The Effects of Firm and Bank Balance Sheet Conditions
to Net Interest Margins:
Evidence from Loan-level Firm Survey Data
Tomoko AIZAWA-Tanemura
Shin-Ichi NISHIYAMA
November 2022
Discussion Paper No. 2215

GRADUATE SCHOOL OF ECONOMICS
KOBE UNIVERSITYROKKO, KOBE, JAPANThe Effects of Firm and Bank Balance Sheet Conditions to Net
Interest Margins: Evidence from Loan-level Firm Survey Data *Tomoko AIZAWA-Tanemura †Shin-Ichi NISHIYAMA ‡College of Commerce,
Nihon University

Graduate School of Economics,
Kobe University

Abstract
This paper uses the interpretation of the monetary transmission channel model inJapan under low interest rates to clarify the factors that determine the net interest
margin (NIM). An analysis using Loan-level data from the Tohoku region from 2012
to 2015 shows that the Capital-to-Asset Ratio of a firm is an important factor in
determining NIM. Even if we consider that firms and banks have suffered Nuclear
Damage, Bad reputation Damage, and Supplier Damage due to the Great East
Japan Earthquake as control variables, the channel through the agency cost of theborrower is effective. Even if we put the policy response of Rents and leases Subsidy,Interest or guarantee fee Subsidy, Interest reductions, and Group Subsidy into theestimation formula as a control variable, the channel through the agency cost of theborrower is effective. On the other hand, the existence of a channel through banks'
agency costs, funding costs of capital and borrowing, and liquidity costs cannot beshown to be stable. In other words, financial institutions can earn high NIMs when
they lend to firms that have relatively small net worth and depend on banks forfunding. Financial institutions in Japan's Tohoku region that wish to profit fromlending need to face the agency problem between borrower firms and lender banks.
Key words: Net Interest Margin, Capital-to-Asset Ratio, Balance Sheet Channel,
We would like to thank Nobuyoshi Yamori and Masahiko Shibamoto for theirinsightful comments. Needless to say, the remaining errors are the responsibilityof the authors. Tohoku University Earthquake Recovery Firm Survey (TERFS)
2012-2015 dataset is provided by Regional Innovation Research Center, Graduate
School of Economics and Management, Tohoku University. Nishiyama gratefully
acknowledges financial support from JSPS Grant-in-Aids for Scientific Research(KAKENHI) No. 18K01687 and No. 16H03608.
† E-mail: aizawa-tanemura.tomoko@nihon-u.ac.jp‡ Corresponding Author.
E-mail: nishiyama@econ.kobe-u.ac.jp
1*

Loan-level Data
JEL Classification: E43, E51, E52, G21

2

Index1.

Introduction

2.

Literature Review

3.

Loan-level Firm Survey and Data Summary

4.

Empirical Framework

5.

Empirical Results

6.

Conclusion

1.1.

2.1.
2.2.

3.1.
3.2.
3.3.
3.4.

4.1.
4.2.

5.1.

Motivation and Research Question

Review of Banking Literature
Review of Macro Literature

About Tohoku Earthquake Recovery Firm Survey
Preliminary Results
Data Descriptions
Summary Statistics

Theory (NIM and Firm CAR, NIM and Bank CAR)
Regression Equations (Model A, B, C, and D)

Results with Tables31. Introduction
1.1.
Motivation and Research Question
The purpose of this paper is to clarify the determinants of the net interest
margin (NIM), which has a significant impact on bank profits under low interest
rates in Japan from 2012 to 2015. The economic environment of low interest rates
continues due to monetary policy, and deposit interest rates are low and constant,
as shown in Figure 1. On the other hand, positive NIM is secured. The lending
interest rate level is considered to change depending on the factors on the firm sideand the factors on the bank side. Therefore, in order to clarify the determinants of
NIM, it is necessary to perform multifaceted analysis using Loan-level Firm Survey
Data.
The uniqueness of this paper is that it focuses on clarifying the determinantsof NIM by utilizing the interpretation of the monetary transmission channel model.
In this paper, we use the monetary transmission channel model and the NIM
determination model to hypothesize that factors on the firm side and/or factors on
the bank side determine NIM. And we will clarify whether the NIM of financial
institutions in the Tohoku region depends on the factors on the firm side or thefactors on the bank side. Specifically, we will clarify which of the borrower's agency
cost, the bank's agency cost, the bank's capital and borrowing funding cost, and the
bank's liquidity cost has a significant effect on NIM. Chapter 2 introduces previous
research. Chapter 3 describes the data. Chapter 4 shows the framework of empiricalanalysis, and Chapter 5 shows the results of empirical analysis. The conclusion issummarized in Chapter 6.

2.
Literature Review
2.1. Review of Banking Literature
In a general equilibrium model, such as the Arrow-Debreu model, firms andhouseholds can take full advantage of financial markets, making banks unable to
make profit and the banking sector becoming unnecessary in the economy. In reality,
financial markets are not perfect, and banks offer their customers a variety offinancial products and services and get their rewards.
Ho and Saunders (1981), pioneers of the NIM model, use portfolio selection
theory for bank hedging and the bank's expected utility maximization model tocreate a model that determines a bank's NIM. The NIM is determined by spreads
due to financial transaction uncertainty, implicit interest expense, opportunity costof reserve requirements, and default premium of lending. NIM is always positive, asbanks face transaction uncertainty. NIM is determined by the degree of risk aversion
4

of bank owners, the size of bank transactions, the market structure of banks, andthe diversification of interest rates. A time-series and cross-section analysis using
data from more than 100 US banks from the fourth quarter of 1976 to the fourth
quarter of 1979 shows that NIM is significantly associated with the diversificationof one-year bond rates. Furthermore, when they analyzed the small banks and thelarge banks separately, it was shown that the small banks had significantly larger
NIMs by 0.3682 percentage points than the large banks.
While Ho and Saunders (1981) assumed uncertainty in the deposit and lending
rates, McShane and Sharpe (1985) create a model that assumes uncertainty in the
short-term money market interest rate in the context of the Australian financial
market. Using 148 observations, they measure the NIM of Australian banks in timeseries and cross-section. As a result, they find that there is a non-linear relationshipbetween the NIM of Australian commercial banks and measures of market
dominance, absolute risk aversion and interest rate uncertainty.
Angbazo (1997) extends Ho and Saunders (1981)'s interest rate margin dealer
model, which investigated the relationship between interest rate risk and NIM, tocreate a model that includes the interaction between default risk and interest raterisk. NIM is determined by Default risk, Interest rate risk, Liquidity risk, Leverage,
Implicit interest payments, Opportunity cost of non-interest bearing reserves,
Management efficiency, and Branching regulation.
They used data from 286 commercial banks and 1400 observations from the
1989-1993 Call Report of the US and conducted a cross-sectional analysis. They findthat banks with higher risk loans and higher interest rate risk exposure achieve
higher NIM. NIM is positively correlated with core capital ratio, non-interest bearing
reserve ratio and efficiency, and negatively correlated with liquidity risk. In the
Angbazo model, as the core capital ratio increases, the cost of funds borrowed
decreases because Solvency Risk decreases. Since the cost of funding by equity ishigher, as equity increases, the average cost of borrowing equity becomes higher.
Thus, an increase in the core capital ratio increases the NIM. The current
assets/liabilities ratio has a negative effect on NIM because an increase in thecurrent assets/liabilities ratio lowers liquidity risk and hence the liquidity premium.
They also conduct empirical analysis by bank size, showing through GLS
estimation that NIMs of large banks are affected by default risk, NIMs of large
regional banks are affected by interest rate risk, and NIMs of small regional banks
are affected by both default risk and interest rate risk.
Hamadi and Awdeh (2012) identify the factors that determine the NIM of banks in
Lebanon. They use a panel data set of 53 banks (32 domestic commercial banks and 21

5

foreign commercial banks) from 1996 to 2009 using the Bilan Banques database to
estimate the NIM in a fixed effects model. They believe that a bank's NIM is determined
firstly by bank-specific factors, secondly by banking industry-specific factors, thirdly bymonetary policy factors, and fourthly by macroeconomic factors. In more detail, the NIM
is determined first by bank-specific factors such as bank asset size, deposit growth rate,
capital adequacy ratio, liquidity ratio, efficiency (cost/income), loan ratio, and nonperforming loan ratio; second by factors related to the structure of the banking sector
such as concentration, interbank interest rates, and foreign currency loan and deposit
ratios; and third by monetary Thirdly, the one-year discount rate of the central bank as
a monetary policy factor; fourthly, the economic growth rate, inflation rate, grossnational savings as a percentage of GDP, and gross investment as a percentage of GDP,
which take into account macroeconomic variables, will be used as explanatory variablesin the empirical analysis.
Their estimation of the fixed effects model shows that the results are very different
for domestic and foreign banks. For domestic banks, bank asset size, liquidity ratio,
efficiency, capital adequacy ratio, nonperforming loan ratio, concentration, foreign
currency loan ratio, and foreign currency deposit ratio have a negative impact on NIM.
Deposit growth rate, loan to value ratio, inflation rate, central bank discount rate, grossnational savings ratio, and gross investment ratio have a significant positive impact on
NIM. On the other hand, for foreign banks, only efficiency and the gross national savings
ratio have an impact on the NIM. This indicates that the NIM of domestic banks issignificantly affected by their own monetary policy and macroeconomic factors, while the
NIM of foreign banks is unaffected by them.
According to the estimation results of Angbazo (1997) and Hamadi and Awdeh(2012), an increase in banks' liquidity ratio has a negative impact on NIM. In Angbazo(1997), an increase in banks' capital adequacy ratio has a positive impact on NIM, while
in Hamadi and Awdeh (2012), an increase in banks' capital adequacy ratio has a negative
impact on NIM.

2.2.

Review of Macro Literature

The transmission mechanisms of monetary policy are explained by Mishkin (1995),
who divides them into Traditional Interest Rate Channels, Exchange Rate Channels,
Equity Price Channels, and Credit Channels. Kuttner and Mosser (2002) show themonetary transmission mechanism in a flowchart. Credit Channels are divided into
Bank Lending Channel and Balance-Sheet Channel.
Bernanke and Gertler (1989) use the overlapping generations model to argue
that the borrower's capital affects agency costs and, as a result, investment. A large
6amount of borrower's capital reduces the agency cost of raising capital investment.
A boom accelerates the economy by increasing capital, lowering agency costs, andincreasing investment. In times of recession, it accelerates in the opposite direction.
Shocks that affect capital stock are factors that cause economic fluctuations. This
indicates that there is a credit channel through the borrower's balance sheet.
Kiyotaki and Moore (1997) build a dynamic economic model with credit
constraints that borrowing is not possible without collateral. The farmer borrows
funds using land as collateral. The farmer produces fruits that can be sold and fruits
that are self-consumed based on the constant returns to scale production function.
The collector lends money using the land as collateral and produces only fruits thatcan be sold based on the decreasing returns to scale production function. As a result,when the farmer experiences a temporary productivity shock, his capital decreases
and he reduces his investment expenditures, including investments in land. The
reduction in investment spending reduces income in the next period, reduces capital,
and further reduces investment due to credit constraints. In the simulation, a
temporary 1% increase in productivity in the first period leads to a 0.36% increasein land prices, a 0.1% increase in farmers' land holdings, and a 0.13% increase in
debt. In the seventh period, farmers' land holdings and debt increase by 0.37% and0.55%, respectively, to reach their peak. Productivity shocks have a persistentimpact on the economy. Thus, in a credit-constrained economy, they show that
temporary shocks to technology and income generate persistent fluctuations in
output and asset prices.
Bernanke et al. (1999) develop a dynamic general equilibrium model based on
the theory of Bernanke and Gertler (1989) and Kiyotaki and Moore (1997). In theirmodel, as in Kiyotaki and Moore (1997), the agency problem is applied to the
producers of the final product who own the permanent capital stock of the economy
in a classical sticky-price setting in a DNK model. Since the borrower owns thecapital stock of the economy, changes in the price of capital have a direct impact on
their net worth.
They create a stochastic growth model that incorporates money, monopolisticcompetition, and nominal price rigidities. Entrepreneurs choose physical capital andlabor to maximize total production according to the Cobb-Douglas production
function. Physical capital is financed by entrepreneurial capital and borrowing.
Capital increases when capital is invested and accumulated in the previous period,or when income from supplying labor is invested. Capital determines the cost of
external financing. More capital reduces the external financing premium because it
reduces the agency problem. Unexpected changes in asset prices will affect the7capital held by entrepreneurs. Unexpected changes in profits affect the financial
position of the firm. Two basic factors of financial accelerators are the endogenouschange in capital stock and the influence of fluctuations in capital stock on the costof capital.
They use an impulse response with a quarterly VAR model to examine theimpact on output, investment, etc. in the presence of four shocks: (1) a monetary
policy shock, (2) a technological innovation shock, (3) a government spending shock,and (4) a single unexpected transfer of assets from households to entrepreneurs. The
results clarify that the presence of a financial accelerator can amplify and propagate
the business cycle.
Bernanke and Gertler (1989), Kiyotaki and Moore (1997), Bernanke et al.
(1999) present a theoretical model of the existence of a firm's balance sheet channel
by introducing agency problems between borrowers and lenders. Furthermore,
Bernanke et al. (1999) show the existence of financial accelerators in which there ismutual feedback between the financial sector and the real economy, and that financeamplifies the business cycle.
Meanwhile, Gertler and Kiyotaki (2010) consider disruptions in financial
intermediaries to be an important feature of the recent historical financial crisis.
There is an agency problem that imposes endogenous balance sheet constraints onfinancial institutions when raising funds from depositors or in the interbank market.
Banks that cannot raise enough funds offer higher lending interest rates to firms.
Banks with low net worth lend at high interest rates.
Kashyap and Stein (2000) show that monetary policy works well for banks witha low ratio of securities to assets, especially small banks. Using 1976Q1-1993Q2 data
for all insured commercial banks in the U.S., they examine whether monetary easing
policies affect securities + Fed funds sold / total assets, which in turn affect lending
volume. In the event of a contractionary monetary shock, financial institutions withlarge liquid assets do not have to change their lending portfolios. The effect ofmonetary policy on lending behavior is stronger for banks with more illiquid balance
sheets, i.e., those with a lower ratio of securities to assets. Moreover, this trend works
strongly for smaller banks, the bottom 95% of the size distribution. This result
supports the existence of bank lending channels.
Gertler and Kiyotaki (2010) show the existence of financial transmission
channels through banks' equity capital, and Kashyap and Stein (2000) show theexistence of financial transmission channels through bank liquidity. These indicate
the existence of a bank lending channel.
There are the following previous studies on empirical analysis using Loan-level8

data. Aysun and Hepp (2013) use individual loan data from the Capital IQ
Compustat database for 15,794 loans from 1995 to 2009 to discuss the relationshipbetween borrower balance sheets and bank liquidity to bank lending. They also findout if monetary policy affects those relationships. First, for each period, NIM isregressed by the total debt / equity ratio (leverage ratio) of borrower i and theliquidity / total asset ratio (liquidity ratio) of lender j, logarithmic value of total assets
and liquidity ratio to total assets as firm-specific control variable vectors, logarithmicvalue and leverage ratio (total debt ratio to equity capital) of total assets as control
variable vectors specific to lenders, loan-specific control variable.
Next, they regress the estimated coefficients of borrowers' leverage ratio andbanks' liquidity ratio as explained variables on the stance of monetary policy, theoutput gap of US GDP, the output gap of quarterly (seasonal) dummy, and the trend
output gap. The results show that the credit channel through borrowers' balance
sheets works effectively due to monetary policy, but the credit channel throughbanks' liquidity does not play an important role.
Hosono and Miyakawa (2014) use a match-level dataset of firms and banksfrom the Nikkei NEEDS Financial Quest since FY1977 to control for loan demandand identify the effects of the business cycle and monetary policy on loan supply.
They use the logarithmic difference in loans as the explained variable, thelogarithmic value of banks' total assets to show banks' financial characteristics,
banks' net business profit/total assets, government bonds/total assets to show banks'
portfolio structure, loans/deposits to measure lending opportunities, net worth/total
assets to measure capital ratios, and Bank liquidity, as well as macro-level variables
and monetary policy variables as explanatory variables, are used in a fixed effects
model for panel data analysis. The results show that financial institutions withhigher net worth ratios, capital adequacy ratios, and liquidity ratios tend to provide
larger loans, while financial institutions with higher holdings of government bonds
tend to lend smaller amounts because they are exposed to market risk arising fromchanges in government bond prices. Second, when economic growth rates are low,
the effect of banks' net worth, capital adequacy, and liquidity ratios on lending isgreater. This indicates that financial institutions with poor bank balance sheet
conditions are less likely to allocate funds to borrower firms when macroeconomic
conditions deteriorate. Third, when policy rates are lower than in the previous year,
the economic importance of banks' capital adequacy ratios becomes less important.
In this paper, according to the analysis method of Aysun and Hepp (2013), it isclarified that the fluctuation of NIM is caused by the firm side through the agency
problem, the Capital-to-Asset Ratio of the bank, or the liquidity problem of the bank.
9

3.
3.1.

Loan-level Firm Survey and Data Summary
About Tohoku Earthquake Recovery Firm Survey

The Center for Recovery from the Earthquake at the Graduate School of Economicsof Tohoku University has prepared unbalanced panel data on the post-earthquake
situation of small and large firms and the names of the financial institutions they
were dealing with at the time of the earthquake. The Tohoku Earthquake Recovery
Firm Survey (TERFS) targets firms with one or more employees headquartered in
the affected areas (Hachinohe City, Aomori Prefecture, Iwate Prefecture, Miyagi
Prefecture, Fukushima Prefecture). In the 2012 survey, questionnaires were
distributed to 30,000 firms, and responses were received from 7,021 firms (survey
form collection rate 23.4%). In the 2013 survey, among the firms that responded to
the 2012 survey, the questionnaire was distributed to 6,983 firms that have survived
in 2013 and 23,017 newly selected firms, for a total of 30,000 firms, and responses
were obtained from 7,481 firms (survey form collection rate 24.9%). In the 2014
survey, the questionnaire was distributed to 6,983 firms that responded to the 2012
survey and 3,973 firms that responded to the new survey in 2013, for a total of 10,956
firms, and 5,713 firms responded (survey form collection rate 52.14%). In the 2015
survey, among the firms distributed in the 2014 survey, the questionnaire wasdistributed to 10,560 firms that did not go bankrupt or closed in 2015, and 4,116
firms responded (survey form collection rate 38.98%). TERFS has loan level data for
SMEs.

3.2.

Preliminary Results

Figure 2 shows that the NIM has increased in 2015. In contrast, the capital adequacy
ratio of banks has remained unchanged between FY2012 and FY2015, while thecapital adequacy ratio of corporations has increased slightly. A closer look at Figure
3 shows that the average NIMs from FY2012 to FY2014 are almost the same, butthere is one NIM with a Frequency above 600 in FY2012, indicating that some policy
effect may have ensured the same lending rate level for all financial institutions. In
FY2015, the standard error became larger, indicating that the policy effect decreased
and new loans may have been made at various lending rate levels. To see the trends
in the data in a simple way, Figure 4 shows an OLS regression of NIMs on corporate
and bank capital ratios. Most of the NIMs are below 5%. The Capital-to-Asset Ratioof firms and NIM shows a positive correlation, and the Capital-to-Asset Ratio ofbanks and NIM shows a negative correlation.
10

3.3.

Data Descriptions
Our purpose is to find out whether it is the firm's or the bank's factors that

determine the NIM. For this purpose, we use data from the TERFS annual data byborrower, including the Capital-to-Asset Ratio, size of the firm, amount of new debt
after the earthquake, and the name of the financial institution from which the firmborrowed the most at the time of the earthquake.
The financial institution that the firm borrowed the most money from at thetime of the earthquake is called the primary bank (PB). We derived the Capital-toAsset Ratio, size, and liquidity of the bank using the Bank's Financial Statement
and statement of accounts data (FFS). Therefore, we use matched panel data of firms
and banks for estimation.
Table 1 shows the sources and details of the data we used. First, we describe
Loan-specific variables. INT indicates the contracted interest rate for the new loan
after the earthquake disaster. DEP is a deposit interest rate calculated using deposit
and interest on deposit from the financial statements and disclosure magazines ofeach financial institution. NIM is Net Interest Margin, which is the difference
between INT and DEP. Log_Loan is a logarithmic value of the new loan amount.
Maturity is the borrowing period (in terms of months) of new loan. Primary_Vintage
is the number of years the firm receiving the new loan has been doing business withthe financial institution. Primary_Share is the ratio of debt from PB to the total debt.
Physical_Inv_Dum is a dummy variable where 1 is for firms that use their new loans
for capital investment and 0 for other firms. Guarantee_Dum is a dummy variable
where 1 is for firms whose new loans are guaranteed by credit guarantee associations
and 0 is for all other firms. Collateral_Dum is a dummy variable where 1 is for firmsthat have collateral or personal guarantees for new loans and 0 is for all other firms.
We describe two Firm-specific variables. One is Capital_Asset_Firm, which isthe firm's asset minus the liability divided by the asset, which indicates the firm's
capital ratio. The other is Log_Asset_Firm, which is a logarithmic value of firm
assets and indicates size.
We use Bank-specific variables to extract the bank side factors. Number_Bankis the total number of banks (including government-affiliated banks, non-banks, etc.)that were borrowing at the time of the Great East Japan Earthquake (as of March
11, 2011). Capital_Asset_Bank is the data obtained by dividing the amount of the
bank's assets minus the liabilities by the assets using the bank's financial statement
data, and indicates the capital adequacy ratio of the bank. Liquidity_Bank is thesum of the bank's cash holding and government bond holding, municipal bonds,
commodities government bonds, commodities local bonds, and call loan, divided by
11the total assets of the bank, and indicates the liquidity ratio of the bank.
Log_Asset_Bank is the logarithm of total assets listed on each bank's balance sheet.
ROA_Bank is the net business profit of each bank divided by the total assets.
Furthermore, unlike the usual case, during this estimation period, the Great
East Japan Earthquake damaged the capital equipment and supply chain of firms
in the Tohoku region, and the capital equipment and human capital of financial
institutions. Therefore, we will clarify the factors that determine NIM after
considering the influence of these special factors as control variables by using Firmspecific damage variables and Bank-specific damage variables.
In addition, after the Great East Japan Earthquake, earthquake insurance andsubsidies were provided, taxes were reduced and exempted, and the debt burden on
existing debt was reduced and exempted. Even if we consider those effects, we will
also investigate whether we can obtain robust results for the channel.

3.4.

Summary Statistics

Table 2 shows the basic statistics for time-varying variables. INT shows that in somecases, financial institutions were lending at almost zero interest rates. This indicatesthat some firms were borrowing through various subsidy programs. Even if a firm's
capital was damaged immediately after the earthquake, the business feasibility of
the firm was evaluated and it was shown that the firm considered to be low risk
could borrow funds with virtually no interest. We see that INT has a large variation,
but DEP does not have much variation. Financial institutions set high interest rates
on high-risk borrowers, so when a firm raises funds, the interest rate changes
according to the risk of the firm. On the other hand, when a bank raises funds froma depositor, it indicates that the depositor finally determines that the risk is almost
the same depending on the bank because of the deposit insurance system. As a result,
INT determines NIM.
The Capital-to-Asset Ratio of a firm is generally higher than that of a bank.
While there are small to large firms, banks are not licensed unless they meet thefinancial basis, so the asset size does not change significantly between banks.
Table 3 shows the basic statistics of time-invariant variables. Firm-specificdamage variables show that there are about half of the firms affected by theearthquake, about 25% of the firms affected by the tsunami, 8% of the firms directly
affected by the nuclear power plant accident, and 18% of the firms indirectly affectedby the nuclear power plant accident. About 30% of the firms have suffered indirect
damage due to damage to their Supplier or Client. In addition, 85% of the companies
said that the damage to their tangible assets was insignificant, but less than 6% of
12the companies said that more than 76% of their tangible assets were destroyed.
Subsidy and aid measure variables show that about 30% of firms have received
earthquake insurance, about 20% have received tax exemption from local
governments, and about 15% have received subsidies.

4.
4.1.

Empirical Framework
Theory (NIM and Firm CAR, NIM and Bank CAR)
In this paper, according to Aysun and Hepp (2013), firstly, the logarithmicvalue of new loans, the borrowing period, the transaction period with PB, and theborrowing ratio from PB determine NIM as factors specific to loans. Secondly, theCapital-to-Asset Ratio of borrower i and the logarithmic value of total assets
determine NIM as factors specific to the firm. Third, we consider that the Capitalto-Asset Ratio, liquidity ratio, logarithmic value of total assets, and ROA of lender jdetermine NIM as factors specific to banks.
In addition to these, in this paper, we will explain with dummy variables
whether new loans were used for capital investment, whether they were guaranteedby the Credit Guarantee Association, and whether they were secured or guaranteed
as factors specific to loans. We add the number of banks as a firm-specific factor.
According to the balance sheet channel of Bernanke and Gertler (1989), as a
firm's Capital-to-Asset Ratio rises, lending interest rates fall. If a firm's Capital-toAsset Ratio significantly lowers NIM, we can indicate the existence of a firm'sbalance sheet channel.
In Gertler and Kiyotaki (2010) and Hamadi and Awdeh (2012), lending interest
rates fall as banks' Capital-to-Asset Ratios rise. On the other hand, in Angbazo(1997), when the bank's core capital ratio rises, the solvency risk decreases, which
lowers the bank's external funding premium. However, the cost of raising capital ishigher, so as capital increases, the average cost of capital increases. Therefore, anincrease in a bank’s Capital-to-Asset Ratio increases NIM. If a bank's Capital-toAsset Ratio significantly affects NIM, we will indicate a channel through the bank's
capital.
AccordingtoKashyap

and

Stein

(2000),

when

banks

have

fewer

securities/assets, lending decreases due to liquidity problems, and as a result, NIMs
increase. Angbazo (1997) also finds that an increase in the liquid assets/liabilities
ratio leads to a decrease in the liquidity risk and thus a decrease in the liquidity
premium, which leads to a decrease in the NIM. Hamadi and Awdeh (2012) also findthat the liquid assets/total assets ratio of domestic banks has a negative impact on
the NIM. If the liquidity ratio of banks has a negative impact on NIM, we can specify
13the channel through the bank's liquidity.

4.2.

Regression Equations (Model A, B, C, and D)

In Model A (Table 4), we use pooling data to determine whether it is the firm's or the
bank's balance sheet conditions that significantly determine the NIM when newloans are made by PBs. In Model A, we estimate the following equation.
𝑁𝑁𝑁𝑁𝑁𝑁 = 𝐹𝐹(𝐿𝐿𝐿𝐿𝐿𝐿_𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿, 𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀, 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃_𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉, 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃_𝑆𝑆ℎ𝑎𝑎𝑎𝑎𝑎𝑎,
𝑃𝑃ℎ𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦_𝐼𝐼𝐼𝐼𝐼𝐼_𝐷𝐷𝐷𝐷𝐷𝐷, 𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺_𝐷𝐷𝐷𝐷𝐷𝐷, 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶_𝐷𝐷𝐷𝐷𝐷𝐷,
𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶_𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴_𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹, 𝐿𝐿𝐿𝐿𝐿𝐿_𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴_𝐹𝐹𝑖𝑖𝑖𝑖𝑖𝑖, 𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁_𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵,𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶_𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴_𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵, 𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿_𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵, 𝐿𝐿𝐿𝐿𝐿𝐿_𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴_𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵, 𝑅𝑅𝑅𝑅𝑅𝑅_𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵)

Then, variables that may have a significant impact on the NIM in the Tohoku
region after the Great East Japan Earthquake, such as earthquake and tsunami
dummies, are added to the explanatory variables as control variables to examine
whether the significance of the explanatory variables determining the NIM remains
unchanged. In Model B (Table 5) we analyze the interest rate on new loans from allbanks, including PBs, and in Model C (Table 6) we analyze the interest rate on newloans from banks other than PBs to show the robustness of the explanatory variables.
In Model D (Table 7), we examine the robustness of the determinants of NIM for newloans from PBs for firms that have not been severely affected by the Great East
Japan Earthquake. Table 4 to Table 7 show the results of the pooled data analysis,
and Table 8 presents the unbalanced panel data analysis for model A to model D.

5.
Empirical Results
5.1.
Results with Tables
P1. Model A (Table 4) Pooled regression results
New loans are made by PB. -Table 4 shows the estimates and standard errorsof the baseline model. In Table 4, we examine the effect of which factors on the NIM
of new loans made by PB. It can be estimated by taking into account the interest rate
on funding, depending on the risk of each financial institution.
Model A1 examines how loan-specific, firm-specific, and bank-specificexplanatory variables affect the NIM. Kasman et al. (2010) show that due to theeffect of economies of scale, an increase in lending volume reduces the cost per unit
and thus reduces the NIM. In this paper, as in Kasman et al. (2010), the NIM
decreases as the loan amount increases; the bank-specific variable, Log_Asset_Bank,is negative and significant for the NIM. As the size of the bank increases, the effect14

of economies of scale kicks in and the NIM decreases.
Petersen and Rajan (1994, 1995) and Berger and Udell (1995) argue that closeand continuous interaction mitigate information asymmetry between firms andfinancial institutions. Ortiz-Molina and Penas (2008) hypothesized that strong
relationships and longer maturities are related. Their hypothesis was rejected.
However, in the estimation of A1-A3, maturity is negative and significant for NIM.
Table 4 analyzes the NIM of new loans lent by the financial institution with thelargest loan amount immediately after the Great East Japan Earthquake. This
result suggests the existence of strong relationships between firms and PBs.
The bank-specific variable Log_Asset_Bank is negative and significant for NIM.
The larger the bank, the higher the effect of economies of scale and the lower the
NIM.
In model A1, we find that the higher the firm's capital-to-asset ratio, thesmaller the NIM. Therefore, the borrower's balance sheet channel presented by
Bernanke and Gertler (1989) is valid. Since the bank's capital-to-asset ratio is
positive and significant, there exists a channel through the bank's equity capital as
indicated by the Angbazo model. The coefficient of the bank's liquidity ratio is not
significant but is consistent with the sign condition of Angbazo (1997) and Hamadi
and Awdeh (2012).
Model A2 adds firm-damage variables as control variables to the explanatory
variables of model A1. Firm-damage variables are control variables, but we try tointerpret them. Nuclear_Dum lowers the NIM, indicating that there was a system
to lower the NIM for SMEs directly affected by the nuclear power plant accident.
Supplierdamage_Dum indicates that the firm may have been rated as risky by the
PB because damage to a supplier would disrupt the firm's business activities, even
if the firm could operate. As in Model A1, the channel through the firm's equity
capital of the Bernanke and Gertler model and the channel through the bank's equity
capital of the Angbazo type are supported. The channel through bank's liquidity ratio
is not significant but consistent with the sign condition of Angbazo (1997) andHamadi and Awdeh (2012).
In model A3, bank-damage variables were added as control variables to the
explanatory variables in model A2. Although they are control variables, when we try
to interpret them, the bank-damage variables Branch_Changed_Dum andBank_VisitFrequencyDecreased_Dum are negative and significant. The NIM
declines when the trading branch is changed or when the frequency of visits by bank
employees declines. However, the bank-damage variables become insignificant inModel A4-A6 when the explanatory variables are subsidies and aid measures for15

firms, and in Model A5-A6 when the time dummy is included. The lender's balance
sheet channel of Angbazo type is also significant for the capital-to-asset ratio, but
not for the liquidity ratio. The lender's balance sheet channel of Angbazo type is alsosignificant for the Capital-to-Asset Ratio, and the sign condition is consistent for the
Liquidity Ratio.
Model A4 is based on the explanatory variables of model A3 with the additionof subsidies and aid measures for firms as control variables. For example, Miyagi
Prefecture's SME loan program is a cooperative effort between the prefecture,
financial institutions, and the Miyagi Credit Guarantee Association. If the loan isapproved, the prefecture will reimburse the SME for the interest paid by the SME
to the financial institution for three years from the date of loan. Lease_Subsidy_Dum
and Interest_Subsidy_Dum were negative and significant, and NIM was lowered byrental lease subsidy and interest subsidy programs. Here, Guarantee_Dum, which
was not significant in model A3, changed to positive and significant. The results
show that the SME loan system in Miyagi Prefecture requires a credit guarantee,
except for the SME central association organized finance. As a result, A1-A3 did notaccurately capture the impact on the NIM when SMEs borrow new funds after theGreat East Japan Earthquake. Furthermore, the degree of freedom-adjusted R
square of Model A3 is 0.027, while the degree of freedom-adjusted R square of Model
A4 is 0.039, which increases the explanatory power of the model. The borrower'sbalance sheet channel is effective in Model 4 as in Models A1-A3. As for the lender'sbalance sheet channel of Angbazo type, it is significant for the capital-to-asset ratio,
and the sign condition is consistent for the liquidity ratio.
Model A5 adds macro variables to the explanatory variables of model A4, andsince we only have time series data for four periods, macro variables have the same
effect as the time dummy introduced in model A6. In Model A5, the credit guarantee
expands the NIM by about 0.26%, and the interest subsidy system reduces the NIM
by about 0.34%. the borrower's balance sheet channel is also effective in Model 5.
The borrower's balance sheet channel is still valid, but neither the bank's channel
through the capital-to-asset ratio nor the channel through liquidity is significant. In
particular, the bank's liquidity channel has different sign condition from Angbazo(1997) and Hamadi and Awdeh (2012).
model A6 is the explanatory variables of model A4 plus time-dummy variables.
Under the interest subsidy program or interest reduction and exemption program,
SMEs receive interest subsidies or reduction and exemption only for the first three
years of borrowing. As a result, the NIM is shown to have increased by 0.97
percentage points in FY2015 when the interest subsidy ended. In the case of lending
16

after the Great East Japan Earthquake, the inclusion of a time dummy captures theimpact of the interest subsidy for three years and avoids the omitted variable bias.
We can improve the explanatory power of the model by including the aid measures
for firms and time dummy in the estimation equation. In other words, the NIM after
the Great East Japan Earthquake should be explained by the subsidies and aid
measures for firms and time dummy instead of the bank-damage variables
introduced in Model A3. In Models A1-A6, the borrower's balance sheet channel isalways effective. On the other hand, the lender's balance sheet channel cannot beshown to exist in a stable manner.

P2.

Model B (Table 5) Pooled regression results

New loan made by primary or other banks. ― In Table 5, we examine the effect of
the borrower's balance sheet channel on the interest rate of new loans made by allbanks. As a result, the Bernanke and Gertler model is strongly supported in models
B1-B5.
Model B1 does not explicitly include the impact of the Great East Japan
Earthquake in the model. The results show that the larger the loan amount and thelonger the maturity, the lower the lending rate. The fact that guarantee_Dum ispositive and significant with respect to the interest rate indicates that riskier firms
may borrow funds under the credit guarantee scheme. Again, Capital_Asset_Firm is
positive and significant. The borrower's balance sheet channel, which reduces thelending rate when Capital_Asset_Firm is high, is also valid here, and the Bernanke
and Gertler model is strongly supported.
Model B2 is a model that takes into account firm-specific damage variables ascontrol variables, but the firm-specific damage variables are not significant. The
other variables are significant as in model B1, and the borrower's balance sheet
channel is effective.
Model B3 is a model that takes into account subsidies and aid measures forfirms as control variables. When subsidies and aid measures for firms are taken into
account, the firm-specific damage variables, Nuclear_Dum, change negatively andsignificantly. Interest_Subsidy_Dum of subsidies and aid measures for firms isnegative and significant. The Interest_Subsidy_Dum for subsidies and aid measures
for firms is negative and significant, the Lease_Subsidy_Dum that was significant
in Table 4 is no longer significant, and the Interest_Reduce_Dum that was not
significant in Table 4 is significant. The inclusion of dummy variables representing
the various subsidies and aid measures for firms may have caused multicollinearity
problems, making the analysis results unstable. However, the dummy variables in17the model, including the subsidies and aid measures for firms and the time dummy
introduced in models B4 and B5, are significantly effective and have highexplanatory power. The borrower's balance sheet channel is also effective in Model
B3.
Model B4 is a model in which macro variables are added to the explanatory
variables of model B3. Model B5 is a model that adds time-dummy variables to the
explanatory variables of model B3. Since the period of interest subsidies and interest
exemptions is three years, we can see that the lending rate increased by 1.15
percentage points in 2015 when the period ended. Comparing the degree of freedomadjusted coefficients of determination of Models B1-B5 in Table 5, the explanatory
power of the B4-B5 model including the subsidies and aid measures for firms andthe time dummy is the highest. As variables explaining the NIM in the Tohoku
region after the Great East Japan Earthquake, the subsidies and aid measures forfirms and time dummy are important variables.

P3.

Model C (Table 6) Pooled regression results
New loans made by other banks only. ― In Table 6, we examine whether the

borrower's balance sheet channel is effective on the interest rate of new loans madeby banks other than PBs. As a result, the borrower's balance sheet channel iseffective in Models C1-C5, and the Bernanke and Gertler model is strongly supported.
In Model C1, it is shown that the loan interest rate is lowered when the size ofthe loan is large or when the credit guarantee system is not used.
Model C2 is a model that takes into account firm-specific damage variables asa control variable; although it is a control variable, we try to interpret it. The factthat Supplierdamage_Dum is negative and significant indicates that banks otherthan PB may have been motivated to lend to the firm by offering lower interest rates
than PB, judging the damage to the firm's suppliers as temporary damage.
Model C3 is estimated by including dummy variables representing subsidies
and aid measures for firms as control variables. Although these dummy variables
are not significant, the coefficient of determination adjusted for degrees of freedom,which was 0.02 in model C2, increases to 0.035 in model C3.
In Model C4, macro variables were added to the explanatory variables in Model
C3. The estimation with macro variables shows the same coefficient as theestimation with time dummy in Model C5, which shows the timing of interest
subsidies and interest exemptions. The estimation with macro variables shows thesame coefficient as the estimation with time dummy in Model C5, which shows thetiming of interest subsidies and exemptions.
18Comparing the coefficient of determination with the degree of freedom adjusted
for Model C1-C5 in Table 6, the explanatory power of the C4-C5 model including thesubsidies and aid measures for firms and its timing dummy is the highest.

P4.

Model D (Table 7) Pooled regression results
The new loans in Table 7 are from primary banks to firms that have notsuffered earthquake damage. We examine the factors that determine the NIM of newloans from PB for firms that were not directly affected or forced to relocate fromTsunami or the nuclear power plant accident. The results show that the borrower'sbalance sheet channel is effective for all models, and the Bernanke and Gertler modelis strongly supported.
Model D1 shows that longer loan maturities and larger bank assets reduce the
NIM. Capital_Asset_Firm is significant at the 5% level and Capital_Asset_Bank issignificant at the 10% level.
Model D5 is a model that adds macro variables to the explanatory variables ofmodel D1. in model D5, Capital_Asset_Firm is significant at the 5% level, and theborrower's balance sheet channel is significant.
In Model D6, we perform estimation with a time dummy indicating the timingof interest subsidies and exemptions. D2015 is positive and significant, indicatingthat firms that were not directly affected or forced to relocate due to the tsunami or
nuclear power plant accident also received benefits such as interest subsidies andexemptions.
Comparing the degree of freedom adjusted coefficients of determination for
Models D1, D5-D6 in Table 7, the explanatory power of the D5-D6 model including
the subsidies and aid measures for firms and the time dummy is the highest.

P5. Panel regression results:
or random-effect model

Pooled regression is better than fixed-effect

In Table 8, we present the Panel Regression Results. Panel data analysis isperformed using the variables in Table 4 - Table 7. The pooling model is adopted inall estimation results.
Model A_FE and model A_RE are the results estimated by the fixed effects
model and the variable effects model using the explanatory variables of model A5.
The pooling model is supported by the F test, the random effects model by the
Hausman test, and the pooling model by the Breusch-Pagan Test. We use four yearsof unbalanced panel data, which ultimately supports the pooling model. Unlike thePooled regression estimation results in Tables 4-7, the Capital_Asset_Firm is not19

significant in either the fixed effects model or the random effects model.
Model B_FE and model B_RE use the explanatory variables of model B4, Model
C_FE and model C_RE use the explanatory variables of model C4, Model D_FE andmodel D_RE use the explanatory variables of model D5, and we estimate with thefixed effects model and the random effects model. In the random effects model, theborrower's balance sheet channel was effective and supported the Bernanke andGertler model, but the fixed effects model could not show it. Even in the above model,
the pooling model is supported by the F test and Breusch-Pagan Test.
In the pooling estimation of Table 4-Table 7, the existence of the lender'sbalance sheet channel cannot be shown stably. On the other hand, the borrower'sbalance sheet channel is effective and results in support for the Bernanke andGertler model. We also show that subsidies and aid measures for firms and the time
dummy indicating their duration and end are important variables that are
indispensable when explaining NIM in the Tohoku region after the Great East Japan
Earthquake. This means that the heavily damaged Tohoku region firms received
policy considerations when they received new loans for restoration andreconstruction, which had a significant impact on NIM.

6.

Conclusion

In this paper, we identify the determinants of NIM that have a significant impact onbank profits. We show that in the era of low interest rates in Japan from FY 2012 toFY 2015, the firm's balance sheet is an important determinant of the NIM when
implementing new loans. Even if firms and banks are damaged by the Great East
Japan Earthquake, and even if government subsidy policies such as the SME Loan
Program, the Credit Guarantee Program, the Interest Subsidy Program, and theInterest Reduction and Exemption Program are taken into account, the agency cost,
the funding cost of equity and debt, and the liquidity cost on the part of banks do notaffect the NIM. We find that the agency costs of firms are the main determinant of
NIM, and the borrower's balance sheet channel is stable and effective. In other words,
financial institutions can obtain higher NIMs when they lend to firms that haverelatively small net worth and are dependent on banks for financing. Financial
institutions in the Tohoku region of Japan can earn higher profits from lending when
they face the agency problem between borrowers (firms) and lenders (banks).

20

References
Angbazo, L. (1997), “Commercial bank net interest margins, default risk, interestrate risk, and off-balance sheet banking”, Journal of Banking & Finance, 21(1),55-87.
Aysun, G, and Hepp, R (2013), “Identifying the balance sheet and the lending
channels of monetary transmission: A loan-level analysis”, Journal of Banking& Finance 37(8): 2812-2822.
Bemanke, B., & Gertler, M. (1989), “Agency costs, net worth, and businessfluctuations”, American Economic Review, 79(1), 14-31.
Berger, A., Udell, G. (1995), “Relationship lending and lines of credit in small firm
finance”, Journal of Business, 68, 351– 381.
Bernanke, B.S., Gertler, M., and Gilchrist, S., (1999), “The financial accelerator in aquantitative business cycle framework” in J.B. Taylor and M. Woodford (ed.),Handbook of Macroeconomics, vol.1, ch.21, pp.1341-1393.
Gertler, M. and Kiyotaki, N., (2010), “Financial Intermediation and Credit Policy inBusiness Cycle Analysis” in B.M. Friedman and M. Woodford (ed.), Handbook of

Macroeconomics, vol.3, ch.11, pp. 547-599.
Hamadi, Hassan and Awdeh, Ali, (2012), “The Determinants of Bank Net Interest
Margin: Evidence from the Lebanese Banking Sector”, Journal of Money,

Investment and Banking, vol.23, pp.85-98.
Ho, T. S., & Saunders, A. (1981), “The determinants of bank interest margins: theory
and empirical evidence”, Journal of Financial and Quantitative analysis, 581600.
Hosono, K. and D. Miyakawa (2014), “Business Cycles, Monetary Policy, and Bank
Lending: Identifying the Bank Balance Sheet Channel with Firm-Bank MatchLevel Loan Data,” RIETI Discussion Paper Series 14-E-026.
Islam, M.S. and Nishiyama, S.I., (2016), “The determinants of bank net interest
margins: A panel evidence from South Asian countries,” Research inInternational Business and Finance, vol.37, pp.501-514.
Isoda, Y., Masuda, S., and Nishiyama, S.I., (2019), “Effects of Post-Disaster Aid
Measures to Firms: Evidence from Tohoku University Earthquake Recovery
Firm Survey 2012–2015,” Journal of Disaster Research, vol.14, pp.1030-1046.
Kashyap, A. K., & Stein, J. C. (2000), “What do a million observations on banks say
about the transmission of monetary policy?”, American Economic Review, 407428.
Kasman, A., Tunc, G., Vardar, G., & Okan, B. (2010), “Consolidation and commercial
21

bank net interest margins Evidence from the old and new European Union
members and candidate countries,” Economic Modelling, 27(3), 648-655.
Kiyotaki, N., and J. Moore (1997), "Credit cycles", Journal of Political Economy
105:211-248.
Kuttner, K. N., & Mosser, P. C. (2002), “The monetary transmission mechanism:
some answers and further questions”, Economic Policy Review, 8(1).
McShane, R. W., & Sharpe, I. G. (1985), “A time series/cross section analysis of thedeterminants of Australian trading bank loan/deposit interest margins: 1962–
1981”, Journal of Banking & Finance, 9(1), 115-136.
Mishkin, F. S. (1995), “Symposium on the monetary transmission mechanism”,

Journal of Economic perspectives, 9(4), 3-10.
Ortiz-Molina, H., & Penas, M. F. (2008), “Lending to small businesses The role ofloan maturity in addressing information problems”, Small Business Economics,
30(4), 361-383.
Petersen, M., & Rajan, R. (1994). The benefits of lending relationships: Evidence
from small business data. Journal of Finance, 49, 3–37.
Petersen, M., & Rajan, R. (1995). The effect of credit market competition on lending
relationships. The Quarterly Journal of Economics, 1109, 407–443.

22

Figure 1
Figure 1: Averages of Borrowing Rate, Deposit Rate, and Net
Interest Margin from 2012 to 2015
(%) 3
2.5
2
1.5
1
0.5
0

2012

2013
Borrowing Rate

2014
Deposit Rate2015
Net Interest Margin

Figure 2

Capital-to-Asset Ratio

Figure 2: Averages of Firm's and Bank's Capital-to-Asset Ratio
from 2012 to 2015(% pt)

0.6

3

0.5

2.5

0.4

2

0.3

1.5

0.2

1

0.1

0.5

0

0

2012

2013

2014

Firm Capital-to-Asset Ratio

2015

Bank Capital-to-Asset Ratio

Net Interest Margin (Right scale)23

Figure 3

Figure 424

Table 1: Variable Description
Variable
Loan-specific variables
INT
DEP
NIM
Log_Loan
Maturity
Primary_Vintage

SourceDescription

TERFS
FFS
TERFS, FFS
TERFS
TERFS
TERFS

Contracted interest rate for the new loan after the earthquake disaster
Deposit interest rate = Interest payment on deposits / Total deposit
NIM = INT - DEP
Logarithm of the firm's new loan
Borrowing period of new loan (in terms of months)
Years of dealing with the primary bank at the time a firm borrowed a new loan
Primary bank share = the firm's total debt from the bank that borrowed a new loan / the firm's
total debt
1: New loan is used for capital investment, and 0: otherwise
1: New loan is guaranteed by the Credit Guarantee Institute, and 0: otherwise
1: New loan is secured by collateral or personal guarantee, and 0: otherwise

Primary_Share

TERFS

Physical_Inv_Dum
Guarantee_Dum
Collateral_Dum
Firm-specific variables
Capital_Asset_Firm
Log_Asset_Firm

TERFS
TERFS
TERFS
TERFS
TERFS

Number_Bank

TERFS

Bank-specific variables
Capital_Asset_BankFFS

Liquidity_Bank

FFS

Firm's capital-to-asset ratio = (Total assets - Labilities) / Total assets
Logarithm of the firm's total assets
Total number of banks (including government financial institutions, non-banks, etc.) that thefirm has borrowed at the time of the Great East Japan Earthquake (as of March 11, 2011)

Log_Asset_Bank
ROA_Bank
Firm-specific damage variables
Earthquake_Dum
Tsunami_Dum
Nuclear_Dum
Nuclear_Ind_Dum
Supplierdamage_Dum
Clientdamage_Dum
Relocation_Dum
Damage_Ratio
Bank-specific damage variables

FFS
FFS

Bank's capital-to-asset ratio = (Total assets - Labilities) / Total assets
Bank's liquidity-to-asset ratio = (Cash holding + Government bond holding + Local
government bonds + Commodity bonds + Commodity municipal bonds + Call loan) / Total
assetsLogarithm of the bank's total assets
Bank's ROA = Net business profit / Total assets

TERFS
TERFS
TERFS
TERFS
TERFS
TERFS
TERFS
TERFS

1: the firm was damaged by the earthquake, and 0: otherwise
1: the firm was hit by the tsunami, and 0: otherwise
1: the firm was directly damaged by the nuclear accident, and 0: otherwise
1: the firm suffered reputational damage from the nuclear accident, and 0: otherwise
1: the firm was indirectly damaged by the damage to its suppliers, and 0: otherwise
1: the firm was indirectly damaged by the damage to its clients, and 0: otherwise
1: the firm was forced to relocate due to tsunami damage or nuclear accident, and 0:
Damage ratio = Damage to tangible fixed assets / Total assets before earthquake

Branch_NotOpened_Dum

TERFS

Branch_Changed_Dum

TERFS

Bank_StaffChanged_Dum

TERFS

Bank_VisitFrequencyDecreased_
TERFS
Dum
Subsidy and aid measure variables
Insurance_Dum

TERFS

Lease_Subsidy_Dum

TERFS

Lumpsum_Subsidy_Dum

TERFS

Taxexemption_Dum

TERFS

Interest_Subsidy_Dum

TERFS

Groupsubsidy_Dum

TERFS

Repay_Allowance_Dum

TERFS

Reschedule_Dum
Interest_Reduce_Dum
Debt_Reduce_Dum

TERFS
TERFS
TERFS

Collateral_Cancel_Dum

TERFS

1: Bank branch could not operate (or not at present) due to earthquake, tsunami and nuclear
accident, and 0: otherwise
1: the bank's branch changed to another branch after an earthquake, tsunami, and a nuclear
accident, and 0: otherwise
1: the person in charge of the bank has changed since the earthquake, tsunami and nuclear
accident, and 0: otherwise
1: Frequency of bank staff visits has decreased due to the situation of the bank since theearthquake, tsunami and nuclear accident, and 0: otherwise
1: the firm had subscribed to earthquake insurance or earthquake risk security contract
before the earthquake, and 0: otherwise
1: the firm has received subsidies for rents and leases from local governments (prefecture,municipalities) since the earthquake, and 0: otherwise
1: the firm received a lump-sum or subsidy from the local government after the earthquake,and 0: otherwise
1: the firm received tax reduction from local governments after the earthquake, and 0:
1: the firm has received interest or guarantee fees from the local government since theearthquake, and 0: otherwise
1: the firm used group subsidy (small and medium-sized enterprise group facilities
restoration maintenance assistance business, maintenance business to temporary store or
temporary construction plant), and 0: otherwise
1: the firm was suspended by the bank to repay the bank loan it had borrowed before theearthquake (hereinafter referred to as past debt), and 0: otherwise
1: the firm has been extended its past debt repayment period by the bank, and 0: otherwise
1: the firm received interest reductions from the bank for past debt, and 0: otherwise
1: the firm has been reduced (exempt, amortize) its past debt by the bank, and 0: otherwise
1: the firm has been reset or reduced by the bank the setting of collateral and personal
guarantee that had been set in the past debt, and 0: otherwise
1: the firm has been subordinated to its past debt by the bank, and 0: otherwise

Debt_Subordinate_Dum
TERFS
Time dummies
D2013
1: In the case of Fiscal Year 2013, and 0: otherwise
D2014
1: In the case of Fiscal Year 2014, and 0: otherwise
D2015
1: In the case of Fiscal Year 2015, and 0: otherwise
TERFS: Tohoku University Earthquake Recovery Firm Survey
FFS : Financial institutions' "Financial Statements" and "disclosure magazines"

25

Table 2: Time-varying variables
Variables
Loan-specific variables
INT
DEP
NIM
Log_Loan
Maturity
Primary_Vintage
Primary_Share
Physical_Inv_Dum
Guarantee_Dum
Collateral_Dum
Firm-specific variables
Capital_Asset_Firm
Log_Asset_Firm
Number_Bank
Bank-specific variables
Capital_Asset_Bank
Liquidity_Bank
Log_Asset_Bank
ROA_Bank

Obs.

Mean Std.Dev.

Min.

25%

Median

75%

Max.

6,761
6,366
4,798
8,519
8,160
7,693
7,201
9,352
8,421
8,284

1.85
0.06
1.82
7.67
76.62
17.27
0.75
0.42
0.50
0.54

1.92
0.03
1.91
1.39
53.09
18.57
1.45
0.49
0.50
0.50

0.00
0.03
-0.28
0.00
0.00
0.00
0.00
0
0
0

1.20
0.04
1.24
6.91
58.00
0.00
0.47
0
0
0

1.50
0.05
1.46
7.60
60.00
11.00
0.70
0
1
1

2.00
0.07
1.96
8.52
120.0030.00
100.00
1
1
1

19.75
0.79
19.72
16.52
720.00100.00100.00
1
1
1

6,015
7,591
8,353

0.35
9.90
2.46

0.24
1.67
1.69

0.00
0.00
0.00

0.15
8.89
1.00

0.31
9.89
2.00

0.50
10.93
3.00

1.00
19.76
24.00

6,366
6,083
6,366
5,760

0.05
0.32
14.37
0.00

0.02
0.09
1.54
0.00

0.02
0.12
10.42
0.00

0.04
0.26
13.45
0.00

0.04
0.34
14.71
0.00

0.05
0.37
15.59
0.00

0.19
0.63
19.09
0.01

26

Table 3: Time-Invariant variables
Freq.

Variables
Firm-specific damage variables
Earthquake_Dum
Tsunami_Dum
Nuclear_Dum
Nuclear_Ind_Dum
Supplierdamage_Dum
Clientdamage_Dum
Relocation_Dum
Damage_Ratio

Bank-specific damage variables
Branch_NotOpened_Dum
Branch_Changed_Dum
Bank_StaffChanged_Dum
Bank_VisitFrequencyDecreased_Dum
Subsidy and aid measure variables
Insurance_Dum
Lease_Subsidy_Dum
Lumpsum_Subsidy_Dum
Taxexemption_Dum
Interest_Subsidy_Dum
Groupsubsidy_Dum
Repay_Allowance_Dum
Reschedule_Dum
Interest_Reduce_Dum
Debt_Reduce_Dum
Collateral_Cancel_Dum
Debt_Subordinate_Dum

27

%

No
Yes
Total
No
Yes
Total
No
Yes
Total
No
Yes
Total
No
Yes
Total
No
Yes
Total
No
Yes
Total
0-0.250.26-0.5
0.51-0.75
0.76-1
Total

13133
12411
25544
19219
6325
25544
23495
2049
25544
20960
4584
25544
18604
6940
25544
17213
8331
25544
24919
625
25544
11759
838
367
777
13741

51.41
48.59
100
75.24
24.76
100
91.98
8.02
100
82.05
17.95
100
72.83
27.17
100
67.39
32.61
100
97.55
2.45
100
85.58
6.1
2.67
5.65
100

No
Yes
Total
No
Yes
Total
No
Yes
Total
No
Yes
Total

8368
477
8845
8733
112
8845
8198
647
8845
8599
246
8845

94.61
5.39
100
98.73
1.27
100
92.69
7.31
100
97.22
2.78
100

No
Yes
Total
No
Yes
Total
No
Yes
Total
No
Yes
Total
No
Yes
Total
No
Yes
Total
No
Yes
Total
No
Yes
Total
No
Yes
Total
No
Yes
Total
No
Yes
Total
No
Yes
Total

17465
7468
24933
24940
407
25347
21525
3822
25347
20423
4924
25347
5302
2983
8285
23005
2822
25827
16688
1192
17880
16972
908
17880
16571
1307
17878
17807
70
17877
17792
85
17877
17793
84
17877

70.05
29.95
100
98.39
1.61
100
84.92
15.08
100
80.57
19.43
100
64
36
100
89.07
10.93
100
93.33
6.67
100
94.92
5.08
100
92.69
7.31
100
99.61
0.39
100
99.52
0.48
100
99.53
0.47
100

Table 4: New loan is made by primary bankModel type
Dependent variable
Estimation method
Log_Loan
Maturity
Primary_Vintage
Primary_Share
Physical_Inv_Dum
Guarantee_Dum
Collateral_Dum
Capital_Asset_Firm
Log_Asset_Firm
Number_Bank
Capital_Asset_Bank
Liquidity_Bank
Log_Asset_Bank
ROA_Bank
Earthquake_Dum

A1
NIM
Robust reg.
-0.099*(0.059)-0.002**(0.001)-0.002(0.003)-0.048(0.079)0.022(0.094)0.098(0.103)-0.033(0.099)-0.480**(0.155)0.028(0.039)-0.019(0.021)15.040**(5.345)-0.784(0.840)-0.090**(0.040)-42.640(32.698)

Tsunami_Dum
Nuclear_Dum
Nuclear_Ind_Dum
Supplierdamage_Dum
Clientdamage_Dum
Relocation_Dum
Branch_NotOpened_Dum

A2
NIM
Robust reg.
-0.101*(0.060)-0.002**(0.001)-0.002(0.003)-0.063(0.081)
0.013(0.098)0.089(0.103)-0.039(0.101)-0.456**(0.155)0.027(0.038)-0.012(0.021)14.801**(5.571)-0.770(0.858)-0.098**(0.041)-48.059(34.167)-0.110(0.094)-0.046(0.132)-0.394**(0.126)-0.036(0.107)0.346**(0.120)-0.042(0.110)0.496(0.477)

Branch_Changed_Dum
Bank_StaffChanged_Dum
Bank_VisitFrequencyDecreased_Dum
Insurance_Dum

A3
NIM
Robust reg.
-0.100*(0.061)-0.002**(0.001)-0.003(0.003)-0.082(0.081)
0.024(0.097)0.107(0.102)-0.015(0.099)-0.426**(0.154)0.024(0.038)-0.009(0.021)15.811**(5.470)-0.934(0.849)-0.104**(0.042)-44.841(33.999)-0.130(0.092)-0.042(0.128)-0.391**(0.128)-0.025(0.108)0.349**(0.122)-0.028(0.110)0.527(0.470)0.185(0.223)-0.362**(0.182)-0.003(0.113)-0.299**(0.141)

Lease_Subsidy_Dum
Lumpsum_Subsidy_Dum
Taxexemption_Dum
Interest_Subsidy_Dum
Groupsubsidy_Dum
Repay_Allowance_Dum
Reschedule_Dum
Interest_Reduce_Dum
Debt_Reduce_Dum
Collateral_Cancel_Dum
Debt_Subordinate_Dum
GDP_Growth

A4
NIM
Pooled-0.125*(0.069)-0.002(0.001)-0.002(0.003)-0.092(0.085)-0.017(0.107)0.267**(0.120)-0.026(0.105)-0.344**(0.161)
0.021(0.043)-0.008(0.020)13.710**(5.548)-0.818(0.871)-0.108**(0.044)-33.888(34.095)-0.135(0.100)-0.028(0.145)-0.334**(0.125)-0.025(0.109)0.374**(0.126)-0.029(0.112)0.627(0.484)0.158(0.251)-0.268(0.207)0.006(0.118)-0.303*(0.165)0.140(0.130)-0.419**(0.177)0.103(0.170)-0.101(0.143)-0.436**(0.110)0.075(0.187)0.469(0.326)0.163(0.277)-0.033(0.188)-0.050(0.185)-0.204(0.388)-0.047(0.346)

Inflation
Log_MB
D2013

A5
NIM
Pooled
-0.143**(0.068)-0.001(0.001)-0.003(0.003)-0.092(0.084)-0.011(0.104)0.258**(0.118)0.048(0.098)-0.336**(0.162)0.030(0.043)-0.009(0.021)
2.714(5.194)0.075(0.811)-0.120**(0.044)-15.046(32.575)-0.117(0.098)0.006(0.142)-0.348**(0.123)-0.064(0.109)0.341**(0.122)-0.024(0.110)0.612(0.465)0.186(0.245)-0.242(0.226)0.038(0.118)-0.258(0.167)0.148(0.127)-0.460**(0.192)0.127(0.168)-0.087(0.140)-0.337**(0.105)-0.004(0.181)0.485(0.315)0.200(0.268)-0.069(0.181)0.135(0.231)-0.665(0.427)0.138(0.358)-4.906*(2.880)-30.440**(7.143)1.086**(0.275)3.983**(0.675)4.059**(0.687)4.320**(0.748)-10.813**(3.693)-0.004(0.068)-0.084(0.076)
0.966**(0.250)4.461**(0.740)

1576
0.025

1569
0.027

1474
0.039

1474
0.074

1474
0.074

D2014
D2015
Constant

3.802**(0.659)

N
1578
0.017
R-sq
Standard errors in parentheses. * p<0.1, ** p<0.05

A6
NIM
Pooled
-0.143**(0.068)-0.001(0.001)-0.003(0.003)-0.092(0.084)-0.011(0.104)0.258**(0.118)0.048(0.098)-0.336**(0.162)0.030(0.043)-0.009(0.021)
2.714(5.194)0.075(0.811)-0.120**(0.044)-15.046(32.575)-0.117(0.098)0.006(0.142)-0.348**(0.123)-0.064(0.109)0.341**(0.122)-0.024(0.110)0.612(0.465)0.186(0.245)-0.242(0.226)0.038(0.118)-0.258(0.167)0.148(0.127)-0.460**(0.192)0.127(0.168)-0.087(0.140)-0.337**(0.105)-0.004(0.181)0.485(0.315)0.200(0.268)-0.069(0.181)0.135(0.231)-0.665(0.427)0.138(0.358)28

Table 5: New loan made by primary or other banks
Model type
Dependent variable
Estimation method
Log_Loan
Maturity
Physical_Inv_Dum
Guarantee_Dum
Collateral_Dum
Capital_Asset_Firm
Log_Asset_Firm
Number_Bank
Earthquake_Dum

B1
INT
Pooled
-0.110**(0.043)-0.001**(0.001)-0.088(0.064)0.177**(0.064)0.022(0.061)-0.384**(0.124)0.015(0.026)0.010(0.017)

Tsunami_Dum
Nuclear_Dum
Nuclear_Ind_Dum
Supplierdamage_Dum
Clientdamage_Dum
Relocation_Dum
Insurance_Dum

B2
INT
Pooled
-0.110**(0.044)-0.001**(0.001)-0.073(0.068)0.175**(0.063)0.020(0.061)-0.386**(0.126)0.019(0.026)0.012(0.017)-0.047(0.062)-0.064(0.079)-0.171(0.108)-0.074(0.075)0.038(0.069)0.020(0.066)0.254(0.275)

Lease_Subsidy_Dum
Lumpsum_Subsidy_Dum
Taxexemption_Dum
Interest_Subsidy_Dum
Groupsubsidy_Dum
Repay_Allowance_Dum
Reschedule_Dum
Interest_Reduce_Dum
Debt_Reduce_Dum
Collateral_Cancel_Dum
Debt_Subordinate_Dum
GDP_Growth

B3
INT
Pooled-0.126**(0.048)-0.001(0.001)-0.072(0.074)0.277**(0.077)0.019(0.063)-0.377**(0.131)0.028(0.028)0.009(0.018)-0.055(0.066)-0.063(0.089)-0.233**(0.090)-0.111(0.076)0.031(0.072)0.049(0.069)0.302(0.283)0.035(0.076)-0.208(0.225)-0.004(0.109)-0.068(0.093)-0.286**(0.078)0.171(0.129)0.215(0.186)0.254(0.222)-0.197**(0.097)2.423(2.608)-0.015(0.235)0.071(0.190)Inflation
Log_MB
D2013

B4
INT
Pooled-0.120**(0.047)-0.000(0.001)-0.081(0.072)0.294**(0.075)0.054(0.061)-0.426**(0.130)0.024(0.028)0.003(0.018)-0.030(0.064)-0.041(0.087)-0.213**(0.088)-0.131*(0.076)0.014(0.070)0.047(0.067)0.329(0.271)0.030(0.074)-0.261(0.217)0.034(0.106)-0.063(0.090)-0.162**(0.075)0.030(0.121)
0.277(0.179)0.273(0.215)-0.254**(0.096)2.364(2.419)-0.250(0.277)0.263(0.196)-5.776**(1.929)-35.869**(4.847)1.296**(0.186)2.701**(0.246)-15.753**(2.663)0.001(0.042)-0.088*(0.047)
1.154**(0.167)2.468**(0.252)

3706
0.018

3706
0.067

3706
0.067

D2014
D2015
Constant

2.669**(0.220)2.664**(0.223)

N
4034
4018
0.010
0.010
R-sq
Standard errors in parentheses. * p<0.1, ** p<0.05

29

B5
INT
Pooled-0.120**(0.047)-0.000(0.001)-0.081(0.072)0.294**(0.075)0.054(0.061)-0.426**(0.130)0.024(0.028)0.003(0.018)-0.030(0.064)-0.041(0.087)-0.213**(0.088)-0.131*(0.076)0.014(0.070)0.047(0.067)0.329(0.271)0.030(0.074)-0.261(0.217)0.034(0.106)-0.063(0.090)-0.162**(0.075)0.030(0.121)
0.277(0.179)0.273(0.215)-0.254**(0.096)2.364(2.419)-0.250(0.277)0.263(0.196)

Table 6: New loan made by other banks
Model type
Dependent variable
Estimation method
Log_Loan
Maturity
Physical_Inv_Dum
Guarantee_Dum
Collateral_Dum
Capital_Asset_Firm
Log_Asset_Firm
Number_Bank
Earthquake_Dum

C1
INT
Pooled-0.120**(0.051)0.000(0.001)-0.150(0.103)0.362**(0.110)0.067(0.092)-0.470**(0.213)-0.004(0.033)0.036(0.029)Tsunami_Dum
Nuclear_Dum
Nuclear_Ind_Dum
Supplierdamage_Dum
Clientdamage_Dum
Relocation_Dum
Insurance_Dum

C2
INT
Pooled
-0.119**(0.053)0.000(0.001)-0.120(0.112)
0.342**(0.106)0.061(0.092)-0.500**(0.219)0.000(0.033)0.036(0.029)-0.015(0.102)-0.068(0.119)-0.022(0.223)0.120(0.150)-0.210**(0.093)0.008(0.092)-0.027(0.394)

Lease_Subsidy_Dum
Lumpsum_Subsidy_Dum
Taxexemption_Dum
Interest_Subsidy_Dum
Groupsubsidy_Dum
Repay_Allowance_Dum
Reschedule_Dum
Interest_Reduce_Dum
Debt_Reduce_Dum
Collateral_Cancel_Dum
Debt_Subordinate_Dum
GDP_Growth

C3
INT
Pooled-0.125**(0.057)0.001(0.001)-0.036(0.115)0.344**(0.125)0.032(0.091)-0.555**(0.236)0.021(0.038)0.035(0.030)-0.056(0.106)0.053(0.129)-0.219(0.133)0.104(0.138)-0.274**(0.093)0.077(0.095)0.074(0.421)-0.051(0.103)-0.201(0.232)-0.112(0.132)-0.119(0.126)-0.140(0.122)-0.160(0.130)0.065(0.292)-0.146(0.412)-0.102(0.145)4.330(4.022)-0.302(0.322)0.341(0.405)

Inflation
Log_MB
D2013

C4
INT
Pooled
-0.110*(0.058)0.001(0.001)-0.053(0.111)0.346**(0.124)0.040(0.088)-0.601**(0.235)0.013(0.039)0.020(0.030)-0.036(0.103)0.052(0.124)-0.220*(0.128)0.126(0.135)-0.282**(0.092)0.103(0.094)0.118(0.405)-0.042(0.101)-0.191(0.213)-0.085(0.130)-0.136(0.123)-0.051(0.118)-0.296**(0.135)0.126(0.282)-0.141(0.394)-0.163(0.146)
4.101(3.775)-0.089(0.355)0.661(0.471)-3.188(3.686)-30.887**(7.644)1.087**(0.297)2.478**(0.334)-12.968**(4.195)0.022(0.079)-0.116(0.074)
0.976**(0.266)2.331**(0.337)

1266
0.035

1266
0.077

1266
0.077

D2014
D2015
Constant

2.578**(0.279)2.613**(0.298)

N
1395
1388
0.021
0.020
R-sq
Standard errors in parentheses. * p<0.1, ** p<0.05

30

C5
INT
Pooled
-0.110*(0.058)0.001(0.001)-0.053(0.111)0.346**(0.124)0.040(0.088)-0.601**(0.235)0.013(0.039)0.020(0.030)-0.036(0.103)0.052(0.124)-0.220*(0.128)0.126(0.135)-0.282**(0.092)0.103(0.094)0.118(0.405)-0.042(0.101)-0.191(0.213)-0.085(0.130)-0.136(0.123)-0.051(0.118)-0.296**(0.135)0.126(0.282)-0.141(0.394)-0.163(0.146)
4.101(3.775)-0.089(0.355)0.661(0.471)

Table 7: New loan is made by primary bank, firms without serious damage
Model type
Dependent variable
Estimation method
Log_Loan
Maturity
Primary_Vintage
Primary_Share
Physical_Inv_Dum
Guarantee_Dum
Collateral_Dum
Capital_Asset_Firm
Log_Asset_Firm
Number_Bank
Capital_Asset_Bank
Liquidity_Bank
Log_Asset_Bank
ROA_Bank
GDP_Growth

D1
NIM
Pooled-0.033(0.038)-0.003**(0.001)0.000(0.004)0.009(0.124)0.124(0.121)
0.031(0.115)-0.023(0.119)-0.547**(0.181)-0.010(0.038)-0.024(0.021)
11.152*(6.723)-0.869(1.002)-0.086*(0.048)-34.143(43.361)

Inflation
Log_MB
D2013

D5
NIM
Pooled
-0.049(0.037)-0.002**(0.001)0.000(0.003)0.025(0.121)0.120(0.119)0.070(0.113)0.041(0.111)-0.528**(0.181)-0.003(0.040)-0.022(0.022)-0.738(6.637)-0.138(0.953)-0.096**(0.048)-11.083(41.253)-5.329*(3.151)-26.662**(7.852)
1.052**(0.299)-10.889**(4.028)0.015(0.072)0.009(0.092)0.939**(0.269)3.899**(0.778)

1098
0.043

1098
0.043

D2014
D2015
Constant

3.755**(0.780)

N
1098
0.009
R-sq
Standard errors in parentheses. * p<0.1, ** p<0.05

31

D6
NIM
Pooled
-0.049(0.037)-0.002**(0.001)0.000(0.003)0.025(0.121)0.120(0.119)0.070(0.113)0.041(0.111)-0.528**(0.181)-0.003(0.040)-0.022(0.022)-0.738(6.637)-0.138(0.953)-0.096**(0.048)-11.083(41.253)

Table 8: Unbalanced panel data estimation results
Model type
Dependent variable
Estimation method
Log_LoanConstant

56.562(51.050)

A_RE
NIM
Random effects-0.135**(0.049)-0.001(0.001)-0.102(0.099)0.009(0.101)0.263**(0.113)0.021(0.097)-0.317(0.223)0.039(0.049)2.036(6.037)-0.069(0.784)-0.123**(0.042)-4.900(51.425)-3.508(4.871)-28.127**(5.108)1.085**(0.162)-0.002(0.003)-0.009(0.030)-0.125(0.104)0.033(0.133)-0.343*(0.195)-0.062(0.127)0.362**(0.112)-0.043(0.109)0.640*(0.369)0.189(0.194)-0.252(0.427)0.020(0.147)-0.244(0.285)0.143(0.114)-0.457(0.322)0.149(0.149)-0.093(0.143)-0.319**(0.113)-0.054(0.169)0.404(0.249)0.244(0.244)-0.034(0.159)0.258(1.242)-0.653(0.553)0.208(0.708)-10.892**(2.376)N
R-sq (Within)
F test that all u_i=0
Prob > F
Hausman Test
Prob > chi2
Breusch-Pagan Test
Prob > chibar2

1681
0.117
1.035
0.342
12.032
0.604
-

1474
1.942
0.082

4390
0.074
0.948
0.878
15.098
0.129
-

3706
0.000
1.000

1595
0.135
1.043
0.340
21.791
0.016
-

1266
0.246
0.310

1164
0.128
1.103
0.169
13.545
0.484
-

1098
2.228
0.068

PooledMost reliable estimation method:Standard errors in parentheses. * p<0.1, ** p<0.05

PooledPooledPooledPooledPooledPooledPooledMaturity
Primary_Share
Physical_Inv_Dum
Guarantee_Dum
Collateral_Dum
Capital_Asset_Firm
Log_Asset_Firm
Capital_Asset_Bank
Liquidity_Bank
Log_Asset_Bank
ROA_Bank
GDP_Growth
Inflation
Log_MBPrimary_Vintage

A_FE
NIM
Fixed effects
0.055(0.109)-0.010**(0.003)-0.055(0.312)0.194(0.245)0.302(0.264)0.165(0.250)-0.285(0.907)0.131(0.209)-19.982(67.097)-0.645(7.734)-6.001(4.331)133.924(160.796)
10.074(11.171)-16.353*(9.354)2.314**(1.035)

Number_Bank
Earthquake_Dum
Tsunami_Dum
Nuclear_Dum
Nuclear_Ind_Dum
Supplierdamage_Dum
Clientdamage_Dum
Relocation_Dum
Branch_NotOpened_Dum
Branch_Changed_Dum
Bank_StaffChanged_Dum
Bank_VisitFrequencyDecreased_Dum
Insurance_Dum
Lease_Subsidy_Dum
Lumpsum_Subsidy_Dum
Taxexemption_Dum
Interest_Subsidy_Dum
Groupsubsidy_Dum
Repay_Allowance_Dum
Reschedule_Dum
Interest_Reduce_Dum
Debt_Reduce_Dum
Collateral_Cancel_Dum
Debt_Subordinate_Dum

B_FE
INT
Fixed effects-0.163**(0.060)-0.002(0.001)

B_RE
INT
Random effects-0.120**(0.032)-0.000(0.001)

C_FE
INT
Fixed effects-0.063(0.132)-0.005*(0.003)

C_RE
INT
Random effects-0.110**(0.052)0.001(0.001)-0.105(0.136)0.155(0.139)-0.051(0.130)-0.009(0.466)-0.139(0.106)-0.081(0.066)
0.294**(0.070)0.054(0.063)-0.426**(0.142)0.024(0.030)-0.009(0.278)0.648**(0.300)-0.574**(0.277)-0.746(0.846)-0.410**(0.200)-0.052(0.100)0.345**(0.109)0.039(0.095)-0.601**(0.213)0.013(0.045)-2.556(5.030)-29.679**(4.971)1.227**(0.148)-5.776*(3.322)-35.869**(3.466)
1.296**(0.102)-9.794(9.842)-35.083**(9.811)1.160**(0.307)-3.177(5.066)-30.885**(5.298)1.086**(0.160)

D_FE
NIM
Fixed effects
0.070(0.128)-0.010**(0.004)-0.144(0.324)0.381(0.289)-0.358(0.302)0.361(0.295)-0.535(1.001)
0.194(0.224)83.319(76.616)
5.628(8.873)-2.815(5.184)388.652**(191.412)-2.486(13.127)-19.358*(10.898)
0.954(1.233)

D_RE
NIM
Random effects-0.048(0.055)-0.002**(0.001)0.024(0.132)0.122(0.113)0.068(0.120)0.039(0.108)-0.528**(0.242)-0.003(0.052)-0.825(7.077)-0.143(0.819)-0.097**(0.045)-10.035(60.499)-5.202(5.548)-26.522**(5.860)1.054**(0.178)0.000(0.003)-0.022(0.030)0.003(0.019)-0.030(0.066)-0.041(0.080)-0.213*(0.120)-0.131(0.081)0.014(0.070)0.047(0.069)0.329(0.205)0.020(0.030)-0.036(0.099)0.052(0.118)-0.220(0.187)0.126(0.130)-0.282**(0.105)0.103(0.103)0.119(0.306)-12.814**(2.263)0.030(0.069)-0.261(0.219)0.034(0.093)-0.063(0.088)-0.162**(0.073)0.030(0.103)0.277*(0.151)0.273*(0.164)-0.254**(0.106)2.364**(0.838)-0.250(0.436)0.263(0.503)-15.753**(1.491)-9.341**(4.628)-0.043(0.104)-0.191(0.333)-0.085(0.146)-0.136(0.137)-0.051(0.115)-0.296*(0.155)0.125(0.223)-0.143(0.282)-0.162(0.172)4.101**(0.965)-0.087(0.847)0.660(1.177)-12.961**(2.339)21.762(60.783)-10.908**(2.613)32