Business Cycles, Monetary Policy, and Bank Lending:Identifying the Bank Balance Sheet Channel with Firm-Bank Match-Level Loan Data
December 2014
Kaoru Hosono† and Daisuke Miyakawa‡
Gakushuin University and Nihon University

Abstract
This paper examines the impact of business cycles and monetary policy on bank loan supply. To thisend, we use a unique firm-bank match-level dataset covering listed firms in Japan that allows us tocontrol for firms’ time-varying unobservable loan demand and endogenous bank-firm matching, sothat we can identify the effects of business cycles and monetary policy on loan supply through thebank balance sheet channel. The estimation results indicate that banks with more liquidity or capitaltend to lend more to their client firms. The quantitative impact of bank liquidity and capital on loan
supply was economically sizable and larger when economic growth was lower. Furthermore, thequantitative impact of bank liquidity on the growth rate of loans more than doubled whenquantitative easing was terminated. Overall, these results imply that changes in economic growthand monetary policy significantly affected loan supply through the bank balance sheet channel. We
also find evidence that fluctuations in economic growth and monetary policy are transmitted tocapital investment through the bank balance sheet channel in the case of firms with high investment
opportunities.

Key words: Bank Balance Sheet Channel; Monetary Policy; Time-Variant Firm Individual Effects
JEL Classification: E44, E51, E52, G21

 We would like to thank Arito Ono, Iichiro Uesugi, Hirofumi Uchida, Nobuyoshi Yamori, Xu Peng, Daisuke Tsuruta,

Daisuke Nakajima, Shinya Tanaka, and other participants of a seminar at RIETI, Bank of Japan IMES, and Otaru
School of commerce for helpful suggestions. K. Hosono gratefully acknowledges financial support from theGrant-in-Aid for Scientific Research No.22223004, JSPS. D. Miyakawa gratefully acknowledges financial support
from the Grant-in-Aid for Scientific Research No. 26885087, JSPS.
† Professor, Department of Economics, Gakushuin University, 1-5-1 Mejiro, Toshima-ku, Tokyo 171-8588, Japan.
E-mail: kaoru.hosono@gakushuin.ac.jp.
‡ Associate Professor, College of Economics, Nihon University, 1-3-2 Misaki-cho, Chiyoda-ku, Tokyo 102-8360
Japan. E-mail: miyakawa.daisuke@nihon-u.ac.jp.

11. Introduction
Contractive monetary policy and economic downturns potentially reduce the supply ofloans provided by banks because their net worth is likely to contract, so that the agency problems
involved in banks’ financing through uninsured debt are severer in such a policy and economic
environment (Stein 1998). Such an adverse impact of contractive monetary policy and economic
downturns is likely to be larger for a bank with less capital or liquid assets as a ratio of total assets,
who are likely to incur larger agency costs. To examine whether such a bank balance sheet channel
really works, it is necessary to identify shocks to loan supply that can be disentangled from shocks toloan demand. In addition, it is necessary to address the assortative matching mechanism that may
arise if better-performing borrowers are likely to borrow from better-performing banks. Few extant
studies, however, successfully overcome these identification problems.
In this paper, we test the bank balance sheet channel hypothesis by addressing theseidentification problems using a unique dataset that contains information on the banks each firmtransacts with, on the amount of outstanding loans each firm has with each bank, and on the balance
sheet variables of each firm and bank. The dataset is a panel that covers firms listed on Japanesestock exchanges and spans almost three decades. Using this dataset, we examine, first, whether
banks’ net worth (measured in terms of capital and liquidity relative to total assets) affects changes
in loan supply and, second, whether tighter monetary policy or lower economic growth strengthen
the effects of bank net worth on changes in loan supply. To disentangle bank loan supply fromdemand, we control for firm-year fixed effects. We thus focus on the variation in changes inoutstanding loans across banks for the same firm and year. Further, we control for bank-firm
matching by including bank-firm fixed effects and focusing on the variation in changes inoutstanding loans over time for the same bank and firm. We therefore can estimate the quantitative
impact of monetary policy and business cycle on changes in outstanding loans through bank net
worth. To the best of our knowledge, the only study that successfully overcomes the identification
problems in testing the hypothesis that bank net worth plays a more important role in a contractive
monetary policy environment or under lower economic growth is that by Jiménez et al. (2012).
Using Spanish data on loan applications, they focus on whether banks accept firms’ loan applications
and whether firms whose applications for loans were rejected could borrow from other banks. On theother hand, we use data on the amounts of loans, which enable us to quantitatively assess the bankbalance sheet channel, i.e., changes in bank lending caused by the changes in the real GDP growthrate and monetary policy. Moreover, we further investigate how the bank balance sheet channel
affects firms’ overall borrowing and investment.
The findings of our analysis can be summarized as follows. First, banks with higher
capital-to-asset ratios and liquidity-to-asset ratios tended to supply more loans. Second, the effects ofbank capital and bank liquidity on loan supply were stronger when the economic growth rate was2lower. Third, the effects of bank liquidity on loan supply were weaker when monetary policy wasloosened and stronger when monetary policy was tightened or the central bank exited fromquantitative easing. Fourth, the effects of bank capital on loan supply were significant during the
2000s, when Japan’s regulatory authorities strictly enforced capital adequacy regulations. Fifth, thequantitative impacts of bank liquidity and capital on the growth rate of loans were both economicallysizable. The impact of bank liquidity more than doubled when quantitative easing was terminated.
Sixth and finally, we also find evidence that fluctuations in economic growth and monetary policy
are transmitted to capital investment through the bank balance sheet channel in the case of firms withhigh investment opportunities. Overall, our findings support the hypotheses that bank net worth
affects loan supply and that the effect depends on monetary policy and economic growth. Moreover,such a bank balance sheet channel has a significant impact on firms’ financing and investment.
The rest of the paper proceeds as follows. Section 2 briefly describes the Japanese loan
market to provide some background to the following analysis. Next, Section 3 reviews the relevant
literature and discusses the contribution of this study, while Section 4 describes the dataset and theempirical methodology. Section 5 then presents and discusses the results, and Section 6 concludes.

2. Background Information on the Japanese Loan Market
As mentioned, the dataset we use for our analysis covers a period of almost three decades.
To provide some background on the Japanese loan market during this period, this section presents
developments in key indicators from the early 1980s onward, including changes in the amount ofloans outstanding, GDP growth, and the monetary policy rate.
We start by looking at the annual growth rate of firms’ outstanding bank borrowing.
Specifically, using the outstanding bank borrowing data from our dataset covering listed firms, we
compute the growth rate of aggregate loans outstanding for listed firms. The results are depicted in
Figure 1, where the solid line shows the growth rate of aggregate loans outstanding, while the dashedline shows the nominal GDP growth rate for each fiscal year. Looking at developments in loans
outstanding and GDP growth together indicates that loans outstanding generally tended to moveprocyclically, although there are some periods, such as the period of the global financial crisis, inwhich they move countercyclically.
Next, in Figure 2, we look at the growth rate of loans outstanding for listed firms and thepolicy rate targeted by the Bank of Japan (BOJ). 1 Although outstanding loans seem to haveincreased in some years when the policy rate was low, the relationship between the two is weak. Thisis probably because the BOJ tends to increase the policy rate when aggregate economic activity isstrong, and loan demand also tends to increase when economic activity is strong. Furthermore, since
The Bank of Japan’s policy target interest rate changed a number of times during our observation period. We
therefore use the official discount rate (Koteibuai) from FY1981 to FY1984 and the annual average of the observed
overnight interbank call rate from FY1985 to FY2010.
1

3

the late 1990s, the policy rate has been set to almost zero, which further obscures any relationshipbetween the policy rate and aggregate loans outstanding. These considerations mean that we need toisolate loan supply shocks from loan demand.
Figure 3 plots the growth rate of aggregate loans outstanding for listed firms from thedatabase and the growth rate of aggregate loans outstanding based on data from the Bank of Japan(shown by the line with round markers), which covers both listed and unlisted firms. While theaggregate loans outstanding of listed firms account for around 30% of the total loans outstanding oflisted and unlisted firms, the trends of these two series are consistent with each other.
Finally, using data from our dataset on loans outstanding measured based on firm-bankmatches, which we will explain in detail further below, we compute the annual loan growth rate foreach firm-bank pair, which provides the distribution of the growth rates of loans outstanding for eachfirm-bank match in each year.2 The results are depicted in Figure 4, where the bold solid and dashedlines labeled “Mean” and “Median” respectively represent the mean and median of the growth rates
of loans outstanding for the firms in our dataset (i.e., listed firms). Figures 4 also depicts two thinlines (with plus and minus markers) that show plus and minus one standard deviation from the meanfor each year. The lower line (i.e., the mean minus one standard deviation) was around minus 40%
until the mid-1990s, after which it fell to around minus 60%. On the other hand, the upper line (i.e.,the mean plus one standard deviation) fluctuates around 50% and does not show a particular trend.
This implies that there was considerable heterogeneity in terms of the growth rate of outstandingloans at the bank-firm match level.

3. Related Literature
Bernanke and Gertler (1989), in their seminal work, show that negative shocks toborrowers’ net worth aggravate agency costs of financing investment and thus reduce investment.
Furthermore, they show that the marginal impact of borrowers’ net worth on agency costs andinvestment is higher when their net worth is smaller. Contractive monetary policy and economic
downturns are likely to reduce borrowers’ net worth, to increase the agency costs borrowers incur,
and hence to reduce investment by borrowers.
Although Bernanke and Gertler (1989) apply their model to firms, it can also be applied tothe banking sector, because banks, in addition to taking deposits, which are insured, also raise funds
by issuing uninsured debt, which is susceptible to agency costs In the case of the banking sector,
Bernanke and Gertler’s argument leads to the hypotheses that banks with smaller net worth supply
fewer loans (Holmstrom and Tirole, 1997) and that the effects of bank net worth on loan supply arestronger when monetary policy is tightened or economic growth is lower. The latter hypothesis
concerning monetary policy transmission is often labeled the “bank-lending view.” According to this2

To compute the growth rate, we only use firm-bank pairs which had a loan relationship in two consecutive years.

4view, monetary policy shifts banks’ loan supply curves and thereby affects investment and otheractivities of bank-dependent borrowers (see, e.g., Bernanke and Blinder, 1988; Kashyap and Stein,1994). Furthermore, shifts in loan supply depend on banks’ balance sheets. For example, when thecentral bank sells securities to a bank through open market operations and decreases the bank’s
reserves, the bank may have to decrease its loans unless it makes up for any shortfall in deposits byselling security holdings or by issuing nonreservable debt. Banks with fewer liquid assets will needto decrease loans more if they cannot issue nonreservable debt or can do so only at a higher cost thandeposits. Stein (1998) develops a formal model of credit rationing in bank financing markets basedon the observation that most of the bank liabilities that escape reserve requirements are not covered
by deposit insurance and hence are likely to be subject to adverse selection.
There is a substantial empirical literature on the role of bank net worth in loan supply.
Examples include the studies by Romer and Romer (1990), Bernanke and Blinder (1992), Kashyap
et al. (1993), Hoshi et al. (1993), Ueda (1993), and Ramey (1993), who use aggregate data to
examine the bank lending channel of monetary policy. On the other hand, Jayaratne and Morgan(2000) use bank-level data to study the relationship between bank liquidity and loans, while
Bernanke and Lown (1991), Peek and Rosengren (1997), Woo (1999), and Ito and Sasaki (2002) use
bank-level data to examine the relationship between bank capital and loans. Meanwhile, Kashyap
and Stein (2000), Favero et al. (1999), and Hosono (2006) use bank-level data to investigate thebank lending channel of monetary policy. Although most of these studies find significant effects ofbank liquidity or capital on lending and that these effects are stronger when monetary policy is tight,
the approach and data they use mean that they cannot clearly disentangle loan supply and demand
shocks.
To isolate loan supply shocks from loan demand shocks, recent studies have employed anumber of empirical strategies, ranging from event studies to the use of natural experiments andloan-level data.3 The strategy most relevant to this paper is the use of loan-level information forfirms with multiple bank relationships. The first to employ a strategy of identifying (bank-specific)
loan supply shocks as changes in loans after controlling for firm-level fixed effects that are assumed
to reflect firm-specific loan demand shocks (as well as aggregate loan supply shocks) were Khwaja
and Mian (2008). They examined whether banks that experienced a larger withdrawal of deposits
due to an exogenous shock (a nuclear experiment) reduced their lending to client firms more thanother banks and found that this was indeed the case. Meanwhile, using data on loan applications in
Spain, Jiménez et al. (2012) examined how changes in aggregate variables such as interest rates andGDP, as well as the interaction between these variables and lender bank characteristics, affect thelikelihood of loans being granted. Extending the empirical strategy employed by Khwaja and Mian
(2008), they controlled for the time-variant quality of potential borrowers by considering either
3

See Hosono and Miyakawa (2013) for a survey.

5firm-month or loan-level fixed effects.4 They found that higher short-term interest rates and lower
GDP growth reduced the probability that a loan was granted and that this tendency was stronger forbanks with low capital (in periods of higher short-term interest rates and lower GDP growth) or low
liquidity (in periods of higher short-term interest rates). While Jiménez et al. (2012) is the only one
that successfully overcomes the identification problems in testing the hypothesis that bank net worth
plays a more important role when monetary policy is contractive or economic growth is low, they do
not estimate the quantitative impacts of bank capital and liquidity on changes in loan supply or the
impacts of the bank balance sheet channel on firms’ total loans and investment. Amiti and Weinstein(2013) also use matched bank-firm loan data to identify idiosyncratic bank shocks, i.e., movements
in bank loan supply net of borrower characteristics and general credit conditions. Using Japanesefirm-bank data, they show that idiosyncratic bank shocks have a large impact on firms’ investment.
However, they do not examine the bank balance sheet channel of monetary policy and GDP growth
rates.

4. Data and Empirical Framework
4.1. Data and Hypotheses
This section provides a description of the data we use for our empirical analysis and setsout our hypotheses. We begin by describing our data. As our variable representing firm-bank levelloan growth, we use data on loans outstanding obtained from Nikkei NEEDs Financial Quest. The
dataset contains for all listed firms in Japan the amount of loans outstanding from each of theirlender banks since FY1977.5 What is more, the information is broken down into the amount ofshort-term and long-term loans outstanding. However, since the dataset only provides the amount ofloans outstanding at the end of each fiscal year, we have no information on gross loan repayments orborrowing, but only the net difference in loans outstanding. We measure the change in total loans
outstanding (i.e., the sum of short-term and long-term loans) between firm j and lender bank i fromtime t-1 to t by computing the log-difference of the amount of outstanding loans from the lenderbank to the firm (LOANS(i,j,t)). To compute the growth rate of loans for a firm-bank pair, Δ
LOANS(i,j,t), we only use firm-bank pair observations where a loan relationship existed both at timet and time t-1.6 When firms borrow from multiple lender banks, there is a set of {i} corresponding to
They utilized information on a firm’s successive loan applications to different banks when they controlled forloan-level fixed effects.
5 The sum of all the firm-bank match-level loans outstanding in our dataset accounts for around 80% of theborrowing recorded in firms’ balance sheets. Given that firms borrow from non-bank institutions and our dataset does
not cover loans outstanding from credit unions and credit cooperatives, the coverage of our data on match-level loans
outstanding is reasonably high.
6 In other words, we specifically limit our analysis in this paper to the study of the change in outstanding loans fromthe banks that the firm has already built relationship with (i.e., the intensive margin of loan relationship) and not
examine the initiation and termination of bank-firm relationship (i.e., the extensive margin). However, the firm-bankrelationship was very stable during the observation period. To illustrate, out of the firm and bank pairs existing in FY
1994, 8.5% of the pair terminated their relations. On the other hand, out of the firm and bank pairs existing in FY
1995, 4.2% of the pair initiated their relations in FY 1995. These numbers imply that our analysis covers a large
46

the lender banks for each (j,t). As detailed in the next subsection, we model the determination of thechange in loans outstanding, Δ LOANS(i,j,t) as a function of firm characteristics, bankcharacteristics, and aggregate-level variables. Other than the time-variant firm-level individual
effects and year-specific effects, the only other data we need are information on bank characteristics
and their interaction with aggregate-level variables. Information for most of the variables
representing banks’ financial characteristics is taken from Nikkei NEEDs Financial Quest.
Specifically, for banks’ size (BSIZE), we use the natural logarithm of banks’ total asset. Banks’profitability (BROA) is measured as the ratio of banks’ operating profit to total assets. To take into
account banks’ portfolio structure, we use the ratio of banks’ Japanese government bond (JGB)
holdings and the ratio of local municipal bond holdings to total assets (BJGB and BLOCALBOND).
We also include banks’ loan-to-deposit ratio (BLTD) to gauge each bank’s lending opportunities.
Banks’ capital ratio is measured in the following two alternative ways. The first is the ratio of banks’
total equity to total assets on their balance sheet, BTETA. Second, given that this variable does not
sufficiently reflect the riskiness of each bank’s assets, we also use the capital adequacy ratio,
REGCAP, which we take from data provided by the Japan Bankers Association. The observation
period is from FY1981 to FY2010 when we use BTETA for banks’ capital ratio, and from FY1993 to
FY2010 when we use REGCAP for banks’ capital ratio, since data for REGCAP are available only
from FY1993 onward. Instead of using the raw value of REGCAP, we adjust REGCAP bysubtracting either 4% or 8% from the original level to take account of the fact that for regulatory
reasons different groups of banks face different minimum capital ratios. Specifically, the minimum
capital adequacy ratio differs depending on whether a bank operates domestically, in which case it is4%, or whether it operates internationally, in which case it is 8%. For REGCAP, we therefore
subtract either 4% or 8% depending on whether a bank operates only domestically or alsointernationally. On the other hand, for BTETA, we do not employ this adjustment. The final variable
for banks’ financial characteristics we use is banks’ liquidity, BLIQ, which we compute as the ratioof (i) the sum of banks’ cash and deposits, loans outstanding in the call market, securities for selling,
and JGBs and local bonds minus required current deposits (i.e., required reserves) at the BOJ to (ii)total assets.7 Given that no information on required reserves is available, we construct a proxy for itin the following two ways. First, as the required reserves for each bank over the period before theintroduction of the zero interest rate policy and quantitative easing (QE) (i.e., until FY 1997), we use
current deposits at the BOJ as the required reserve, given that banks had no incentive to hold excess
reserves. Next, from FY 1997 to FY 2010, we compute the amount of required reserves using thedata on the amount of deposits classified into various types, which we take from Nikkei NEEDs
Financial Quest. Specifically, multiplying the required reserve ratio set by the BOJ by the amount of

portion of the firm and bank pairs in our data set.
7 In Japan, banks’ cash does not count toward reserves, and only the current account balance at the BOJ does.

7

deposits of each type, we compute the required reserves. These estimated required reserves on
average take a value of around 100% to 130 % of the actual current deposits at the BOJ over theperiod FY 1990-FY1996, which implies that our estimation is reasonably accurate. Given that a
number of banks were involved in mergers and acquisitions (M&A) during our observation period,
we drop the acquiring banks from our sample in the year of the M&A, because the balance sheet
variables of such banks are likely to swing considerably as a result of such acquisitions.
As for aggregate-level variables, we use the growth rate of real GDP, GDPGROWTH, aswell as various dummy variables accounting for changes in monetary policy. The first monetary
policy variable, POLRATE_UP (POLRATE_DOWN), is a dummy variable taking a value of one
when the targeted policy rate (i.e., either the official discount rate (Koteibuai) or the overnight (O/N)
call rate) becomes strictly higher (lower) than the previous year. The change in the policy rate ismeasured using the official discount rate (Koteibuai) until FY1984 and the O/N call rate after
FY1984.8 The target rates are calculated as the annual average of monthly levels. As Table 1 shows,the target rate did not change in FY1988 and FY2005, which allows us to use the two variables inthe estimation simultaneously when these two years are included in the observation period. The
second monetary policy variable, QE_LOOSENING (QE_TIGHTENING), is a dummy variabletaking a value of one when the target of the BOJ’s quantitative easing policy (QE target), banks’current account balances at the BOJ, becomes strictly larger (smaller) than the previous year. The QE
target level is measured as the average of the BOJ’s target for banks’ current account balances foreach fiscal year. In the calculation of this average, we use the number of months for each target levelas a weight and compute the weighted average of the QE target. The third variable representingchanges in monetary policy we use is MPLOOSENING (MPTIGHTENING), which is a dummy
variable taking a value of one when the BOJ strictly relaxes (tightens) monetary policy in terms of
the policy rate or the QE target. MPLOOSENING is equal to zero when the policy target remains
unchanged or represents a tightening (i.e., a higher policy rate or a lower target for current accountbalances), while MPTIGHTENING is equal to zero when the policy target remains unchanged or
represents a loosening of monetary policy (i.e., a lower policy rate or a higher target for current
account balances).
Based on the theoretical discussion above regarding the bank balance sheet channel ofmonetary policy transmission, we hypothesize that banks with higher capital- and liquidity-to-asset
ratios tend to provide larger amounts of loans. Thus, the first hypothesis we test is as follows:

Hypothesis 1: Banks with a higher BTETA, REGCAP, or BLIQ provide larger amounts of loans.

We also hypothesize that this mechanism is enhanced when economic growth slows. This8

For the O/N call rate, we use the actually observed O/N call rate in the market.

8

is because banks with a weaker balance sheet face larger frictions or do not have sufficient funds toallocate to borrower firms as macroeconomic conditions deteriorate.

Hypothesis 2: The positive marginal impact of BTETA, REGCAP, and BLIQ becomes smaller(larger) when GDPGROWTH is higher (lower).
We further hypothesize that the mechanism governing banks’ loan provision also interacts
with changes in monetary policy. For example, banks with less liquidity are expected to provide
smaller amounts of loans to firms. Such banks, however, could provide larger amounts of loans
under expansionary monetary policy. This is because even banks with low liquidity can securesufficient amounts of funding to provide loans when the policy rate and hence actual short-term
interest rates are low. In other words, we expect that the link between a strong balance sheet and loan
amounts is less pronounced in an expansionary monetary policy environment.

Hypothesis 3: The positive marginal impact of BTETA, REGCAP, and BLIQ becomes smaller(larger) when POLRATE_DOWN, QE_LOOSENING, and/or MPLOOSENING take a value ofone (POLRATE_UP, QE_TIGHTENING, and/or MPTIGHTENING take a value of one).

A full list of variables used in our estimation, their definitions, and summary statistics is provided inTable 2. Our observations cover the period from FY1981 to FY2010. The dataset contains
observations on between 907 firms (in FY1982) and 1,666 firms (in FY2008) and 109 banks (inFY2010) and 153 banks (in FY1981) in any particular year.

4.2. Empirical Framework
The aim of our investigation is to examine the marginal impact of (i) bank balance sheet
variables on banks’ loan provision and (ii) their interaction with the aggregate-level variables while
controlling for various firm characteristics, including time-variant loan demand. For this purpose, weestimate equation (1) below. In this equation, we take unobservable time-variant individual effects,η(j, t), where subscript j denotes the firm and t the year, that determine loan demand into account.
Incorporating the time-variant individual effects into our analysis, we control for all firm-year level
shocks, including, for example, firms’ loan demand, changes in firms’ investment opportunities, andchanges in the availability of other financing measures (e.g., corporate bonds and CP). POLICY(t)denotes either (a) POLRATE_DOWN, POLRATE_UP, QE_LOOSENING, and QETIGHT, (b)MPLOOSENING, or (c) MPTIGHTENING. To control for year-specific effects, we also include
YEAR(t) in this estimation. Since we include year-specific effects in this equation, we cannot
include GDPGROWTH or POLICY on their own, but only in their interacted form.
9

ΔLOANS(i, j, t) = η(j, t) + β1 BSIZE(i, t − 1) + β2 BROA(i, t − 1) + β3 BJGB(i, t − 1)
+ β4 BLOCALBOND(i, t − 1) + β5 BLTD(i, t − 1) + β6 BCAP(i, t − 1)
+ β7 BLIQ(i, t − 1) + YEAR(t)+ γ1 BCAP(i, t − 1)GDPGROWTH(t)+ γ2 BLIQ(i, t − 1)GDPGROWTH(t) + γ3 BCAP(i, t − 1)POLICY(t)+ γ4 BLIQ(i, t − 1)POLICY(t) + ε(i, j, t)(1)

For the last term in the equation, ε(i, j, t), we employ three alternative assumptions. The
first is that ε(i, j, t) ≡ ϵ(t) is a random error. The second is that ε(i, j, t) ≡ δ(i) + ϵ(t), where δ(i)is the bank-level fixed effect. This captures unobservable bank-specific time-invariant factors.
Finally, for the most comprehensive model, we assume ε(i, j, t) ≡ δ(i, j) + ϵ(t), where δ(i, j) is thefirm-bank match fixed effect. This captures unobservable firm-bank relationship-specifictime-invariant factors. Controlling for such firm-bank match fixed effect is useful if some firms andbanks have a special relationship that affects loan growth. Note that when we control for δ(i, j), thebank-level fixed effect is automatically controlled for.
We include interaction terms between the aggregate-level variables on the one hand andbanks’ capital ratio, BCAP (i.e., BTETA or REGCAP), and their liquidity ratio, BLIQ, on the other.
The coefficients of these interaction terms capture how the marginal effects of banks’ capital ratio
and liquidity ratio vary as the aggregate-level variables change. For example, suppose the interaction
term between BTETA and GDPGROWTH has a negative coefficient, while BTETA on its own has apositive coefficient. This means that banks with a higher capital ratio tend to lend more than bankswith a lower capital ratio, and this relationship is stronger when economic growth is weak.
To estimate this equation, we first eliminate the time-variant firm-level individual effectη(j, t) by taking the difference between i’s for the same j and t. As mentioned, in our analysis, we
specifically focus on firms borrowing from multiple lender banks. Suppose firm j has I(j,t) andI(j,t-1) lender banks at the end of year t and t-1, respectively. Then the number of banks we use to
estimate (1) for firm i in year t is min{I(j,t), I(j,t-1)}. This leads to equation (2) below, which isindexed by (i1, i2, j, t). In this equation, BCHAR(i, t − 1) represents the vector of bank i’scharacteristics at the end of t-1. We estimate this equation under the three different assumptions forε(i, j, t) described above. The equation looks as follows:.

10

ΔLOANS(i1, j, t) − ΔLOANS(i2, j, t)= 𝛃{BCHAR(i1, t − 1) − BCHAR(i2, t − 1)}+ γ1 {BCAP(i1, t − 1) − BCAP(i2, t − 1)}GDPGROWTH(t)+ γ2 {BLIQ(i1, t − 1) − BLIQ(i2, t − 1)}GDPGROWTH(t)+ γ3 {BCAP(i1, t − 1) − BCAP(i2, t − 1)}POLICY(t)+ γ4 {BLIQ(i1, t − 1) − BLIQ(i2, t − 1)}POLICY(t) + ε(i1, j, t) + ε(i2, j, t)(2)

In the next section, we show the estimation results based on the model and discuss theimplication of the results. Our main interest is in the signs of 𝛃 and {γ1 , γ2 , γ3 , γ4 }.

5. Estimation Results
5.1. Baseline Estimation Results
The baseline results of our empirical analysis are presented in Tables 3 to 5. Each table has
two sets of columns corresponding to either BCAP=BTETA (i.e., the first set of columns) or
BCAP=REGCAP (i.e., the second set of columns). Because, as mentioned above, REGCAP data are
available only from FY1993 onward, the sample size in the latter case is only about half of that in
the former case. In each case, we show the results for the three models based on the alternative
assumptions for ε(i, j, t). The results in column (i) are based on the assumption that ε(i, j, t) ≡ ϵ(t),those in column (ii) on the assumption that ε(i, j, t) ≡ δ(i) + ϵ(t), and those in column (iii) on theassumption that ε(i, j, t) ≡ δ(i, j) + ϵ(t). In all the estimations, we run the regression using
specification (2) above. More specifically, when we assume ε(i, j, t) ≡ ϵ(t), we run a pooled
ordinary least squares (OLS) regression, while in the case of ε(i, j, t) ≡ δ(i) + ϵ(t) we include thetwo bank-level fixed effects for banks i1 and i2. When we assume ε(i, j, t) ≡ δ(i, j) + ϵ(t), we
employ a fixed-effect panel estimation with (i1, i2, j)-level fixed effects.
Table 3 shows the results when using {POLRATE_DOWN, POLRATE_UP,
QE_LOOSENING, QETIGHT} for POLICY(t). The actual value for each dummy variable and year
is shown in Table. 1. We find the following. First, consistent with Hypothesis 1, banks with a higher
capital ratio and liquidity ratio tend to provide larger amounts of loans to their client firms. In
particular, the results in column (iii) imply that even after taking into account firm-bankrelationship-specific factors (e.g., the geographical proximity between a firm and its bank or thesector specialization of the bank), we find a systematic pattern whereby banks with a better balance
sheet (i.e., higher BCAP and BLIQ) tend to lend more than banks with a weaker balance sheet. We
also find that banks that are larger in size, are more profitable, and that have a lower share ofJapanese government bonds in their portfolio tend to provide larger amounts of loans. Among theseresults, the negative impact of Japanese government bonds is in contrast with the positive impact of11liquid assets that include cash and deposits. This difference may be due to the market risk arisingfrom the change in bond prices that government bonds are exposed to. The result suggests that banks
taking larger market risk are more reluctant to take credit risk through loan provision. These results
are robust to the inclusion of different levels of individual effects. On the other hand, we do not
obtain consistent estimates for the loan-to-deposit ratio and the share of local municipal bonds, theresults for which depend on the specification of the error term and the measure for banks’ capitalratio.
Second, the results for the coefficients on the interaction term between bank characteristics
and GDPGROWTH indicate that the positive marginal impact of these bank variables becomes
smaller when GDPGROWTH is higher, supporting Hypothesis 2. In other words, the marginal
impact of banks’ capital and liquidity ratios on loan supply becomes larger when economic growth isslower. This may reflect that banks with a weaker balance sheet face larger friction in their own
financing or that they do not have sufficient funds to allocate to borrower firms whenmacroeconomic conditions worsen. To illustrate how the positive marginal impact of BLIQ, forexample, becomes smaller when GDPGROWTH is higher, Figure 5 plots the point estimate for theslope coefficient associated with BLIQ conditional on GDPGROWTH as well as its 95% confidence
interval.
Third, as for BLIQ, the introduction of QE mitigates its marginal impact on loan supply
except for specification (iii) when using REGCAP as BCAP, while the termination of QE enhances it.
This result implies that under QE, the economic importance of bank liquidity declines, while it
increases when QE is terminated. To illustrate how the positive marginal impact of BLIQ becomeslarger when QE is terminated, Figure 6 plots the point estimate for the slope coefficient associatedwith BLIQ conditional on the variable QETIGHT as well as its 95% confidence interval. As for
decreases in the policy rate, the marginal impact of BCAP has a similar implication to that of BLIQ
and QE (see specification (iii)). Namely, the negative coefficient on the interaction term of BCAP
and POLRATE_DOWN implies that the economic importance of BCAP becomes smaller when thepolicy rate is lowered. These results are consistent with Hypothesis 3. On the other hand, somewhat
surprisingly, the interaction term between BCAP and QE_TIGHTENING has a negative coefficient,which means that the economic importance of BCAP became weaker when the BOJ exited from QE.
As we will see in the discussion on the results in Tables 4 and 5, however, this result is not robust to
the use of alternative measures of monetary policy.
Let us consider the quantitative implications of these results. Specifically, let us focus on
the results for the baseline model (i.e., the right-most model in Table 3). The coefficient on BLIQ inthis estimation is 0.382, that on the interaction term of BLIQ and GDPGROWTH is -5.781, and thaton the interaction term of BLIQ and QE_TIGHTENING is 0.504. Suppose that the liquidity ratio oflender bank i for firm j declines by one standard deviation (i.e., 0.068) in year t-1 and that monetary12policy does not change in year t, while GDPGROWTH is 0 in year t. Given the estimated parameters,
the model predicts that ΔLOANS(i, j, t) will be 0.382 × (0.068) = 2.6% smaller than in the casethat bank liquidity had not declined. Considering that the sample mean and the standard deviationofΔLOANS(i, j, t) are -0.3% and 48.8%, respectively, this implies that bank liquidity has an
economically sizable impact on the growth rate of loans. Next, suppose that the economy is in asevere recession (i.e., GDPGROWTH=-0.02) in year t. Assuming again a one-standard deviation
decline in BLIQ, ΔLOANS(i, j, t) will be 0.382 × (0.068) + (−5.781) × (0.068) × (−0.02) =3.4% smaller than in the absence of a decline in bank liquidity. Finally, suppose that quantitativeeasing is terminated (i.e., QETIGHT=1). Then ΔLOANS(i, j, t)

will be 0.382 × (0.068) +0.504 × (0.068) × 1 = 6.0% smaller than without a decline in bank liquidity. Thus, the impact ofbank liquidity more than doubles when quantitative easing is terminated when compared with thecase of no change in monetary policy.
As another example, suppose the capital ratio (REGCAP) of lender bank i for firm jdeclines by one standard deviation (i.e., 2.671%) in year t-1 and that the real GDP growth rate iszero, while there is no change in monetary policy. Then, given the estimated parameters, the model
predicts that ΔLOANS(i, j, t) will be 0.016 × (2.671) = 4.3% smaller than if bank capital had
not declined. Thus, the quantitative impact of bank capital is economically sizable and comparable tothat of bank liquidity. If the economy is in a severe recession (i.e., GDPGROWTH=-0.02) in year t,
the model predicts that ΔLOANS(i, j, t) will be 0.016 × (−2.671) + (−0.216) × (−2.671) ×(−0.02) = 5.4% smaller than in the case with no decline in bank capital.

5.2. Alternative POLICY Measures
Next, Tables 4 and 5 show the results when using MPLOOSENING or MPTIGHTENING
for POLICY(t), respectively. The results are very similar to those in Table 3. Specifically, BCAP
and BLIQ have a positive marginal effect on loan growth in all cases, and the marginal effect isstronger when the economy is in recession. In addition, the interaction terms of BLIQ on the one
hand and MPTIGHTENING (i.e., Table 5) or MPLOOSENING (i.e., Table 4) on the other indicatethat tighter monetary policy increases the economic importance of BLIQ, while more expansionary
monetary policy reduces it. On the other hand, the marginal impact of BCAP does not change
consistently in response to MPTIGHTENING or MPLOOSENING.
Table 6 shows results for the same regressions as in Tables 4 and 5 but for two different
subperiods. The two subperiods are chosen so that we have sufficient variation in POLICY to beable to conduct our estimation. The first set of columns shows the results based on observations forthe period FY1993-2004, while the second set of columns shows the results for FY2001-2010. The
latter period is characterized by the adoption and termination of the quantitative easing policy. We
use the model with all the individual effects (i.e., specification (iii)) and REGCAP for BCAP in this13

estimation.
Table 6 shows that the coefficients on BSIZE, BROA, and BJGB are all significant for
both subperiods. More interestingly, the coefficients on BLIQ and the interaction term of BLIQ and
POLICY are significant only for the second subperiod. This implies that the role of bank liquidity
became more important in the second subperiod than in the first subperiod and was magnified
specifically by changes in the quantitative easing policy in the 2000s. On the other hand, thecoefficient on the interaction term of BLIQ and GDPGROWTH is significant only for the first
subperiod. Furthermore, BCAP is also found to have a positive marginal effect only in the second
subperiod. This likely reflects the strict enforcement of capital adequacy regulation in the early
2000s. Similarly, a significant negative coefficient on the interaction term between BCAP and
GDPGROWTH is found only for the second subperiod. Finally, the interaction term between BCAP
and changes in monetary policy is not significant in either subperiod.

5.3. Subsample Analysis
In this section, we apply the same model as in the previous section to various subsamples.
The purpose of this additional analysis is to compare the magnitude of the estimated coefficients
between two sets of firms with different characteristics and hence consider the mechanisms
governing loan transactions in more detail. One may expect, for example, that firms with different
characteristics face different responses from their lender banks when macroeconomic conditions ormonetary policy change. Although we control for unobservable time-variant firm-specific individual
effects in the baseline estimation, we implicitly assume that the effects of bank characteristics (e.g.,
BCAP and BLIQ) and their interaction with GDPGROWTH and POLICY on loan growth are thesame for all firms in our sample. In the following subsample analysis, we relax this assumption.
Throughout the analysis that follows, we use MPLOOSENING and MPTIGHTENING as thevariables representing POLICY and we employ the model with all the individual effects (i.e.,
specification (iii)) and REGCAP for BCAP.
In our first subsample analysis, we divide firms by size, because firm size is often used asa proxy for opaqueness. Specifically, using total assets as our measure of firm size, we split the
sample into firms whose total assets are equal to or smaller than the sample median and those whose
total assets are larger than the median. The results are presented in Table 7(a) and indicate that all the
results obtained in the previous section also hold for the smaller firms. On the other hand, for thelarger firms, while the coefficients on the interaction terms of BCAP and BLIQ with GDPGROWTH
are significant, the coefficients on the interaction terms of BCAP and BLIQ with POLICY are notsignificant. Given that firm size is used to proxy for the opaqueness of firms, this result implies thatbanks with a weaker balance sheet are more reluctant to supply loans to smaller firms especiallyduring economic recession or under tighter monetary policy.
14

Next, we divide our observations into two subsamples based on the number of banks firms
borrow from, adjusted for firm size. That is, given that the number of firms’ lender banks is highly
correlated with firm size, we use the ratio of the number of lender banks to the natural logarithm offirms’ total assets. We conjecture that dispersed borrowing relations weaken the ties between thefirm and each lender bank. If this is the case, the role of bank liquidity is more important for firms
with a relatively large number of lender banks during recessions, because in such times banks withscarce liquidity are likely to lend more to closely-tied firms than to remote firms. Again, theobservations are split into firms who fall below and above the sample median. The results are shownin the sets of columns labeled (i) and (ii) in Table 7(b). We find that the coefficients on theinteraction terms of BLIQ and the POLICY variables are significant only for firms with a larger
number of banks. This is consistent with our conjecture that the role of bank liquidity is more
important for firms with a relatively large number of lender banks when monetary policy is tight.
In the two sets of columns in Table 7(b) labeled (ii-a) and (ii-b), we further split theobservations for firms with a larger number of banks into two subsamples based on the loan share of
the top lender. The purpose of this analysis is to examine how asymmetry in loan shares among
lenders, which we try to capture by the top lender bank’s share, affects the result obtained above. We
find that the coefficient on BLIQ is significant only for firms whose top lender’s loan share is above
the median. Further, the absolute value of the coefficients on the interaction terms of BLIQ and the
POLICY variables is larger for firms with a larger top lender loan share. In sum, the results in Tables
7(a) and (b) imply that, among firms with a large number of lender banks, the role of bank liquidity
is important mainly for firms with a highly asymmetric loan share structure, that is, firms which have
only weak ties with many banks other than their top lender.

5.4. Size and Direction of the Endogeneity Bias
All the empirical models we have been examining so far assume the existence oftime-variant firm-specific individual effects. Omitting these firm-specific individual effects
potentially results in biased estimates for the parameters seeking to capture bank balance sheet
effects. In this section, we quantify these potential biases. For this purpose, we start by estimating
the following equation:

ΔLOANS(i, j, t) = β1 BSIZE(i, t − 1) + β2 BROA(i, t − 1) + β3 BJGB(i, t − 1) + β3 BJGB(i, t − 1)
+ β4 BLOCALBOND(i, t − 1) + β5 BLTD(i, t − 1) + β6 BCAP(i, t − 1)
+ β7 BLIQ(i, t − 1) + YEAR(t)+ γ1 BCAP(i, t − 1)GDPGROWTH(t) + γ2 BLIQ(i, t − 1)GDPGROWTH(t)+ γ3 BCAP(i, t − 1)POLICY(t) + γ4 BLIQ(i, t − 1)POLICY(t) + δ(i, j)+ ϵ(t)(3.1)
15

This equation is almost identical to the baseline model given by equation (1) with theassumption that ε(i, j, t) ≡ δ(i, j) + ϵ(t), but it does not include η(j, t). In that regard, we consider(3.1) to be incorrectly specified. What we are interested in is how much the coefficients on the bankbalance sheet variables and their interaction terms with the aggregate-level variables {γ1 , γ2 , γ3 , γ4 }differ from the coefficient estimates obtained in Sections 5.1 and 5.2. If the two sets of coefficientestimates differ greatly, this would suggest that the bias due to endogeneity is quite substantial. For
this estimation, we use the same sample as we used to obtain the results in Table 6.9
Table 8, under the columns labeled “Without Firm Characteristics,” presents the results forthe estimation based on (3.1) and on the right reproduces the results from column (ii) in Table 6 forease of comparison. We use REGCAP for BCAP in this estimation. The table shows the following.
First, the absolute value of the coefficient on BLIQ in the estimation based on (3.1) is substantiallysmaller than in the estimation based on equation (1). In fact, the estimate coefficient is notstatistically different from zero. This means that the economic importance of bank liquidity in theprovision of loans is underestimated when, as in (3.1), the model is incorrectly specified. This
downward bias occurs when the firm time-varying individual effect, which captures firm-specificloan demand, is negatively correlated with bank liquidity. This could be the case, for example, if
firms with good investment opportunities, and hence with larger loan demand, tend to borrow fromboth banks with low liquidity and banks with high liquidity, while firms with poorer investment
opportunities and hence lower loan demand tend to borrow only from high-liquidity banks. As aresult, the coefficient on BLIQ in the incorrectly specified model is closer to zero. Second, we alsofind that the absolute value of the negative coefficient on the interaction terms between BLIQ and
GDPGROWTH or MPLOOSENING is smaller than that in the estimation based on equation (1).
This implies that the economic impact of the bank balance sheet channel is underestimated if we do
not properly control for the time-variant component of firm characteristics. This downward bias
emerges when the firm time-varying individual effect, representing firm-specific loan demand, ispositively correlated with the interaction of BLIQ and MPLOOSENING or with the interaction of
BLIQ and GDPGROWTH. One interpretation of this bias is that firms with larger loan demand in abetter economic environment or under expansionary monetary policy tend to borrow from bankswith more liquidity. Therefore, if we do not control for such loan demand, the coefficient on theinteraction term between BLIQ and GDPGROWTH or MPLOOSENING becomes closer to zero.
This confirms the presence of endogeneity bias in the models if firms’ loan demand is not properly
controlled for.

9

To be more precise, we use the data of firms with equal to or more than two lender banks, which we used for theestimation of (2). The differences in the number of observations and the number of groups originate from the fact thatthe unit of observation for the estimation of (3.1) is firm-bank match while the unit of observation for the baseline
estimation is firm and two lender banks (i.e., triplet).

16

Next, we examine to what extent the bias arising from omitting unobservable firm loan
demand is reduced when we add observable firm characteristics that likely represents firms’ loan
demand. Specifically, we estimate the following equation, which is the same as equation (3.1) but
incorporates a number of additional firm characteristics (F_CHAR):

ΔLOAN(i, j, t) = β1 BSIZE(i, t − 1) + β2 BROA(i, t − 1) + β3 BJGB(i, t − 1) + β3 BJGB(i, t − 1)
+ β4 BLOCALBOND(i, t − 1) + β5 BLTD(i, t − 1) + β6 BCAP(i, t − 1)
+ β7 BLIQ(i, t − 1) + YEAR(t)+ γ1 BCAP(i, t − 1)GDPGROWTH(t) + γ2 BLIQ(i, t − 1)GDPGROWTH(t)+ γ3 BCAP(i, t − 1)POLICY(t) + γ4 BLIQ(i, t − 1)POLICY(t)+ 𝛌𝐅_𝐂𝐇𝐀𝐑(𝐣, 𝐭 − 𝟏) + δ(i, j) + ϵ(t)(3.2)

The additional characteristics include firms’ price-to-book ratio, the number of lenderbanks relative to the logarithm of total assets, the natural logarithm of their total assets, the ratio ofcurrent profits to total assets, the ratio of liquidity assets to liquidity liabilities, the ratio of total debt
to total assets, the ratio of short-term to long-term liabilities, the ratio of bank borrowing to total
liabilities, and the ratio of fixed assets to total assets.10 The results for the estimation of (3.2) are
shown in the columns labeled “With Firm Characteristics” in Table 8 and have the same
implications as the estimation results for (3.1). That is, even when we include a considerable rangeof firm characteristics in the estimation, the results still appear to be biased. Moreover, given that theinclusion of this long list of firm characteristics rules out most other potential sources of bias, we canbe fairly confident that the source of the bias is firms’ loan demand, which is taken account of byallowing for time-variant firm-specific effects.
To illustrate the importance of controlling for loan demand, let us compare the economic
impact of liquidity on loan provision implied in the baseline model and the incorrect specification.
Specifically, we focus on the estimation that does not control for firms’ unobservable loan demand
but does include the various firm characteristics and that uses MPTIGHTENING as the POLICY
variable (i.e., the fourth set of columns in Table 8). The coefficient estimates for BLIQ and theinteraction term of BLIQ and MPTIGHTENING are 0.102 (not significantly different from 0) and
0.378 respectively, while in the baseline case (the last set of columns in Table 8), they are 0.695 and0.288. Suppose that the liquidity ratio of lender bank i for firm j declines by one standard deviation(i.e., 0.068) in year t-1 and that monetary policy is tightened (i.e., MPTIGHTENING=1), while
GDPGROWTH=0 in year t. Based on the parameter estimates from the correct specification, loans
would fall by 6.7% (i.e., ΔLOANS(i, j, t)= 0.695 × (−0.068) + 0.288 × (−0.068) × 1 = −6.7%).

10

The estimation results for these variables are suppressed in Table 8 to conserve space. They are available from theauthors upon request.

17

Given that the sample mean ofΔLOANS(I, j, t) is -0.4%, this is an economically sizable impact. On
the other hand, the incorrectly specified model predicts that loans would fall by only 2.6% (assumingthat the coefficient on BLIQ is zero given the insignificant coefficient,ΔLOANS(i, j, t) = 0 ×(−0.068) + 0.378 × (−0.068) × 1 = −2.6%). This means that the bias gives rise to a substantial
underestimation of the economic impact of the deterioration in the balance sheet and the tighteningof monetary policy.

5.5. Impact of Bank Balance Sheets on Firm-Level Borrowing and Investment
The results obtained so far imply that changes in the real GDP growth rate and monetary
policy are transmitted to firms through a change in bank lending especially when the bank has scarce
capital or liquid assets. However, a decrease in loans from banks with weak balance sheets may beoffset by an increase in loans from banks with strong balance sheets. If such credit substitution fully
occurs, firms’ overall borrowing and real economic activities is unlikely to be affected by thedecrease in loans from weak banks. In this section, we look at this possible substitution mechanism
by examining how the bank balance sheet channel affects firms’ overall borrowing and investment.
We start by estimating two equations which use the change in firm j’s total borrowing
between t-1 to t as the dependent variable. We regress this firm-level variable taken from firms’balance sheet information on the average level of BCAP in the first equation and BLIQ in the second
equation (both computed over the lender banks to firm j at the end of t-1), the interaction terms
between these two variables and GDPGROWTH and MPTIGHTENING, and various firm
characteristics, 𝐅_𝐂𝐇𝐀𝐑(𝐣, 𝐭 − 𝟏). Specifically, we include the price-to-book ratio (FPBR), itssquared value (FPBR_SQ), the natural logarithm of the firm’s total assets (FSIZE), the ratio ofcurrent profits to total assets (FROA), and the ratio of fixed assets to total assets (FTANGIBLE).
Since the average BCAP and BLIQ are highly correlated (with a correlation coefficient of around0.8), we include only one of these variables in each equation.
We use the average BCAP and BLIQ of a firm’s lender banks to measure the extent towhich the bank balance sheet channel affects firms. For example, suppose a firm is borrowing fromone bank with low BLIQ and another bank with high BLIQ. In this case, even if the former bankcannot extend a loan to the firm in an economic downturn, the latter might be able to do so to meetthe firm’s loan demand. However, if the latter bank’s BLIQ is also low and it therefore, too, cannot
lend to the firm, this would affect the firm’s ability to borrow. Thus, we expect that (i) the
coefficients on the average BLIQ and BCAP are positive, (ii) the coefficients on the interaction
terms of these two variables with GDPGROWTH are negative, and (iii) the coefficients on theinteraction terms on these two variables with MPTIGHTENING, which we use as the POLICY
variables, are positive. Concretely, the specifications we estimate are as follows:18̅̅̅̅̅̅̅(j, t − 1) + α2 BCAP̅̅̅̅̅̅̅(j, t − 1)GDPGROWTH(t)ΔLOAN(j, t) = α1 BCAP
+ α2 ̅̅̅̅̅̅̅
BCAP(j, t − 1)POLICY(t) + YEAR(t) + 𝛌𝐅_𝐂𝐇𝐀𝐑(𝐣, 𝐭 − 𝟏) + ϵ(t)(4.1)

ΔLOAN(j, t) = α1 ̅̅̅̅̅̅̅BLIQ(j, t − 1) + α2 ̅̅̅̅̅̅̅BLIQ(j, t − 1)GDPGROWTH(t)̅̅̅̅̅̅̅(j, t − 1)POLICY(t) + YEAR(t) + 𝛌𝐅_𝐂𝐇𝐀𝐑(𝐣, 𝐭 − 𝟏) + ϵ(t)+ α2 BLIQ(4.2)

Column (i) in Tables 9(a) and (b) show the estimation results for equations (4.1) and (4.2)
respectively. We find that the coefficients on the interaction terms of the average BCAP and BLIQ
with GDPGROWTH are negative, meaning that firms whose banks are less well capitalized or lessliquid (i.e., lower average BCAP or BLIQ) obtain fewer loans in a recession. Moreover, thistendency is more pronounced for firms facing better investment opportunities, as shown in column(ii) in Tables 9(a) and (b), where equations (4.1) and (4.2) are estimated separately for firms whose
price-to-book ratio (FPBR), used to proxy investment opportunities, falls below or above the median.
Specifically, Table 9(b) indicates that for firms with an FPBR above the median, the liquidity of theirlending banks has a greater impact on their borrowing than for other firms. Moreover, this link issignificantly stronger when the economy is in downturn and/or monetary policy is tightened. On theother hand, as shown in column (ii) in Table 9(a), no similar link is observed when using BCAP
instead of BLIQ. In both panels, most of the results for the control variables are consistent withpreceding studies. For instance, FPBR takes a positive and significant coefficient, suggesting thatfirms with better investment opportunities demand more loans.
Finally, we apply the same framework to the determination of firms’ capital investment
ratio. Using the same specification as in (4.1) and (4.2) except that the dependent variable now is theratio of gross investment to the capital stock at the end of the previous period, we estimate thefollowing equations:11

ΔINVETMENTRATIO(j, t)̅̅̅̅̅̅̅(j, t − 1) + α2 BCAP̅̅̅̅̅̅̅(j, t − 1)GDPGROWTH(t)= α1 BCAP+ α2 ̅̅̅̅̅̅̅
BCAP(j, t − 1)POLICY(t) + YEAR(t) + 𝛌𝐅_𝐂𝐇𝐀𝐑(𝐣, 𝐭 − 𝟏) + ϵ(t)(5.1)ΔINVESTMENTRATIO(j, t)̅̅̅̅̅̅̅(j, t − 1) + α2 BLIQ
̅̅̅̅̅̅̅(j, t − 1)GDPGROWTH(t)= α1 BLIQ+ α2 ̅̅̅̅̅̅̅BLIQ(j, t − 1)POLICY(t) + YEAR(t) + 𝛌𝐅_𝐂𝐇𝐀𝐑(𝐣, 𝐭 − 𝟏) + ϵ(t)(5.2)

Columns (iv), (v), and (vi) in Tables 9(a) and (b) show the results for (5.1) and (5.2)
respectively for all firms, firms with an above median FPBR, and firms with an FPBR equal to or
lower than the median. Similar to the results for ΔLOAN(j, t), firms with better investment

11

To compute gross investment in t, we add depreciation during t to the change in the stock of tangible assets fromt-1 to t.

19

opportunities tend to invest more when their lender banks are more liquid, and this link is stronger
during an economic downturn. These results show that the bank balance sheet channel examined inthis paper has a significant impact on firm borrowing and investment activities. Again, most of thecontrol variables are consistent with many preceding studies.

6. Conclusion
This paper examined the impact of changes in economic growth and monetary policy onbanks’ supply of loans. To do so, we used a unique firm-bank match-level dataset of listed firms inJapan that allows us to control for firms’ time-varying unobservable loan demand and endogenousbank-firm matching, so that we can identify the effects of monetary policy and economic growth on
loan supply through the bank balance sheet channel. The estimation results indicate that banks withmore liquidity or capital tended to lend more to their client firms. The quantitative impact of bankliquidity and capital on loan supply was economically sizable and larger when economic growth was
lower. Furthermore, the quantitative impact of bank liquidity on the growth rate of loans more thandoubled when quantitative easing was terminated. Overall, these results imply that changes in
economic growth and monetary policy significantly affected loan supply through the bank balance
sheet channel. We also find evidence that fluctuations in economic growth and monetary policy aretransmitted to capital investment through the bank balance sheet channel in the case of firms with
better investment opportunities
The research presented in this study could be expanded in a number of directions. One
such direction would be to extend our analysis to examine various other kinds of firm dynamics suchas those with regard to market exit and overseas investment. A further potentially interesting
extension would be to use the model in this study to analyze changes in the list of banks that firms
borrow from. Firms may stop transacting with some banks – maybe because such banks no longer
want to lend to them – and may start transaction with banks from which they have never borrowed
before. Such changes in lender banks potentially represent the transmission of shocks through thebank balance sheet channel, but because we focused on loans from banks with which firms had loans
outstanding for at least two consecutive years, our analysis does not cover this aspect. Examining
this aspect as well would further deepen our understanding of the bank balance sheet channel. We
believe all of these extensions would provide further insights to gain a better understanding of thebank balance sheet channel of business cycle and monetary policy on firm activities.

20References
Amiti, M. and Weinstein, D. E. (2013). How much do bank shocks affect investment? Evidence frommatched bank-firm loan data. NBER Working Paper 18890, National Bureau of Economic
Research.
Bernanke, B. S. and A. Blinder, A. S. (1988). Credit, money, and aggregate demand, Amer. Econ. Rev.
(Papers and Proceedings) 78 (2), 435-439.
Bernanke, B. S. and Blinder, A. S. (1992). The federal funds rate and the channels of monetary
policy transmission, Amer. Econ. Rev. 82 (4), 901-21.
Bernanke, B. S. and Gertler, M. (1989). Agency costs, net worth, and business fluctuations. Amer.
Econ. Rev. 79 (1), 14-31.
Bernanke, B. S. and Lown, C. S. (1991). The credit crunch, Brookings Pap. Econ. Act. 2, 205-248.
Favero, C. A., Giavazzi, F., and Flabbi. L. (1999). The transmission mechanism of monetary policyin Europe: Evidence from banks’ balance sheets, NBER Working Paper 7231, National
Bureau of Economic Research.
Holmstrom, B. and Tirole, J. (1997). Financial intermediation, loanable funds, and the real sector,
Quart. J. Econ. 112 (3) 663-691.
Hoshi, T., Scharfstein, D., and Singleton, K. J. (1993). Japanese corporate investment and Bank ofJapan guidance of commercial bank lending, in Japanese Monetary Policy (K. J. Singleton,Ed.), pp. 63-94, Univ. of Chicago Press, Chicago.
Hosono, K. (2006). The transmission mechanism of monetary policy in Japan: Evidence from banks’balance sheets, J. Japanese Int. Economies 20 (3), 380-405.
Hosono, K. and Miyakawa, D. (2013). Bank lending and firm activities: Overcoming identification
problems, Hitotsubashi Project on Real Estate, Financial Crisis and Economic Dynamics
Working Paper 1.
Ito, T. and Sasaki, Y. N. (2002). Impacts of the Basle capital standard on Japanese banks’ behavior, J.
Japanese Int. Economies 16 (3), 372-397.
Jayaratne, J. and Morgan, D.P. (2000). Capital market frictions and deposit constraints at banks, J.
Money, Credit, Banking 32 (1), 74-92.
Jiménez, G., Ongena, S., Peydro, J., and Saurina, J. (2012). Credit supply and monetary policy:
Identifying the bank balance-sheet channel with loan applications, Amer. Econ. Rev. 102(5), 2301-2326.
Kashyap, A. K. and Stein, J. C. (1994). Monetary policy and bank lending, in Monetary Policy (N. G.
Mankiw, Ed.), pp. 221-256, Univ. of Chicago Press, Chicago and London.
Kashyap, A. K. and Stein, J. C. (2000). What do a million observations on banks say about thetransmission of monetary policy? Amer. Econ. Rev. 90 (3), 407-428.
Kashyap, A. K., Stein, J. C., and Wilcox, D. W. (1993). Monetary policy and credit conditions:21

Evidence from the composition of external finance, Amer. Econ. Rev. 83 (1), 78-98.
Khwaja, A. I. and Mian, A. (2008). Tracing the impact of bank liquidity shocks: Evidence from anemerging market, Amer. Econ. Rev. 98 (4), 1413-1442.
Peek, J. and Rosengren, E. S. (1997). The international transmission of financial shocks: The case ofJapan, Amer. Econ. Rev. 87 (4), 495-505.
Ramey, V. A. (1993). How important is the credit channel in the transmission of monetary policy,
Carnegy-Rochester Conference Series on Public Policy, 39, 1-45.
Romer, C. D. and Romer, D. H. (1990). New evidence on the monetary transmission mechanism,
Brookings Pap. Econ. Act. 1, 149-198.
Stein, J. C. (1998), An adverse selection model of bank asset and liability management with
implications for the transmission of monetary policy, RAND J. Econ. 29 (3), 466-86.
Ueda, K. (1993). A comparative perspective on Japanese monetary policy: Short-run monetarycontrol and the transmission mechanism, in Japanese Monetary Policy (K. J. Singleton,Ed.), pp. 7-29, Univ. of Chicago Press, Chicago.
Woo, D. (1999). In search of “capital crunch”: Supply factors behind the credit slowdown in Japan,
IMF Working Paper 99/3, International Monetary Fund.

22

Figure 1. Change in Loans Outstanding and GDP Growth
The figure shows the change in loans outstanding and the nominal GDP growth rate.

0.08

0.25
0.2

0.06
0.15

0.04

0.1
0.05

0.02

Listed Firms
Loan Growth(Data used inthis paper)

-0.05

1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
2009

0

-0.1

0

-0.02

Growth Rate ofReal GDP(Right Axis)

-0.15

-0.04
-0.2

-0.06

-0.25

Figure 2. Change in Loans Outstanding and the Policy Rate
The figure shows the change in loans outstanding and the policy rate.
0.25

9.0

0.2

8.0

0.15

7.0

0.1

6.0

0.05
5.0

Listed Firms
Loan Growth(Data used inthis paper)

-0.05

1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
2009

0

4.0
3.0

-0.1

2.0

-0.15
-0.2

1.0

-0.25

0.0

23

Policy Rate: %(Right Axis)

Figure 3. Changes in Loans Outstanding for Listed and All Firms
The figure shows the changes in loans outstanding for listed firms and all (i.e., listed and unlisted) firms.
0.25
0.2
0.15

Listed Firms
Loan Growth(Data used inthis paper)

0.1
0.05

1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
2009

0
-0.05
-0.1

Aggregate
Loan Growth
(BOJ)

-0.15
-0.2
-0.25

Figure 4. Distribution of the Change in Firm and Bank Match-Level Loans Outstanding
The figure shows the distribution of the change in loans outstanding measured at the firm-bank match-level.

0.8
0.6
Mean
- 1Std. Dev.

0.4
0.2

Mean
+ 1Std. Dev.

-0.2

1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
2009

0

-0.4

Mean

Median

-0.6
-0.8

24

Figure 5. Conditional Slope Coefficient of BLIQ on GDP Growth Rate
The figure shows the point estimate of the conditional coefficient associated with BLIQ on GDP growth rate and its

Conditional slope coefficient of BLIQ

95% confidence interval.

-0.04

0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
-0.1 0
-0.2

-0.02

0.02

0.04

0.06

GDPGROWTH
slope_BLIQ_onGDP

upper95_slope_BLIQ_onGDP

lower95_slope_BLIQ_onGDP

Figure 6. Conditional Slope Coefficient of BLIQ on QE_TIGHTENING
The figure shows the point estimate of the conditional coefficient associated with BLIQ on QE_TIGHTENING and
its 95% confidence interval.

Conditional slope coefficient of BLIQ

1.4
1.2
1

0.8
0.6
0.4
0.2
0
0

1

QE_TIGHTENING
slope_BLIQ_onQETIGHTENING
upper95_slope_BLIQ_onQETIGHTENING
lower95_slope_BLIQ_onQETIGHTENING

25

Table. 1 Aggregate-Level and Policy Variables
The change in the policy rate (ΔPolicy Rate) is the difference of the policy target rate (i.e., either the Koteibuai or the
O/N call rate) from the previous year. The change in the policy rate is measured using the Koteibuai until FY1984
and the O/N rate thereafter. The target rate for each year is calculated as the average over each period. The extent of
quantitative easing (QE target) is measured as the average of the BOJ’s target volume of reserves. In the calculationof this average, we use the number of months for each target level as a weight and compute the weighted average of
the QE target. POLRATE_UP (POLRATE_DOWN) is a dummy variable taking a value of one when the policy rate
becomes strictly lower (higher) than in the previous year. QE_LOOSENING (QE_TIGHTENING) is a dummy
variable taking a value of one when the QE target becomes strictly larger (smaller) than in the previous year.
MPLOOSENING (MPTIGHTENING) is a dummy variable taking a value of one when the BOJ strictly relaxes(tightens) monetary policy in terms of the policy rate or the QE target.

FY

Growth Rate ofReal GDP

Policy Rate
Koteibuai:
%

1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010

0.03940.03140.03530.0476
0.06280.01890.06100.06400.04570.06200.02340.0071-0.00480.01500.02280.0288-0.0002-0.01480.00730.0255-0.0079
0.01080.02110.01980.02280.02300.0182-0.0408-0.02420.02406.00005.50005.25005.00004.83333.20832.50002.50003.68755.75005.25003.25002.06251.75000.70830.50000.50000.50000.50000.46670.16250.10000.10000.10000.10000.38330.75000.55830.30000.3000

Policy Rate
ΔPolicy Rate fromQE Target
QE
QE
MP
MP
O/N CallPOLRATE POLRATEPrevious FY:(Weight Average):_LOOSE _TIGHT LOOSE TIGHT
Rate
_UP
_DOWN
%
Trillion Yen
NING
ENING NING ENING%
n.a.
-2.0000
0
0
1
0
0
1
0
n.a.
-0.5000
0
0
1
0
0
1
0
n.a.
-0.2500
0
0
1
0
0
1
0
n.a.
-0.2500
0
0
1
0
0
1
0
6.7010-0.1667
0
0
1
0
0
1
0
4.4424-1.6250
0
0
1
0
0
1
0
3.5403-0.7083
0
0
1
0
0
1
0
3.95720.0000
0
0
0
0
0
0
0
5.7432
1.1875
0
1
0
0
0
0
1
7.76562.0625
0
1
0
0
0
0
1
6.8928-0.5000
0
0
1
0
0
1
0
4.1228-2.0000
0
0
1
0
0
1
0
2.7596-1.1875
0
0
1
0
0
1
0
2.1819-0.3125
0
0
1
0
0
1
0
0.7746-1.0417
0
0
1
0
0
1
0
0.4800-0.2083
0
0
1
0
0
1
0
0.4683-0.0117
0
0
1
0
0
1
0
0.3008-0.1675
0
0
1
0
0
1
0
0.0267-0.2742
0
0
1
0
0
1
0
0.15330.1267
0
1
0
0
0
0
1
0.0080-0.1453
7.1
0
0
1
0
1
0
0.0019-0.0061
15.0
0
0
1
0
1
0
0.0011-0.0008
28.7
0
0
1
0
1
0
0.0009-0.0002
32.5
0
0
1
0
1
0
0.00110.0002
32.5
0
0
0
0
0
0
0.21860.2175
0.0
0
0
0
1
0
1
0.50470.2861
0
1
0
0
0
0
1
0.3627-0.1420
0
0
1
0
0
1
0
0.1022-0.2605
0
0
1
0
0
1
0
0.0909-0.0113
0
0
1
0
0
1
0

26

Table 2. Summary Statistics
This table shows the summary statistics for the variables we use in the regression analysis. For the first group ofvariables (i), the statistics are computed over all firm-bank matches and years. For the second group of variables (ii), thestatistics are computed over all firms and years. ΔLOANS is the log-difference of the outstanding loan amount fromeach lender bank to each firm. BSIZE is the natural logarithm of banks’ total assets. BROA is the ratio of banks’operating profits to total assets. BJGB and BLOCALBOND are the ratio of banks’ JGB and local municipal bondholdings to total assets, respectively. BTETA is the ratio of banks’ total equity to total assets. REGCAP is the capital
adequacy ratio taken from data provided by the Japan Bankers Association. For REGCAP, we subtract 4% in the case ofdomestically operating banks and 8% in the case of internationally operating banks. BLIQ is the ratio of (i) the sum ofbanks’ cash and deposits, loans outstanding in the call market, securities for selling, and JGBs and local bonds minus
required reserve at the BOJ, to (ii) total assets.

Variable

Obs.

Mean

Std. Dev.

ΔLOANS
BSIZE

425,415-0.0030.488-7.931

7.585

459,272

16.1531.358

10.90418.852

BROA

459,272

0.002

0.009

-0.252

0.027

BJGB

459,272

0.066

0.046

0

0.278

459,272

0.014

0.016

0

0.119

BLOCALBOND
BLTD(i) Computed over
all firm-bankmatches and years

Min.

Max.

459,272

0.607

0.499

0

4.030

BTETA

459,272

0.037

0.016

-0.491

0.127

REGCAP

243,989

3.921

2.671

0.010

20.450

BLIQ

459,272

0.191

0.068

0.020

0.485

FPBR

29,6932.090

18.4550.001

1220.000

FSIZE

30,02810.7181.536

516.722

29,820

0.019

0.059

-1.9620.945

29,749

0.458

0.194

0

0.989

ΔFLOAN1

25,218-0.0040.306-3.405

4.135

ΔFLOAN2

30,196-0.011

0.483-6.223

6.097

FINVRATIO

29,602

0.121

0.434

-0.95430.024FROAFTANGIBLE(ii) Computed over
all firms and years

27Table 3. Estimation Results Including Four Monetary Policy Variables
This table shows the estimation results for equation (2). Heteroskedasticity-robust standard errors are reported. ***, **, and * indicate significance at the 1, 5, and 10% level, respectively. The
dependent variable (ΔLOANS(t)) is the log difference between t-1 and t of the total loans outstanding for each firm-bank pair. The columns labeled “BCAP=BTETA” are for the case where theratio of banks’ equity to total assets (BTETA) is used as banks’ capital ratio (BCAP), while the columns labeled “BCAP=BREGCAP” are for the case where banks’ capital adequacy ratio(BREGCAP) is used as BCAP.

Dependent Variable:
ΔLOANS(t)(i)

BCAP = BTETA
(ii)
Coef.
SD

Coef.

SD

Coef.

SD

(iii)

(i)

BCAP = REGCAP
(ii)
Coef.
SD

Coef.

SD

(iii)

Coef.

SD

BSIZE(t-1)
BROA(t-1)
BJGB(t-1)
BLOCALBOND(t-1)
BLTD(t-1)
BCAP(t-1)
BLIQ(t-1)

0.015
1.687
-0.1900.376-0.0380.377
0.246

0.001 ***
0.155 ***
0.039 ***
0.067 ***
0.003 ***
0.382
0.085 ***

0.069
1.306
-0.324-0.186-0.0011.384
0.285

0.005 ***
0.162 ***
0.048 ***
0.105 *
0.007
0.395 ***
0.088 ***

0.124
1.354
-0.352-0.353
0.020
1.759
0.213

0.007 ***
0.177 ***
0.057 ***
0.119 ***
0.008 **
0.511 ***
0.095 **

0.015
1.742
-0.4290.188-0.037-0.0020.388

0.002 ***
0.161 ***
0.056 ***
0.087 **
0.004 ***
0.003
0.137 ***

0.098
1.442
-0.673-0.054-0.0270.008
0.370

0.008 ***
0.175 ***
0.068 ***
0.160
0.011 **
0.004 **
0.143 ***

0.164
1.416
-0.717-0.133
0.016
0.016
0.382

0.012 ***
0.190 ***
0.084 ***
0.187
0.015
0.005 ***
0.186 **BCAP(t-1)×GDPGROWTH(t)BCAP(t-1)×POLRATE_UP(t)BCAP(t-1)×POLRATE_DOWN(t)BCAP(t-1)×QE_LOOSENING(t)BCAP(t-1)×QE_TIGHTENING(t)-13.505-0.086-0.198-1.669-0.8694.379 ***
0.421
0.385
0.423 ***
0.570

-19.547-0.501-0.813-1.495-0.8874.749 ***
0.428
0.398 **
0.427 ***
0.569

-14.452-1.201-1.221-0.858-1.382

5.557 ***
0.519 **
0.501 **
0.545
0.639 **

-0.1180.010-0.002-0.005-0.0190.046 ***
0.004 ***
0.004
0.003
0.005 ***

-0.2060.010-0.007-0.006-0.0190.047 ***
0.004 ***
0.004 *
0.004
0.005 ***

-0.2160.002-0.014-0.009-0.0290.055 ***
0.005
0.005 ***
0.005 *
0.005 ***

BLIQ(t-1)×GDPGROWTH(t)BLIQ(t-1)×POLRATE_UP(t)BLIQ(t-1)×POLRATE_DOWN(t)BLIQ(t-1)×QE_LOOSENING(t)BLIQ(t-1)×QE_TIGHTENING(t)-8.306-0.060-0.106-0.4040.602

0.745 ***
0.085
0.079
0.099 ***
0.179 ***

-8.081-0.077-0.057-0.4020.573

0.778 ***
0.085
0.080
0.101 ***
0.179 ***

-7.395-0.065-0.073-0.2760.513

0.881 ***
0.089
0.086
0.110 **
0.204 **

-7.935-0.252-0.114-0.3240.683

1.429 ***
0.144 *
0.135
0.139 **
0.197 ***

-7.400-0.163-0.084-0.3340.589

1.491 ***
0.148
0.141
0.144 **
0.198 ***

-5.781-0.208-0.198-0.2980.504

1.728 ***
0.186
0.182
0.182
0.244 **

CONSTANT

0.002 0.004
Yes
Yes
No
No

Year EffectFirm Time-Variant FE
Bank-Level Time-Invariant FE
Match-Level Time-Invariant FE
Number of Obs.
Number of Groups
F-Value
R-Squared (Overall)

N.A.
19.71
0.0038-0.190 196.848
Yes
Yes
Yes
No
299,196
N.A.
.
0.00780.032 0.005 ***
Yes
Yes
Yes
Yes

0.013 0.010
Yes
Yes
No
No

52,10913.03
0.0009N.A.
23.24
0.006226

0.249

0.125 **
Yes
Yes
Yes
No
156,722N.A.
.
0.01310.087 0.016 ***
Yes
Yes
Yes
Yes
40,37414.34
0.0018

Table 4. Estimation Results Including Dummy for Loosening Monetary Policy
This table shows the estimation results for equation (2). Heteroskedasticity-robust standard errors are reported. ***, **, and * indicate significance at the 1, 5, and 10% level, respectively. The
dependent variable (ΔLOANS(t)) is the log difference between t-1 and t of the total loans outstanding for each firm-bank pair. The columns labeled “BCAP=BTETA” are for the case where theratio of banks’ equity to total assets (BTETA) is used as banks’ capital ratio (BCAP), while the columns labeled “BCAP=BREGCAP” are for the case where banks’ capital adequacy ratio(BREGCAP) is used as BCAP.

Dependent Variable:
ΔLOANS(t)BCAP = BTETA
(ii)
Coef.
SD

Coef.

SD

***

0.007
0.176 ***
0.056 ***
0.119 ***
0.008 ***
0.277 *
0.058 ***

(i)

(iii)

Coef.

SD

BSIZE(t-1)
BROA(t-1)
BJGB(t-1)
BLOCALBOND(t-1)
BLTD(t-1)
BCAP(t-1)
BLIQ(t-1)

0.016
1.589
-0.1950.405-0.038
0.062
0.251

0.001
0.154 ***
0.038 ***
0.067 ***
0.003 ***
0.218
0.049 ***

0.070
1.290
-0.355-0.221
0.004
0.836
0.276

0.005
0.161 ***
0.046 ***
0.104 **
0.007
0.227 ***
0.052 ***

0.123
1.373
-0.374-0.366
0.021
0.530
0.204

BCAP(t-1)×GDPGROWTH(t)BCAP(t-1)×MPLOOSENING(t)-15.917 4.408 ***-0.324 0.222-21.062-0.4994.784 ***
0.230 **

BLIQ(t-1)×GDPGROWTH(t)BLIQ(t-1)×MPLOOSENING(t)-8.189 0.734 ***-0.158 0.042 ***-7.901-0.0980.770 ***
0.043 **

CONSTANT

0.003 0.004
Yes
Yes
No
No

-0.196 .
Yes
Yes
Yes
No
299,196
N.A.
.
0.0075

Year EffectFirm Time-Variant FE
Bank-Level Time-Invariant FE
Match-Level Time-Invariant FE
Number of Obs.
Number of Groups
F-Value
R-Squared (Overall)

N.A.
21.07
0.0033***

BCAP = REGCAP
(ii)
Coef.
SD

Coef.

SD

***

0.012 ***
0.189 ***
0.081 ***
0.186
0.014
0.003 **
0.091 ***

(i)

(iii)

Coef.

SD

0.016
1.689
-0.451
0.208-0.037-0.0030.448

0.002
0.160 ***
0.052 ***
0.086 **
0.004 ***
0.002 *
0.075 ***

0.101
1.480
-0.677-0.070-0.018
0.006
0.462

0.008
0.174 ***
0.064 ***
0.159
0.011 *
0.002 ***
0.078 ***

0.168
1.476
-0.667-0.139
0.022
0.006
0.373

-12.200 5.645 **0.057 0.261-0.165 0.042 ***-0.002 0.002-0.199-0.0040.043 ***
0.002 **

-0.160 0.051 ***-0.001 0.002-7.346 0.872 ***-0.086 0.046 *-8.638 1.437 ***-0.218 0.070 ***-8.393-0.234

1.496 ***
0.073 ***

-6.103 1.732 ***-0.220 0.080 ***0.032 0.005 ***
Yes
Yes
Yes
Yes

0.012 0.010
Yes
Yes
No
No

0.251

0.074 0.015 ***
Yes
Yes
Yes
Yes

52,10914.36
0.0009N.A.
25.31
0.00520.123 **
Yes
Yes
Yes
No
156,722N.A.
.
0.0122

27

***

***

40,37414.77
0.0016

Table 5. Estimation Results Including Dummy for Tightening Monetary Policy
This table shows the estimation results for equation (2). Heteroskedasticity-robust standard errors are reported. ***, **, and * indicate significance at the 1, 5, and 10% level, respectively. The
dependent variable (ΔLOANS(t)) is the log difference between t-1 and t of the total loans outstanding for each firm-bank pair. The columns labeled “BCAP=BTETA” are for the case where theratio of banks’ equity to total assets (BTETA) is used as banks’ capital ratio (BCAP), while the columns labeled “BCAP=BREGCAP” are for the case where banks’ capital adequacy ratio(BREGCAP) is used as BCAP.

Dependent Variable:
ΔLOANS(t)

(i)
Coef.

SD

BCAP = BTETA
(ii)
SD
Coef.

Coef.

SD
0.007
0.176 ***
0.056 ***
0.119 ***
0.008 ***
0.174 ***
0.034 ***

0.016
1.673
-0.4300.207-0.037-0.006
0.231

Coef.

SD
0.012 ***
0.189 ***
0.080 ***
0.186
0.014
0.001 ***
0.051 ***

(iii)

0.100
1.472
-0.656-0.075-0.018
0.002
0.228

0.008
0.174 ***
0.064 ***
0.159
0.011 *
0.001 *
0.042 ***

0.167
1.474
-0.655-0.151
0.023
0.005
0.156

-8.514 5.450-0.516 0.267 *-0.151 0.041 ***0.001 0.002-0.1760.002

0.042 ***
0.002

-0.121 0.050 **-0.002 0.002-7.100 0.828 ***0.081 0.048 *-8.134 1.412 ***0.207 0.075 ***-8.0340.244

1.465 ***
0.077 ***

-5.901 1.707 ***0.221 0.086 ***0.032 0.005 ***
Yes
Yes
Yes
Yes

0.007 0.010
Yes
Yes
No
No

0.243

0.068 0.015 ***
Yes
Yes
Yes
Yes

52,10914.42
0.0009N.A.
25.08
0.00520.123 **
Yes
Yes
Yes
No
156,722N.A.
.
0.01220.016
1.584-0.191
0.404-0.038-0.2180.090

0.001
0.154 ***
0.037 ***
0.067 ***
0.003 ***
0.123 *
0.026 ***

0.070
1.279
-0.362-0.226
0.004
0.424
0.178

0.005
0.161 ***
0.046 ***
0.104 **
0.007
0.141 ***
0.030 ***

0.124
1.372
-0.378-0.370
0.022
0.642
0.118

BCAP(t-1)×GDPGROWTH(t)BCAP(t-1)×MPTIGHTENING(t)-14.060 4.311 ***0.087 0.242-17.451
0.104

4.643 ***
0.247

BLIQ(t-1)×GDPGROWTH(t)BLIQ(t-1)×MPTIGHTENING(t)-7.841 0.708 ***0.156 0.045 ***-7.7000.099

0.742 ***
0.046 **

CONSTANT

0.003 0.004
Yes
Yes
No
No

-0.195 .
Yes
Yes
Yes
No
299,196
N.A.
.
0.0074

N.A.
20.86
0.0033SD
0.002
0.160 ***
0.051 ***
0.086 **
0.004 ***
0.001 ***
0.032 ***

BSIZE(t-1)
BROA(t-1)
BJGB(t-1)
BLOCALBOND(t-1)
BLTD(t-1)
BCAP(t-1)
BLIQ(t-1)

Year EffectFirm Time-Variant FE
Bank-Level Time-Invariant FE
Match-Level Time-Invariant FE
Number of Obs.
Number of Groups
F-Value
R-Squared (Overall)

Coef.

BCAP = REGCAP
(ii)
SD
Coef.
***

***

***

(i)

(iii)

28

***

***

40,37414.71
0.0016

Table 6. Subperiod Estimation Results
This table shows the estimation results for equation (2). Heteroskedasticity-robust standard errors are reported. ***, **, and * indicate significance at the 1, 5, and 10% level, respectively. The
dependent variable (ΔLOANS(t)) is the log difference between t-1 and t of the total loans outstanding for each firm-bank pair.

BCAP = REGCAP
Dependent Variable:
ΔLOANS(t)Coef.

(i) t=
1993-2004
SD
Coef.
0.180
1.422
-0.582-0.365
0.000
0.000
0.032

SD

Coef.
0.232
2.348
-1.279
0.194
0.083
0.017
0.937

0.180
1.422
-0.582-0.365
0.000
0.006
0.159

BCAP(t-1)×GDPGROWTH(t)BCAP(t-1)×MPLOOSENING(t)BCAP(t-1)×MPTIGHTENING(t)-0.031 0.088-0.006 0.004 *BLIQ(t-1)×GDPGROWTH(t)BLIQ(t-1)×MPLOOSENING(t)BLIQ(t-1)×MPTIGHTENING(t)-5.800 2.179 ***-0.127 0.100

CONSTANT

0.046 0.010 ***0.046 0.010 ***
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
121,977
31,77114.92
14.92
0.00140.0014

Year EffectFirm Time-Variant FE
Bank-Level Time-Invariant FE
Match-Level Time-Invariant FE
Number of Obs.
Number of Groups
F-Value
R-Squared (Overall)

0.014 ***
0.200 ***
0.096 ***
0.218 *
0.016
0.004
0.112

0.014 ***
0.200 ***
0.096 ***
0.218 *
0.016
0.002
0.057

BSIZE(t-1)
BROA(t-1)
BJGB(t-1)
BLOCALBOND(t-1)
BLTD(t-1)
BCAP(t-1)
BLIQ(t-1)-0.031 0.088(ii) t=2001-2010SD
Coef.
0.026 ***
0.379 ***
0.146 ***
0.373
0.052
0.004 ***
0.166 ***

-0.312 0.058 ***-0.003 0.003

0.00634 0.004 *-5.800 2.179 ***

0.225
2.321
-1.265
0.164
0.075
0.015
0.695

SD
0.026 ***
0.379 ***
0.146 ***
0.373
0.052
0.002 ***
0.128 ***

-0.262 0.056 ***-0.003 0.003-2.211 2.640-0.249 0.130 *

0.127 0.100-1.849 2.523
0.288 0.146 **0.092 0.015 ***0.088 0.015 ***
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
65,592
24,41514.19
14.17
0.00170.001729

Table 7(a). Subsample Estimation: Firm Size
This table shows the estimation results for equation (2). Heteroskedasticity-robust standard errors are reported. ***, **, and * indicate significance at the 1, 5, and 10% level, respectively. The
dependent variable (ΔLOANS(t)) is the log difference between t-1 and t of the total loans outstanding for each firm-bank pair. We use observations from FY2001 to FY2010 for this estimation.
The first subsample consists of firms with total assets that are equal to or smaller than the median of all firms. The second subsample consists of firms with total assets that are larger than the
median.
(ii) LN(Total Assets)> Median
Coef.
SD
Coef.

Coef.

(i) LN(Total Assets)<= Median
Coef.
SD

BSIZE(t-1)
BROA(t-1)
BJGB(t-1)
BLOCALBOND(t-1)
BLTD(t-1)
BCAP(t-1)
BLIQ(t-1)

0.165
1.609
-0.908
0.007
0.041
0.012
0.473

0.018
0.283 ***
0.124 ***
0.313
0.023 *
0.004 ***
0.141 ***BCAP(t-1)×GDPGROWTH(t)BCAP(t-1)×MPLOOSENING(t)BCAP(t-1)×MPTIGHTENING(t)-0.247 0.076 ***-0.003 0.004

BLIQ(t-1)×GDPGROWTH(t)BLIQ(t-1)×MPLOOSENING(t)BLIQ(t-1)×MPTIGHTENING(t)-4.499 2.592 *-0.243 0.123 **CONSTANT

0.071 0.019 ***0.074 0.019 ***
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
73,806
21,6717.65
7.74
0.00120.0012

BCAP = REGCAP
Dependent Variable:
ΔLOANS(t)Year EffectFirm Time-Variant FE
Bank-Level Time-Invariant FE
Match-Level Time-Invariant FE
Number of Obs.
Number of Groups
F-Value
R-Squared (Overall)

***

0.163
1.604
-0.885-0.007
0.043
0.009
0.237

SD
0.018
0.283 ***
0.123 ***
0.313
0.023 *
0.002 ***
0.082 ******

-0.191 0.074 ***

0.185
1.329
-0.398-0.230
0.006
0.004
0.242

0.016
0.258 ***
0.108 ***
0.228
0.019
0.003
0.121 *****

-0.137 0.068 **-0.003 0.003

0.016 ***
0.258 ***
0.107 ***
0.228
0.019
0.002
0.067

-0.109 0.066 *0.001 0.003-0.0023 0.004-4.102 2.549

0.184
1.330
-0.396-0.241
0.007
0.001
0.087

SD

-6.937 2.333 ***-0.156 0.106-6.946 2.297 ***0.169 0.111

0.225 0.134 *

0.098 0.021 ***0.103 0.021 ***
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
82,916
20,3548.26
8.27
0.00230.002330

Table 7(b). Subsample Estimation: Standardized Number of Lender BanksThis table shows the estimation results for equation (2). Heteroskedasticity-robust standard errors are reported. ***, **, and * indicate significance at the 1, 5, and 10% level, respectively. The
dependent variable (ΔLOANS(t)) is the log difference between t-1 and t of the total loans outstanding for each firm-bank pair. We use observations from FY2001 to FY2010 for this estimation.
The first subsample (i) consists of firms whose number of lender banks divided by the firm’s total assets is smaller than or equal to the median, while the second subsample (ii) consists of firms
with a ratio above the median. The group of columns labeled (ii.a) is for firms in subsample (ii) for which the loan share of the top lender bank is smaller than or equal to the median, while thegroup of columns labeled (ii.b) is for firms in subsample (ii) for which the loan share of the top lender bank is above the median.
BCAP = REGCAP
Dependent Variable:
ΔLOANS(t)(i) No. of Lender Banks /LN(Total Assets)<= Median
SD
Coef.
SD
Coef.
0.017 ***
0.267 ***
0.116 ***
0.258 **
0.021
0.004 *
0.124 ***

0.163
1.326
-0.748-0.646
0.013
0.007
0.178

0.017 ***
0.267 ***
0.116 ***
0.257 **
0.021
0.002 ***
0.070 **

BSIZE(t-1)
BROA(t-1)
BJGB(t-1)
BLOCALBOND(t-1)
BLTD(t-1)
BCAP(t-1)
BLIQ(t-1)

0.164
1.329
-0.764-0.645
0.011
0.007
0.328

BCAP(t-1)×GDPGROWTH(t)BCAP(t-1)×MPLOOSENING(t)BCAP(t-1)×MPTIGHTENING(t)-0.107 0.069
0.000 0.003

BLIQ(t-1)×GDPGROWTH(t)BLIQ(t-1)×MPLOOSENING(t)BLIQ(t-1)×MPTIGHTENING(t)-6.280 2.325 ***-0.158 0.108

CONSTANT

0.051 0.021 **0.054 0.021 ***
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
86,96423,8827.63
7.66
0.00140.0014

Year EffectFirm Time-Variant FE
Bank-Level Time-Invariant FE
Match-Level Time-Invariant FE
Number of Obs.
Number of Groups
F-Value
R-Squared (Overall)-0.086 0.067(ii) No. of Lender Banks /LN(Total Assets)> Median
SD
Coef.
SD
Coef.
0.176
1.597
-0.579
0.468
0.034
0.006
0.475

0.017 ***
0.268 ***
0.113 ***
0.277 *
0.020 *
0.004
0.136 ***

-0.248 0.077 ***-0.004 0.004

0.092 0.117

0.017 ***
0.267 ***
0.113 ***
0.277
0.020 *
0.002
0.076 *

-0.187 0.075 **

0.221
0.987
-0.489
0.318
0.000
0.005
0.036

0.031 ***
0.386 **
0.183 ***
0.400
0.031
0.005
0.189

-0.317 0.136 **-0.005 0.005-6.485 2.597 **-0.327 0.119 ***-6.874 2.561 ***-1.716 3.954-0.126 0.166

0.399 0.126 ***0.060 0.018 *** 0.058 0.018 ***
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
69,75817,4458.14
8.17
0.00210.0026

31

0.222
0.992
-0.5100.284-0.0010.001-0.1050.031 ***
0.385 ***
0.183 ***
0.401
0.031
0.003
0.120

-0.264 0.131 **(ii b) Main bank share > Median
SD
Coef.
SD
Coef.
0.176
2.098
-0.625
0.318
0.054
0.007
0.703

0.023 ***
0.383 ***
0.161 ***
0.435
0.030 *
0.005
0.189 ***

-0.226 0.102 **-0.005 0.005

0.052 0.033
Yes
Yes
Yes
Yes

3.42
0.0033-3.359 3.944

0.175
2.092
-0.604
0.301
0.055
0.002
0.266

0.023 ***
0.382 ***
0.160 ***
0.435
0.030 *
0.003
0.109 **

-0.159 0.099-0.001 0.005

0.001 0.006-0.002 0.004-0.002 0.003-5.500 2.294 **

0.176
1.597
-0.570
0.447
0.034
0.003
0.145

(ii a) Main bank share <= Median
SD
Coef.
SD
Coef.

-9.132 3.548 ***-0.441 0.166 ***-8.864 3.509 **0.297 0.170 *

0.469 0.178 ***0.051 0.033
Yes
Yes
Yes
Yes
30,9358,884
3.39
0.0053

0.096 0.023 *** 0.092 0.024 ***
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
38,823
11,8744.85
4.92
0.00150.0015

Table 8. Endogeneity Bias
This table shows the estimation results for equation (2) and (3). Heteroskedasticity-robust standard errors are reported. ***, **, and * indicate significance at the 1, 5, and 10% level,
respectively. The dependent variable (ΔLOANS(t)) is the log difference between t-1 and t of the total loans outstanding for each firm-bank pair. We use observations from FY2001 to FY2010
for this estimation. In the column labeled “(i.b) With Firm Characteristics,” we control for firms’ price-to-book ratio, the number of lender banks, the natural logarithm of total assets, the ratioof current profits to total assets, the ratio of liquidity assets to liquidity liabilities, the ratio of total debts to total assets, the ratio of short-term to long-term liabilities, the ratio of bank borrowing
to total liabilities, and the ratio of fixed assets to total assets, while in the column labeled “(i.a) Without Firm Characteristics” we do not.
Not Controlling for Loan Demand(i a) Without Firm Characteristics(i b) With Firm Characteristics
Coef.
SD
Coef.
SD
Coef.
SD
Coef.
SD

(ii) Controlling for Loan Demand(From Table 6)
Coef.
SD
Coef.
SD

BSIZE(t-1)
BROA(t-1)
BJGB(t-1)
BLOCALBOND(t-1)
BLTD(t-1)
BCAP(t-1)
BLIQ(t-1)

0.250
1.871
-0.438-0.044
0.028
0.018
0.156

0.232
2.348
-1.279
0.194
0.083
0.017
0.937

BCAP(t-1)×GDPGROWTH(t)BCAP(t-1)×MPLOOSENING(t)BCAP(t-1)×MPTIGHTENING(t)-0.260 0.058 ***-0.002 0.003

BLIQ(t-1)×GDPGROWTH(t)BLIQ(t-1)×MPLOOSENING(t)BLIQ(t-1)×MPTIGHTENING(t)-0.199 2.168-0.080 0.099

0.338 0.117 ***0.378 0.120 ***0.288 0.146 **CONSTANT-4.318 0.305 ***-4.051 0.303 ***
Yes
Yes
No
No
Yes
Yes
Yes
Yes
Yes
Yes
89,557
22,13636.16
36.32
0.00080.0009-2.611 0.356 ***-2.367 0.355 ***
Yes
Yes
No
No
Yes
Yes
Yes
Yes
Yes
Yes
85,852
21,25647.74
48.10
0.00090.00100.092 0.015 ***0.088 0.015 ***
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
65,592
24,41514.19
14.17
0.00170.0017

BCAP = REGCAP
Dependent Variable:
ΔLOANS(t)Year EffectFirm Time-Variant FE
Firm Time-Invariant FE
Bank-Level Time-Invariant FE
Match-Level Time-Invariant FE
Number of Obs.
Number of Groups
F-Value
R-Squared (Overall)

0.018 ***
0.313 ***
0.130 ***
0.370
0.034
0.003 ***
0.140

0.239
1.824
-0.460-0.111
0.016
0.018
0.060

0.018 ***
0.313 ***
0.130 ***
0.369
0.034
0.002 ***
0.112

-0.195 0.054 ***

0.253
1.607
-0.507
0.103
0.024
0.019
0.199

0.019 ***
0.308 ***
0.129 ***
0.379
0.034
0.003 ***
0.139

-0.271 0.059 ***-0.002 0.003-0.006 0.003 **-1.354 2.035

0.241
1.566
-0.533
0.031
0.011
0.019
0.102

0.019 ***
0.308 ***
0.128 ***
0.378
0.034
0.002 ***
0.111

-0.207 0.055 ***0.026 ***
0.379 ***
0.146 ***
0.373
0.052
0.004 ***
0.166 ***

-0.312 0.058 ***-0.003 0.003-0.006 0.003 **-0.038 2.164-0.080 0.098

32-1.413 2.023

0.225
2.321
-1.265
0.164
0.075
0.015
0.695

0.026 ***
0.379 ***
0.146 ***
0.373
0.052
0.002 ***
0.128 ***

-0.262 0.056 ***-0.003 0.003-2.211 2.640-0.249 0.130 *-1.849 2.523

Table 9(a). Firm Level Estimation
This table shows the estimation results for equations (4) and (5). Heteroskedasticity-robust standard errors are reported. ***, **, and * indicate significance at the 1, 5, and 10% level,
respectively. The dependent variables (ΔBorrowing(t) and Investment Ratio (t)) are the log difference between t-1 and t of the total loans outstanding for each firm and the capital investment
ratio of each firm. We use observations from FY2001 to FY2010 for this estimation. The columns labeled (i) and (ii) are for all firm observations. The columns labeled (iii) and (iv) are for firms
whose price-to-book ratio (FPBR) is above the median, while the columns labeled (v) and (vi) are for firms whose FPBR is equal to or smaller than the median.

BCAP = REGCAP(i) ΔBorrowing (t)Full sample
Coef.
SD

(ii) ΔBorrowing (t)FPBR>Median
Coef.
SD

(iii) ΔBorrowing (t)FPBR<=Median
Coef.
SD

(iv) Investment Ratio (t)Full sample
Coef.
SD

(v) Investment Ratio (t)FPBR>Median
Coef.
SD

(vi) Investment Ratio (t)FPBR<=Median
Coef.
SD

BCAP(t-1)

0.002

0.002

0.006

0.004

-0.002

0.003

0.002

0.002

0.005

0.005

-0.0020.002

BCAP(t-1)×GDPGROWTH(t)BCAP(t-1)×MPTIGHTENING(t)-0.1460.005

0.075 *
0.004

-0.2440.009

0.129 *
0.006

-0.060
0.000

0.093
0.005

0.005
-0.003

0.062
0.004

-0.132
0.000

0.098
0.006

0.053
-0.004

0.058
0.003

FPBR(t-1)
FPBR_SQ(t-1)
FSIZE(t-1)
FROA(t-1)
FTANGIBLE(t-1)
CONSTANT
Year EffectFirm Time-Invariant FE
Number of Obs.
Number of Groups
R-Squared (Overall)

3.140E-03 8.11E-04 ***-4.120E-06 9.42E-07 ***-0.1870.021 ***
0.831
0.108 ***
0.174
0.079 **
1.840
0.231 ***
Yes
Yes
15,2572,401
0.00322.08E-03 7.85E-04 ***-3.17E-06 8.97E-07 ***-0.2850.035 ***
1.349
0.215 ***
0.201
0.150
2.906

0.389 ***

3.36E+00 1.75E+00 *1.86E-04 2.80E-03-0.1390.031 ***
0.403
0.129 ***
0.204
0.092 **
1.234

Yes
Yes
7,135
1,695
0.00420.326 ***

Yes
Yes
8,122
1,474
0.003933

3.92E-03 1.26E-03 ***-3.32E-06 1.42E-06 **-0.316
0.068 ***
0.482
0.096 ***
-1.6120.164 ***
4.198

0.734 ***

Yes
Yes
18,6112,730
0.01692.63E-03 1.10E-03 **-2.09E-06 1.21E-06 *-0.4950.119 ***
0.918
0.190 ***
-2.1230.316 ***
6.385

1.305 ***

Yes
Yes
9,257
2,017
0.0225

2.09E-01 3.24E+00
2.96E-03 4.76E-03-0.2000.096 **
0.106
0.112
-1.1980.159 ***
2.700

0.922 ***

Yes
Yes
9,354
1,635
0.0051

Table 9(b). Firm Level Estimation
This table shows the estimation results for equations (4) and (5). Heteroskedasticity-robust standard errors are reported. ***, **, and * indicate significance at the 1, 5, and 10% level,
respectively. The dependent variables (ΔBorrowing(t) and Investment Ratio (t)) are the log difference between t-1 and t of the total loans outstanding for each firm and the capital investment
ratio of each firm. We use observations from FY2001 to FY2010 for this estimation. The columns labeled (i) and (ii) are for firm observations. The columns labeled (iii) and (iv) are for firms
whose price-to-book ratio (FPBR) is above the median, while the columns labeled (v) and (vi) are for firms whose FPBR is equal to or smaller than the median.

BCAP = REGCAP(i) ΔBorrowing (t)Full sample
Coef.
SD

(ii) ΔBorrowing (t)FPBR>Median
Coef.
SD

(iii) ΔBorrowing (t)FPBR<=Median
Coef.
SD

(iv) Investment Ratio (t)Full sample
Coef.
SD

(v) Investment Ratio (t)FPBR>Median
Coef.
SD

(vi) Investment Ratio (t)FPBR<=Median
Coef.
SD

BLIQ(t-1)

0.062

0.080

0.237

0.139 *

-0.112

0.097

0.100

0.088

0.281

0.158 *

-0.1450.087 *

BLIQ(t-1)×GDPGROWTH(t)BLIQ(t-1)×MPTIGHTENING(t)-5.9590.172

2.459 **
0.112

-8.2470.311

3.996 **
0.176 *

-3.260-0.074

2.972
0.148

-1.484-0.015

1.825
0.107

-6.4120.102

3.035 **
0.176

2.931
-0.174

1.638 *
0.077 **FPBR(t-1)
FPBR_SQ(t-1)
FSIZE(t-1)
FROA(t-1)
FTANGIBLE(t-1)
CONSTANT
Year EffectFirm Time-Invariant FE
Number of Obs.
Number of Groups
R-Squared (Overall)

3.14E-03 8.12E-04 ***-4.12E-06 9.42E-07 ***-0.1870.021 ***
0.832
0.107 ***
0.177
0.079 **
1.898
0.230 ***
Yes
Yes
15,2572,401
0.0032

2.07E-03 7.85E-04 ***-3.15E-06 8.92E-07 ***-0.2830.035 ***
1.350
0.215 ***
***

0.206

0.150

3.36E+00 1.75E+00 *1.50E-04 2.80E-03-0.1390.031 ***
0.404
0.128 ***
0.204
0.092 **
1.251

Yes
Yes
7,135
1,695
0.00410.325 ***

Yes
Yes
8,122
1,474
0.004134

3.92E-03 1.27E-03 ***-3.32E-06 1.42E-06 **-0.3150.068 ***
0.482
0.096 ***
-1.6110.164 ***
4.159

0.733 ***

Yes
Yes
18,6112,730
0.017

2.64E-03 1.10E-03 **-2.10E-06 1.21E-06 *-0.4920.119 ***
0.913
0.190 ***
-2.1210.316 ***
6.294

1.290 ***

Yes
Yes
9,257
2,017
0.0226

1.84E-01 3.24E+00
2.99E-03 4.75E-03-0.202
0.096 **
0.106
0.112
-1.1990.159 ***
2.731

0.918 ***

Yes
Yes
9,354
1,635
0.0052