1.0410
c_LEVERAGE	Ratio of total liability to total asset	24804	0.6522	0.1883	0.0482	8.3381
c_LNSIZE	Natural log of total asset	24804	10.7030	1.3690	6.1759	16.2883
c_ROA	Ratio of EBITDA to total asset	24669	0.0865	0.0680	-1.0464	3.2691
c_BANK_DEPENDENCY	Ratio of bank borrowing (including short and total borrowing) to total liability	21698	0.3531	0.1818	0.0007	0.9693
c_LIQUIDITY_RATIO	Ratio of liquidity asset to liquidity liability	24804	1.4250	0.7596	0.0441	20.3485
c_INTANGIBLE_ASSET_RATIO	Ratio of intangible asset to total asset	24804	0.0063	0.0171	0.0000	0.4734
c_SHORTLOAN_RATIO	Ratio of short-term bank borrowing to total bank borrowing	21698	0.6619	0.2375	0.0010	1.0000
c_PBR	Price-to-Book Ratio	24691	7601	88366	13	5330745
c_R&D_INTENSITY	Ratio of R&D expenditure to total sales	4446	0.0244	0.0703	0.0000	2.4459
b_EFFIC	Credit risk-adjusted FISIM output of bank divided by its operational cost	24804	1.4993	0.7416	-1.9961	4.3007
b_EFFIC Pre-Risk Adjusted	"Pre" credit risk-adjusted FISIM output of bank divided by its operational cost	24804	1.5391	0.7460	-1.6286	3.8303
TOTAL_LOANSHARE	Share of top lender's total loan out of total loan outstanding	24804	0.3421	0.1733	0.0759	1.0000
b_EFFIC × TOTAL_LOANSHARE	24804	0.4999	0.3539	-1.9961	3.6454

Note: c and b denote the firm and bank variables, respectively.
## Table-2: Correlation Table

LN_TFP	LN_TFP (t+3) - LN_TFP (t)	LN_TFP (t+5) - LN_TFP (t)	LEVERAGE	c_LOA	c_BANK_DEPENDENCE_RATIO	c_LIQUIDITY_RATIO	c_INTANGible_ASSET_RATIO	c_SHORT_LOA_RATIO	c_PBR	c_R&D_INT_N_RATIO	c_EFFIC	b_EFFIC	b_
b_PROD = TOP LENDER's b_PROD	(v) DIF (τ=5)	(vi) DIF (τ=5)	(vii) LEVEL (τ=5)	(ix) DIF (τ=5, no R&D Intensity)
All c_LEVERAGE	c_LEVERAGE > Median	c_LEVERAGE > Median	c_LEVERAGE > Median
Dependend Variable
"DIF": LN_TFP (t+τ) - LN_TFP (t)
"LEVEL": LN_TFP (t+τ)
c_LEVERAGE (t-1)	0.019	0.132	0.017	0.134	-0.499	0.217 **	-0.554	0.192 ***	-1.744	0.772 **	-1.896	0.699 ***
c_LNSIZE (t-1)	0.009	0.080	0.009	0.081	0.015	0.139	0.017	0.136	0.101	0.422	0.055	0.371
c_ROA (t-1)	0.113	0.101	0.112	0.105	0.106	0.161	0.090	0.188	-0.501	0.480	-0.565	0.447
c_BANK_DEPENDENCY (t-1)	0.021	0.097	0.022	0.099	0.077	0.119	0.081	0.118	1.155	0.447 **	1.206	0.414 ***
c_LIQUIDITY_RATIO (t-1)	0.019	0.018	0.019	0.018	-0.132	0.072 *	-0.105	0.069	-0.778	0.393 *	-0.558	0.322 *
c_INTANGIBLE_ASSET_RATIO (t-1)	0.065	0.592	0.072	0.601	0.194	1.089	-0.216	1.249	0.983	3.129	-2.786	2.523
c_SHORTLOAN_RATIO (t-1)	0.113	0.048 **	0.113	0.048 **	-0.022	0.076	-0.020	0.076	-0.039	0.397	-0.038	0.351
c_PBR (t-1)	0.000	0.000 **	0.000	0.000 **	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
c_R&D_INTENSITY (t-1)	-0.435	0.800	-0.446	0.847	-0.152	0.714	-0.326	0.689	2.629	1.788	1.452	1.539
b_EFFIC (t-1)	0.012	0.017	0.010	0.027	0.045	0.026 *	-0.011	0.029	0.371	0.114 ***	0.066	0.150
b_EFFIC (t-1)
× TOTAL_LOANSHARE (t-1)	0.007	0.062	0.194	0.110 *	1.043	0.452 **
_cons	-0.201	0.903	-0.206	0.911	0.365	1.575	0.345	1.561	-1.799	4.933	-1.450	4.430
# Obs	416	416	191	191	146	146
# Groups	263	263	118	118	89	89
Obs per group min	1.0	1.0	1.0	1.0	1.0	1.0
Obs per group avg	1.6	1.6	1.6	1.6	1.6	1.6
Obs per group max	3.0	3.0	3.0	3.0	3.0	3.0
Prob > F	0.0124	0.0155	0.0061	0.0029	0.0000	0.0000
R-sq (within)	0.0944	0.0945	0.2257	0.2690	0.3972	0.4559

Note: All the estimation employ the fixed-effect model, which is selected through the standard model specification test (F-test to compare Fixed-effect and Pooling OLS, Hausman-test for comparing Fixed-effect and Random-effect models. $ *^{***} $ : 1%, $ *^{**} $ : 5%, $ *^{*}): 10%. Dependent variable is either (a) the growth in firm TFP, which is measured as the deviation from the industry average, in τ periods from t ("DIF"), or (b) the natural log of firm TFP at t+τ ("LEVEL"). c_LEVERAGE is the ratio of total liability to total asset (c_ denotes firm variables), c_LNSIZE is the natural log of total asset, c_ROA is the ratio of EBITDA to total asset, c_BANK_DEPENDENCY is the ratio of bank borrowing (including short and total borrowing) to total liability, c_LIQUIDITY_RATIO is the ratio of liquidity asset to liquidity liability, c_INTANGIBLE_ASSET_RATIO is the ratio of intangible asset to total asset, c_SHORTLOAN_RATIO is the ratio of short-term bank borrowing to total bank borrowing, c_PBR is the Price-to-Book Ratio, and c_R&D_INTENSITY is the ratio of R&D expenditure to total sales. b_EFFIC is the credit risk-adjusted FISIM output of bank divided by its operational cost. TOTAL_LOANSHARE is the share of top lender's total loan out of total loan outstanding. The group for panel estimation is the match between a firm and its top lender. All the standard error is adjusted for clusters.
b_PROD = TOP LENDER's b_PROD	(ix) DIF (τ=3)	(x) LEVEL (τ=3)	(xi) DIF (τ=5)	(xii) LEVEL (τ=5)
c_LEVERAGE > Median	c_LEVERAGE > Median	c_LEVERAGE > Median	c_LEVERAGE > Median
Dependend Variable
"DIF": LN_TFP (t+τ) - LN_TFP (t)	Coef.	Robust	Coef.	Robust	Coef.	Robust	Coef.	Robust
"LEVEL": LN_TFP (t+τ)	Std.	Std.	Std.	Std.	Std.	Std.	Std.	Std.	Std.	Std.
c_LEVERAGE (t-1)	0.222	0.132 *	0.224	0.133 *	1.582	0.800 **	1.671	0.815 **	-0.428	0.249 *	-0.423	0.248 *
c_LNSIZE (t-1)	0.083	0.038 **	0.082	0.039 **	-0.154	0.177	-0.183	0.185	0.015	0.143	0.013	0.141
c_ROA (t-1)	0.091	0.099	0.093	0.099	0.045	0.524	0.048	0.528	0.157	0.143	0.156	0.145
c_BANK_DEPENDENCY (t-1)	-0.063	0.076	-0.067	0.074	0.224	0.296	0.204	0.303	0.075	0.117	0.073	0.119
c_LIQUIDITY_RATIO (t-1)	0.092	0.042 **	0.095	0.043 **	0.388	0.265	0.428	0.271	-0.115	0.072	-0.118	0.075
c_INTANGIBLE_ASSET_RATIO (t-1)	1.103	0.590 *	1.188	0.576 **	8.363	2.779 ***	9.263	2.957 ***	-0.453	0.864	-0.422	0.871
c_SHORTLOAN_RATIO (t-1)	0.075	0.046	0.079	0.049	0.154	0.305	0.206	0.304	-0.026	0.082	-0.023	0.082
c_PBR (t-1)	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
c_R&D_INTENSITY (t-1)	-0.185	0.452	-0.171	0.457	0.166	4.906	0.299	4.874	-0.454	0.510	-0.458	0.509
b_EFFIC (t-1) Pre-Risk Adjusted	-0.035	0.040	-0.050	0.048	0.015	0.284	-0.129	0.270	0.015	0.045	0.024
b_EFFIC (t-1) Pre-Risk Adjusted	0.047	0.074	0.495	0.375	-0.029	0.112
_cons	-1.150	0.487 **	-1.142	0.493 **	-1.742	2.319	-1.600	2.355	0.335	1.574	0.354	1.564
# Obs	548	548	380	380	191	191
# Groups	309	309	217	217	118	118
Obs per group min	1.0	1.0	1.0	1.0	1.0	1.0
Obs per group avg	1.8	1.8	1.8	1.8	1.6	1.6
Obs per group max	5.0	5.0	5.0	5.0	3.0	3.0
Prob > F	0.0065	0.0101	0.0000	0.0000	0.0154	0.0146
R-sq (within)	0.2323	0.2335	0.3535	0.3597	0.1827	0.1833

Note: All the estimation employ the fixed-effect model, which is selected through the standard model specification test (F-test to compare Fixed-effect and Pooling OLS, Hausman-test for comparing Fixed-effect and Random-effect models. $ * $ ：1%, $ * $ ：5%, $ * $ ：10%. Dependent variable is either (a) the growth in firm TFP, which is measure as the deviation from the industry average, in $ \tau $ periods from t ("DIF"), or (b) the natural log of firm TFP at $ t+\tau $ ("LEVEL"). c_LEVERAGE is the ratio of total liability to total asset (c_denotes firm variables), c_LNSIZE is the natural log of total asset, c_ROA is the ratio of EBITDA to total asset, c_BANK_DEPENDENCY is the ratio of bank borrowing (including short and total borrowing) to total liability, c_LIQUIDITY_RATIO is the ratio of liquidity asset to liquidity liability, c_INTANGIBLE_ASSET_RATIO is the ratio of intangible asset to total asset, c_SHORTLOAN_RATIO is the ratio of short-term bank borrowing to total bank borrowing, c_PBR is the Price-to-Book Ratio, and c_R&D_INTENSITY is the ratio of R&D expenditure to total sales. b_EFFIC is the "pre" credit risk-adjusted FISIM output of bank divided by its operational cost. TOTAL_LOANSHARE is the share of top lender's total loan out of total loan outstanding. The group for panel estimation is the match between a firm and its top lender. All the standard error is adjusted for clusters.
##

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