“Business Cycles, Monetary Policy, and Bank Lending:Identifying the Bank Balance Sheet Channel with
Firm-Bank Match-Level Loan Data”
CIGS End of Year Macroeconomics Conference in Tokyo
Dec. 26th 2014
Kaoru Hosono (Gakushuin Uni.)
Daisuke Miyakawa (Nihon Uni.)

1. Introduction & Motivation
 Bank lending view E.g., Stein (RAND 1998)
Bank variables
Firm variables

Identification problem!

Aggregate variables
 An Empirical Strategy: “Within-firm estimator”
Take advantage of multiple loan relations b/w firm & banks
Control for unobservable change in loan demand
Khawaja & Mian (AER 2008), Jimenez et al. (AER 2012)

1 /25

Shock(e.g., aggregate variables↑↓, inst’ change, natural disaster etc.)
Bank-aFirm-jBank-bBank-aFirm-jBank-bBank-cBank-ct-1

tPanel data could be used as far as the “shock” is cleanly identified

2 /25

2. This paper Identify banks’ balance sheet channel
Impact of the changes in business cycle & monetary policy
through banks w/ different liquidity and capital ratio With controlling for…
Firm: Time-variant observable/unobservable characteristics⇔ E.g., loan demand （★）
Bank: Observable characteristics
Firm-bank match: Time-invariant unobservable characteristic⇔ E.g., assortative matching Subsample analysis based on firms’ characteristics
 Size of bias originating from missing （★）
 Also, impact on Firm-Level Borrowing and Investment

3 /25

W/ banks more well capitalized
or more liquid on averageVS.

W/ banks less well capitalized
or less liquid on averageBank

Firm-1’sborrowing
Investment

Bank

Bank

Bank

Firm-2’sborrowing
Investment

Bank

Bank

4 /25

3. Literature (1): Khawaja and Mian (AER 2008)
 Data: Loan-level data (i.e., match-level data) in Pakistan
 Regressions(1) DLij = b j + b1DDi + F j + e i . j(lending channel)△Lij : change in loans from bank i to firm j△Di : change in deposits at bank i
In the wake of the test
Fj : firm-level fixed effectof the nuclear device(2) DY j = b 1F DD j + h j(borrowing channel)△Yj : firm j’s default rate (i.e., outcome variable)
DD j: average change in deposits for the banks that lent to firm i
 Results
β1>0, β1F>0 (for small firms)
 Our paper: Using extended panel data
5/25

3. Literature (2): Jimenez et al. (AER 2012) Data: Granted / rejected loan applications in Spain (discrete var)
 Regression:
Dep var: Probability(Application by firm i to be granted by bank j)
Indep var: Bank char, firm char, Macro variables (i.e., short-term
interest rate, GDP), Interaction between macro variables &bank char, Firm-month (or loan-level) fixed effect Results:
Higher short-term interest rates or lower GDP growth reduce
the probability that a loan application is granted
This tendency is stronger for banks with low capital (inperiods of higher short-term interest rates and lower GDP
growth) or low liquidity (in periods of higher short-term
interest rates) Our paper: Studying quantitative implication
6/25

3. Data (1): Match & Bank NEEDs-FQ: Firm-bank match-level panel data in Japan from
1981FY to 2010FY including…
 Firm-Bank match-specific loan outstanding• Long-term, short-tem loan outstanding, and total loan
outstanding (we use this) Bank’s financial statement information• SIZE (BSIZE), ROA (BROA), JGB/Total Asset (BJGB), Local
Bond/Total Asset (BLOCALBOND), Loan-to Deposit Ratio(BLTD)
7 /25

Distribution of the Change in Firm and Bank Match-Level Loans Outstanding
0.8
0.6
Mean
- 1Std. Dev.

0.4
0.2

Mean
+ 1Std. Dev.

-0.2
-0.4
-0.6
-0.8

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

0
Mean

Median3. Data (2): “Key” Bank Variables• Capital ratio (BCAP):⇔Either Total Equity/Total Asset (BTETA: 1981-2010FY) or
Capital Adequacy Ratio (REGCAP: 1993-2010FY from JBA)
Subtract 4% or 8%• Liquidity (BLIQ):⇔The ratio of (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 at the BOJ to(ii) total assets.
(i) Current deposits at the BOJ until FY 1997(ii) Multiplying the required reserve ratio by the amount of deposits ofeach type from FY 1997 to FY 20109 /25

3. Data (3): Firm
 Firm’s financial statement information• PBR, total assets, ROA, liquidity ratio, fixed assets to total
assets ratio• Debts to total assets ratio, the ratio of short-term to longterm liabilities, the ratio of bank borrowing to total
liabilities, and• Number of lender banks
We use the firms withat least two lender banks

 Sample size: Observations on between 907 firms (in FY1982)
and 1,666 firms (in FY2008) and 109 banks (in FY2010) and153 banks (in FY1981) in any particular year
10 /25

3. Data (4): Policy Real GDP growth rate (GDPGROWTH) Variables related to monetary policy (POLICY)
Change in policy-rate• Koteibuai or O/N call rate
• POLRATE_UP, POLRATE_DOWN• Base=no change, QE period
Change in quantitative easing policy• QE_LOOSENING, QE_TIGHTENING• Base=no change, pre-QE period
Combined indicator• MPLOOSENING• MPTIGHTENING

We use these separately(due to potential multicollinearity)
11 /25Structure of “POLICY”
FY

Growth rate ofreal GDP

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
0.01080.02110.01980.02280.02300.0182-0.0408-0.02420.0240

Policy Rate Policy Rate ΔPolicy Rate from
Koteibuai: O/N call rate
previous FY:%%%
6.00005.50005.25005.00004.83333.20832.50002.50003.68755.75005.25003.25002.06251.75000.70830.50000.50000.50000.50000.46670.16250.10000.10000.10000.10000.38330.75000.55830.30000.3000

n.a.
n.a.
n.a.
n.a.
6.70104.4424
3.5403
3.95725.7432
7.76566.89284.12282.75962.18190.77460.48000.46830.30080.02670.15330.00800.00190.00110.00090.00110.21860.50470.36270.10220.0909-2.0000-0.5000-0.2500-0.2500-0.1667-1.6250-0.70830.00001.18752.0625-0.5000-2.0000-1.1875-0.3125-1.0417-0.2083-0.0117-0.1675-0.27420.1267-0.1453-0.0061-0.0008-0.00020.00020.21750.2861-0.1420-0.2605-0.0113

QE target
POLRATE POLRATE
QE
QE
MP
MP
(Weight Average):_UP_DOWN _RELAX _TIGHT RELAX TIGHT
Trillion Yen
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
7.1
15.0
28.7
32.5
32.5
0.0
0
0
0
0

0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
1
0
0
0

1
1
1
1
1
1
1
0
0
0
1
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
1
1
1

0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0

1
1
1
1
1
1
1
0
0
0
1
1
1
1
1
1
1
1
1
0
1
1
1
1
0
0
0
1
1
1

0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
1
1
0
0
0

4. Empirical Strategy (1) Model choice depends on our identification assumption
Change in loan amount depends on firm’s loan demand,bank’s loan supply, and aggregate-level variablesε: (i) Error term, (ii) error & bank-FE, (iii) error & match-FE
Accounting forloan demand

Account for assortative
matching up to some extent

ΔLOANı i, j, tı = ηı j, tı + β1 BSIZEı i, t − 1ı + β2 BROAı i, t − 1ı + β3 BJGBı i, t − 1ı
+ β3 BJGBı i, t − 1ı + β4 BLOCALBONDı i, t − 1ı + β5 BLTDı i, t − 1ı
+ β6 BCAPı i, t − 1ı + β7 BLIQı i, t − 1ı + YEAR(t)+ γ1 BCAPı i, t − 1ı GDPGROWTHı tı + γ2 BLIQı i, t − 1ı GDPGROWTHı tı+ γ3 BCAPı i, t − 1ı POLICYı tı + γ4 BLIQı i, t − 1ı POLICYı tı + εı i, j, tı

Main interest

Two key variables

13 /25

4. Empirical Strategy (2) Additional analyses
Subsample based on firms’ characteristics• Balance sheet channel interacts w/ firm characteristics?
– Different loan provision to firms with different size?
– Different loan provision to firms with dispersed (i.e.,weak) loan relations?
Results from wrong model• Omit η(j,t)• Include firms’ characteristics• Compute the difference in predicted change of ΔLOAN(I,j,t)
14 /25

4. Empirical Strategy (3) Firm-level estimation
FIRM(j,t) = Δloan(j,t) or ΔINVESTMENTRATIO(j,t)

Main interest

Accounting fore.g., loan demand

15 /25

5. Empirical Analyses (1) Three hypothesesHypothesis 1: Banks with a higher BTETA, REGCAP, or BLIQ
provide larger amounts of loansHypothesis 2: The positive marginal impact of BTETA,REGCAP, and BLIQ becomes smaller (larger) when
GDPGROWTH is higher (lower)
Hypothesis 3: The positive marginal impact of BTETA,REGCAP, and BLIQ becomes smaller (larger) when
POLRATE_DOWN, QE_LOOSE, and/or MPLOOSE take avalue of one (POLRATE_UP, QE_TIGHT, or MPTIGHT take avalue of one)
16 /25

5. Empirical Analyses (2): Baseline Estimation
Dependent Variable:
ΔLOANS(t)BCAP = BTETA
(iii)
Coef.
SD

BCAP = REGCAP
(iii)
Coef.
SD

***

***

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

0.124
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 **

0.164
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)-14.452-1.201-1.221-0.858-1.382

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

-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)-7.395-0.065-0.073-0.2760.513

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

-5.781-0.208-0.198-0.2980.504

1.728 ***
0.186
0.182
0.182
0.244 **

CONSTANT

0.032 0.005 ***0.087 0.016 ***

Yes
Yes
Yes
Yes
299,196
52,10913.03
0.0009

Yes
Yes
Yes
Yes
156,722
40,37414.34
0.0018Year 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)H1 supported

H2 supported
H3 supported
Not robust in otherspecification
H2 supported
H3 supported(Exit from QE)
17 /25

Conditional slope coefficient of BLIQ

-0.04

-0.02

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

0.04

0.06

GDPGROWTH
slope_BLIQ_onGDP

upper95_slope_BLIQ_onGDP

lower95_slope_BLIQ_onGDP

18 /25

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

19 /25

5. Empirical Analyses (3): Quantitative Impacts
 BLIQ↓ by 1 Std. (0.068) in t-1
 GDPGROWTH = 0 & No Change in monetary policy in year t• ΔLOAN(i,j,t) is smaller by 0.382×(0.068) = 2.6%
⇔ Sample mean of ΔLOAN(i,j,t) is -0.3%⇔ 7% of the standard deviation of ΔLOAN(i,j,t) (48.4%)
 GDPGROWTH = -0.02 & No Change in monetary policy in t
• ΔLOAN(i,j,t) is smaller by 3.4%
 GDPGROWTH = -0.02 & QETIGHT=1 in t
• ΔLOAN(i,j,t) is smaller by 6.0%
The impact of bank liquidity more than doubles whenquantitative easing is exited under economic downturns

20 /25

5. Empirical Analyses (3)’: Quantitative Impacts
 REGCAP↓ by 1 Std. (2.671%) in year t-1
 GDPGROWTH = 0 & No Change in monetary policy in year t• ΔLOAN(i,j,t) is smaller by 4.3%
• The quantitative impact of bank capital is economicallysizable and comparable to that of bank liquidity

 GDPGROWTH = -0.02 & No Change in monetary policy in t
• ΔLOAN(i,j,t) is smaller by 5.4%
21 /25

5. Empirical Analyses (4): Quantifying the Bias
 Our model vs. Wrong model
Comparing the impact of 1 STD↓ (-0.068) in bank liquidity
Zero GDP growth and BOJ tightened monetary policy⇔True model: 0.695×(-0.068)+0.288×(-0.068)×1 = △6.7%⇔Wrong model: 0×(-0.068)+0.375×(-0.068)×1 = △2.6%
BCAP = REGCAP
Dependent Variable:
ΔLOANS(t)Not Controlling for Controlling for Loan
Loan Demand
DemandWith Firm
Characteristics(From Table 6)

Coef.

Coef.

SD

SD

BLIQ(t-1)

0.102 0.111

0.695 0.128 ***BLIQ(t-1)×GDPGROWTH(t)BLIQ(t-1)×MPTIGHTENING(t)-1.413 2.023
0.378 0.120 ***-1.849 2.523
0.288 0.146 **22 /25

5. Empirical Analyses (5): Firm-Level Results Average levels of BLIQ and BCAP among lender banks
BCAP = REGCAP(i) ΔBorrowing (t)(iv) Investment Ratio (t)Full sample
Coef.

FPBR>Median
Coef.

Full sample
Coef.

FPBR>Median
Coef.

BLIQ(t-1)

0.062

0.237 *

0.100

0.281 *

BLIQ(t-1)×GDPGROWTH(t)BLIQ(t-1)×MPTIGHTENING(t)-5.959 **0.172-8.247 **0.311 *

-1.484-0.015-6.412 **0.102

3.14E-03 ***-4.12E-06 ***-0.187 ***0.832 ***
0.177 **

2.07E-03 ***-3.15E-06 ***-0.283 ***1.350 ***

3.92E-03 ***-3.32E-06 **-0.315 ***0.482 ***
-1.611 ***2.64E-03 **-2.10E-06 *-0.492 ***0.913 ***
-2.121 ***4.159 ***

6.294 ***FPBR(t-1)
FPBR_SQ(t-1)
FSIZE(t-1)
FROA(t-1)
FTANGIBLE(t-1)
CONSTANT

1.898 ***

***

0.206

23 /25

6. Summary Banks’ characteristics matter Banks’ balance sheet channel exists
 Sizable endogeneity bias
 Firms whose banks are less well capitalized or less liquid Obtain fewer loans in a recession
 Pronounced for firms w/ better investment opportunities Firms w/ better investment opportunities tend to invest more
 When their lender banks are more liquid This link is stronger during an economic downturn⇔ Bank balance sheet channel has a significant impact
24 /25

7. What’s Next?
 Future studies:Study the extensive margin of relations (i.e., initiation andtermination) in the same framework?

How about unlisted firms (e.g., BSBSA with COSMOS2)?

Other financial relations (e.g., Venture Firm & VC)?

25 /25

Appendix

X1. Empirical Analyses: MP Variables
Dependent Variable:
ΔLOANS(t)BCAP = BTETA
(iii)
Coef.
SD

BCAP = REGCAP
(iii)
Coef.
SD

RELAX

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

0.012 ***
0.189 ***
0.081 ***
0.186
0.014
0.003 **
0.091 ***BCAP = BTETA
(iii)
Coef.
SD

BCAP = REGCAP
(iii)
Coef.
SD

TIGHT

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

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

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

0.123
1.373
-0.374-0.366
0.021
0.530
0.204

BCAP(t-1)×GDPGROWTH(t)BCAP(t-1)×MPLOOSENING(t)-12.200 5.645 **0.057 0.261-0.160 0.051 ***-0.001 0.002-8.514 5.450-0.516 0.267 *-0.121 0.050 **-0.002 0.002

BLIQ(t-1)×GDPGROWTH(t)BLIQ(t-1)×MPLOOSENING(t)-7.346 0.872 ***-0.086 0.046 *-6.103 1.732 ***-0.220 0.080 ***-7.100 0.828 ***0.081 0.048 *-5.901 1.707 ***0.221 0.086 ***CONSTANT

0.032 0.005 ***
Yes
Yes
Yes
Yes
299,196
52,10914.36
0.00090.074 0.015 ***
Yes
Yes
Yes
Yes
156,722
40,37414.77
0.00160.032 0.005 ***
Yes
Yes
Yes
Yes
299,196
52,10914.42
0.00090.068 0.015 ***
Yes
Yes
Yes
Yes
156,722
40,37414.71
0.0016

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)

0.168
1.476
-0.667-0.139
0.022
0.006
0.373

0.124
1.372
-0.378-0.370
0.022
0.642
0.118

0.167
1.474
-0.655-0.151
0.023
0.005
0.156

X2. Empirical Analyses: Subsample by Periods
BCAP = REGCAP
Dependent Variable:
ΔLOANS(t)Coef.

(i) t=
1993-2004
SD
Coef.
***

Coef.
***

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

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

SD

0.180
1.422
-0.582-0.365
0.000
0.000
0.032

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

-0.031 0.088

0.232
2.348
-1.279
0.194
0.083
0.017
0.937

(ii) t=2001-2010
SD
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 ***0.127 0.100

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 *-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
24,41514.17
14.19
0.00170.0017

X3. Empirical Analyses: Subsample by Firm Char
BCAP = REGCAP
Dependent Variable:
ΔLOANS(t)Coef.

(i) LN(Total Assets)<= MedianSD
Coef.

(ii) LN(Total Assets)> Median
Coef.
SD
Coef.

SD

SD

BCAP(t-1)
BLIQ(t-1)***

0.012 0.004
0.473 0.141 ******0.009 0.002
0.237 0.082 ***0.004 0.003
0.242 0.121 **0.001 0.002
0.087 0.067

BCAP(t-1)×GDPGROWTH(t)BCAP(t-1)×MPLOOSENING(t)BCAP(t-1)×MPTIGHTENING(t)-0.247 0.076 ***-0.003 0.004-0.191 0.074 ***-0.137 0.068 **-0.003 0.003-0.109 0.066 *BLIQ(t-1)×GDPGROWTH(t)BLIQ(t-1)×MPLOOSENING(t)BLIQ(t-1)×MPTIGHTENING(t)-4.499 2.592 *-0.243 0.123 **CONSTANT

0.074 0.019 ***-0.0023 0.004-4.102 2.549

0.001 0.003-6.937 2.333 ***-0.156 0.106

0.225 0.134 *0.071 0.019 ***-6.946 2.297 ***0.169 0.111

0.103 0.021 ***0.098 0.021 ***(i) No. of Lender Banks /LN(Total Assets)<= Median
Coef.
SD
Coef.
SD

(ii) No. of Lender Banks /LN(Total Assets)> Median
Coef.
SD
Coef.
SD

BCAP(t-1)
BLIQ(t-1)

0.007 0.004 *0.328 0.124 ***0.007 0.002 ***0.178 0.070 **0.006 0.004
0.475 0.136 ***0.003 0.002
0.145 0.076 *BCAP(t-1)×GDPGROWTH(t)BCAP(t-1)×MPLOOSENING(t)BCAP(t-1)×MPTIGHTENING(t)-0.107 0.069
0.000 0.003-0.086 0.067-0.248 0.077 ***-0.004 0.004-0.187 0.075 **BLIQ(t-1)×GDPGROWTH(t)BLIQ(t-1)×MPLOOSENING(t)BLIQ(t-1)×MPTIGHTENING(t)-6.280 2.325 ***-0.158 0.108

CONSTANT

0.054 0.021 ***BCAP = REGCAP
Dependent Variable:
ΔLOANS(t)-0.002 0.003-5.500 2.294 **-0.002 0.004-6.485 2.597 **-0.327 0.119 ***0.399 0.126 ***0.092 0.117
0.051 0.021 **-6.874 2.561 ***0.060 0.018 ***0.058 0.018 ***<Contact Information> Comments are welcome!!!
Kaoru Hosono:
Professor
Gakushuin University, Department of Economics
E-mail: 20030014@gakushuin.ac.jp
Daisuke Miyakawa:
Associate Professor
College of Economics, Nihon University
E-mail: miyakawa.daisuke@nihon-u.ac.jp30