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令和5年10月〔研究論文〕◇COVID-19(新型コロナウィルス)危機が主要国の信用リスクに与えた影響の検証◇日本における新型コロナウィルスの地方債市場へのインパクトとリスク管理に関する考察（共同研究）◇ESG投資とイスラム金融：制度的類
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からの分析

〔参

・・・・・・

明
商

治

大

学
部

教

授 伊藤

隆康 ・・・・・・ 1

・・・・・・

西南学院大学
経 済 学 部

教授 丹波

靖博 ・・・・・・ 31

西南学院大学商学
部

准教授 原口 健太郎

九 州 大 学
経済学研究院

教

授 大石

桂一

・・・・・・

同志社大学大学院
ビジネス研究科教授 吉田悦章 ・・・・・・ 63

・・・・・・

敬
経

学
部

教

授 和田

良子 ・・・・・・ 85

・・・・・・

筑 波 大 学人 文 社 会 系

教

授 内藤

久裕 ・・・・・・ 115

学

愛
済

大
学

考〕

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176◇ゆうちょ財団の研究助成について**************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************

一般財団法人 ゆう ちょ 財団

ルス
要

の

ス

に

た

の

COVID-19

2020
2020

1

2

CDS

COVID-19

1

Tokic (2020)

COVID-19

COVID-19

CDS

4
COVID-

19

2
COVID-19

CDS
CDS

(
5

)

CDS

5

CDS
Datastream
2

1
21
24

1

2

(
(

A)

2018

3

23

2020

2

B)

2020

2

24

2022

1

1

CDS

－3－

CDS

)
01
0 0

0 0
0

Johansen(1988)

CDS

－4－

CDS

(

CDS

Augmented Dickey-Fuller (ADF)

Toda/Yamamoto 1996

CDS

COVID-19

CDS

4

)

COVID -19

2021/12/21

2021/10/21

2021/8/21

2021/6/21

2021/4/21

2021/2/21

2020/12/21

2020/10/21

2020/8/21

2020/6/21

2020/4/21

2020/2/21

2019/12/21

2019/10/21

2019/8/21

2019/6/21

2019/4/21

2019/2/21

2018/12/21

2018/10/21

2018/8/21

2018/6/21

2018/4/21

2018/2/21

40

bp

35

30

25

20

15

10

5

0

図 1 CDSスプレッドの推

1

Kwiatowski-Phillips-Schmidt-

Shin (KPSS)
I(1)

I (1)

(

Grager2000(2020))

COVID-19

CDS
CDS

CDS

5

: COVID-19

CDS

－5－

5

(

COVID-19－6－

COVID-19

CDS
4

2020
4

2

CDS

CDS

COVID -19
CDS

COVID-19
CDS
CDS

4

2000

(2020))

COVID-19

CDS
CDS

CDS

5

: COVID-19

CDS

－7－

5

(

1.はじめに
COVID-19

2020
2020

1

2

CDS

COVID-19

1

Tokic (2020)

COVID-19

COVID-19
CDS
4
COVID-19

2

CDS(2020)

CDS

CDS
CDS

COVID-19
CDS

1

CDS

－8－

2.先行研究2.1 CDS
Alter and Beyer (2014)
CDS

VAR (Vector

Autoregressive)
Beirne and Fratzscher (2013)

31

CDS
Calice et al. (2013)
CDS
Gorea and Radev (2014)

2007

2011
CDS

Grammatikos and Vermeulen (2012)
CDS
Kalbaskaa andGatkowski (2012)

Ito (2015)

PIIGS
5

CDS
CDS

PIIGS

2015
1

2.2 CDSCOVID-19

Andries et al. (2021)

GDP

CDS

－9－

Daehle et al. (2021)
GDP
Haddad et al. (2021)

CDS

COVID-19(2020)CDS

COVID-19

5

(

)

5

2020

2
Ivanov et al. (2020)

10

COVID-19

CDS

Jinjarak et al. (2021)

2020

3

COVID-19

CDS

Liu et al. (2021)

COVID-19

CDS

Wei and Han (2021)

CDS

2.データ
CDS

(
5

)

CDS

CDS

－ 10 －

01
0 0

0 0
0
1

－ 11 －

図 1 CDSスプレッドの推

1
(
A)
2018
3
23
2020
2
21

2
(
B)
2020
2
24
2022
1
24

2021/12/21

2021/10/21

2021/8/21

2021/6/21

2021/4/21

12021/2/21

2020/12/21

2020/10/21

2020/8/21

2020/6/21

2020/4/21

2020/2/21

2019/12/21

2019/10/21

2019/8/21

2019/6/21

2019/4/21

2019/2/21

2018/12/21

2018/10/21

2018/8/21

2018/6/21

2018/4/21

2018/2/21

5
Datastream
2

1

40

bp

35

30

25

20

15

10

5

0

表1

統計
変数

平

準

最小

最大

14.91

1.99

11.81

18.47

15.13

国

13.21

1.24

9.31

16.20

13.40

ドイ

5.84

0.66

5.00

8.17

5.71

国

20.59

4.92

11.84

30.29

21.35

11.30

4.51

5.53

22.76

10.39

12.10

3.96

8.96

29.41

10.59

6.25

2.08

4.78

13.78

5.52

12.04

7.05

5.04

37.11

10.78

A
日

01
0 0

0 0
0

1

3.分析手法3.1

tGranger and Newbold (1974)

Spurious Regression

Phillips(1986)

(1)
(2)
2

Plosser(1982)

3.2
AR

(1)
(2)
2
AR(1)

－ 12 －

Nelson and

yt

yt 1

ut

(1)

(1

L) y t

ut

(2)

AR(1)
1

L

L)

(1

( Ly tyt 1 )

L 1/

0
1

L

1
1

1

1994

1

AR(p)

(1
(1

1

1

L

L

2

L2 

2

L2 

p

p

Lp ) yt

Lp ) yt

(3)

ut

p

2

i

L
2

(

 2 1)

3.3
Augmented Dickey-Fuller (ADF)

Kwiatowski-Phillips-Schmidt-

Shin (KPSS)
I(1)ADF
1)DF Dickey/Fuller

KPSS

ADF Augmented Dickey/Fuller

ADF
Fuller(1976)

Dickey and Fuller(1979) Dickey and Fuller(1981)
AR(1)

yt

yt 1

(4)

ut

| | <
1

1

2(

−

t

)

－ 13 －( OLS) / SE ( OLS )

t
1

T(

1)

OLS

DickeyT(
Dickey/Fuller

t

1)

OLS

DF
AR(1)

ut
1

p

(1

1

L

yt

2

L2 

p 1

yt 1

1

i 1

(5)

p

Lp ) yt

yt 1

(5)

ut

(6)

ut

(6)

t

(6)

T(

OLS

Dickey1
DF1)
FullerAugmented Dickey/Fuller
DF
ADF

AIC

SBC

(2) KPSS (Kwitakowski/Phillips/Schmidt/Shin)

ADF

Kwitakowski et al.(1992

KPSS

ADF

yt

xt ~ I (1)x t ~ I (0)u t ~ I (0)txt ~ I (1)

ut

(7)

3

yt
yt

t

xt

t

t

xt

xt

xt 1 u t
ut

yt
xt

xt

xt 1

t

－ 14 －

ut

0

xt

xt 1

I(1)

(7)

T

T 2

St

T
i 1

ei

T

St

i 1

uˆ i

s 2 (k ) T 1
T

T

1
t

et2
1

2T

k

1
s

T

w( s , k )
1

t

s

et et

û t

(8)

S t2 / s 2 (k )t 1s 2 (k )

xt

T
t 1

s

1

k

ut2 2T 1Tw( s, k )s 1

uˆt uˆt s

(9)

t s 1

w (s,k) = 1 – s / (k + 1)
Bartlett

window

xt

yt

yt

xt

0

u

u

3.4
I (1)

Johansen(1988)kJohansen

p

Xt
Xt

Xt

VAR

1

Xt 1 

p

k

Xt k

k

VAR

ut

(10)

I(1)

0

ut

0

Xt

I

1

k 1



i

I

I
Xt

Xt 1

1
1



Xt k 1

Xt k

(11)

ut

, (i 1,  , k 1)

k

I(1)

(10)

－ 15 －

Xt 1

I(0)

0

rank ( )pXt
0rank ( )PXr rprank ( )'Johansen

Xt

Xt 1

1

(11)

 ' Xt k

Xt k 1

t k

(12)

ut

'

 'Xt kI(0)Johansen

(12)

ECM

r

VAR

r
r

rank ( )p1

JohansenCDS

r+1

rank ( )

(

)

CDS
4

CDS

3.3

x

Granger

y

x

y

y

x
x

13
F

y

(13)

x

H0

(14)

H0
F

x

y

－ 16 －

(14)

y

p

xt

0

p

i xt ii 1

i 1

 i yt i(13)ut

H0 :  1  2  p 0

 i 0 (i 1,2,, p)

H1 :

p

yt

0

H0 :

i 1
1

p

i xt i

2

i 1



H1 :

 i yt i

(14)

vt

0

p

0 (i 1,2, , p )iGranger

Toda and Yamamoto(1995)

VAR

Granger

y

x x
x

y

H0

y

p

p

1

t

+1

xt

p 1

t

0

i xt i

i 1

p 1
i 1

 i yt j(15)

ut

H0 :  1  2  p 0

 i 0 (i 1,2,, p)

H1 :
yt

0

H0 :

i 1
1

2

H1 :
15

p 1

t

i xt i



p

i

p 1
i 1

 i yt j

(16)

vt

0

0 (i 1,2,  , p )
F

(16)
(15)

y

H0
F

(16)

x

H0

－ 17 －

x

y
(17)
CDS

CDS
(AIC)

𝑚𝑚𝑓𝑓𝑚𝑚𝑚𝑚𝑚𝑚 =

0+

𝑐𝑐 +

+ 𝑢𝑢
𝑚𝑚𝑝𝑝𝑚𝑚𝑚𝑚 =

0+

=

0+

𝑐𝑐 +

𝑐𝑐 +

1

𝛼𝛼 𝑚𝑚𝑝𝑝𝑚𝑚𝑚𝑚 −1 +

𝑝𝑝 1
1

𝑝𝑝 1
1

𝛼𝛼

0+

𝑐𝑐 +

𝑝𝑝 1
1

𝑚𝑚𝑓𝑓𝑚𝑚𝑚𝑚𝑚𝑚(18)

𝛼𝛼 𝑚𝑚𝑝𝑝𝑚𝑚𝑚𝑚 −1 +

+ 𝑢𝑢
𝑈𝑈 =

𝑝𝑝 1

−1 +

𝛼𝛼 𝑚𝑚𝑝𝑝𝑚𝑚𝑚𝑚 −1 +

+ 𝑢𝑢

𝑝𝑝 1

𝑝𝑝 1

𝑝𝑝 1
1

(20)

−1 +

𝑈𝑈

1

𝑈𝑈

−1 +

𝑝𝑝 1

−1 +

𝑚𝑚𝑓𝑓𝑚𝑚𝑚𝑚𝑚𝑚

1

(19)

𝑈𝑈

1

(17)

+ 𝑢𝑢
𝑈𝑈

𝑝𝑝 1

−1 +

𝑝𝑝 1
1

𝑈𝑈

1

𝑝𝑝 1
1

𝑝𝑝 1
1

−1 +

𝑈𝑈

𝑈𝑈

𝑚𝑚𝑓𝑓𝑚𝑚𝑚𝑚𝑚𝑚

𝑝𝑝 1

−1 +

−1 +

−1 +

4.分析結果
4.1.
ADF

KPSS
2
ADF

I (1)
4

5

－ 18 －

KPSS

3

1

𝜀𝜀

𝑚𝑚𝑓𝑓𝑚𝑚𝑚𝑚𝑚𝑚

𝑝𝑝 1
1

𝑝𝑝 1
1

𝑝𝑝 1
1

−1

𝜀𝜀 𝑚𝑚𝑝𝑝𝑚𝑚𝑚𝑚 −1

𝜀𝜀 𝑈𝑈

−1

𝜀𝜀 𝑈𝑈

−1

表 2 ADF

定

変数

トレンドなしトレンドあり-0.6682.935

国-1.183-5.817

ドイ-0.410-2.073

国-0.295-1.208-1.745-2.862-0.903-1.728-0.639-2.401-1.046-2.993A日

注 : *は5％5%

表3 KPSS

準で有意であることを示す。
は-2.86(トレンドなし) 、 -3.41(トレンドあり
01
0 0
1
0 0
0

定
Lag=0

Lag=12

VariableA
2.418*

2.244*

0.239

0.222*

44.418*2.008*

3.718*

0.288*

8.209*

6.627*

0.799*

0.606*

9.570*

9.569*

0.771*

0.771*

7.338*

5.923*

1.205*

0.504*

30.729*7.647*

2.490*

0.658*

23.136*5.365*

1.902*

0.474*

40.361*

5.613*

3.214*

0.520*

B

注: * は5 %5%準で有意であることを示す。
は0.463(レベル定 性)、 0.146 (トレンド定 性)である。
はレベル定 性。
はトレンド定
01
0 0
1
0 0
0

－ 19 －性。

表4 ADF

定 一次

分

変数

トレンドなしトレンドあり-9.156*-9.093*

国-10.840*-10.821*ドイ-8.137*-8.147*

国-27.422*-27.318*-7.854*-7.819*-9.578*-9.714*-7.904*-8.354*-5.987*-6.013*A日

注 : *は5％5%

表5 KPSS

準で有意であることを示す。
は-2.86(トレンドなし) 、 -3.41(トレンドあり
01
0 0
1
0 0
0

定 一次

分
Lag=0

Lag=12

Variable
A
0.042

0.016

0.095

0.036

0.005

0.005

0.017

0.017

0.032

0.008

0.102

0.027

0.352

0.032

0.473

0.048

0.081

0.084

0.061

0.063

0.046

0.084

0.092

0.086

0.084

0.082

0.084

0.086

0.142

0.142

0.090

0.090

B

注: * は5 %5%準で有意であることを示す。
は0.463(レベル定 性)、 0.146 (トレンド定 性)である。
はレベル定 性。
はトレンド定
01
0 0
1
0 0
0

性。

4.2
Johansen()－ 20 －

A

1

B
CDS

4

CDS
6

表6 Johansen

和分

仮説

仮説

対

定
定統計量
最大

A

有

定統計量

5%

定

トレース

5%

定

r=0

r=1

40.77*

28.14

64.96*

53.12

1

r=2

15.97

22.00

24.19

34.91

2

r=3

6.63

15.67

8.22

19.96

3

r=4

1.59

9.24

1.60

9.24

r=0

r=1

18.79

28.14

39.64

53.12

1

r=2

10.88

22.00

20.85

34.91

2

r=3

7.64

15.67

9.97

19.96

3

r=4

2.33

9.24

2.33

9.24

B

注: * は5 %準で有意であることを示す。
はOsterwald-Lenum (1992)からの 用である。
01

0 0

0 0

0

1

4.3 Granger
GrangerA
B

7－ 21 －

表7 Granger因果性

定

変数

定統計量

変数

A

定統計量

B

日

国

0.047

日

国

1.984

日

ドイ

0.631

日

ドイ

7.313*

日

国

0.952

日

国

3.578*

国
日

日

0.915

ドイ

0.961

国

3.131*

国

4.607*

ドイ

国
ドイ

日

国
日

日

0.584

ドイ

0.867

国

2.157

日

7.224*

ドイ

国

2.308*

ドイ

国

0.890

ドイ

国

7.350*

ドイ

国

4.080*

2.079

国

国

日

日

4.297*

国

国

1.727

国

国

5.954*

国

ドイ

0.414

国

ドイ

9.742*

注: * は5 %準で有意であることを示す。
01
0 0
0 0
0

1

5.まとめ
COVID-19

CDS
4

2020

2

CDS
CDS

COVID -

19
CDS

COVID-19
CDS

CDS
4

2000

(2020)－ 22 －

5()

COVID-19

CDS
CDS

CDS

5

－ 23 －

1 CDS と ー

Ito (2015)

の

2009

1

29

2011

2

9

16

PIGGS
CDS
PIIGS

CDS
PIIGS
5

CDS

CDS

A1

A1

bp
6, 0005, 0004, 000Portu g al

3, 000

IrelandItaly

2, 000

GreeceSpain

1, 0002011/7/29

2011/5/29

2011/3/29

2011/1/29

2010/11/29

2010/9/29

2010/7/29

2010/5/29

2010/3/29

2010/1/29

2009/11/29

2009/9/29

2009/7/29

2009/5/29

2009/3/29

2009/1/29

0Figure A1 Movement of CDS
Notes : CDS prices of PIIGS (Portugal, Ireland, Italy, Greece, and Spain) are shown.
The sample period is from January 29, 2009 to September 16, 2011.
The data source is Bloomberg.

－ 24 －

Table A1 Descriptive statistics
Variable

Average

SD

Min

Max

MedianItaly

327.278280.62143.1341214.856

274.575

Spain

371.393238.748110.2971180.500

257.302

Portugal

159.395

76.07457.718504.000

154.888

France736.511662.711100.500

5034.450

720.662

Germany185.455

94.84853.233429.652

197.010

Note: The sample period is from January 29, 2009 tp September 16, 2011.

5

PIIGS

CDS
PIGGS

CDS

PIIGS
PIIGS
PIIGS

－ 25 －

2 CDS と COVID-19

Ito (2020)

COVID-19

5
)

CDS

2020
EU
)

5

5

12

2

21

2020

5

5

6

2020

5

5

(

CDS

Datastream

2
2020

(

2

(

A)

2019

12

6

(

B)

2020

2

24

CDS

5
CDS

A2

A2

bp
180
160
140
120
100
80
60
40
20

Italy

Spain

Portu g al

FranceGermany

Figure A2 CDS Premium
Notes: Data source is Datastream.
Sample A is from December 6, 2019 to February 21, 2020.
Sample B is from February 24, 2020 to May 5, 2020.

－ 26 －

01/05/2020

24/04/2020

17/04/2020

10/04/2020

03/04/2020

27/03/2020

20/03/2020

13/03/2020

06/03/2020

28/02/2020

21/02/2020

14/02/2020

07/02/2020

31/01/2020

24/01/2020

17/01/2020

10/01/2020

03/01/2020

27/12/2019

20/12/2019

13/12/2019

06/12/2019

0

COVID-19
CDS
5.

Table A2 Descriptive statistics
Variable

Average

SD

Min

Max

Median

Italy

55.37

6.09

45.98

70.89

55.90

Spain

17.43

3.21

10.44

19.98

19.34

Portugal

20.68

2.42

16.33

22.85

22.09

France

6.57

0.35

6.07

7.09

6.58

Germany

5.07

0.00

5.07

5.08

5.07

Italy

115.65

30.17

53.02

169.35117.59

Spain

50.67

19.35

12.45

86.72

56.20

Portugal

56.36

19.75

16.54

87.92

60.28

France

17.04

6.16

5.59

26.14

19.22

Germany

10.16

2.69

5.07

13.78

10.99

Sample A

Sample B

Notes: Sample A is from December 6, 2019 to February 21, 2020.
Sample B is from February 24, 2020 to May 5, 2020.

－ 27 －

参考文献(1994)

13

4,

97-129.
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本に

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２．先行研究レビューと本研究の概要
２－１．先行研究レビュー
,

,

,

,

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,

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2011b
,
,

,
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,

,

2012

,

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,
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,

,
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,
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,
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,
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－ 38 －

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－ 39 －

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－ 40 －

,

#

変数

内容

#

0 TokyoYield

対東京都債イールド(目的変数)

23 percost

住民一人当たり行政コスト.万円#

内容

24 perliabilities

住民一人当たり負債額.万円

25 balance

基礎的財政収支.百万円

変数

1 population

人口

2 YRSTOMAT

銘柄の残存年限3 ISSAMT

銘柄の当初発行高対数

4 RepMem

市場価格報告者数

5 AveRating格付け

6 zaisei

財政力指数

7 keijo

経常収支比率

8 jissitsu

実質公債比率

9 syourai

将来負担比率10 ebbalance

臨時財政対策債期末残高

11 assets

資産合計

12 liabilities

一人あたり負債合計

13 retirement

一人あたり退職手当引当金額

14 infra

固定資産等形成分

15 netassets

純資産合計

16 revenue

財源

17 NAR

純資産比率

18 rNAR

修正純資産比率20 revasset

住民一人当たり資産額.万円
歳入額対資産比率.年.

21 depasset

有形固定資産減価償却率

22 future

将来世代負担比率

19 perasset

変数

内容

26 benefit

受益者負担比率27 popToukei

一人あたり決算統計上の地方債残高

28 popdiftoukei

一人あたり公会計財務諸表上の負債と決算統計上の地方債との差

29 popresidual

一人あたり公会計財務諸表上の負債（退職手当引当金以外）と決算統計上の地方債との差30 popretirement
31 sd
32 lgcode
33 prefdummy
34 COUP
35 MATDATE
36 MOODY.IR.
37 S.P.IR.
38 R.IQ
39 ebissue
40 rasu
41 bond_toukei
42 JointGov
43 PRICEDT44 ISSUEDATE

一人あたり退職手当引当金
スプレッド標準偏差
自治体コード番号
県ダミー
クーポン
満期日
Moody's格付け
S&P格付けR&I格付け
期中発行額
ラスパイレス指数
差引現在高共同発行債フラグ
価格評価日
発行日

45 Difference

売買参考統計値上の取引価格の最大と最小の差*11~30は公会計指標

,
,
,

,
,
,
,
,
2019

,

0.5

(

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2020
,

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10

430

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,

,
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－ 41 －

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自治体名
北海道
札幌市
宮城県
仙台市
秋田県
福島県
茨城県
栃木県
群馬県
埼玉県
千葉県
千葉市
東京都
神奈川県
横浜市
川崎市
相模原市
新潟県
福井県
山梨県
長野県
岐阜県
静岡県
静岡市
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0
961
351
146
39

年限

5
3018
1014
699
201
106
523
279
328
668
3307
1260
810
4997
3464
3135
974
402
660
280
363
201
344
3882
176
4468

103
106
39
72
754
172
184
1138
848
553
396
39
117
46
115
39
106
1032
36
1259

年限

10
1003
702
268

年限

201
200
162
198
497
1899
648
579
2801
1781
1480
961
134
350
140
164
164
2430
103
3001

15
575
312

114,416

年限

20
335
156

26

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335
681
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134
456
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938
1089
984
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603
430
516
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752

521

335

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総計

自治体名

5959
2574
1113
365
307
826
547
565
1706
7912
2527
1573
11013
7612
6668
3961
575
1244
718
642
240
614
9078
315
10470

名古屋市
滋賀県
京都府
京都市
大阪府
大阪市
兵庫県
神戸市
奈良県
島根県
岡山県
岡山市
広島県
広島市
徳島県
高知県
福岡県
北九州市
福岡市
長崎県
熊本県
熊本市
大分県
鹿児島県
総計

28,604

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0
158
39
699
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1677
688
626
386
39
72
106
67
533
444
39
542
195
565
138

56
74
16242

年限

年限
年限
年限
年限
年限
5
10
15
20
25
30
878
560
224
219
112
28
328
200
2416 1642
369
134
67
39
1745
909
318
214
5794 2557
430
1622121 1834
502
341
240
134
2359 2129 1244
603
67
39
1141
871
508
430
268
252
200
333
106
134
240
28
670
402
335
201
2253 1339
371
304
67
974
402
45
301
192
115
233
200
2293 1764 1110
500
296
212
507
558
253
39
1566
738
427
520
39
224
56
272
97
300
200
197
98
166
63056 36738 13527 8391 3311 1755

４．スプレッド推定モデルの構築
４－１．スプレッド推定モデルの構築と説明力の比較
2019

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rf

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2179
567
5366
3634
10620
5860
7067
3856
239
913
1178
603
4867
2166
346
433
6717
1552
3855
280
507
500
351
240
143020

rf

,

rf

,

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変数
TokyoSpread
YRSTOMAT
jissitsu
COUP

定義
各銘柄の対東京債スプレッド (各銘柄の残存年限
各自治体の実質公債比率
各銘柄のクーポン金利2019－ 43 －

変数)

9

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Random Forest Regressor
パラメーターn_estimators
criterion
max_features
max_depth
min_samples_split
min_samples_leaf
min_weight_fraction_leaf
max_leaf_nodes
min_impurity_split
bootstrap
oob_scoren_jobsrandom_stateverbosewarm_start

設定 内容
100 バギングに用いる決定木の個数
mse 決定木のデータを分割する指標
auto 最適な分割のための特徴量数。auto は特徴量数のルート個
None 決定木の深さの最大値
2
ノード分割のための必要な最小サンプルサイズ
1葉を構成するのに必要な最小限のサンプル数0葉における重みの総和の最小加重率
None 生成される決定木における最大の葉の数
None 決定木成長における停止の閾値
TRUE ブートストラップサンプリングの実行
FALSE 各ブートストラップサンプリングでサンプリングされなかったデータの利用
1
フィットおよび予測の際に用いるスレッドの数
123 乱数シード
0
モデル構築過程のメッセージ有無
FALSE 既にフィットしたモデルへの学習の追加

４－２．ランダムフォレストモデルの概要
R2

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－ 44 －

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(2023b)５－１．特徴量の重要度,,
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Feature Importance

PFI

PFI

,

,

,
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,

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PFI

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ICE

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５－４．目的変数に対して行った予測の理由－ 47 －

SHapley
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SHAP

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,

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Intelligence

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,

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６－１．特徴量の重要度
PFI

,

,2019

,2020

,

2019

,2020

,

,

,

,
2019

,2020

－ 48 －,

■ PFI による特徴量の重要度の結果
2019

PFI

Permutation

2020

PFI

Permutation

SHAP
,

2019

,2020

,

,

,

,

,

,2020
,
0.84bp 3,

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,

2019
,
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－ 49 －

2020

2020

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■ SHAP による特徴量の重要度の結果
2019

SHAP

2020

SHAP

６－２．特徴量と予測値の平均的な関係（PD）とインスタンスごとの関係（ICE）
,

PD
2019

,

2020

,

PD

15
15

－ 50 －

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2020

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15
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0.03

0.02

,
,
,

－ 51 －

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2020

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,
2020

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,

2019
PD

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,

PD
,

－ 52 －

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2020

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11,

12
,2020

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11

12

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0.02

－ 53 －

0.02, 2020

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2020

ICE

６－３．目的変数に対して行った予測の理由
SHAP

13

,

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jissitsu,, jissitsu, jissitsu,,,－ 54 －,

13 2019

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14 2020

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,

,

,

,
,

13

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－ 55 －

15 2019

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15

16
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－ 56 －

17 2019

18 2020YRSTOMAT
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SHAP

0.02

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,

18

5

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0.04,

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19 2019

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0.03

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0.06
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７．まとめ
,
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AI XAI,,23－ 58 －

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,
2019

2020

,
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5
,
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,

,

,

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,
,
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24

－ 59 －

,
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,

,

,

,
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,

,

,

,
,
,

参考文献
2007
11

2

31 45

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No.2007-01,

2019(2021) XAI(

AI)--

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2008
58

,1 16

2012
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2021
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3

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(

)
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2023a
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(

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Haraguchi,K. and K. Oishi (2019) The International Differences in the Relationship between
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Liu,P. and A. V. Thakor (1984) Interest Yields,Credit Ratings,and Economic Characteristics ofState Bonds: An Empirical Analysis, Journal of Money, Credit and Banking,16 (3), pp.344351.
Lundberg, S.M. G. Erion, H. Chen, A. DeGrave, J. M Prutkin, B. Nair, R. Katz, J. Himmelfarb,
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URL
FTSE Russell

The Yield Book Inc

https://www.yieldbook.com/m/products/yield-book/index-jpn.shtml
https://www.yu-cho-f.jp/ar_center/research_aid/ronbunkensaku.html
謝辞
2022

,

JSPS KAKENHI Grant Number JP19K23214, JP20K02058, JP21K13412,
The Yield Book Inc.

,

APPENDIX

発行額 8.4056 以上？
Yes

標準偏差 0.0467 以上？

Yes

No

No

27

－ 62 －,FTSE

とイスラム
と

行

度的
の

の

ESG

ESG

ESG
ESG
(1)

ESG

(2)
(3)
(4)

2006

(5)

35

1975

PRI
2020

GSIA

2021
Statista

(6)

ESG
ESG
ESG

ESG

ESG
ESG

－ 65 －

ESG
E
G
ESG
ESG
ESG
S&P
Sustainalytics
CDP
Fitch Solutions

Truvalue LabsFTSE Russell

ESG

Moody's

DNV

MSCI

JCR
R&I
NEEDS

Sustainalytics
(

ESG

ESG

－ 66 －)

S

(1)ESG160

2015/1/1 = 100

150

150

140

140

130

130

120

120

110

110

100

100

90

90

80

2018/6/1 = 100

70

80

2015

2016

2017

2018

2019

FTSE Japan

2020

2021

2018

2022

2019

2020

MSCI IMI Top 700

FTSE Blossom

2021

2022

MSCI J-ESG Select Leaders

(2)
200

2015/1/1 = 100

160

2015/1/1 = 100

150

180

140

160

130
120

140

110

120

100

100

90
80

80

2015

2016

2017

2018

SPJ 500

2019

2020

2021

2015

2022

2016

2017

2018

FTSE JapanSPJ 500 Shariah

2019

2020

2021

2022

FTSE Japan Shariah

Bloomberg

ESG

ESG

ESG
ESG

ESG

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ESG

－ 67 －

ESG
2023

１．イントロダクション
ESG

SDGs
ESG
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ESG

－ 68 －

6

ESG

２．制度的類似性──ESG 投資とイスラム金融
ESG
ESG
ESG
ESG

ESG
ESG
(1)

ESG

(2)

(3)
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2006

1975
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35

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Statista(6)－ 69 －

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ESG
2019

ESG
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ESG
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ESG

－ 70 －

ESG
2019: 8
6
GPIF
Government Pension Investment Fund

ESG

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2019: 8

ESG
ESG
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S&P
Sustainalytics
CDP
Fitch Solutions

Truvalue LabsFTSE Russell

ESG

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DNV

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(

ESG

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－ 71 －)

GPIF
2019: 116
ESG

ESG

[2017][fatwa]ESG
S&P Global, FTSE, MSCI

ETF
ETF
ESG

ESG

－ 72 －

ESG

ESG
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ESG

ESG
（２）スクリーニングの対象・目的
ESG
ESG
E S G

ESG

ESG

ESG
ESG

ESG

ESG
ESG
ESG

ESG

－ 73 －

３．ＥＳＧ投資におけるスクリーニングに関する課題
ESG

ESG

2021

ESG

600

（１）ＥＳＧ投資におけるスクリーニングの個別課題
600
ESG
ESG

ESG

ESG

ESG

ESG
600

100
15
2008
ESG
ESG

－ 74 －

55

[

]

ESG

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ESG

(1)
(2)

－ 75 －

ESG

Semenova and Hassel (2015)D

ner et al. (2015)(2016) Berg et al. (2019)

Chatterji et al. (2016)

Howard

ESG

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2020

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(2)
2002

(1)

ESG

ESG
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－ 76 －

ESG

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ESG
（2）ESG のスクリーニング上の課題に関する小括

ESG

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ESG
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－ 77 －

ESG

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AI
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４．株価パフォーマンスの比較を通じた検証（1）株価指数を比較の対象とする背景
ESG

2017

ESG

ESG
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GSIA [2020]
ESG
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ESG

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ESG
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－ 78 －

ESG

（2）株価指数による両者の比較
ESG

(
ESG

FTSE Blossom JapanFTSE JapanESG

MSCI Japan

MSCI

Japan

IMI

)

2015/12022/122018/62022/12

Top 700
S&P Japan Shariah 500

S&P Japan 500

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150

150

140

140

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130

120

120

110

110

100

100

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2021

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2020

2021

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(2)
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2015/1/1 = 100
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150

180

140

160

130

140

120
110

120

100

100

90
80

80

2015

2016

2017

2018

2019

2020

2021

2015

2022

2016

2017

2018

2019

FTSE JapanSPJ 500 Shariah

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: SDGs2017[]

CHATTERJI, A. K., DURAND, R., LEVINE, D. I., & TOUBOUL, S. (2016). “DO
RATINGS OF FIRMS CONVERGE? IMPLICATIONS FOR MANAGERS,INVESTORS AND STRATEGY RESEARCHERS”. Strategic Management
Journal, 37(8), pp.1597 1614
0

“Measuring the level and risk ofcorporate responsibility approaches”, Journal of Asset Management 16, pp. 450-466
Global Sustainable Investment Alliance (GSIA), (2020) “Global Sustainable Investment
Review 2020”
Howard, A. (2016) “Painting by Numbers”,

Professional Investors, Expert Magazine, Schroders.

－ 83 －

Semenova, N. and Hassel. L. G. (2015) “On the Validity of Environmental Performance
Metrics”, Journal of Business Ethics, vol. 132, issue 2, pp.249-258－ 84 －

ま
ト

よ

ま

ント

力

の

ート

度

た
データとの

よ

な

の
に

フ

によるモデルのールドCAPM
Klibanoff, Marinacci, and Mukerji 2005
KMM
2
,
.

Wakker2004-maxmin-contraction

Epstein(2010),.
Maccheroni, maxminMarinacci (2002)( , )= ( − )[, Ghirardato-maxmin]+－ 87 －

𝒂𝒂𝒙𝒙

[

]

∈

∈

→

( )[ (𝒙𝒙)]

0

∈

⊂

( )

-contraction(Gajdos, Hayashi, Tallon and Vergnaud (2008))
P=1( , ) = ( − )
=

( )[

] +

∈ ( − ){ ( )} +

s(P)

[

∈

]

[

]

2018

2

.

.
DICE

𝜀𝜀

𝑐𝑐𝑓𝑓𝑚𝑚𝑐𝑐𝑓𝑓𝑚𝑚𝑐𝑐𝑐𝑐𝑚𝑚𝑓𝑓𝑚𝑚 𝑚𝑚𝑓𝑓𝑚𝑚𝑚𝑚𝑚𝑚

TN－ 88 －

𝛼𝛼 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 𝑚𝑚𝑓𝑓𝑚𝑚

係数

Odds

5.87E-01

2.405140.487

0.6264-3.82E+00

0.00043-2.1090.035 *

-4.83E-02

0.90826-0.0870.9305

6.64E-03

1.079390.123

0.9018

性別（女性＝１）-1.96E-01

1.07416-0.5190.6037

年齢

3.98E-02

0.79949

2.321

0.0203 *扶養人数-1.60E-01

1.07939-0.7510.4528

可処分所得-2.39E-02

0.95215-1.2170.2235

現金（含む普通預金）-7.38E-05

0.99984

-0.61

0.542

定期性預金

-8.91E-04

0.99819-1.5040.1325定数
相対的リスク回避度

ｚ値

1

2023

5

84
odds

－ 89 －

0.000429

ｐ値

1

2023
226
2005

11

2022

10 2

39.8

3

19.8

Mehra and

Equity Premium
Prescott,1985

MO Riegera and Wang
Epstein and Zin (1991)(GEU)
Benartzi and Thaler 1995

1

2,043

rwada@u-keiai.ac.jp－ 90 －

Gul

1991(2006)
Barro

Hens and Wöhrmann
Rietz 1988

Mehra and Prescott 1988

2005

and Duffie 1996

Constantinides
Krebs 2000
(Constantinides etal., 2002)(Bansal and Coleman,

1996; Holmström 1998)

Capital Asset
Pricing Model

Epstein and Schneider (2007)
Schneider (2003), recursive multiple-priors

Epstein andmulti-prior model Gilboa and Schmeidler (1989)

Ju and Miao (2012)

recursive multiple-priors utility
Hara and Honda (2020)
Klibanoff,
Marinacci, and Mukerji (2005)

KMM
2－ 91 －

Bossaerts et al. (2010),
Levin (2011

Cohen,Tallon and Vergnaud

Lauriola and2011 Cohen,Tallon and Vergnaud

Wakker 2004prior

Gilboa and Schmeidler (1989)2011

maxminprior
geographic

maxmin

prior

prior

geographic

Baillon, Halevy and Li (2022)
maxmin-contraction.

Holt and Laury 2002
Multi Pricing List

MPL

Becker, DeGroot andMarschak (1964

deception

Dominiak, and Duersch (2015)
Telling Nothing (TN)Snake and Ladder
DICE
TN－ 92 －

KMM

Hayashi and Miao 2011

EpsteinHara 2020

2010
maxminMaccheroni

Ghirardato

Marinacci (2204)-maxmin𝑈𝑈(𝑃𝑃, ℎ) = (1 − 𝛼𝛼) 𝑚𝑚𝑚𝑚𝑚𝑚 𝐸𝐸𝑝𝑝 [𝑢𝑢 ∘ ℎ] + 𝛼𝛼 𝑚𝑚𝑚𝑚𝑚𝑚 𝐸𝐸𝑝𝑝 [𝑢𝑢 ∘ ℎ]𝑝𝑝 ∈ 𝑃𝑃𝑢𝑢 ∘ ℎ: Ω → 𝑅𝑅ℎ(𝜔𝜔)[𝑢𝑢(𝑚𝑚)],𝑓𝑓𝑓𝑓𝑓𝑓⊂ ∆(Ω)𝐸𝐸𝑝𝑝𝜔𝜔 ∈ Ω

Gajdos, Hayashi, Tallon and Vergnaud (2008)-contraction

Ps(P)𝑈𝑈(𝑃𝑃, ℎ) = (1 − 𝜀𝜀) 𝐸𝐸𝑠𝑠(𝑃𝑃) [𝑢𝑢 ∘ ℎ] + 𝜀𝜀 min 𝐸𝐸𝑝𝑝 [𝑢𝑢 ∘ ℎ]=min𝑝𝑝∈(1−𝜀𝜀){𝑠𝑠(𝑃𝑃)}𝐸𝐸𝑝𝑝 [𝑢𝑢 ∘ ℎ]

－ 93 －𝑝𝑝∈𝑃𝑃ℎ ∈ 𝐻𝐻

0

=1
0

3
3
180
90

3

(

)

(𝑝𝑝𝑅𝑅 , 𝑝𝑝𝐵𝐵 )2, (𝑝𝑝𝑅𝑅 , 𝑝𝑝𝐵𝐵 , 𝑝𝑝𝑌𝑌 )3= {(𝑝𝑝𝑅𝑅 , 𝑝𝑝𝐵𝐵 ): 𝑝𝑝𝑅𝑅 , 𝑝𝑝𝐵𝐵 ≥ 0, 𝑝𝑝𝑅𝑅 + 𝑝𝑝𝐵𝐵 = 1}

𝑪𝑪 = {(𝑝𝑝𝑅𝑅 , 𝑝𝑝𝐵𝐵 ): 𝑝𝑝𝑅𝑅 ≥ 𝑝𝑝𝐵𝐵 ≥ 0, 𝑝𝑝𝑅𝑅 + 𝑝𝑝𝐵𝐵 = 1}

𝑫𝑫 = {(𝑝𝑝𝑅𝑅 , 𝑝𝑝𝐵𝐵 , 𝑝𝑝𝑌𝑌 ): 𝑝𝑝𝑅𝑅 , 𝑝𝑝𝐵𝐵 , 𝑝𝑝𝑌𝑌 ≥ 0, 𝑝𝑝𝑅𝑅 + 𝑝𝑝𝐵𝐵 + 𝑝𝑝𝑌𝑌 = 1}

Dominiak and Duersch (2015)

0

Carbone, Dong and Hey (2017)－ 94 －

90

Oechssler andRoomets (2015)

Ellesberg

Hayashi and Wada (2010)
Snakes and Ladders
DICE.

TN Telling Nothing
H

3

B,C,D

.

2000
.

H

(0.5, 0.5)

1000
100

2000

100

1100

1000

H
1100

1

B

(𝑝𝑝𝑅𝑅 , 𝑝𝑝𝑌𝑌 ) =H
HD－ 95 －

,

BCD

05

55

,
A05 A55

A

11
A05

05

55

05

55

05

TN

A

5

A55

55

B,C,D

DICE

B

C

B

61,119B
D

X={B, C, D}pe(X)Probability Equivalent-maxminPEpe(B) = pe(C) = pe(D)-construction

pe(C)

pe(B)

1/2

pe(D)

pe(B)

2/3

500
1000

30

77
2000
DICE

,

PC
PC
2

2

55

phython－ 96 －

2

PC

BOX B

.
2022

23

34

2022

4

3

3

84

2018
16

2018

4

3

4

DICE

PC

－ 97 －

242

DICE
TN

0.279

DICE

0.098
DICE
DICE

3

B

TN,

DICE

TN
Bossaerts et al. (2010)

Lauriola and Levin (2011

DICE
Cohen,Tallon and Vergnaud 2011TN,

- maxminpe(D) = pe(B)2/3 * pe(B)1－ 98 －,- contractionpe(D)H0 α-maxmin
ALL
Sample size
257
pe(B) = pe (D)t-value
10.268p-value(0.0000)****

TN118
5.86450.0000)****

DICE
139
8.6806(0.0000)****18.917(0.0000)****

12.447(0.0000)****

17.122(0.0000)****

H0 :ϵ-contractionALLpe(D) =2 / 3 pe(B) t-value
2.1910p-value(0.01512)**TN
2.4388(0.02954)**

DICE
1.26790.2061pe(C) =1 / 2 p e (B) t-value
18.917p-value(0.0000)****

12.447(0.0000)****

17.122(0.0000)****

pe(C) =1/2 pe (B)p-value**5t-value**** 0.1- contraction

pe(D)

TN

2/3 * pe(B)

DICE

.

pe(B) = pe(D)TN DICEpe(B)> pe(D
0
DICE

DICE

0.645

TN
4

B

－ 99 －

TN

0.692

-maximin-contractionpe(C) = 1/2 * pe(B)DICE TN=0.000

DICE
B

C
C

DICE

TN
5

DICE

C

－ 100 －

D

**

5

Bossaerts et al.(2010)

Dimmock,
Kouwenberg, Mitchell and Peijnenburg (2016)

,
DICE

5
5 1

2017

Ahn et al. (2014)

and Zame (2010)－ 101 －

Bossaerts, Ghirardato, Guarnaschelli,implied ambiguity aversion

DICE

5

2012
6

5

18
45

5
42

31
47
2022

5

6

－ 102 －

11.8

7

Dimmock et al.(2016)

804

900

8

7

Dimmock et al. (2016)8－ 103 －

1

Baillon and Placido (2019)

84
A.
39.5

B.

－ 104 －

C.

54.7

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17
2022
40

54
71
30

40

80

13
8

－ 105 －

A-1.
35

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wifi

－ 106 －

3 87

Median00Bu(𝒙𝒙) = 𝒙𝒙𝒂𝒂

－ 107 －

Mean

0

0

4
*

10

**

5

5

odds

Dimmock et al. (2016)

0.000429

odds

0. 99
H
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－ 108 －

Power

5
0.047
0.088

2018

2

KMM
𝜀𝜀

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𝑐𝑐𝑓𝑓𝑚𝑚𝑐𝑐𝑓𝑓𝑚𝑚𝑐𝑐𝑐𝑐𝑚𝑚𝑓𝑓𝑚𝑚 𝑚𝑚𝑓𝑓𝑚𝑚𝑚𝑚𝑚𝑚

, TN

84

odds

DICE

0.004289
0.799

10－ 109 －

𝛼𝛼 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 𝑚𝑚𝑓𝑓𝑚𝑚

Economic Science Association 2023 European meeting

DICE.

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－ 113 －

モ
る

イル

ーの
ン

は
のデータ

の分析

要

約

世界銀行の推定によれば、先進国では 94%の成人が銀行口座を保有しているが、途上国では金融機関への距離が遠いなどの理由で 63%の成人しか銀行口座を保有していない。これらの口座なしの人々は、貯蓄手段として、家畜の購入、タンス預金、教会等への預けいれを通して貯蓄をしている。その結果、不作、災害、失業などの経済的な負のショックを経験した
場合、その負のショックの影響を緩和する手段が十分でない。またビジネスや将来の子供への教育のために貯蓄を行う場合にも困難がある。一方近年、携帯電話を使った技術的イノベーションによりその状況が劇的に変化しつつある。それが、携帯電話を使った金融サービス、
いわゆる「モバイルマネー」である。例えば、ケニアでは、伝統的な銀行口座の成人保有率は、55%であるが、モバイルマネーの成人使用率は 77%である。タンザニアでは 2008 年にモバイルマネーが認可され、2017 年にはその個人使用率が 55%になったのに対して、伝統的な銀行口座保有率はほぼ 21%で一定である。今回の研究対象国であるジンバブエでは、世界銀行によるサーベイによれば、銀行口座保有率は、17％であるのに対して、モバイルマネーの使用率は 32％である。また、将来的なモバイルマネーの可能性は大きい。携帯電話の世界
団体 GSMA は、９６か国に 310 の携帯電話送金サービス会社があり、毎日 20 億ドルが送金されていると報告している。また現在、amazon や space-X 社によって、スターリンク等の大規模な衛星コンステレーションが準備されている。現在世界では 18 億人の口座なしの人々がいる。人工衛星コンステレーションで地球のあらゆる場所でインターネット接続が可能になると、
これら 18 億の人々のモバイルマネーへのアクセスが可能になる。またそれは、金融機関にとって大きなビジネスチャンスとなる。
経済学でのこれまでの先行研究は、モバイルマネーの拡大は、送金の受け入れを容易にし、その結果、世帯が負のショックを経験したときに消費の平準化が可能になるという観点から分析を行ってきた。(Jack and Suri(2014), Munyegera and Matsumoto (2016) ,Riley(2018)).
今回の研究では、先行研究と異なり、サブサハラアフリカのジンバブエを対象に、モバイル
マネーの拡大が、貯蓄のポートフォリオと借り入れの容易さを変化させるという仮説を実証した。

－ 117 －

ジンバブエは、サブサハラアフリカの 1 国であるが、近年モバイルマネーの使用が急拡大している。下の表は、伝統的な銀行でのＭ3 の量とモバイルマネーでやり取りされている送金額を示している。２０１１年にモバイルマネーが導入されて以来、２０１５年において、すでにモバ
イルマネーの量が伝統的な銀行でのＭ３の量にほぼ近づいていることがわかる。

この研究で注意しなければならないのは、モバイルマネーの使用自体が、経済的な内生変数であるということである。言い換えれば、貯蓄や借り入れをしたい人が、モバイルマネーを使用する可能性があるため、逆の因果関係が存在する可能性があるということである。その場合、単純な最小二乗法では、因果関係の意味で、モバイルマネーの使用が、貯蓄にどのように影響を与えるかをみることはできない。今回の研究では、この内生性の問題を解決するため、携
帯電話会社のモバイルネットワークマップを用い研究を行った。具体的には、各世帯の GPS
情報と携帯電話会社のモバイルネットワーク地図を用い、各世帯がどの程度モバイルネットワ
ークの内側あるいは外側にいるかを定量化した。そのうえで、そのモバイルネットワークの境
界からの距離を用いて内生性をコントロールした。以下の地図は、ジンバブエにおけるある地
区における各世帯の位置とモバイルネットワーク地図を示している。赤い点が、世帯が存在している場所であり、背景が LANDSAT の衛星画像である。紫色で示されている場所がモバイ
ルネットワークが行き届いているエリアである。右下に示されている直線は白と黒の両方で－ 118 －

5km である。下の地図からわかるように隣接している世帯でモバルネットワークによってカバーされているエリアとそうでないエリアがある。また統計的にはモバイルネットワークに入っている
世帯ではモバイルマネーを使う確率が近い。つまり似たような状況の世帯において、モバル
ネットワークエリアに入っている世帯はモバイルマネーを使う確率が高く、エリアに入っていな
い世帯はモバイルマネーを使う確率が低い。この情報を使って内生性をコントロールする。

そのことを示したのが次の表である。この表の横軸は、他の影響をコントロールしたうえでのネットワークに入っているダミー変数を縦軸はモバイルネットワークを使う確率を示している。このグラフは、ネットワークに入る確率が上がれば、モバイルマネーを使う確率が上がることを示している。この情報を使って、内生性をコントロールしたうえで、モバイルマネーの使用が、貯蓄、借入、送金受け取りにどのような影響を与えるかを分析した。

－ 119 －

研究結果は、次のようなものが得られた。
14

12
45

71
Space-X－ 120 －

－ 121 －Does Use of Mobile Money Affect Saving Decision?
Evidence from the Data in Zimbabwe

Hisahiro Naito ∗†Program in Economic and Public Policy
Graduate School of Humanities and Social Sciences
University of Tsukuba, Japan
Research Report submitted to Yucho-Foundation
June 2023∗

Address: Tennodai 1-1-1, Tsukuba, Ibaraki 305-8573, Japan; e-mail: naito@dpipe.tsukuba.ac.jp
I appreciate the financial support from Yu-cho Foundation. I am responsible for all remaining
errors.
†－ 122 －

Abstract
According to estimates by the World Bank, only 63% of adults in developing countries
have a bank account due to reasons such as the distance to financial institutions. Lacking a formal bank account makes it difficult for households to save safely and prepare
for potential future negative shocks. However, recent technological developments havestarted to change the financial access of non-bank users due to the development ofmobile money technology. This study examines the effect of the use of mobile moneyon saving and borrowing using information on mobile phone network coverage and
2SLS estimation strategy Zimbabwe. I find that the use of mobile money increases theprobability of saving and borrowing by 14 percent and 12 percentage points. I alsofind that the use of mobile money increase the probability of receiving remittance by
45 percentage point. On the other hand, the effect of negative shock on borrowing andreceiving remittance does not depend on the use of mobile money. This implies theuse of mobile money increases the opportunity of saving, the probability of receiving
remittance, borrowing money regardless of the negative shocks. Finally, we also foundthat the use of mobile money increase in saving in mobile money account but it does
not increase saving in a bank account. In short, the availability of the mobile network
makes households more accessible to mobile money and make them more financially
active and increase saving, borrowing and receiving remittance. In our data, the mobile money is not used to buffer negative shocks, contrary to the claims of the previous
studies such as Jack and Suri (2014) and Riley (2018).

－ 123 －

1

Introduction

According to estimates by the World Bank, 94% of adults in developed countries havea bank account, while only 63% of adults in developing countries have a bank accountdue to reasons such as the distance to financial institutions. Lacking a formal bankaccount makes it difficult for households to save safely and prepare for potential future
negative shocks. However, recent technological developments have started to change
the financial access of non-bank users due to the development of mobile money technology. Mobile money allows the holder of a SIM card of a mobile phone to transfer
money to another holder with a different SIM card.1 In addition, mobile money operators often offer a savings account in which customers can save with a reasonableinterest rate by depositing money with the nearest mobile money agent.2
According to a financial inclusion survey by the World Bank (World Bank, 2019),
only 55, 19, and 17 percent of adults have a bank account in Kenya, Tanzania, andZimbabwe, respectively, whereas 58, 32, and 32 percent of adults already have mobile
money accounts.
The mobile money has proliferated at an accelerated pace. In Zimbabwe, mobile
money was officially introduced in 2011, and, by 2015, the amount of money transferredthrough mobile money was almost the same as the amount of money circulating in the
traditional banking system. In Tanzania, mobile money was officially introduced in2008. In 2009, the user rate of mobile money was just 1.1 percent; however, this rose
to 32 percent in 2013 and 55.8 percent in 2017.
Given this high speed market penetration, a natural question is to what degree
mobile money affects the saving and borrowing of households. In developing countries,
the lack of access to a safe method of saving can lead to insufficient saving. Insufficientsaving and insufficient credit access in the face of negative shocks make it difficult for1

A mobile money account is attached to a mobile phone SIM card, not the mobile phone itself. In
developing countries, each individual often owns a SIM card, but shares a mobile phone with others,
especially in rural areas. Hence, even in such cases, it is possible for each individual to hold his or her
own mobile money account as long as he or she owns a SIM card.
2
The cost of the equipment needed to become a mobile money agent is much lower than the cost ofsetting up a bank branch or ATM. One needs only a personal computer and mobile network access to
become a mobile money agent. In sub-Saharan countries, owners of small grocery shops often become
mobile money agents. This implies that mobile money agents are more available than bank branches
and ATMs.

1－ 124 －

a household to buffer negative shocks. Hence, the availability of safe saving methods
through mobile money might make it easier to smooth consumption.
Theoretically, there are several channels through which the use of mobile money
affects saving. First, when a household lacks access to a formal financial institution,
mobile money can allow households to save in a safe and liquid way, thereby preparing
them for negative shocks(new source channel). Second, on the contrary, if a household
already has access to saving methods from a formal financial institution, the use ofmobile money allows it to switch the source of those saving methods to a mobile money
operator (substitution channel). This, however, would not affect the probability andthe amount of saving. Third, having a mobile money account makes it easy for ahousehold to saving from relatives or friends in the face of negative shocks because of
the low transfer fee, which decrease the need of saving(connection channel). Fourth,the presence of low-cost money transfers might increase the possibility of households
forming mutual insurance groups (Jack and Suri, 2014) (insurance channel). This
insurance effect is likely to increase the amount of saving as a group. Fifth, a mobile
money user can receive more remittances because of the low cost of transferring money
for altruistic reasons (Agarwal and Horowitz, 2002; Vanwey, 2004). Hence, when ahousehold can receive more remittances, the need for saving for precautionary reason
falls (remittance precautionary effect). On the other hand, receiving more remittancecan increase household disposable income, which in turn can increase the amount ofsaving.
Regarding borrowing behavior, similar arguments hold. The new source effect will
increase borrowing. The substitution effect will not change the total amount of borrowing but the composition of different saving methods will. The insurance effect will
decrease borrowing. The income effect is likely to decrease borrowing. Thus, fromthese theoretical points, it is not clear whether the use of mobile money will increaseor decrease saving and borrowing.
Therefore, in this study, first I examine the causal effect of the use of mobile moneyon saving by using Finscope Survey 2014 in Zimbabwe. For estimating the causal
effect of mobile money, several considerations are needed. First, using mobile money
is a choice variable. It is possible that a household that is financially distressed mightset up a mobile money account. This would introduce endogeneity bias. Second, an
2－ 125 －

important variable, which is not in the list of control variables, that might affect theschooling decision might be correlated with mobile money usage. This would lead toomitted variable bias.
To treat those problems, in this study, I apply the 2SLS estimation and use information on the G2 mobile phone network coverage as an instrumental variable while
including the observed demographic characteristics and enumeration area dummies ascontrol variables. In other words, I use the cross-sectional variation of the mobile phone
network coverage near the border of the network in each enumeration area as the key
exogenous variation. In the two-staged stratified sampling, each enumeration area isconstructed to ensure that households within each enumeration area is homogenous. In
Zimbabwe, each enumeration area is constructed to ensure that it includes 150 households in the national sampling frame based on the census, and those households are
expected to be very similar in terms of household demographic characteristics. Thisimplies that after I control the enumeration area, restrict the sample to households
who live near the border of the network area, and control observed demographic characteristics, the unobserved household characteristics is not likely to be correlated with
the network dummy and the outcome variable.
In addition, to examine the robustness of my estimates, I conduct several robustness
checks. First, in another specification, I restrict the sample to the households wholive close to the border of the network area (within 10km, 8km, or 6km from theborder), and run 2SLS with control variables including enumeration area dummies andhousehold characteristics. The idea to use those who live close the border of the mobile
network is that the household who live in those area are relatively more similar andthe bias due to unobserved characteristics seem less likely.
Second, I conduct the coefficient stability test which is proposed by Altonji, Elderand Taber (2005) and later refined by Oster (2019). The potential criticism to theidentification strategy of my 2SLS estimation is that there might be some unobserved
differences between those who live in the network covered area and those who live inthe uncovered area and those unobserved differences affect the outcome variable even
if I restrict the sample households to those who live within 6km from the border of the
network. The coefficient stability test proposed by Altonji, Elder and Taber (2005) and3－ 126 －

Oster (2019) address this issue directly.3 The coefficient stability test shows that even
if unobservable variables as strongly affect as all observed variables, the estimatedconfidence interval of the effect of the use mobile money does not include the zerotreatment effect.
Although my robustness checks show the estimated coefficients are robust, one
might still argue that there is unobserved difference of the household characteristics of
those who live under the mobile network coverage and those without mobile network
coverage even though they live within 6km from the border and that those differences
might affect the outcome. In addition, one might argue that although the use ofmobile money might ease the financial difficulty in sending children to school, theuse of mobile money might not improve the education outcome since the effect on
educational outcome is not strong enough.
The identification strategy in this study raises the question whether the instrumental variable, the network coverage dummy, causes substantial variation in the use ofmobile money after I include the enumeration area dummies and observed characteristics as control variables. However, even after including enumeration area dummies andobserved demographic characteristics, the instrumental variable, the G2 mobile phone
network coverage dummy, has a very strong predictive power for explaining the mobile
money usage (Kleibergen-Paap rank Wald statistics, which is the robust F-value inthis case, > 600); it shows that the instrumental variables pass the standard test of
the weak instrumental variable.
This study contributes to the existing literature in three ways. First, I estimate
the causal effect of the use of mobile money on saving and borrowing. Although manystudies examine the effect of the use of mobile money on receiving remittance, theeffect on saving and borrowing is quite limited. Second, I use the network coverage
map as the source of exogenous variation. My identification assumption is that once I
control the enumeration fixed effect, the variation of the coverage of the mobile network
is exogenous. Using this information, I construct the network coverage dummy and3

The coefficient stability test proposed by Altonji, Elder and Taber (2005) and Oster (2019) become
very influential in empirical research. For example, Oster (2019), which was published in 2019, hasmore than 1000 citations in January 2020 in google scholar. Their technique is now widely used in
papers published major economic journals (Mian and Sufi, 2014; Michalopoulos and Papaioannou,
2016).

4－ 127 －

use it as an instrumental variable while controlling enumeration areas and restricting
the sample to the households who live close to the border of the network area. The
households that live very close to the border of the network area in the same enumeration area will be very similar, irrespective of whether they live inside or outside the
network area. This instrumental variable turns out to be a very powerful instrumental variable, and the estimated coefficients are quite stable with various specifications.
This instrumental variable can be used for conducting different research in future.
Third, I use detailed information of the data on remittance, borrowing, and saving.
This information allows me to examine whether mobile money only increases remittanceor it also increases borrowing and saving.
Regarding the main results, using the coverage by mobile phone network as the
instrumental variable while controlling enumeration areas and other covariates, I findthat a household whose location is covered by the network has a 74 percentage points
higher probability of using mobile money than a household whose location is not covered by the network. The 2SLS estimates consistently show that using mobile money
increases the probability of receiving remittances, borrowing money, and saving by 45,
12, and 14 percentage points, respectively.
The remainder of this paper is organized as follows. In section 2, I discuss the historical development of mobile money and the educational system in Zimbabwe. In section
3.1, I explain my data set. In section 3.2, I discuss the empirical strategy. In sections
4.1, I present summary statistics and the results of the ordinary least squares (OLS)
and 2SLS estimations. In Subsection 4.2, I explore a possible mechanism. Section 5
provides a discussion and conclusions.
2

Literature Review

Our study is related to several strands of the literature. Given the rapid increase inmobile money usage, researchers have started to examine its effect on the economy(Aker et al., 2016; Muralidharan et al., 2016; Asongu and Asongu, 2018; Asongu, 2018;
Okello Candiya Bongomin et al., 2018; Okello Candiya Bongomin and Munene, 2021;
Blumenstock et al., 2015; Dupas and Robinson, 2013a; Jack and Suri, 2014; Munyegera
and Matsumoto, 2016; Blumenstock et al., 2016; Riley, 2018; Gosavi, 2018; Suri and5－ 128 －

Jack, 2016; Abiona and Koppensteiner, 2020; Riley, 2020).Aker et al. (2016) and Muralidharan et al. (2016) analyze the role of the secure payment method in Niger andIndia, respectively. Asongu and Asongu (2018) examine the effect of mobile money
usage on economic development. Asongu (2018) analyze the determinants of mobile
money penetration in African countries. Okello Candiya Bongomin et al. (2018) andOkello Candiya Bongomin and Munene (2021) examine the role of the social context
for the adoption of mobile money. Blumenstock et al. (2015) conduct a randomized
experiment to test the effectiveness of using mobile money to pay salaries. Dupas andRobinson (2013a) analyze the role of mobile money as a secure way to deposit daily
cash in microenterprises in Kenya. Jack and Suri (2014) theoretically show that the development of mobile money decreases the transaction cost of risk sharing and increases
the means to absorb a negative income shock on a household through an increase
in remittances. Additionally, the authors empirically demonstrate that, in Kenya, ahousehold that uses mobile money does not decrease consumption when faced with anegative income shock. Munyegera and Matsumoto (2016) show that, in Uganda, amobile money user receives remittances more frequently and has higher real per capita
consumption than a non-user. Blumenstock et al. (2016) and Riley (2018) analyze
whether mobile money is useful to smooth consumption for households that experience
negative shocks. Gosavi (2018) studies the effect of the usage of mobile money forfirms’ financing. Suri and Jack (2016) analyze the long-run effect of the use of mobile
money and find that 2 percent of Kenyan households have moved out of poverty since
its availability in the country because of increases in saving and financial resilience.
Abiona and Koppensteiner (2020) analyzes the effect of the use of mobile money on
education expenditure in Tanzania. Riley (2020) finds, using field experiments, that
disbursing loans through a mobile money account to female business borrowers has amore significant effect on profit than disbursing loans in cash. Naito et al. (2021) examine the effect of the use of mobile money on saving, borrowing and receiving remittancein Tanzania when a household experience a negative shocks. They found that the useof mobile money increase the probability of saving and receiving remittance. They also
found that the effect of negative shock on receiving remittance does not depends on
the use of mobile money.
To the best of our knowledge, however, no study focuses solely on the effect of
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mobile money on household saving, saving methods, and borrowing except my own
paper with co-authors.
Second, several studies examine the effect of having a bank account on financial
behavior. Burgess and Pande (2005) find that the state-led bank expansion in rural
India has reduced poverty. Bruhn and Love (2009) analyze the expansion of a Mexicanbank that offered both saving and credit products. They estimate that the new bankopening led to 7 percent higher income for both men and women. Dupas and Robinson(2013b) show that providing a safe place to save increases health-related saving by 60
percent in Kenya. Agarwal et al. (2017) analyze the effect of a large financial inclusion
program in India and find that the region exposed to the program now lends more toborrowers. Dupas et al. (2018) analyze the effect of having a bank account on saving
using field randomization in three countries, Uganda, Malawi, and Chile. They find no
discernible intention-to-treat effects on savings, but a large treatment-on-the-treatedeffect due to the low take-up rate.
3

Institutional Background in Zimbabwe

In 2000s, the economy of Zimbabwe experienced some ups and downs. In November
2008, the monthly inflation rate peaked and it reached 500 billion percent (Hanke
and Kwok, 2009). To address this ultra-hyperinflation, the government of Zimbabwe
abandoned its own currency in 2009. Consequently, the inflation rate became normal
and the economy started to grow by more than 10 percent from 2009. The annual
growth rate from 2009 to 2012 was more than 12 percent. However, due to the lack offoreign currency, the economy went into recession from 2013 and the economic growthrate became 2.0 percent in 2013. In 2016, the government of Zimbabwe issued an orderon the daily limit of cash withdrawals from formal financial institutions. However, in2014 and 2015, years in which the survey of Finscope data and Demographics Health
Survey 2015 were conducted, there was no official limit on cash withdrawal, although
the economy was in the recession.
In Zimbabwe, for most of the population, how to transfer money safely and cheaply
is a very important issue. In our sample, in the last 12 months, 36 percent of thehouseholds sent remittance to someone in Zimbabwe, and 1.5 percent of households
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Table 1: Economic Condition of Zimbabwe after Hyperinflation
Year
2007
2008 2009 2010 2011 2012 2013 2014 2015 2016
GDP Growth Rate (%)-3.7-17.7 12 12.6 15.4 14.8 5.5 2.1 1.7 0.6
Inflation Rate (%)
24411.0 89 S n.a. 3.0 3.3 3.9 1.6 -0.2 -2.4 -1.6
Lending Interest Rate (%)
579
1175 n.a. n.a. n.a. 11.6 9.7 8.5 7.1 9.5
Source: IMF fanancial statistics with the exception of the 2008-inflation rate.
Notes: The 2008-inflation rate is taken from the study by Hanke and Kwok (2015). They
estimated that the inflation of Zimbabwe in 2008 is 89 sextillion percent; n.a. shows that the data
is not available because of the chaotic situation of the country. All numbers are annual rates.

sent remittance to foreign countries. For receiving remittance, in the last 12 months,
46 percent of the households received remittance from someone in Zimbabwe, and 13
percent of the households received remittance from someone from other countries.
There are three major mobile network operators (MNOs) in Zimbabwe : Telecel, NetOne, and Econet. In September 2011, Econet, the largest mobile operator in
Zimbabwe, introduced a mobile money service called Ecocash.4 Econet Wireless has
registered significant success signing up users to its EcoCash mobile money service,with 1.7 million subscribers just a year after launch. As of 2015, the market shareof the total transaction value of mobile money of the three MNOs, Telecel, Netone,
and Econet, are, 3.1 percent, 0.01 percent, and 96.9 percent, respectively (Postal and
Telecommunications Regulatory Authority of Zimbabwe, 2015).5 By 2015, the amountof money transferred through the mobile money system had grown to match the amountof money circulating in the traditional banking system (See Table 2). Given that mobile money system was officially introduced in 2011, the mobile money system has
proliferated in Zimbabwe at an accelerated pace.
Econet wireless acquired the Steward Bank in February 2013; this allowed theaccount holders of the Steward bank to link their bank accounts with their mobile
4

Before 2011, mobile network operators had started small-scale projects using mobile money totest the implementation of mobile money. This implies the existence of a few mobile money businesses
before 2011. However, the size of the mobile money market was very small compared to its size inlater years. For statistics on the development of mobile money, see Table 1.
5
Note that as in many other developing countries, in Zimbabwe consumers often own several SIM
cards and register to multiple mobile money companies but do not rarely use some of mobile moneycompanies. For example, the share of subscription of Netone is 11.1 percent but people rarely use it
to transfer money. As a result, the market share of the transaction value is 0.01 percent. Thus, it ismore appropriate to use the transaction value based market share instead of the subscription based
market share. The subscription-based market shares of Telecel, Netone, and Econet are 14.3 percent,11.1 percent and 74.3 percent.

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money accounts. Other MNOs made similar arrangements. The partnership between
mobile money operators and traditional banks implies that mobile money can be either
a substitute for or a complement to traditional banks.6
Table 2: Development of Mobile Money and M3 in Zimbabwe
Year
Mobile Money
M3*
Mobile Money/M3*
2009
0.62
1,381.25
0.000
2010
1.21
2,327.61
0.001
2011
7.87
3,100.40
0.003
2012
381.61
3,719.00
0.103
2013
2,091.01
3,888.00
0.538
2014
3,634.40
4,377.00
0.830
2015
4,645.89
4,736.00
0.981
Source: Internal documents of the Ministry of Finance of Zimbabwe
Notes: The unit of mobile money and M3* is US million dollars. M3* is the amount of M3
minus the amount of mobile money. For calculating M3, the IMF definition of M3 is used.
Although mobile money was officially introduced in Zimbabwe in 2011, the mobile network
operators started small-scale projects on mobile money to test the implementation of mobile
money even before 2011. Thus, the amount of mobile money before 2011 is not zero.

According to a financial inclusion survey by the World Bank in 2014 (World Bank,2014), in Zimbabwe, 32 percent of the adults have mobile money accounts, while theproportion of the adult population with traditional bank accounts is 17 percent.
Concerning the ownership of mobile phones, the ownership rate of a mobile phone
is 85 percent, although only 62 percent of the households’ locations are covered by themobile phone network. This implies that many households would still use a mobile
phone even if their residential location is not covered by the mobile phone network.
However, as we demonstrate in the first stage regression of the 2SLS estimation, when
the households’ locations are covered by the mobile phone network, the probability
that a household would use mobile money is about 70 percentage point higher thana household whose location is not covered by the mobile phone network, even after
controlling enumeration area and observed characteristics.
6
Currently, Econet has a partnership with the Western Union. This implies the possibility oftransferring the money from foreign countries to a domestic mobile money account in Zimbabwe by
linking two accounts in the Western Union and Econet. For example, a customer who has an accountin the Western Union in Zimbabwe can link own Western Union bank account with own mobile money
account. When a relative or a friend who lives abroad sends the money to the Western Union’s bankaccount in Zimbabwe, the money is automatically transferred to the mobile money account. Many
mobile money operators in other countries have similar services.

9－ 132 －

4
4.1

Data set and Empirical Strategy
Data set

The data set that I use in this study is the Finscope data 2014, which was commissioned by the Ministry of Finance of Zimbabwe and the National Statistical Agency ofZimbabwe. The survey was conducted by FinMark, an independent trust.7 The sampling frame was developed by the National Statistics Agency of Zimbabwe based on amaster frame developed from the 2002 Population Census of Zimbabwe. The samplingframe was constructed and the weighting of the data undertaken to obtain a representative individual-based sample of each province of Zimbabwe for the population aged
18 years and older. The sampling was based on a two-stage stratified sampling, and 662
enumeration areas were selected. Among the 662 enumeration areas, 4000 households
were interviewed.
Administratively, there are 10 provinces in Zimbabwe, each comprising 59 districts,
and each district is composed of 1200 wards (municipalities). In each ward, there
are enumeration areas. The enumeration area is selected in the first stage in a twostage stratified sampling method. Enumeration areas are delineated to ensure thathouseholds in each enumeration area are homogeneous. There are about 150 households
in each enumeration area in the national sampling frame based on the census sample.
In the analysis, I construct two samples: the main sample and subsample. For themain sample, I restrict to households that have at least one household member who is
younger than 18 years of age and that provide information on the financial difficulty
in sending children to school and information on demographic characteristics. This
main sample contains 2,621 households. For the subsample, I drop households that did
not answer the question about the distance to school. The size of subsample is 1,789
households.
The Finscope data 2014 collects information on income, remittances, financial inclusion, location, distance, transportation methods to specific locations, such as a market
and school, schooling of children, and demographic characteristics. However, the ques7
FinMark Trust, an independent trust based in Johannesburg, South Africa, was established in
March 2002 and is funded primarily by UKaid from the Department for International Development(DIFD) through its Southern Africa office. For information on this trust, see http://www.finmark.
org.za/.

10－ 133 －

tion about income is not specific enough to exclude the amount of remittance received.
It is possible that the respondent includes the amount of remittance received in own
income when answering questions about income. Thus, I did not use this informationin this study.
The main dependent variable is the following variables: having saved in the last 12
months, having borrowed in the last 12 months, having received remittance in the last
12 months.
For distance and transportation methods to school and market, the data set provides
information as categorical variables. For distance, it provides 5 categories (less than
10 minutes, 11–20 minutes, 21-30 minutes, 30–60 minutes, and 1–2 hours), and for thetransportation methods, it provides 4 categories. I constructed dummy variables forthese variables and their interaction.
For the network coverage map, I use the network coverage map of the G2 mobile
phone provided by Econonet, which is the largest mobile money operator in Zimbabwe,with a mobile money market share exceeding 90 percent in Zimbabwe. Using thelatitude and longitude information of the households, I construct a dummy variableindicating whether a household is covered by this G2 mobile phone network.
Table 3 shows the summary statistics (mean and standard deviation) of the key variables in my data set. Regarding schooling, about 46 percent of the sampled households
stated that they had never experienced financial difficulty in sending their children toschool in the last 12 months. About 8 percent of the sampled households stated thatthey always (more than 10 times a year) experienced difficulty in sending their children
to school for a financial reason in the last 12 months.
Regarding the use of mobile money and mobile phone’s network coverage, about
51 percent of the sampled households use mobile money, while about 62 percent of
the sampled households are covered by the network. Of the sampled households, 85
percent own mobile phones. Regarding saving, 41 percent of the households statedthat they have saved in the last 12 months. The average amount of saving is 80 USD.
Regarding remittance, about 51 percent of the sampled households receive remittances.
The average frequency of receiving remittances is 4.2 times per year.
Concerning borrowing, 51 percent of the households stated that they borrowed in
the last 12 months, 45 percent of the households stated that they borrowed from family11－ 134 －

Table 3: Summary Statistics of Some Selected Variables
N
Variables
mean
s.d
Mobile Network Coverage
2,621
Mobile Money Dummy
0.508
0.500
2,621
Network Coverage Dummy
0.624
0.485
2,621
Distance to the Network (km)-1.885
8.814
2,621
Mobile Phone Ownership
0.849
0.358
Savings
2,621
Having Saved in the last 12 months
0.421
0.494
2,621
Amount of Saving (USD)
80.0
360.0
Borrowing
Having Borrowed in the last 12 months
2,621
0.510
0.500
Borrowed from Relatives or Friends
2,621
0.548
0.498
Borrowed from Mobile Money Companies
2,621
0.198
0.399
Borrowed from Savings Club
2,620
0.0927
0.290
Negative Shocks
Negative Shock Dummy
2,621
0.423
0.494
Death of the Main Earner
2,621
0.06070.239
Family Illness
2,621
0.105
0.306
Low Harvest Volumes
2,621
0.265
0.441
Low Price on Output
2,621
0.227
0.419
Remittance
2,621
Receipt of Remittance
0.510
0.500
2,621
Frequency of Receiving Remittance
4.178
6.389
Frequency of having difficulty in sending to school
2,621
always ( more than 10 times in a year)
0.08390.277
2,621
1 or 2 times a year
0.149
0.356
2,621
Never
0.465
0.499
Demographic Characteristics
2,621
Age of the Head of the Household
44.64
14.36
2,621
Gender of the Head of the Household
0.758
0.428
2,621
Household Size
4.858
1.820
2,621
Number of Children
2.381
1.491
Notes: The number of observations for the Distance to School variable is smaller than thenumber of observations of the other variables due to the limited response rate to thisquestionnaire.

or relatives, 20 percent of the households stated that they borrowed from mobile moneycompanies, and 9 percent of the households stated that they borrowed from a savings
club.
Concerning negative shocks, 40 percent of the households experienced some kind of12－ 135 －

negative shocks in the last 12 months, 16 percent of the households experienced deathor illness of family members, 26 percent of the households experienced lower harvest
volumes, and 26 percent of the households experienced a low price on the output.
4.2

Empirical Strategy

4.2.1

Analysis using Finscope Data

I consider the following Model, based on previous studies (Jack and Suri, 2014; Munyegera and Matsumoto, 2016),
Yi = β0 + β1 Mobilei + β2 xi + 2i(1)where Yi is a dummy variable indicating having saved in the last 12 months. This
dummy variable is equal to 1 if a household states that it has saved in the last 12
months, and 0 otherwise. Mobilei is a dummy variable that is equal to 1 if household
i uses mobile money. xi is a vector of household characteristics and geographicalvariables, such as ward and enumeration area dummies, distance to market and school,
and transportation methods, and their interactions. Additionally, xi includes a mobile
phone ownership dummy, following the specification used in Jack and Suri (2014) and
Munyegera and Matsumoto (2016).8 My parameter of interest is β1 .
There are several reasons why applying OLS to equation (1) would generate aninconsistent estimate of β1 . First, having mobile money is a choice variable. Thus, ahousehold that wants to save (higher value of 2i ) might decide to have a mobile money
account. In such a case, the mobile money dummy and the error term 2i are positively
correlated, and estimating (1) by OLS does not generate a consistent estimate of β1 .
In such a case, the estimate of the effect of mobile money on saving is biased upward.
Alternatively, a household that is very poor might decide to have a mobile money
account to receive remittances. Such poor households might have difficulty in saving
8

Although the mobile phone ownership dummy is included as a control variable in previous studies
on the effect of the use of mobile money on consumption (Jack and Suri, 2014; Munyegera andMatsumoto, 2016), I recognize that the mobile phone ownership dummy is also an endogenous variable
and including it as a control variable would introduce bias in the estimated coefficient of β1 even whenmobile money use is instrumented. I discuss the effect of including mobile phone ownership dummyin the estimating equation on the estimation of β1 , later in this subsection.

13－ 136 －

Figure 1: The Zoomed Network Coverage Area of One Area in Zimbabwe

Notes: The purple-coloured area is the area covered by the G2 mobile phone network of
the Econet. Red points are the location of the households. Background is the landsat
satellite image of the location in 2015

even if they received remittances. In such a case, the estimate of the effect of mobile
money on saving has a downward bias.
Second, a household that uses mobile money can be different from a householdthat does not use mobile money in terms of other characteristics. When all household
characteristics that influence schooling and mobile money are not observed, estimation(1) by OLS generates an inconsistent estimate of β1
To solve the endogeneity bias and omitted variable bias that occur as a result ofapplying OLS to the equation (1), I estimate (1) by the 2SLS estimation, by using theG2 mobile phone network coverage as an instrumental variable while using the observed
covariates, enumeration area dummy and mobile phone ownership as control variables.
Figure 3 shows the relationship between the average use rate of mobile money and the
distance from the border of the network area. If the distance is negative, it implies thatthe location is inside the network area. As Figure 2 shows, there is a clear relationshipbetween the coverage by the mobile phone network and mobile money use rate.
To apply the 2SLS estimation, I estimate the following first-stage equation:
Mobilei =α0 + α1 Networki + α2 xi + 1i(2)where Networki is a dummy variable indicating whether a household location is coveredby the G2 mobile phone network of Econet. xi is the same as that used in (1) and14－ 137 －

includes demographic characteristics, ward or enumeration area dummies, the mobile
phone ownership, the source of income, distance to market and school, transportation
methods to those locations, and their interaction.
The basic idea of using the G2 mobile phone network coverage as an instrumental
variable while including other covariates as control variables is that the mobile money
coverage affects easing financial difficulty in sending to school only through the usageof mobile money once I control demographic characteristics, enumeration area and experience of negative shocks. With this exclusion restriction assumption, in my 2SLS,
I compare households whose locations are covered by the mobile phone network withhouseholds that live in the same enumeration area and that have the same characteristics but whose locations are not covered by the mobile phone network.
There are several reasons why coverage by mobile phone network is likely to be
exogenous once I control demographic characteristics, enumeration area and experience of negative shocks. First, Figure 1 shows the zoomed map of the mobile phone
network coverage. Figure 1 shows that among households who live very closely, there
are households that are covered by the network and households that are not coveredby the network. Since they live very closely, whether a household is covered by the
network or not is more likely to be exogenous once I control demographic and economic
characteristics, enumeration area, experience of negative shocks and distance to school
and markets.
Second, the enumeration area is the first unit that is selected in the first stage inthe two-stage stratified sampling design; each enumeration area in Zimbabwe has 150
households in the national census sampling frame, and it is designed to ensure thathouseholds are homogeneous as much as possible. Thus, once I control enumeration
areas, the observed household characteristics and the experience of the negative shock,
it is reasonable to assume that the network coverage is uncorrelated with financial
difficulty in sending children to school except through a channel of using mobile money.
Third, the mobile money was officially introduced in 2011, and the survey was
conducted in 2014. The mobile phone network was established much before 2011. Thus,
it is very unlikely that mobile phone company, which is also a mobile money operator,
designed the mobile phone network to provide radio access to particular households thatmight be interested in using mobile money. Certainly, it is possible that the demand15－ 138 －

for voice and tex (demand for mobile phone) is correlated with experience of financial
difficulty and the network coverage is positively correlated with the demand for mobile
phone. In this case, the experience of financial difficulty is positively correlated with the
network coverage. But, if this is true, then when I control the mobile phone ownership,
the estimated coefficient of the use of mobile should change. On the contrary, when I
includes ownership of the mobile phone as a control variable, I found that the estimatedcoefficient of the use of mobile money in the 2SLS estimation is not sensitive to theinclusion of the mobile phone ownership. This suggest that such a channel is not likely.
Fourth, due to the short gap after the introduction of mobile money, it is very
unlikely that a household would have moved its location to the network area for using
mobile money within 3 years. Additionally, when I restrict the sample to homeowners
or farmers, which have less mobility due to their attachment to own land, I observea similarity between the estimated coefficients and the estimated coefficient obtained
from the unrestricted sample. This suggests that a household’s relocation is not likely
to be driven by a need for using mobile money, and the network coverage is likely to
be exogenous.
However, one might argue that there are still unobserved difference between areas
covered by the network and areas not covered by the network. Although I include many
variables in the control variables, still it is possible that unobserved characteristics areboth correlated with the financial difficulty and the network coverage. To answer sucha concern, I conduct two additional analyses.
First, I conduct the coefficient stability test which was initially proposed by Altonji,
Elder and Taber (2005) and later refined by Oster (2019) and obtain the robust region of
the coefficient of the network dummy in the reduced form. This calculation is based on
the observation that it is reasonable to assume that at maximum unobservable factor iscorrelated with the network dummy when many control variables are already included.
In Supplemental material section, I explain how we obtain the robust region of the
network dummy. whether unobservable factors are big enough to cancel the size of thecoefficient of the network dummy. I find that even if there is unobserved factors which
are correlated with the network coverage and financial difficulty in sending children toschool, the effect of such unobservable factors is not big enough to cancel the size of
my estimated coefficient.
16－ 139 －

Since the estimated coefficient of 2SLS is the estimated coefficient of the reduced
form divided by the estimated coefficient of the first stage. Thus, consider the reduced
form regression which regress the outcome variable on the network coverage and control
variables. The reduced form regression is represented as
Yi = γ1 zi + φ1 x1i + φ2 x2i + wi + i(3)where zi is the network coverage dummy and x2 is a minimum set of control variables
such as enumeration area dummies which is used in any specification. x3 are control variables such as demographic characteristics, distance to market, experience ofnegative shock. wi is an unobserved factor that influences the outcome.
The proportional selection assumption by Altonji, Elder and Taber (2005) andOster (2019) states thatδCOV(z, w)
COV(z, φ2 x2 )=Var(φ2 x2 )Var(w)(4)δ is the degree of proportionality. It represent to what extent the unobservable factor
is correlated with the instrumental variable, zi , compared with φ2 x2i . Altonji, Elderand Taber (2005) state that δ =1 at the maximum is reasonable in a situation wherea large number of observable control variables are already chosen.
To see the relationship between δ and the degree of bias, let Rl be the R-squarewith a long list of control variables, x1i and x2i and Rs be the R-square with a shortlist of control variable with only x1i . Let Rmax be the maximum R-square with x1i , x2i
and wi . Similarly, let γ1l be the estimated coefficient of a long list of control variable,
x1i and x2i and let γ1s be the estimated coefficient with a short list of control variables,
x1i . Oster show that the bias due to unobservable wi is approximately equal tobias = δRmax − Rl× (γ1l − γ1s )Rl − Rs(5)

Note that (γ1l − γ1s ) and Rl − Rs are the change of the estimated coefficient of the key
explanatory variable and R-square as the set of explanatory variable is expanded. δ isdefined in equation (4). Thus, the above equation shows that the bias becomes smaller
as the change of R-square become larger and the change of the estimated coefficient of
17－ 140 －

the key explanatory variable become smaller as the set of control variables is expanded.
Altonji, Elder and Taber (2005) and Oster (2019) argue that it is reasonable to assume
that the maximum value of δ is close many observable control variables are included,
which is true in our case. Oster (2019) argues that Rmax = R1 × 1.3 is reasonablefrom surveying top economic journals. Thus, assuming that δ = 1, we can calculate
the bound of the estimated coefficient of Zi as [γ1 + bias, γ1 ] if bias is negative and[γ, γ1 + bias] if bias is positive. Alternatively, we can calculate δ that is needed to havezero coefficient of the network dummy. In the Result section, I estimate the bound of
the coefficient of the network coverage dummy assuming different value of Rmax .9 If thesign of the bounds do not change, it shows that my estimated coefficient is robust by
the effect of unobserved characteristics.
Endogeneity of Mobile Phone Ownership

In my 2SLS estimation, I include the mobile phone ownership dummy as a control
variable in xi or xtci . Although the inclusion of this mobile phone ownership dummy
follows the specification of the previous literature (Jack and Suri, 2014; Munyegera
and Matsumoto, 2016), the inclusion of the mobile phone ownership variable can bias
the estimate of β1 . This is because mobile phone ownership is the outcome variable,
and controlling it introduces bias in estimating the causal effect (Angrist and Pischke,2008). To examine in which direction the inclusion of the mobile phone ownershipbias, observe that the asymptotic bias of 2SLS can be written as follows (Hahn and
Hausman (2005), equation 3.1):
plim β̃1 − β1 =α1 σz̃2R2 σỹ2 ỹ2(6)where β̃1 is 2SLS estimator of β1 , the coefficient of the use of the mobile money dummyin the second stage equation. ỹ2 is the residual from regressing endogenous variable,
y2 , on the control variables. z̃ is the residual from regressing instrumental variable,z, on control variables. R2 is R-square when ỹ2 is regressed on z̃, which shows the
explanatory power of the instrumental variable after partial out the effect of control
9Note that the equation (5) holds only in approximation. To calculate the bound precisely, weneed to solve polynomial equation.

18－ 141 －

variables. σỹ2 ỹ2 is the variance of ỹ2 , the use of mobile money dummy after controllingthe effect of control variables. α1 is the coefficient of the instrumental variable in thefirst stage equation and it is positive. σz̃2 is the covariance between z̃ and the second
stage error term, 2 . σz̃2 shows the partial covariance between the instrumental variable
and the second stage error term, 2 while holding the control variables constant.
In SI2, I show that 2SLS estimate of β1 is biased downward and I examine how β1 is
sensitive to the inclusion and exclusion of mobile phone ownership. Regression results
show that it is not sensitive to the inclusion of mobile phone ownership dummy.
5

Results

OLS and 2SLS Estimates

Table 4 shows the estimated coefficients of the mobile money use dummy and the mobile
phone ownership dummy when I use the saving dummy as the dependent variable in
the OLS estimation of equation (1).
In all the specifications (1)–(5), the dependent variable is a dummy variable, indicating that the household had saved in the last 12 months. The control variables
in all the specifications include the enumeration dummies. In column (2), I include
the mobile phone ownership as an additional control variable. In columns (3),I add
household head characteristics such as the age, education and gender of the household
head and household characteristics such as the number of household members. In (4), I
add the distance to nearest market and transportation method to the market and theirinteraction as control variables. In (5), I add the income sources as control variables.
Column (5) of Table 4, using mobile money increases the probiblity of having saved in
the last 12 month by about 10 percentage points.
It must be noted that, as discussed in section 3, due to an endogeneity, we cannot
interpret the coefficient of mobile money as the causal effect of the use of mobile moneyon experiencing difficulty in sending children to school, and I use the 2SLS to estimate
its causal effect.
Figure 2 shows the graphical relationship of the distance from the border of themobile phone network and the rate of the use of mobile money. Note that both Table
5 and Figure 2 show the unconditional difference of the use of mobile between the19－ 142 －

Table 4: The Effect of Mobile Money Use on the Probability of Having Savedin the Last 12 months in OLS Estimation
Dependent variable
Having Saved in the Last 12 months
Variables
(1)
(2)
(3)
(4)
(5)
0.145*** 0.146*** 0.116*** 0.116*** 0.108***
Mobile Money Dummy(0.0201)(0.0201)(0.0210)(0.0210)(0.0209)
Mobile Phone Ownership 0.0965*** 0.0954*** 0.0747*** 0.0747*** 0.0743***(0.0271)(0.0271)(0.0272)(0.0272)(0.0272)
Negative Shock0.0392* 0.0499** 0.0499** 0.0540***(0.0205)(0.0207)(0.0207)(0.0206)
Control Variables
Enumeration Areas
yes
yes
yes
yes
yes
Mobile phone ownership
yes
yes
yes
yes
HH Characteristics
yes
yes
yes
Distance to Markets
yes
yes
Income Sourcesyes
R-squared
0.031
0.031
0.058
0.058
0.066
N
2,621
2,621
2,621
2,621
2,621
Notes: Robust standard errors are in parentheses. All specifications have enumeation
fixed effects. The distance to market is the time distance (five categories), transportaton
methods (four categories), and their interaction. Demographic variables include the age
and gender of the head of the household; age, gender, education, and the head of thehousehold dummy of the respondent; the number of children; the household size; andincome source dummies. Income source dummies are dummy variables indicating thesource of income (eight categories). Specification (5) has a smaller sample size due tothe limited response rate to the question about the distance to school. *** p<0.01, **p<0.05, and * p<0.1

household who live in the network covered areas and those who live under the network
non-covered areas. For example, Figure 2 maps households’ average distance and its
rate of the use of mobile money regardless of enumeration areas and demographic
characteristics10 . However, from the point of causal inference, we are interested in
the conditional difference of the use of mobile money between the households who livein the covered area and households who live uncovered area because the area that iscovered the area that is covered in the network and the area that is not covered can
10

Figure 2 might lead the readers to suggest the use of the (geographical) regression discontinuity
design because the regression discontinuity is considered the most credible identification strategy inthe evaluation literature (Lee and Lemieux, 2010). However, the data requirement of conducting thegeographical regression discontinuity design is quite stringent. It requires that, at all relevant pointsof the border, I need to find a sequence of household locations that converge to a corresponding border
point; subsequently, it entails a comparison between the right limit and left limit of the sequence ofhouseholds at each point of the border(Dell, 2010). In the data used in this study, for each enumerationarea, I have only about 15 households. Thus, it is not feasible to find a sequence of households at theboth sides of each point of the border.

20－ 143 －

Figure 2: The Use of Mobile Money and Distance from the Border of the Network Area

0

Ratio of Mobile Money Use
.2
.4
.6
.8

1

| Distance from the border | <=20km

−20

−10
0
10
Distance from the border of the Network
Differnce of the mean is 0.66 and SE is .04.

20

Notes: Each dot shows the average mobile money use rate at each distance from theborder of the network area. The difference in the mean of not experiencing financial
difficulty in sending children to school between covered and uncovered areas is 0.66,
and the standard error is 0.04.

be very different in other dimension. For example, it is possible that the covered area
is mainly urban area and uncovered area is mainly rural area.
Table 5 shows the result of the first- and second stages of the 2SLS estimation.
Panel A shows the first stage estimation results and Panel B shows the second stage
estimation results. Panel C shows the list of control variables that apply to both thefirst- and second stage estimations, which regress the mobile money use dummy on
the mobile phone network coverage dummy with other covariates. In all specifications,
I include enumeration area dummy and mobile phone ownership dummy. Inclusionof enumeration area dummy implies that we compare the household who live in the
network covered area and the household who live in the uncovered area in the same
enumeration area. Column (1) of Panel A of Table 5 shows that when a household iscovered by the mobile phone network, the probability of using mobile money increases
by 76 percentage points. Kleibeg-Papp-Wald Statistics (Kleibergen and Paap, 2006),
which is also equal to the robust version of the first-stage F-statistic (Baum, Schaffer
and Stillman, 2007) in our case, is greater than 10 and shows that our instrumental
variable, mobile money network dummy, is not a weak instrumental variable. Thissuggests that there is a substantial variation in the network coverage dummy within

21－ 144 －

Mobile Money Use Dummy after Controlling Covariates
−1
−.5
0
.5
1

Figure 3: The Network Coverage and Mobile Money Usage While Controlling Covariates
First Stage Regression After Controlling Covariates

−1

−.5
0
.5
Network Coverage Dummy after Controlling Covariates
The estimated slope is 0.74 and SE is .03. The size of bin is 0.001.

1

Notes: Horizontal axis is the residual from regressing the network coverage dummyon enumeration area dummy, mobile phone ownership dummy, demographic characteristics, income sources and distance to market and school and their interaction to
the transportation mode. Vertical axis is the residual regressing the mobile money use
dummy on the same covariates. Then, for each bin of the first residual, the averagevalue of the second residual is plotted on the graph. The size of bin is 0.001.

each enumeration area, and this network coverage affects the use of mobile moneystrongly within each enumeration.
In column (2), I add the experience of negative shock dummy as additional control
variable. Column (2) shows that the estimated coefficient in column (2) is almost identical to the estimated coefficient in column (1). In columns (3), I include demographic
characteristics such as age, gender and education of the respondents as control variables. In addition, I also include the distance to market and transportation method tomarket and its interaction as control variables in column (3). In column (4), I include
the income source dummy (6 categories) as additional control variables. In column(5), I include the distance to school dummies, mode to school dummies and their interaction as additional control variables, respectively. Since the response rate to thequestion about the distance to school is lower than that to other variables, the sample
size of column (5) is smaller than the other columns. In summary, Panel A of Table 6
shows that when a household’s location is covered by the mobile phone network, themobile money usage increases about 73 percentage points.

22－ 145 －

Table 5: The Effect of Mobile Money Use on the Probability of Having Savedin the Last 12 months in 2SLS Estimation
A. First Stage Estimation
Mobile Money Dummy
Dependent variable
(1)
(2)
(3)
(4)
(5)
Variables
Network Coverage Dummy 0.759*** 0.759*** 0.723*** 0.723*** 0.719***(0.0181)(0.0180)(0.0185)(0.0185)(0.0188)
0.165*** 0.166*** 0.133*** 0.133*** 0.136***
Mobile phone ownership(0.0212)(0.0212)(0.0203)(0.0203)(0.0202)-0.0286*-0.0202-0.0202-0.0195
Negative Shock Dummy(0.0153)(0.0148)(0.0148)(0.0148)
0.402
0.417
0.460
0.460
0.463
R-squared
Kleibergen-Paap rk Wald
1302
1307
1100
1100
1053
B. Second Stage Estimatoin
Having Saved in the Last 12 months
Dependent variable
(1)
(2)
(3)
(4)
(5)
Variables
Mobile Money Dummy
0.191*** 0.191*** 0.170*** 0.170*** 0.154***(0.0314)(0.0314)(0.0337)(0.0337)(0.0339)
Mobile Phone Ownership 0.0837*** 0.0829*** 0.0627** 0.0627** 0.0642**(0.0278)(0.0278)(0.0277)(0.0277)(0.0277)
negative_shock
0.0405** 0.0509** 0.0509** 0.0547***(0.0205)(0.0207)(0.0207)(0.0207)
0.028
0.029
0.056
0.056
0.064
R-squared
Control Variables
Enumeration Dummies
yes
yes
yes
yes
yes
Mobile phone ownership
yes
yes
yes
yes
yes
Negative shocks
yes
yes
yes
yes
HH Characteristics
yes
yes
yes
Distance to Markets
yes
yes
Income Sources
yes
N
2,621
2,621
2,621
2,621
2,621
Notes: Robust standard errors are in parentheses. Network Coverage Dummy is equal to
1 if a household's location is covered by the G2 mobile phone network. The
specification of control variables in each column is the same as that in Table 4.
Kleibergen-Paap Rank Wald statistics shows the Kleibergen-Paap rank Wald statisticsof the weak identification test. Notes in Table 4 apply. *** p<0.01, ** p<0.05, and *p<0.1

Figure 3 show the graphical relationship between the mobile phone network coverageand the use of mobile money while controlling the effect of enumeration areas, demographic characteristic and experience of negative shocks. Figure 4 show that there is aclear linear relationship between the network coverage dummy and the mobile moneyuse dummy even after controlling all covariates.
Panel B of Table 5 shows the results of the second stage estimation of 2SLS. The
specifications of the control variables are the same as those of Panel A of Table 5 (first23－ 146 －

stage). Column (1) shows that the use of mobile money increases the probability of notexperiencing financial difficulty in sending children to school by 12.4 percentage points
while controlling mobile phone ownership. In column (2), I add negative shock dummy
as an additional control variable. The estimated coefficient of column (2) is identicalto the estimated coefficient of column (1). In column (3), I include demographic
characteristics and distance to school as additional control variables. In this case,the coefficient changes from 12.4 percentage points to 12.9 percentage points. Column(4) shows that when the information on the source of income are included, the useof mobile money increases the probability of not experiencing financial difficulty insending children to school by 12.0 percentage points. In column (5), I include thedistance to school dummies, transportation methods to school, and their interaction asadditional control variables. Column (5) shows that the use of mobile money increases
the probability of not experiencing financial difficulty in sending children to school by
14.3 percentage points.
Overall, Panel B of Table 5 shows that the estimated coefficients of mobile money
usage are quite stable with the inclusion of various control variables. Panel B of Table
5 shows that the use of mobile money increases the probability of not experiencing
financial difficulty in sending children to school by 15–19 percentage points.
It is worth noticing that the estimated coefficient of OLS is downward-biased. Thisimplies that, in the OLS estimation, a household which uses mobile money is a household that is experiencing financial difficulty in sending children to school and that ahousehold which does not use mobile money is a household that does not experiencefinancial difficulty in sending children to school.
Table 6 shows the reduced-form regression, which regresses a dummy variable of
not experiencing financial difficulty in sending children to school dummy, on a dummy
variable of mobile phone network coverage with other covariates. The specifications of
the control variables are the same as those of Tables 4 and 5. Table 6 shows that being
covered by the mobile phone network increases the probability of having saved in thelast 12 months by 11–15 percentage points. Again, Table 6 shows that the relationshipbetween the network coverage dummy and a dummy variable indicating having savedin the last 12 months is quite stable, regardless of control variables. This indicatesthat those control variables do not change the relationship between the mobile phone24－ 147 －

Table 6: Reduced Form Regression
The Effect of Mobile Network Coverage on the Probability of Having Savedin the Last 12 months in OLS Estimation
Having Saved in the Last 12 months
Dependent variable
(1)
(2)
(3)
(4)
(5)
Variables
Network Coverage Dummy 0.145*** 0.145*** 0.123*** 0.123*** 0.111***(0.0240)(0.0240)(0.0244)(0.0244)(0.0244)
Mobile Phone Ownership
0.115*** 0.115*** 0.0853*** 0.0853*** 0.0851***(0.0270)(0.0270)(0.0272)(0.0272)(0.0272)
Negative Shock Dummy
0.0350* 0.0474** 0.0474** 0.0517**(0.0204)(0.0206)(0.0206)(0.0206)
0.023
0.025
0.056
0.056
0.064
R-squared
Control Variables
Enumeration Dummies
yes
yes
yes
yes
yes
Mobile phone ownership
yes
yes
yes
yes
yes
Negative shocks
yes
yes
yes
yes
HH Characteristics
yes
yes
yes
Distance to Markets
yes
yes
Income Sources
yes
N
2,621
2,621
2,621
2,621
2,621
Notes: Robust standard errors are in parentheses. Network Coverage Dummy is equal to 1
if a household's location is covered by the G2 mobile phone network. The specification ofcontrol variables in each column is the same as that in Table 4. Kleibergen-Paap Rank
Wald statistics shows the Kleibergen-Paap rank Wald statistics of the weak identification
test. Notes in Table 4 apply. *** p<0.01, ** p<0.05, and * p<0.1

network coverage and having saved in the last 12 months.
Figure 4 show the graphical relationship between the network coverage dummy
and the dummy that indicates that a household did not experience financial difficulty
sending children in school. The Figure shows that the relationship between the network
coverage and not experiencing financial difficulty sending in children to school is not
driven by the outlier.
The Effect of the Use of Mobile Money in Different Types of Savings

In Table 5 and Table 6, we have shown that the use of mobile money increases theprobability of saving in the last 12 months. A natural question would be in what formof saving households increases when they use mobile money. Table 7 examine such a
relationship. In Table 7, the dependent variable is a dummy variable indicating thata household has saved in a mobile money account or that a household has saved in abank count. The result of Table 7 shows that a household who used the mobile money
increases the probability of having saved in a mobile money account. However, Table
25－ 148 －

-1

-.5

0

.5

Figure 4: The Network Coverage and Having Saved in the Last 12 Months

-1

-.5

0
Network Dummy

Saving Dummy.51Fitted values

Notes: Horizontal axis is the residual from regressing the network coverage dummy on
enumeration area dummy, mobile phone ownership dummy, demographic characteristics, income sources and distance to market and their interaction to the transportation
mode. Vertical axis is the residual from regressing having saved dummy on the same
covariates.

7 shows that the use of mobile money does not increase the probability of saving in abank account.
Do Unobservable Factors Generate the Result of 2SLS Estimates ?

In my estimation, I have included enumeration area dummies in addition to observablecovariates. In other words, I am comparing the outcome of the household in the same
enumeration area who live within the network covered area and the household who live
outside the network covered area while controlling demographic characteristics andexperience of the negative shocks.
However, one might still argue that, even after controlling for observed characteristics enumeration areas and experience of negative shocks and enumeration area, there
might be unobserved characteristics that might be correlated with the network coverage and not experiencing financial difficulty in sending children to school. In thiscase, my 2SLS coefficient of the use of mobile money captures not only the effect ofmobile money but also the effect of the unobserved difference in the household characteristics correlated with the network coverage. To address this concern, I show threeevidences that my results are robust to unobserved difference of the characteristics of26－ 149 －

Table 7: The Effect of Mobile Money Use on
Different Types of Saving Form in 2SLS Estimation
A. Having Saved in Mobile Money AccountHaving Saved in the Mobile Money Account
Dependent variable
Variables
(1)
(2)
(3)
(4)
(5)
Network Coverage Dummy 0.0971*** 0.0973*** 0.0841*** 0.0841*** 0.0833***(0.0146)(0.0146)(0.0151)(0.0151)(0.0151)
Mobile Phone Ownership-0.0119-0.0117-0.0144-0.0144-0.0122(0.0123)(0.0123)(0.0120)(0.0120)(0.0119)
Negative Shock Dummy-0.0132-0.0109-0.0109-0.0118(0.0103)(0.0103)(0.0103)(0.0102)
0.033
0.034
0.069
0.069
0.074
R-squared
B. Having Saved in a Bank AccountHaving Saved in a Bank Account
Dependent variable
Variables
(1)
(2)
(3)
(4)
(5)
Network Coverage Dummy 0.0899*** 0.0898*** 0.0459** 0.0459**
0.0305(0.0188)(0.0188)(0.0192)(0.0192)(0.0189)
Mobile Phone Ownership
0.0351** 0.0350**
0.0238*0.0238*0.0195(0.0151)(0.0151)(0.0144)(0.0144)(0.0139)
Negative Shock Dummy
0.00626
0.00652
0.00652
0.0179(0.0135)(0.0130)(0.0130)(0.0125)
0.023
0.023
0.129
0.129
0.163
R-squared
Control Variables
Enumeration Dummies
yes
yes
yes
yes
yes
Mobile phone ownership
yes
yes
yes
yes
yes
Negative shocks
yes
yes
yes
yes
HH Characteristics
yes
yes
yes
Distance to Markets
yes
yes
Income Sources
yes
N
2,621
2,621
2,621
2,621
2,621
Notes: Robust standard errors are in parentheses. Network Coverage Dummy is equal to 1 if
a household's location is covered by the G2 mobile phone network. The specification ofcontrol variables in each column is the same as that in Table 4. Kleibergen-Paap Rank Waldstatistics shows the Kleibergen-Paap rank Wald statistics of the weak identification test.
Notes in Table 4 apply. *** p<0.01, ** p<0.05, and * p<0.1

the households between in the network covered area and network uncovered area.
First, I estimate the reduced form equation and 2SLS equation by dropping one
control variable and repeat this excercise for all control variables. This exercise shows
how much my reduced form estimates and 2SLS are sensitive the omission of one control
variable. Figure 5 show the estimated coefficient of the network coverage dummy in27－ 150 －

Figure 5: The Effect of Omission of kth Control Variable from the list of Control
Variables in Reduced Form and 2SLS Regression
Sensitivity Check of 2SLS

0

10

20

30

k

40

Estimated Coefficient of Network Dummy
LWB-CI50

60

70

Estimated Coefficient of Network Dummy
.05
.1
.15
.2
.25

Estimated Coefficient of Network Dummy.02 .04 .06 .08 .1 .12 .14 .16 .18 .2

Sensitivity Check of Reduced Formk th control variable is dropped from the list of control variables

UPB-CIk th control variable is dropped from the list of control variables

0

10

20

30

k

40

50

Estimated Coefficient of Mobile Money Dummy
LWB-CI(a) Reduced Form

60

70

UPB-CI(b) Second Stage of 2SLS

Notes: The vertical axis shows the estimated coefficient of the network coverage dummy in the
reduced form regression (Figure (a)) and the estimated coefficient of the mobile money use dummy
in 2SLS regression (Figure (b)) when k-th control variable except enumeration area dummies isomitted from the full list of control variables in the reduced form and 2SLS, respectively. The
horizontal axis shows the index of k-th variable. For example, the vertical height at k=12 show
the estimated coefficient when 12th control variable is dropped from the list of control variable.
Each graph shows that estimated coefficients of the network coverage dummy in the reduced form
regression and the use of mobile money dummy in 2SLS are quite robust regarding the omissionof different control variables.

the reduced form and the estimated coefficient of the mobile money dummy when k-th
control variable is omitted from the full list of control variable except enumeration
dummies. The Figure 5 shows that the estimated coefficients are not sensitive to theomission of one control variables. Also comparison of column (1) and column (5) of
both Table 7 and Table 8 shows that when all control variables omitted, the estimatedcoefficients of the network coverage dummy and the use of mobile money dummy do notchange. This suggest that the estimated coefficient of the mobile money use dummy is
not sensitive to omission of observable control variables. Given that we have already62 control variables, it is very likely that omission of the unobservable control variable
is not likely to affect the estimated coefficient of the network coverage dummy and theuse of mobile money dummy.
Second, I conduct the coefficient stability test proposed by Altonji, Elder and Taber
28－ 151 －

(2005) and later refined by Oster (2019) using the reduced form regression.
Dependent variable
VariableA
Specification
Mobile Money Dummy
B
Specificatoin
Mobile Money Dummy

Table 8: Coefficient Robustness to Unobservable Factors
Having Saved in the last 12 months
(1)
(2)
(3)
(4)
Baseline Effect Controlled Effect R-max δ for  =0
(Std. Err), [R2]
(Std Err),[R2](5)
Identified Setof 

R-max=1.3 ×R-square of the regression with full control
Control Variables in the baseline estimation: Limited Control Variable
0.116 **
0.667
4.39
[ 0.116, 0.156]
0 .096 ***(0.0261),[0.0009] (0.0426), [0.51]
R-max=1.5 ×R-square of the regression with full control
Control Variables in the baseline estimation: Limited Control Variables
0.116 **
0.77
2.67
[ 0.116, 0.243 ]0 .096 ***(0.0261),[0.0009] (0.0426), [0.51]

C
Specificatoin

R-max=1.3 ×R-square of the regression with full control
Control Variable in the baseline estimation: no control variable
0.116 **
0.667
2.28
[ 0.116, 0.145 ]Mobile Money Dummy
0.100 ***
(0.0257 ),[0.0001] (0.0426), [0.51]
Notes: The table shows coefficient robustness to unobsrvable factors based on Oster (2019) using the
reduced form equation. The column (1) show the estimated coefficient of Network dummy, its standard
error and R-square in the baseline model. In Panel A and B, the baseline model includes province
dummies, rural area dummy, distance to market dummies interacted with transportaton meothds. In PanelC, there is no control variable in the baseline model. Column (2) shows the estimated coefficient of the
netowrk dummy, its standard error and R-square when all control variables are used. Column (3) shows
the R-max value, the maximum R-square when all unobservable are hypothetically included in the control
variables. Oster (2019) argue that 1.3 times the R-square when all observable control variables are used
is appropriate. Column (4) shows the degree in which uonbservable factors need to be important to zeroout the estimated coefficient of the Network Dummy (. Column (5) shows the potential region of theestimated coefficient of the network dummy when uonbservable is correlated with the network dummy asMore specifically, I estimate the reduced form equation and identify a possible
region of the coefficient using Oster’s test by assuming that unobservable error term
can be correlated with the Network dummy as the same degree as observable. Table
7 show the results of this exercise. The column (1) shows the estimated coefficient of
the network coverage dummy in the baseline equation where the control variables areminimized. In Panel A and B of Table 7, I include as the control the province dummy,rural dummy, variables related with distance to market and distance to school. In
Panel C of Table 7, I do not include any control variable. Column (2) shows the29－ 152 －

estimated coefficient of the Network dummy, its standard error and R-square when allcontrol variables are included. Column (3) show the R-max, which is the hypotheticalvalue of R-square when not only observable but also all unobservable covariates areincluded. Following Oster, I use R-max as R-square in column (3) times 1.3 in Panel
A and Panel C. In Panel B, I use R-square times 1.5 to examine the robustness of theresult of Panel A. Column 5 calculates the robust region of the coefficient of the network
dummy assuming that unobservable is correlated with the network dummy in the sameas in the degree as the observable is correlated with network coverage dummy. In PanelA, column (5) show that the estimated robust region of the coefficient of the network
dummy is [0.116, 0.156] , which is away from zero and it is economically significant.
Column (4) conducts another thought experiment. The column calculates the degree
that unobservable need to be correlated for making the true coefficient of the network
work dummy zero. The column (4) of Panel A shows that it is more than 2. Altonji
and Oster argue that the reasonable maximum number is one. Therefore, it suggeststhat it is very unlikely that our results are driven by unobservable factors.
In Panel B, I calculate delta and the robust region of the coefficient when the R-max
is 1.5 times R-square of the regression when all control variables are included. Again,
the delta is 2.67, which is higher than one. In Panel C, I use a different specificationof the baseline estimation.
In Panel C, I do not put any control variable in the baseline estimation and check
whether my test is sensitive to the specification in the baseline estimation. The estimated identified set is [0.116, 0.145]. Thus, Table 8 shows that my estimation results
is robust to possible correlation between network dummy and unobservable factors.
Restricting the Sample to Households close to the Border of the Network Area
and Over-Identification Test

In my 2SLS estimation of equation (1) with equation (2), in Table 6, I have included
enumeration area dummies (or ward dummies) in addition to observable covariates.
The reason behind having enumeration area dummies is attributed to the similarities
found between households within each enumeration area that are covered by the network area and not covered by the network, after controlling for the observed covariates.
This facilitated comparison between the two types of households when examing the use30－ 153 －

of mobile money and the rate of not experiencing financial difficulty in sending children
to school after controlling for observed characteristics.
Table 9: Robustness Checks (2): Controlling Migration Problem
Restricting the Sample to Households living within 10km
but not within 5km from the Border in 2SLSHaving Saved in the last 12 months
Dependent variable
(1)
(2)
(3)
(4)
(5)
0.264*** 0.263*** 0.246*** 0.246*** 0.196***
Mobile Money Dummy(0.0587)(0.0587)(0.0637)(0.0637)(0.0633)
851
851
851
851
851
N
240.7
238.6
173.7
173.7
167.5
Kleibergen-Paap Rank Wald
0.042
0.042
0.118
0.118
0.149
R-squared
Control Variables
Enumeration Dummies
yes
yes
yes
yes
yes
Mobile phone ownership
yes
yes
yes
yes
yes
Negative shocks
yes
yes
yes
yes
HH Characteristics
yes
yes
yes
Distance to Markets
yes
yes
Income Sources
yes
Notes: Robust standard errors are in parentheses. A negative distance implies that the location is
inside the network area. The specification of control variables are the same as the specifications inTable 4. Notes in Table 4 apply. *** p<0.01, ** p<0.05, and * p<0.1

However, one might still argue that, even after controlling for observed characteristics and enumeration areas, there might be unobserved characteristics that might becorrelated with the network coverage and not experiencing financial difficulty in sending children to school. In this case, it must be noted that my 2SLS coefficient of theuse of mobile money captures not only the effect of mobile money but also the effect of
the unobserved difference in the household characteristics correlated with the network
coverage. To address such a criticism, in Panels A, B, and C of Table 8, I restrict
my sample to households that live within a certain km (10km, 8km, or 6km) from theborder of the mobile phone network area, regardless of a household’s location withinor outside the network area.
In Table 8, a negative distance implies that a household lives within the network
area and a positive distance implies that a household live outside of the network area.
The specification of control variables is the same as the control variables in Table 6.
Columns (1)–(4) use the main sample, and the column (5) uses the subsample of house31－ 154 －

holds that have information on the distance to school. Panel A shows the estimatedcoefficient of the use of mobile money when the sample is restricted to households
within 10 km from the border of the network area. It shows that when a household
uses mobile money, the probability of not experiencing financial difficulty in sending
children to school increases by 24 percentage point. Column (2) controls demographic
characteristics, column (3) includes ward area dummies, and column(4) includes enumeration area dummies. Column (4) shows that when demographic characteristics andenumeration areas are controlled and when a household uses mobile money, the probability of not experiencing financial difficulty in sending children to school increases
by 15 percentage points. Panel B shows the estimated coefficient when the sample isrestricted to households that live within an 8-km area from the network area. The
Panel B shows that the estimated coefficient is very similar to the estimated coefficientin Table 6. Panel C restricts the sample to households that live within 6km from theborder of the network area. Panels A, B, and C of Table 8 show that the unobserved
characteristics do not change significantly when the sample is restricted to households
who live close to the border of the network. In Table 6, if the unobserved characteristics cause the bias, then the estimated coefficients would change accordingly as
we restrict the sample on account of the similarities among the households that live
close to the border of the network. This suggests that our identification assumptionthat the network coverage is exogenous, once we control observed characteristics andenumeration areas, is likely to be valid.
Does Migration within 6km from the Border Cause Bias?

In Table 8, I have shown that even if I restrict the sample to household who live within
6km from the border of the mobile phone network area, the estimated coefficients of themobile money dummy are similar to the coefficients obtained from unrestricted sample
and argue that it is unlikely that the 2SLS results is generated by the difference ofunobserved household characteristics who live in the network area and who live outside
the network area. However, one might argue that a household who is initially locatedjust outside of the border of the network and who has a strong demand of using mobile
money might move into the area where the mobile phone network is available. In this32

－ 155 －

case, the estimated coefficient might be biased. 11
Table 10: Robustness Checks (2): Controlling Migration Problem
Restricting the Sample to Households living within 10km from the Borderbut not within 5km from the Border in 2SLSHaving Saved in the last 12 months
Dependent variable
(1)
(2)
(3)
(4)
(5)
0.264*** 0.263*** 0.246*** 0.246*** 0.196***
Mobile Money Dummy(0.0587)(0.0587)(0.0637)(0.0637)(0.0633)
851
851
851
851
851
N
240.7
238.6
173.7
173.7
167.5
Kleibergen-Paap Rank Wald
0.042
0.042
0.118
0.118
0.149
R-squared
Control Variables
Enumeration Dummies
yes
yes
yes
yes
yes
Mobile phone ownership
yes
yes
yes
yes
yes
Negative shocks
yes
yes
yes
yes
HH Characteristics
yes
yes
yes
Distance to Markets
yes
yes
Income Sources
yes
Notes: Robust standard errors are in parentheses. A negative distance implies that the location is
inside the network area. The specification of control variables are the same as the specifications inTable 4. Notes in Table 4 apply. *** p<0.01, ** p<0.05, and * p<0.1

To examine whether endogenous migration might affect our estimates, I rerstrict
the sample to households living within 10km from the Border but not within 5km
from the Border. Table 9 shows the estimated coefficients of 2SLS estimation using
this sample. The estimated coefficients do not change from Table 5 suggesting that
migration is not likely to be the issue.
Summary on the Effect of Mobile Money Use on Saving Decision

In section 4.1, I examined the effect of using mobile money on not experiencing financial difficulty in sending children to school by using different data sets through theapplication of the 2SLS estimation method. Irrespective of whether I restrict the sample to households that live close to the border of the network area, exclude households
that live close to the border of the network area, restrict the sample to a homeowners’
11

The direction of the bias depends on the amount of remittance that a household receives. If theamount of sufficient, then the estimated coefficient of the network coverage dummy is underestimated.
If the amount of sufficiently large, the estimated coefficient is upward biased.

33－ 156 －

sample or rural household, the estimated coefficients of the use of mobile money remainquite similar. Those robustness check suggests that the effect of mobile money use on
not experiencing financial difficulty in sending children to school is quite robust.
5.1

Mechanism

As I discussed in the Introduction, from a theoretical point of view, there are fourchannels through which the use of mobile money affects a saving decision (new methodchannel, substitution channel, connection channel, insurance channel, remittance precautionary channel, remittance channel) 12 Among the six channels, the first four channels involve remittance and/or borrowing. Thus, we estimate the following equation
by 2SLS:
Yi = β0 + β1 Mobilei + β2 xi + 5i(7)where Yi = Remittancei or Yi = Borrowingi . Remittancei is a dummy variable indicating that a household received remittance in the past 12 months, and Borrowingi
is a dummy variable indicating that the household borrowed the money in the last
12 months. The above equation examines whether the use of mobile money increases
remittance and borrowing.
One of the key issues regarding an increase in receiving remittance or borrowing iswhether an increase in remittance or borrowing is related to an intrinsic negative shock
to recipient householders. If this is true, then it would suggest that the use of mobile
money will affect not experiencing financial difficulty through informal risk sharing or
buffer channel through borrowing. If it is not true, then it would suggest that otherchannels are likely to be working. Let Ni be a dummy variable indicating whether ahousehold experienced at least one negative shock in the past 12 months. I make a
12
These six channels are not mutually exclusive. For example, the use of mobile money may provide abanking opportunity to the households that do not have access to formal financial institutions, provide
a method of safe saving, and, simultaneously, promote a form of informal insurance network through alower cost of remittance. Additionally, the differences in some channels are quite subtle. For example,
the difference between insurance channel and borrowing channel is the obligation to return the moneyin the case of the latter. In the case of borrowing, a recipient household has an obligation to returnthe borrowed money, while, in the case of informal insurance, a recipient household has an obligation
to help other households when other households are hit by negative shocks.

34－ 157 －

negative shock dummy if a household experiences one of the following in the past 12
months: (i) the death of the main income earner, (ii) illness of a family member thatwould lead to a substantial medical expenditure, (iii) a lower level of harvest volume,or (iv) a lower price on the agricultural output. Subsequently, I estimate the following
equation in 2SLS:Yi = β0 + β1 Mobilei + β2 xi + β3 Ni + β4 Ni × Mobilei + 5i(8)where Yi is the outcome variable, such as the receipt of remittance, borrowing, or not
experiencing financial difficulty in sending children to school. In the above equation, I
treat Mobile and Ni ×Mobilei as endogenous variables and use Networki and Networki ×
Ni as instrumental variables.13

Effect on Remittance and Borrowing

Panel A of Table 10 shows the estimated coefficients of the effect of the use of mobile
money on receiving remittances. It shows that the use of mobile money increases theprobability of receiving remittances by 45 percentage points. Panel B of Table 11 showsthe estimated coefficients of the effect of the use of mobile money on the frequency ofreceiving remittances in the past 12 months. The result shows that the frequency of
the remittance receipt by a household that uses mobile money increases by 4.3 times
than a household that does not use mobile money.
Table 11 presents 2SLS estimates of the effect of the use of mobile money on borrowing. In Panel A of Table 12, the dependent variable is a dummy variable indicating
borrowing that is equal to one if a household has borrowed money in the last 12
months. Panel A shows that having a mobile money account increases the probabilityof borrowing money in the last 12 months by 12 percentage points. In Panels B, C,
and D of Table 12, I look at the source of borrowing. In Panel B of Table 12, thedependent variable is a dummy variable that indicates whether a household borrowed
money from friends or relatives in the last 12 months. In Panel C of Table 12, the dependent variable is a dummy variable indicating whether a household borrowed money
13

In Table A2, I examine the orthogonality of negative shock dummy to the network coveragedummy.

35－ 158 －

Table 11 : The Effect of Mobile Money on Remittance in 2SLS
(1)
(2)
A. The Effect of Mobile Money on Receiving Remittance

(4)

(5)

Remittance Receipt Dummy

Dependent variable
Mobile Money Dummy(3)

0.481***

0.462***

0.415***

0.422***

0.446***(0.0294)(0.0311)(0.0325)(0.0323)(0.0391)

N

2,621

2,621

2,621

2,621

R-squared

0.212

0.245

0.430

0.450

2,621
0.530

B. The Effect of Mobile Money on Frequency of Receiving Remittances
Frequency of Receiving Remittances

Dependent variable
Mobile Money Dummy

4.164***

4.095***

3.815***

3.918***

4.327***(0.349)(0.370)(0.393)(0.391)(0.491)

N

2,621

2,621

2,621

2,621

1,789

R-squared

0.109

0.124

0.303

0.333

0.443

C. Control Variables in Panel A and B
Enumeration Areas

yes

Mobile phone ownership

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

HH Characteristics
Distance to Markets

yesIncome Sources
R-squared

yes
yes

0.031

0.031

0.058

0.058

0.066

N
2,621
2,621
2,621
2,621
2,621
Notes: Robust standard errors are in parentheses. In Panel A, the remittance receipt dummy isequal to 1 if a household has received a remittance in the last 12 months, and 0 otherwise. In
Panel B, the frequency of receiving remittance is the number of remittances received in the past
12 months. The specification of control variables in each column is the same as that in Table 4.
Notes in Table 4 apply. *** p<0.01, ** p<0.05, and * p<0.1

from mobile money companies in the last 12 months. In Panel D of Table 12, thedependent variable is a dummy variable that indicates whether a household borrowed
money from savings clubs in the last 12 months. In Panel B of Table 12, the use ofmobile money increases the probability of borrowing money from friends or relatives
in the last 12 months by 7 percentage points, and it is significant marginally at the
10-percent significance level. Panel C shows that the use of mobile money increases
the probability of borrowing money from mobile money companies by 10 percentage
points at a 1-percent significance level in all specifications. Panel D of Table 12 showsthat the use of mobile money does not affect the probability of borrowing money from36

－ 159 －

Table 12 : The Effect of Mobile Money on borrwoing in 2SLS
(1)
(2)
(3)
(4)

(5)

A. The Effect of Mobile Money on Borrowing
Borrowed Money
Dependent variable
0.0547*0.05450.0774** 0.0940*** 0.121***
Mobile Money Dummy(0.0320) (0.0339) (0.0351) (0.0352) (0.0437)
N
2,621
2,621
2,621
2,621
2,621
0.050
0.074
0.116
0.305
0.380
R-squared
B. The Effect of Mobile Money on Borrowing from Friends or Relatives
Dependent variable
Borrowed from Friends or Relatives
Mobile Money Dummy-0.0191 -0.00172
0.05380.0570*0.0704*(0.0309) (0.0332) (0.0332) (0.0335) (0.0402)
N
2,621
2,621
2,621
2,621
2,620
0.093
0.108
0.197
0.389
0.469
R-squared
C. The Effect of Mobile Money on Borrowing from Mobile Money Companies
Dependent variable
Borrowed from Mobile Money Companies
Mobile Money Dummy
0.108*** 0.0980*** 0.114*** 0.0816*** 0.0950***(0.0248) (0.0263) (0.0277) (0.0274) (0.0325)
N
2,621
2,621
2,621
2,621
2,621
0.060
0.083
0.141
0.343
0.436
R-squared
D. The Effect of Mobile Money on Borrowing from a Savings Club
Borrowed from a Savings Club
Dependent variable
Mobile Money Dummy
0.02850.02000.02080.01890.0313(0.0192) (0.0200) (0.0208) (0.0212) (0.0261)
N
2,621
2,621
2,621
2,621
2,621
0.018
0.032
0.076
0.280
0.392
R-squared
Notes: Robust standard errors are in parentheses. The borrowing dummy is 1 if the
respondent borrowed the money in the last 12 months, and 0 otherwise. The specificationof control variables in each column is the same as that in each column in Table 11. Notes
in Table 4 apply. *** p<0.01, ** p<0.05, and * p<0.1

a savings club.14 . In summary, tables 11 and 12 show that the use of mobile money
increases the probability of receiving remittance and borrowing money.
Interaction of Mobile Money and Negative ShockAmong the six channels through which the use of mobile money affects schooling,four channels are related to remittance or borrowing. Among the four channels, twochannels are related to following the negative shocks: the insurance channel and buffer
14

I also examined the effect of the use of mobile money on borrowing from employers. The 2SLS
regression shows that the use of mobile money does not affect the probability of borrowing from
employers. The estimation results are available from the author upon request.

37－ 160 －

through the borrowing channel. In tables 12 and 13, I estimate the equation (8) in
2SLS and examine whether the use of mobile money affects receiving remittance orborrowing when a household experiences a negative shock such as the death of themain earner or lower harvest volumes.
Table 13 : The Effect of a Negative Shock on Receiving Remittance in 2SLS
(1)
A. The Effect on Receiving Remittances
Dependent variable
Mobile Money Dummy
Mobile Money Dummy×Negative Shock Dummy
Negative Shock Dummy
N
Kleibergen-Paap Rank Wald
R-squared

(2)

(3)

(4)

(5)

Remittance Receipt Dummy
0.427***(0.0455)
0.0790(0.0537)

0.422***(0.0456)
0.0576(0.0530)

0.381***(0.0471)
0.0455(0.0528)0.387***(0.0468)
0.0466(0.0528)0.425***(0.0609)0.0193(0.0667)

0.0123(0.0330)
2,621
895.6
0.214

0.0160(0.0328)
2,621
766.7
0.246

0.0351(0.0321)
2,621
667.6
0.432

0.0319(0.0321)
2,621
548
0.451

0.0480(0.0396)
2,621
360.1
0.531

B. The Effect on Frequency of Receiving Remittances
Frequency of Receiving Remittances
Dependent variable
Mobile Money Dummy
Mobile Money Dummy×Negative Shock Dummy

3.556***(0.582)0.903(0.676)

3.526***(0.585)0.829(0.671)

3.155***(0.626)0.915(0.749)3.176***(0.623)1.032(0.752)

3.795***(0.745)0.665(0.831)

Negative Shock Dummy

0.106
0.0892
0.228
0.191
0.216
(0.349)(0.351)(0.389)(0.390)(0.409)
N
2,621
2,621
2,621
2,621
2,621
Kleibergen-Paap Rank Wald
895.6
766.7
667.6
548
360.1
R-squared
0.111
0.126
0.305
0.335
0.444
Notes: Robust standard errors are in parentheses. Endogenous variables are mobile money use
dummy and mobile money use dummy × negative shock dummy. The instrumental variables are
network coverage dummy and network coverage dummy ×negative shock dummy. The
specification of control variables in each column is the same as that in Table 11. Notes in Table 4
apply. *** p<0.01, ** p<0.05, and * p<0.1

In Table 12, the dependent variable is a variable related to receiving remittance.
In Table 12, I examine the coefficient of the interaction term of the mobile money38－ 161 －

dummy and the negative shocks dummy. The estimated coefficients of the interaction
term in Table 12 show that the effects of experiencing negative shocks on on receiving
remittances are the same between the mobile money users and non-users.
In Table 13, I estimate the equation (8) when the dependent variable is a dummy
variable, indicating that a household borrowed money in the last 12 months. Table 13
shows that the estimated coefficient of the interaction term of the use of mobile moneyand negative shock dummy is very small and statistically insignificant.
One concern in Tables 12–13 is the orthogonality of negative shocks. One mightargue that negative shocks are endogenous. In such a case, it is possible that they arehighly correlated with the instrumental variables and, as a result, the interaction term is
not estimated precisely. To check such a possibility, in Table A1, with other covariates,
I regress the negative shocks dummy on the instrumental variable, the network coveragedummy. Table A1 shows that the negative shock dummy is orthogonal to the network
dummy.
Previous studies emphasize the role of the use of mobile money as a method to buffer
the negative shocks (Jack and Suri, 2014; Riley, 2018). More specifically, previous
studies in Kenya and Uganda showed that the use of mobile mobile money increases
the probability and frequency of receiving remittance when household received negative
shocks. In contrast, in our paper, we did not find such a pattern. Table 13 shows thatthe effect of the negative shock on receiving remittance does not depends on the useof the mobile money. Also, it also show that
In summary, tables 12–14 show that the effects of the use of mobile money onreceiving remittance and borrowing money are not different between mobile moneyusers and non-users.
6

Discussion and Conclusion

In this study, I have examined the effect of the use of mobile money on saving behavior.
My 2SLS estimation results show that the use of mobile money increases the probabilityof receiving remittances in the last 12 months by 45 percentage points and the frequency
with which households receive remittances in the last 12 months increases by 4.3 times
when compared to a household that does not use mobile money. I also find that the39－ 162 －

Table 14: The Effect of Negative Shock and Mobile Money on Borrowing in 2SLS
(1)

(2)

(3)

(4)

(5)

0.0631(0.0408)-0.0143(0.0583)
0.0670*(0.0348)0.0561(0.0418)-0.00373(0.0579)
0.0802**(0.0347)

0.0689(0.0423)0.0358(0.0588)
0.0686*(0.0353)

0.0861**(0.0418)0.0158(0.0581)0.0722**(0.0352)

0.105*(0.0555)
0.0196(0.0726)0.0884**(0.0433)

2,621
0.054

2,621
0.079

2,621
0.285

2,621
0.309

2,621
0.385

A. The Effect on Borrowing
Dependent variable
Mobile Money Dummy
Mobile Money Dummy×Negative Shock Dummy
Negative Shock DummyN
R-squared

Borrowed Money

B. The Effect on Borrowing from Relatives or Friends
Borrowed Money from Relatives or Friends
Dependent variable
Mobile Money Dummy
Mobile Money Dummy×Negative Shock Dummy
Negative Shock DummyN
R-squared

0.0154(0.0395)-0.0709(0.0566)
0.102***(0.0336)

0.0256(0.0409)-0.0598(0.0564)
0.105***(0.0335)

0.0578(0.0399)-0.0191(0.0553)0.100***(0.0334)

0.0677*(0.0395)-0.0275(0.0546)0.100***(0.0333)

0.0657(0.0507)-0.00626(0.0676)0.119***(0.0400)

2,621
0.061

2,621
0.084

2,621
0.320

2,621
0.343

2,621
0.436

C. The Effect on Borrowing from Mobile Money Companies
Borrowed Money from Mobile Money Companies
Dependent variable
Mobile Money Dummy
Mobile Money Dummy×Negative Shock Dummy
Negative Shock Dummy

0.118***(0.0309)-0.0197(0.0442)0.0265(0.0236)0.102***(0.0319)-0.00787(0.0438)
0.0360(0.0238)0.0704**(0.0327)0.0271(0.0444)-0.00276(0.0250)

0.0722**(0.0324)0.0214(0.0440)-0.00268(0.0250)0.0883**(0.0409)
0.0167(0.0524)-0.0293(0.0285)

N
2,621
2,621
2,621
2,621
2,621
R-squared
0.020
0.033
0.267
0.283
0.396
Notes: Robust standard errors are in parentheses. The specification of control variables in eachcolumn is the same as that in each column of Table 11. Notes in Table 4 apply. Kleibergen-Paap
Rank Wald statistics is the same as Table 13 because the endogenous and instrumental variables are
the same as the ones used in Table 13 *** p<0.01, ** p<0.05, and * p<0.1

use of mobile money increases the probability of borrowing money and saving by 11
and 14 percentage points, respectively. On the other hand, I find that the effects40－ 163 －

Table 15 : The Effect of Network Dummy on Negative Shock (OLS)
(1)
(2)
(3)
(4)
(5)
Negative shock dummy
Dependent variable
Network Coverage Dummy-0.0205
0.003420.01250.01250.0531(0.0209) (0.0212) (0.0272) (0.0275) (0.0387)
N
2,621
2,621
2,621
2,621
2,621
R-squared
0.125
0.153
0.373
0.392
0.457
Enumeration Dummies
yes
yes
yes
yes
yes
Mobile phone ownership
yes
yes
yes
yes
yes
Negative shocks
yes
yes
yes
yes
HH Characteristics
yes
yes
yes
Distance to Markets
yes
yes
Income Sources
yes
Notes: Robust standard errors are in parentheses. The specification of control variables ineach column is the same as that in Table 4. Notes in Table 4 apply. *** p<0.01, **p<0.05, and * p<0.1

of experiencing negative shocks on receiving remittance and borrowing are the same
between mobile money users and mobile money non-users.
Previous studies emphasize the role of the use of mobile money as a method tosmooth consumption (Jack and Suri, 2014; Riley, 2018). However, we did not find sucha role. Instead, we found that the effect of experiencing negative shock on receiving
remittance and borrowing money does not depend on the use of mobile money. Also,
we find that once we control the use of mobile money, experience of negative shock
does not affect receiving remittance while the experience of negative shock certainly
increases the probability of borrowing.
This would suggests that the effect of the use of mobile money on easing financial
constraints and smoothing consumption is not operating in our data set. The factthat the use of mobile money increases savings might suggest a possibility that mobile
money helps households to be engaged in various financial activities and to accumulate
wealth, which decrease the need of receiving remittance when a household experiencea negative shock.
My empirical results have several implications. First, having more mobile network
increases the probability of using mobile money and increasing saving, borrowing andreceiving remittance. Second, this increase of the use of mobile money is not for buffering negative shocks. Third,my empirical results have an implication for the regulationof mobile money. As the amount of money transferred through mobile money becomesalmost the same as that of money in the traditional banking system, regulatory au41

－ 164 －

thorities of the governments of developing countries started taking interest in imposing
strict regulations on mobile money providers. Any regulatory policy needs to be balanced between the costs and benefits of regulations. Previous studies identified thebenefits of using mobile money, such as the benefits of consumption smoothing (Jackand Suri, 2014; Riley, 2018) and enhancing the efficiency of implementing a welfare system (Aker et al., 2016; Muralidharan et al., 2016) and a payment system (Blumenstock
et al., 2015). My results show that the use of mobile money will provide an additional
benefit for easing financial constraints in schooling. This factor needs to be taken into
consideration in designing a policy for mobile money providers.
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参

考

成

成

1
2

成

成

成

－ 173 －

成
03-5275-1814
03-6831-8970-

k-miyashita@yu-cho-f.jp

成
)

成

.

(

成)

.
成

成

成
成
成

－ 174 －

成

成

成
成

A

A

成

成
Web

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成
成

This study was

supported by the Yu-cho Foundation (Grant-in-Aid for Research , 2022)－ 175 －

研究助成一覧
年度

応 募
件 数

平成
3
1992

7

平成
4

6

助

成

件

数

研

究

テ

ー

マ

研

究

者

個人研究

１

(1) 銀行信用重視のマクロ経済モデル

神戸大学

助教授

瀧

川

好

夫

共同研究

1

(2) 金融恐慌と預金保険 （共同研究）

東京都立大学

助教授

金

谷

貞

男

横浜市立大学

助教授

酒

井

良

清

東北学院大学

教授

上

田

良

光

(2) 内外価格差のマクロ的分析

京都学園大学

助教授

坂

本

信

雄

(3) 日英郵貯マーケティングの比較研究

福岡大学

教授

山

中

豊

国

(4) 地方拠点都市整備における財政投融資の役割に

金沢大学

教授

佐々木

雅

幸

個人研究

４

(1) アルゼンチンとブラジルにおける郵便貯金
の比較研究

1993

関する研究

平成
5

13

個人研究

４

(1) 貯蓄と課税に関する理論的実証的研究

東京大学

助教授

井

堀

利

宏

共同研究

1

(2) 定額郵便貯金のオプション性評価 （一般家計と

岡山大学

助教授

谷

川

寧

彦

長崎大学

教授

内

田

機関投資家との比較）

1994(3) 公的金融機関行動と地域金融サービス需給に関滋する研究(4) 流動性制約に関する実証分析

慶応義塾大学教授

牧(5) 短期金利の変動に関する理論的実証的研究

横浜国立大学

助教授

森

田

洋

教授

笹

井

均

（共同研究）

平成
6

15

個人研究

６

〃

厚

克

志

(1) 家計の貯蓄性向の決定要因

長崎大学

教授

松

浦

(2) 安全第一基準に基づくポートフォリオ選択問題の

広島大学

助手

土

肥

巳

名古屋市立大学

教授

根

津

永

二

名古屋市立大学

助教授

福

重

元

嗣

神戸学院大学教授

高島

鹿児島経済大学

助教授

梅

原

英

治

小樽商科大学

教授

川

浦

昭

彦

(2) 日本の財政投融資の経営的課題

千葉商科大学

教授

齊

藤

壽

彦

(3) 今後の地方財政の役割と地方債資金を通じた財

明海大学

教授

兼

村

高

投資金の運用方法 （共同研究）

明星大学

助教授

星

野

(4) 「市場の失敗」と公的金融サービス正

理論的・実証的研究

1995(3) 地域金融の地域経済成長への影響についての実
証分析(4) 大都市圏における郵便貯金と銀行預金の競合・補完関係(5) 郵便貯金事業創業・進展の役割と明治期金融財

博

政に関する財政学的研究 （明治財政と郵政事業活動展開の一つの理論的・実証的研究：序説）(6) 地方単独事業の拡大と地方債・地方交付税措置の財政効果 （財政力指数の高い自治体と低い
自治体の比較分析）

平成
7

12

個人研究

３

共同研究

３

1996

(1) 明治期経済発展における郵便貯金・政策金融の
役割

広島大学

教授

小

村

衆

統

－各国比較に基づく実証研究－

〃

教授

北

岡

孝

義

（共同研究）〃

専任講師

ジョセ・ミゲル・ドュアルト・ライト・ドス・
サ
ン
ト
ス

教授

高

(5) 生活基盤社会資本整備における郵貯の役割

熊本学園大学(6) 進展する情報化・国際化の下での社会構造の流

シンガポール国立大学大学院

動化と貯蓄行動の変化 －消費行動との関連分
析、日・米比較分析を含めて－

学生

15

個人研究

１

共同研究

５

(1) 社債市場における資金の運用と管理に関する先泰之NG MIEN WOON

大阪大学

教授

Ｃ．Ｐ．ＲＡＤ

教授

仁

科

一

彦

端的な方法の研究(2) 公共投資の地域間配分と地域間格差

1997

瀬

Old Dominion University U.S.A（共同研究）

平成8

文
泉（共同研究）(3) 地域経済における郵貯資金の活用のあり方-高齢

三重大学

教授

焼

田

四日市地域経済研究所

研究員

朝

日

愛媛大学

教授

小

淵

党
幸

代
港

化先進地域への資金活用と地場産業の育成とい

〃

助教授

松

本

う視点から－ （共同研究）

〃

講師

丹

下

晴

貴

富山大学

教授

古

田

俊

吉

(4) 公的金融と準公共財供給の現状と課題・展望（共同研究）(5) 電子マネーの決済システム、金融機関・郵貯、利用者に与える影響の研究 （共同研究）(6) マルチメディアのユニバーサル・サービスと郵貯資金 （共同研究）－ 176 －

朗

〃

助教授

中

村

和

之

名古屋大学

教授

千

田

純

一

〃

助手

西

垣

鳴

人

大阪大学大学院

教授

辻

正

次

名城大学

教授

手

帝塚山大学

教授

森

嶋

正

章
徹

平成
9

8

個人研究

４

(1) アメリカにおける住宅関連公的金融の保証、リファ

共同研究

２

イナンス、民営化のコストに関する実証的研究

中央大学

教授

井

村

進

哉

(2) 日本の経済協力の現状と効率性

福岡大学

講師

高

瀬

浩

一

(3) 沖縄県経済における郵貯資金の役割に関する研

沖縄国際大学

教授

富

川

盛

武

－日米の比較の視点から－

1998

究 －地域振興の観点から－

〃

助教授

広

瀬

牧

人

（共同研究）

〃

助教授

前

村

昌

健

〃

講師

安

里

〃

講師

鵜

池

〃

講師

大

井

(4) 最適な公的金融システムの設計についての

肇
幸

雄
肇

滋賀大学

助手

丸

茂

俊

彦

神戸大学

教授

滝

川

好

夫

新潟大学

教授

宮

越

龍

義

神奈川大学

講師

宮

原

勝

一

京都学園大学

専任講師

井

手

幸

恵

(2) 広域型トータルヘルスケア・システムへの郵

埼玉大学

教授

小笠原

浩

一

貯資金活用の可能性に関する研究

〃

助教授

後

藤

和

子

埼玉県地方自治センター

主任

平

野

方

紹

埼玉県立衛生短期大学

助手

林

裕

栄

澤
伏
木

由
良
祥

美
明
弘

喜

典

一試論 （共同研究）(5) 地域金融機関の効率性の計測－確率的フロンティア生産関数－(6) 社会資本整備の地域社会への経済的効果－生活関連、通信分野の社会資本整備の地域貢献

平成
10

13

個人研究

７

共同研究

２

1999

(1) 金融不安時における郵便貯金に対する女性の意識と実態（共同研究）(3) ベンチャー支援と郵貯資金の活用について(4) 郵貯資金の有価証券市場における関りと役割

石巻専修大学
大阪府立大学

教授
助教授

野
木
黒

(5) 金融規制改革と地域における中小企業金融

青森公立大学教授

今(6) 公的資金の市場運用と株主行動主義

神戸大学

教授

榊

原

茂

樹

(7) 日本の国債管理政策 －近年における「満期構

上智大学

助教授

竹

田

陽

介

新潟大学大学院の変化

成の短期化」がマクロ経済に及ぼす影響について(8) 債券ポートフォリオの理論的実証的研究（共同研究）

平成
11

14

個人研究

5

共同研究

3

2000

一橋大学
〃

教授

三

浦

良

造

専任講師

大

上

新

吾

(9) イールドカーブの形状に関するリスク分析

横浜国立大学

助教授

森

田

洋

(1) 公的金融機関の貸出行動と企業の設備投資に与横浜国立大学

助教授

井

上

徹

える効果の実証研究(2) ＡＴＭ相互接続におけるネットワーク外部性の分析

関西大学

専任講師

岡

村

秀

(3) 混合寡占的金融市場における公的金融の役割

新潟大学

助教授

芹

澤

伸

(4) 情報・通信基盤等の社会資本整備が経済成長に

上智大学

専任講師

中

里

夫
子
透

与える影響に関する実証的研究(5) 非対称情報下での社債発行の理論（共同研究）(6) 郵貯資金運用手段の多様化と財政規律に関する

神戸大学

助教授

原

千

秋

一橋大学

助教授

大

橋

和

彦

長崎大学

教授

深

浦

厚

之

研究 －資産担保証券を中心に－(7) 地方自治体の公共サービス供給と郵便貯金の役
割 （共同研究）(8) １９７０年以降の日本における金融仲介

9

個人研究

4

共同研究

3

森

教授

稲

垣

秀

徹
夫

坂

恒

治

高千穂バンキング研究会
教授

宮

高千穂商科大学

教授

原

岐阜大学

助教授

大

藪

千

〃

教授

杉

原

利

治

日本福祉大学

助教授

小

木

紀

親

(3) 地域金融におけるメインバンク機能

摂南大学

助教授

加

納

正

二

(4) 財投改革後の公的金融の課題－アカウンタビリ

千葉商科大学

教授

齊

藤

壽

彦

(1) 国民の貯蓄行動・金融資産選択に対する郵便貯金事業のＩＴの意義 （共同研究）(2) 郵政事業におけるマーケティング戦略

2001

教授

四日市大学
代表：高千穂商科大学（共同研究）

平成12

名古屋市立大学

司 郎
ほか５名
穂

－ポスタル・マーケティング戦略の展望－

ティを中心として－ （共同研究）(5) 金融システムの安定化策と公的資金の役割－「予算制約のソフト化」をいかに防ぐか－〃
名古屋市立大学

〃

講師

山

根

寛

隆

助教授

櫻

川

昌

哉

助教授

細

野

薫

（共同研究）(6) 「証券トラブル」についての実態調査

神戸大学大学院

教授

滝

川

好

夫

(7) エクイティファイナンスと郵貯資金の活用

北海道大学

教授

濱

田

康

行

－ 177 －

平成
13

13

個人研究

４

共同研究

２

2002

(1) 支出税としての401(K)年金プランと生涯税負担の

名城大学

助教授

鎌

田

繁

則

(2) 証券市場における銀行の役割に関する実証研究

一橋大学大学院

助教授

小西(3) 経済発展における公的金融の役割と家計行動

名古屋文理大学

助教授

関川－東南アジア諸国と日本の比較考察－

中京学院大学

助教授

山

中

高

光

（共同研究）(4) スワップマーケット情報を用いた債券流通市場
分析

高千穂大学

教授

高

橋

豊

治

(5) 日本における郵貯制度と消費者保護システム

関西学院大学

教授

春

井

久

志

名古屋大学大学院

助教授

家

森

信

善

助教授

西

垣

鳴

人

京都教育大学

教授

田

岡

文

夫

大阪大学大学院
〃

正

次

助教授

今

川

拓

郎

神戸大学大学院

助教授

砂

川

信

幸
子

水平的公平性大靖－イギリス金融サービス機構(FSA)との比較を中心に－(6) 諸外国における公的金融サービスの再評価〃について （共同研究）

平成
14

2

個人研究

1

共同研究

1

2003

(1) 遠隔医療、遠隔教育事業への郵貯資金活用の可能性と方法に関する研究(2) 地域活性化政策に対する郵貯資金の活用に関する研究 （共同研究）

平成
15

11

個人研究

5

共同研究

1

2004

(1) 金融機関の支援行動と公的資金注入の経済合

15

個人研究

5

共同研究

1

2005

(2) 公表情報、私的情報と金融危機

横浜私立大学

助教授

武

田

史

(3) ﾘｽｸ･ﾌﾟﾚﾐｱﾑとﾏｸﾛ経済活動

同志社大学

助教授

植

田

宏

文

(4) 金融業におけるﾕﾆﾊﾞｰｻﾙ･ｻｰﾋﾞｽと金融排除問題

関西学院大学

助教授

岡

村

秀

夫

(5) 公的企業のｶﾞﾊﾞﾅﾝｽ

新潟大学大学院

教授

芹

澤

伸

子

(6) 長期金融ｼｽﾃﾑ安定のための郵便貯金の役割

九州産業大学

教授

益

村

眞知子

長崎県立大学

助教授

矢

野

生

子

(1) ｾｸﾀｰ･ｽﾌﾟﾚｯﾄﾞを利用した債券理論時価の導出

東京国際大学

教授

渡

辺

信

一

(2) 財政運営の安定性と公的金融の役割についての

上智大学

助教授

中

里

(3) 日本の国債市場における郵便貯金資金

駒澤大学

教授

代

田

(4) わが国長期国債先物市場のﾏｲｸﾛｽﾄﾗｸﾁｬ

一橋大学大学院

教授

釜

江

廣

〃

講師

山

根

寛

隆

東北大学

助教授

渡

部

和

孝

11

個人研究

2

共同研究

3

2006

公正取引委員会経済取引局

荒

井

弘

毅

助教授

吉

川

卓

也

(1) 日本郵政公社の企業価値推定に関する実証研究

佐賀大学

教授

大

坪

(2) ｺｰﾎﾟﾚｰﾄ･ｶﾞﾊﾞﾅﾝｽ改革の要因･効果と郵便貯金

北九州市立大学

助教授

内

田

交

(3) ｸﾚｼﾞｯﾄｶｰﾄﾞの普及と決済口座利用動向に関する

長崎大学

教授

須

齋

正

幸

助教授

山

下

耕

治

助教授
専任講師
助教授

春
杉
岩

日
浦
崎

教
史
一

測
和
郎
治

(4) 移行経済諸国における貯蓄銀行の比較研究（共同研究）

一橋大学(5) 郵便貯金資金及び財政投融資と奨学金制度・

早稲田大学大学院

2

共同研究

2

(1) 地方における郵便局の配置と経済性（共同研究）(2) 郵便貯金の市場運用への移行ﾌﾟﾛｾｽが資金循環

2007に与える金融連関分析とｼﾐｭﾚｰｼｮﾝ

大学院生

白

川

優

小

島

佐恵子

鹿児島大学

助教授

永

田

邦

和

鹿児島大学

教授

石

塚

孔

信

ソ

ク

2008

個人研究

3

玄

慶應義塾大学

連携21COEﾌﾟﾛｸﾞﾗﾑ研究員
助教授

阿

萬

弘

行

秋田経済法科大学

講師

宮

崎

浩

伸

(4) 郵便貯金銀行の外資への売却によって生じうる
ﾏｸﾛ経済構造の変化：ﾆｭｰｼﾞｰﾗﾝﾄﾞのｹｰｽ

龍谷大学

助教授

鈴

木

智

也

(1) 資本主義の精神と証券市場の役割

埼玉大学

教授

相

沢

幸

悦

(2) 郵便貯金と地域金融市場

関東学院大学

准教授

黒

川

洋

行

(3) 郵便貯金銀行は地域金融機関を混乱させるのか

神戸大学大学院

教授

滝

川

好

夫

（共同研究）4謹

長崎大学(3) 金融ｼｽﾃﾑ安定化とｼｽﾃﾐｯｸﾘｽｸ波及の研究

平成
19

稔

同上

政策の関係についての研究 （共同研究）
個人研究

志中村学園大学

研究 （共同研究）

7純(6) 家計の金融資産選択行動の長期的変化の合併 （共同研究）

平成
18

透

実証的研究

(5) BIS規制の金融機関の行動への影響、金融機関

平成
17

辻

理性

（共同研究）

平成16教授－ 178 －

平成
20

8

個人研究

3

2009
平成
21

9

個人研究
共同研究

3
1

2010

平成
22

(1) 地域金融機関の貸出しにおける横並び行動

関西大学

准教授

中

川

(2) 証券化市場の拡大とメインストリート金融

茨城大学

教授

内

田

聡

(3) 金融コングロマリットのリスクと資本規制

武蔵大学

非常勤講師 茶野努(1) 欧州金融市場での金融危機と実体経済への影響

関西大学

教授

屋

(2) 東京証券取引所の改革と証券市場の透明性

名古屋市立大学

講師

坂

和

秀

晃

大阪大学

助教

生

方

雅

人

(3) 金融機関のリスク資本の評価・管理

神奈川大学

准教授

菅

野

正

泰

(4) アメリカのコミュニティ投資と個人金融

ｿｰｼｬﾙ・ﾌｧｲﾅﾝｽ

代表

唐

木

宏

一

(1) 世界金融危機における資金調達の逼迫度に
関する研究

新潟大学

教授

伊

藤

隆

康

(2) 戦前日本の地方預貯金市場の実証的研究

東京大学

博士課程

早

川

大

介

佐賀大学

准教授

三

好

祐

輔

(4) 現代女性のライフコースと金融行動 -生活経済

ﾆｯｾｲ基礎研主任研究員 栗林

敦子

リスクとしての非婚・晩婚・離婚に女性はどう対応

ﾆｯｾｲ基礎研

研究員

井

上

智

紀

(1) 地域金融機関に関する経済の外部性効果の計測- 愛知県の工業メッシュデータを用いた例 -

愛知大学

教授

打

田

委千弘

(2) イギリスにおける金融排除問題への取組みに関する

成城大学

研究員

峯

岸

信

哉

(3) 固定資産税を活用した地域再生ファンドの可能性

東海大学

准教授

川

崎

一

泰

(4) 銀行業における財務業績の価値関連性の国際比較

東京富士大学短期大学部

准教授

井

手

健

二

武蔵大学

非常勤講師 松澤孝

紀(1) 長期不況下における郵便貯金の資金的役割- 定額貯金満期資金をめぐって -青山学院大学

助教

伊

藤

真利子

(2) リテールバンキングの変容と金融機関行動の研究

和歌山大学

講師

簗

田

優

福岡女子大学

准教授

張

厦門大学

副教授

劉

(4) 金融商品取引法の証券市場への影響

立命館大学

講師

(5) 家計調査資料を用いた日韓貯蓄行動に関する

横浜市立大学教授(1) 複雑な金融商品の評価に伴う外部専門家の利用に関する国際比較研究

流通経済大学准教授

岡(2) ニュージーランドの住宅取引及び住宅金融に関する

滋賀大学大学院

博士課程

中

近畿大学

准教授

マルデワ・グジェゴ
シュ

立命館大学

教授

井

澤

裕

司

北海道大学大学院

教授

鈴

木

輝

好

神戸大学大学院

教授

滝

川

好

夫

（共同研究）

6

個人研究
共同研究

3
1

2010

高

竜

一

定

美

- 新潟県を事例に (3) 企業が証券会社及び銀行に求める保険的役割に関する実証研究するか-（共同研究）

平成
23

9

個人研究
共同研究

3
1

2011

考察 - クレジットユニオン業界を中心として -（共同研究）

平成
24

9

個人研究
共同研究

4
1

2012

- 日英米の住宅金融をめぐって (3) 世界金融危機下の日中米株式市場の比較分析（共同研究）

渡

艶
振

涛

直

樹

重

鎬

本

紀

明

尾

彰

彦

辺
鞠

比較分析

平成
25
2013

11

個人研究
共同研究

4
1

調査分析－我が国の住宅金融への示唆－(3) 最適貯蓄計画の数値解析手法の開発と経済実験による検証 （共同研究）(4) 銀行救済における公的資金の最適配分問題とその経済効果(5) ゆうちょ銀行 vs. 民間預金取扱金融機関の店舗展開の決定要因：全国市区町村データを用いた実証研究－ 179 －

平成
26

13

6

個人研究

(1) 近年の流動性供給における金融商品価格に関する

中京大学

准教授

英

邦

広

長崎県立大学

准教授

小

原

篤

次

中央大学

准教授

鯉

渕

賢

成蹊大学

教授

永

野

護

名古屋市立大学

准教授

坂

和

秀

晃

東京大学大学院

博士課程

税

所

真

也

裕

研究
(2) 中国の外貨準備資金によるアクティブ株式運用の

2014

実証研究
(3) 大規模金融機関縮小のインパクト－公社化以降の郵貯減少が都道府県別預金市場に与えた影響の分析(4) 銀行リテール事業における最適店舗チャネルの研究(5) 機関投資家が市場流動性に及ぼす影響に関する実証分析(6) 金融機関における成年後見制度の必要性－地域金融機関による見解と認識の分析を通して－

平成
27

18

5

個人研究
共同研究

1

(1) 大学生の金融リテラシーと金融教育

明治大学

准教授

浅

井

義

(2) 新規公開により企業の知名度は向上するのか

名古屋大学大学院

講師

高

橋

秀

徳

関西学院大学

教授

岡

田

克

彦

邦

（共同研究）

2015(3) 日本の銀行業の市場規律

鹿児島大学

教授

永

田

(4) 高速取引の普及が個人投資家の投資環境に

長崎大学

教授

森

保

洋

和

神戸大学大学院

講師

田

中

光

(6) ＥＳＧ（環境・社会・ガバナンス）の情報と評価基準

愛知学泉大学

講師

浅

野

礼美子

(1) 戦時期の郵便貯金

静岡英和学院大学

准教授

伊

藤

真利子

もたらす影響(5) 個人少額貯蓄の地域経済に対する社会経済的
な影響

平成
28

9

5

個人研究
共同研究

1

ー1930年代預貯金市場を中心として

2016(2) どうすれば景気に左右されずに資産を運用できるのか

北海学園大学

准教授

吉

川

大

介

(3) アベノミクスは成長期待を高めたのか

東京理科大学

専任講師

土

屋

陽

一

明治大学

教授

伊

藤

隆

康

関西大学

教授

高

屋

定

美

ー株価予測の考察から(4) マレーシアにおけるイスラム金融と従来型金融の比較
分析：金融政策の予想が市場に与える影響の検証(5) マイナス金利政策の経験
ーユーロ圏と欧州小国の経験から学ぶ
神戸大学大学院

准教授

宮

崎

智

視

産業別データを用いた分析

流通科学大学

准教授

小

塚

匡

文

（共同研究）

東海大学

准教授

平

賀

一

希

神戸大学大学院准教授

井上

名古屋大学大学院

教授

清

水

克

俊

名古屋大学大学院

教授

渡

辺

直

樹

決定要因分析－あいまいさ選好、リスク選好、

敬愛大学

教授

和

田

良

子

時間選好率および金融リテラシー

慶應義塾大学

修士課程

大

塚

崇

夫

一橋大学大学院

教授

安

田

行

宏

(6) 公共投資と証券市場との関係に関する実証研究：

平成
29

21

4

個人研究
共同研究

1

(1) 郵便ネットワークの金融包摂効果に関する研究

武－戦後日本の所得格差に与えた影響(2) 非伝統的金融政策が日本の金融市場や家計の2017

貯蓄・消費行動に与えた影響に関する分析(3) 大規模災害が金融市場にどのような効果を与えるか(4) 実験経済学による個人のポートフォリオ選択の（共同研究）(5) 資金余剰経済における銀行リスクの決定要因」－アベノミクス導入後における検証

平成
30
2018

15

4

個人研究
共同研究

2

(1) Financial Acceleratorと金融政策

東京大学大学院

専任講師

平

野

智

裕

(2) 同族企業が市場流動性に与える効果の検証

名古屋市立大学大学院

准教授

坂

和

秀

晃

(3) クラウドファンディングの成功要因に関する実証研究

神戸大学大学院

教授

藤

原

賢

哉

(4) 長生きリスクに対するリスク・マネジメント

早稲田大学

准教授

尾

﨑

祐

介

明治大学

准教授

藤

井

陽一朗
喜美枝

：介護予防に向けた政策提言（共同研究）(5) 日本銀行によるETF買入政策と日経平均銘柄への影響 （共同研究）(6) 日本における消費者のキャッシュレス化に関する実証研究－ 180 －

中央大学

教授

原

田

オーストラリア国立大学

准教授

沖

本

竜

義

城西大学

教授

竹

村

敏

彦

2019

18

個人研究

6

(1) デフレ下における資産価格変動と金融政策運営

専修大学

教授

奴田原

健

悟

(2) 低金利政策下における銀行のリスクテイク行動と

長崎大学

教授

式

雅

代

見

銀行間競争
(3) 視線計測実験による金融商品選択行動の研究（共同研究）

早稲田大学大学院

博士課程

小

林

マーサージャパン㈱

アナリスト

新

井

伸
拓

也

(4) 取締役会の構造・個人ネットワークが日本企業の

埼玉大学

准教授

長

田

不正発生・発覚に与えた影響（共同研究）

日本銀行

企画役

橋

本

武

健
敏

(5) 金融システム安定化と銀行規制一元化への挑戦

金沢大学

准教授

佐

藤

秀

樹

愛知大学

准教授

早

川

大

介

三

浦

一

輝

見

誠

良

：ヨーロッパから学ぶシステミックリスクの事前防止策(6) 1903年の普通銀行は預金銀行だったか

2020

16

個人研究

6

『銀行通信録』データを用いた分析

常葉大学

准教授

（共同研究）

法政大学

名誉教授

日本大学

教授

菅

野

正

泰

神戸大学大学院

准教授

宮

崎

智

視

(1) 新型コロナウイルス感染症(COVID–19)が金融市場に及ぼすリスクの分析(2) 独立財政機関と国債市場：国際比較と日本への政策的含意

KKBS
associate professor
佐 藤
神戸大学経済経営研究所 ｼﾞｭﾆｱ・ﾘｻｰﾁ・ﾌｪﾛｰ(3) 経営者報酬契約とESG投資(4) 非伝統的金融政策がマクロ経済変数に与えた影響

横浜市立大学大学院 客員准教授

中

園

善

(5) 定性情報が企業価値評価に与える影響

九州大学

三

輪

宏太郎

(6) 商業銀行の役員報酬制度が金融システムの安定に

Asia School of Business in collaboration
with MIT Sloan ファイナンス助教授

竹

井

郁

夫

及ぼす影響の実証分析

2021

15

個人研究

3

共同研究

3

愛

(1) 金融の不安定性の元での銀行合併の分析（共同研究）(2) 企業の環境配慮的活動は危機における企業パフォー

准教授

行

横浜国立大学

講師

猪

野

明

生

長崎大学

准教授

松

木

佑

介

筑波大学

准教授

作

道

真

理

承

子

マンスを支えるのか～日本からのエビデンス(3) 性格特性と投資スタイル～Big Fiveによる測定～㈱パパラカ研究所社長/大阪大学招聘研究員（共同研究）山

根㈱パパラカ研究所副社長/慶応義塾大学訪問研究員(4) コロナ禍の財政破綻懸念が家計の貯蓄行動に与える荒木

池田泉州キャピタル 会長

野

田

宏

子

愛媛大学

准教授

新

関

剛

史

隆

影響
県立広島大学

准教授

塚

原

一

郎

常葉大学

准教授

土

村

宜

明

(6) 銀行の産業別貸出データによる金融政策効果の分析

横浜市立大学教授

清遠(1) COVID-19（新型コロナウイルス）危機が主要国の信用

明治大学

教授

伊

藤

隆

康

(5) 日本における投資家センチメントの特徴と株式市場への影響（共同研究）

2022

11

個人研究

4

共同研究

1

随

リスクに与えた影響の検証(2) 日本における新型コロナウイルスの地方債市場へのイン
パクトとリスク管理に関する考察（共同研究）(3) ESG投資とイスラム金融：制度的類似性と株価・企業行

西南学院大学

教授

丹

波

靖

博

九州大学

教授

大

石

桂

一

西南学院大学

准教授

原

口

健太郎

同志社大学大学院

教授

吉

田

悦

章

敬愛大学

教授

和

田

良

子

筑波大学

教授

内

藤

久

裕

動等への影響の検証(4) あいまいさ回避度およびあいまいな情報のトリートメント能力が家計の株式保有にもたらす影響―経済実験およびフィールドデータとの整合性によるモデルの検証―(5) モバイルマネーの使用は貯蓄を増加させるか？
ジンバブエのデータからの分析－ 181 －

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