Doctoral Dissertation
Banking Crises, Macroprudential Policies and Financial Inclusion

UCH RAKSMEY

Graduate School for International Development and Cooperation
Hiroshima University
September 2021

Banking Crises, Macroprudential Policies and Financial Inclusion

D186752
UCH RAKSMEYA Dissertation Submitted to
the Graduate School for International Development and Cooperationof Hiroshima University in Partial Fulfillmentof the Requirement for the Degree ofDoctor of Philosophy
September 2021

v

Acknowledgement
First, I would like to express my sincere thanks to Japanese people and Japanese government
for their kind support for the development of Cambodia, particularly in offering scholarship
to Cambodian scholars including me to pursue further study in Japan. I also would like toexpress my heartful thanks to the Royal Government of Cambodia and the National Bank of
Cambodia, specifically the management team for giving me the opportunity in joining a Ph.D
program in Japan.
My fruitful achievement and successful completion of this Ph.D program could not be realized
without the great and obliging supervision, encouragement and support of the following
people.
I would like to pay my sincere gratitude and thank to my supervisor Prof. KAKINAKA
Makoto for his great support, motivation, patience, and immense knowledge, in particular theknowledge relates to my research. His supportive and accommodative supervision strategies
are helpful in guiding me to achieve each step of my research work and complete all therequirement by program. In addition, I would like to extend my genuine thanks to Prof.
ICHIHASHI Masaru, Prof. TAKAHASHI Shingo, Prof. LIN, CHING-YANG, and Prof.
GOTO Daisaku for their insightful and valuable comments and encouragement which helped
to improve my research substantially.
I would like to extend my thanks to all friends and colleagues for their spiritually supporting
me in several ways.
Last but not the least, I would like to thank my family, particularly my beloved wife for her
resilience and supportive roles during this most difficult time in taking care all the three little
kids without me.

vi“Train your mind to be calm in every situation. Everything comes to you at the right time. Be
patient.”(Anonymous)

i

Table of Contents
Table of Contents ....................................................................................................................... i
List of tables ............................................................................................................................iii
List of figures ........................................................................................................................... vi
Chapter 1: Introduction ............................................................................................................. 1
Chapter 2: Effects of a banking crisis on credit growth in developing countries ..................... 4
2.1 Introduction ..................................................................................................................... 4
2.2 Methodology and data ..................................................................................................... 5
2.3 Results ............................................................................................................................. 8
2.4 Robustness checks......................................................................................................... 14
2.4.1 Smooth transition function approach............................................................................142.4.2 Alternative measure of financial development .............................................................15
2.4.3 Augmented regression-adjusted estimation approach ..................................................16
2.4.4 International Monetary Fund (IMF) economies groupings ..........................................17

2.5 Conclusion .................................................................................................................... 18
Chapter 3: Macroprudential policy and banking crises: Targeting borrowers or financial
institutions? ............................................................................................................................. 22
3.1 Introduction .................................................................................................................. 22
3.2 Literature review .......................................................................................................... 25
3.2.1 Financial crises ............................................................................................................25
3.2.2 Financial crisis and regulatory frameworks.................................................................27
3.2.3 Macroprudential policies .............................................................................................28

3.3 Data and empirical specification .................................................................................. 29
3.3.1 The database of macroprudential policies ....................................................................29
3.3.2 Measuring macroprudential policy instruments ...........................................................32
3.3.3 Observations on the MP instruments ............................................................................34
3.3.4 Empirical specification .................................................................................................41

3.4 Empirical results .......................................................................................................... 46
3.5 Robustness checks........................................................................................................ 53
3.5.1 Factor analysis .............................................................................................................54
3.5.2 Instrumental variables estimation ................................................................................57
3.5.3 Monetary and fiscal policy stances..............................................................................60

3.6 Conclusion ................................................................................................................... 62
Chapter 4: Macroprudential policy and financial inclusion: Any difference between
developed and developing countries? ..................................................................................... 75

ii

4.1 Introduction ................................................................................................................... 75
4.2 Related Literature .......................................................................................................... 78
4.3 Methodology ................................................................................................................. 80
4.3.1 Empirical model ...........................................................................................................81
4.3.2 Financial inclusion........................................................................................................82
4.3.3 Macroprudential policy ................................................................................................84
4.3.4 Other control variables .................................................................................................87

4.4 Empirical results ........................................................................................................... 91
4.1 Developing countries .................................................................................................... 92
4.4.2 Mobile banking.............................................................................................................97
4.4.3 Developed countries ...................................................................................................100
4.4.4 Other control variables ...............................................................................................104

4.5 Conclusion .................................................................................................................. 104
Chapter 5: Conclusion .......................................................................................................... 131

iii

List of tables
Table 2-A1. Data description and sources of variables ........................................................ 8
Table 2-A2. The effect of banking crisis on credit growth (baseline), developed anddeveloping countries ............................................................................................................. 9
Table 2-A3. The effect of banking crisis on credit growth, financial cycles, developed anddeveloping countries ........................................................................................................... 11
Table 2-A4. The effect of banking crisis on credit growth, financial development,
developing countries ........................................................................................................... 12
Table 2-A5. The effect of banking crisis on credit growth, financial cycles and financial
development, developing countries..................................................................................... 13
Table 2-A6. The effect of banking crisis on credit growth, financial cycles and financial
development, developing countries (smooth transition function) ....................................... 15
Table 2-A7. The effect of banking crisis on credit growth, financial development (basedon the credit to GDP ratio), developing countries .............................................................. 16
Table 2-A8. The effect of banking crisis on credit growth, financial cycles and financial
development (AIPW), developing countries ....................................................................... 17
Table 3-1. Definitions of macroprudential policy tools ...................................................... 31
Table 3-2. Groups of macroprudential policy instruments ................................................. 33
Table 3-3. MP instruments and banking crises (1993-2015) .............................................. 40
Table 3-4. Correlation matrix of MP instruments ............................................................... 41
Table 3-5. List of countries (60 countries) .......................................................................... 44
Table 3-6. Data description and sources of variables ......................................................... 45
Table 3-7. Descriptive of Statistics ..................................................................................... 45
Table 3-8. Macroprudential policy instruments and banking crises ................................... 50
Table 3-9. Subgroup of financial institutions- and loan-targeted policy instruments andbanking crises ...................................................................................................................... 52
Table 3-10. Factor analysis of macroprudential policy instruments ................................... 56
Table 3-11. Macroprudential policies and banking crises (Linear probability model) ....... 59
Table 3-12. Macroprudential policies and banking crises with monetary and fiscal policy
stances……………………………………………………………………………………. 61
Table 3-A1. Credit booms and banking crises .................................................................... 69
Table 3-A2. MP instruments and banking crises (1993-2010) ........................................... 70
Table 3-A3. Macroprudential policy (Cerutti et al., 2017) and banking crises .................. 71
Table 3-A4. Quantitative LTV_average and banking crises............................................... 72

iv

Table 3-A5. Macroprudential policies and banking crises (Reinhart and Rogoff, 2011) ..73
Table 3-A6. List of countries (107 countries) ..................................................................... 74
Table 3-A7. List of countries (30 countries) ....................................................................... 74
Table 4-1. Definitions of macroprudential policy tools ...................................................... 85
Table 4-2. Groups of macroprudential policy (MP) instruments ........................................ 87
Table 4-3. Data description and sources of variables ......................................................... 89
Table 4-4. List of developing and developed countries…………………………………. 90
Table 4-5. Descriptive of Statistics ..................................................................................... 91
Table 4-6. Macroprudential policy and financial inclusion ................................................ 91
Table 4-7. Overall measure of MP (developing countries) ................................................. 93
Table 4-8. Borrower- and financial institution-targeted MP (developing countries) ......... 94
Table 4-9. Capital- and loan supply-targeted MP (developing countries) .......................... 96
Table 4-10. Mobile money agent outlets and MP in developing countries (access tobanking services)................................................................................................................. 98
Table 4-11. Mobile money agent outlets and MP in developing countries (use of banking
services) .............................................................................................................................. 99
Table 4-12. Overall measure of MP (developed countries) .............................................. 101
Table 4-13. Borrower- and financial institution-targeted MP (developed countries) ....... 102
Table 4-14. Capital- and loan supply-targeted MP (developed countries) ....................... 102
Table 4-A1. Overall measure of MPs (developing countries): system-GMM .................. 117
Table 4-A2. Borrower- and financial institution-targeted MPs (developing countries):
system-GMM .................................................................................................................... 118
Table 4-A3. Capital- and loan supply-targeted MPs (developing countries): system-GMM........................................................................................................................................... 119
Table 4-A4. Factor analysis (developing countries) ......................................................... 120
Table 4-A5. Overall measure of MPs with additional controls (developing countries) ... 121
Table 4-A6. Borrower- and financial institution-targeted MPs with additional controls(developing countries)....................................................................................................... 122
Table 4-A7. Capital- and loan supply-targeted MPs with additional controls (developing
countries) ........................................................................................................................... 123
Table 4-A8. Overall measure of MPs (developed countries): system-GMM ................... 124
Table 4-A9. Borrower- and financial institution-targeted MPs (developed countries):
system-GMM .................................................................................................................... 125
Table 4-A10. Capital- and loan supply-targeted MPs (developed countries): systemGMM………………………………………………………………………………… 126

v

Table 4-A11. Factor analysis (developed countries) ........................................................ 127
Table 4-A12. Overall measure of MPs with additional controls (developed countries)... 128
Table 4-A13. Borrower- and financial institution-targeted MPs with additional controls(developed countries) ........................................................................................................ 129
Table 4-A14. Capital- and loan supply-targeted MPs with additional controls (developed
countries) ........................................................................................................................... 130

vi

List of figures
Figure 2-1: Effects of banking crisis ..................................................................................... 9
Figure 2-2: Financial cycles ................................................................................................ 10
Figure 2-3: Financial development ..................................................................................... 12
Figure 2-4: Financial cycles and financial development ………………………………... 13
Figure 3-1. States of MP instruments.................................................................................. 34
Figure 3-2. Changes of MP instruments ............................................................................. 37

1

Chapter 1: Introduction
The Global Financial Crisis during 2007-2008 has brought about the discussion on theresilience of the international financial system and the roles of financial regulatory andsupervisory frameworks, especially, macroprudential policies, in mitigating accumulation offinancial vulnerabilities and safeguarding financial stability. Although there is no formallyagreed on the definition, the adoption of macroprudential policy is to reduce the accumulationof financial imbalance and systemic risk and their consequence on the real economy. Crockett(2000) states that the objective of macroprudential policies (MPs) can be defined as limiting
the costs to the economy of financial distress, including limiting the likelihood of the failure,
and the corresponding costs to significant portions of the financial system, which is oftenloosely referred to as limiting “systemic risk”. Systemic risk can be understood as market
failure in the financial system or financial crises (Butzbach, 2016). There are many types offinancial crisis including banking crises, currency crises, debt crises and inflation crises.
However, the accumulation of financial vulnerabilities which generally associated withexcessive credit expansion in economy lead to occurrence of a banking crisis (Borio & Lowe,
2002, 2004; Borio & Drehmann, 2009; Babecký et al., 2014, Caprio et al., 2014, Hasanov &Bhattacharya, 2018).
Banking crises often impose enormous negative effects on economic and financial
system. Some study show that a banking crisis have a negative effect on output or growth(Demirguc-Kun et al., 2006; Dell’Ariccia et al., 2008; Furceri & Zdzienicka, 2012; Fernandez
et al., 2013). However, the existing literature has not fully addressed how financial conditions
alter the impact of a banking crisis. Thus, we try to fill this gap by examine the effects of abanking crisis on the credit growth in developing countries in Chapter 2 of this dissertation.
Our empirical findings indicate that banking crises during financial boom decrease credit
growth more substantial than that during financial slump and the effects of banking crisis are

2magnified for the countries with the high level of financial development than the countries
with low level of financial development.
Many studies demonstrate that MP is effective in limiting the level of financial
indicators associated with financial failure, such as credit growth and housing prices (Alam etal., 2019; Olszak et al., 2018; Cerutti et al., 2017; Fendoğlu, 2017). Credit and asset pricebooms, however, are not the only driving forces that increase systemic risk and induce banking
crises. In the literature, there is very limited evidence on the effect of MP on various factors,
such as the externalities generated from the strategy of financial institutions, which are crucial
in intensifying systemic risk. One possible reason that these factors are abstract in the literatureis the difficulty in measuring them accurately. In contrast to previous studies, our study
attempts to evaluate the MP role in limiting systemic risk in the financial system by taking theoccurrences of banking crises as the proxy for systemic risk. The results of the empiricalfindings show that, in addition to the overall MP measured in aggregate, borrower-targeted
MP instruments are negatively associated with the likelihood of a banking crisis. The finding
also reveals that several specific objective-oriented MP instruments, such as capital, loan
supply, and loan related macroprudential policy, also have a significant negative relationshipwith the likelihood of a banking crisis.
Though, the adoption of macroprudential policy would help achieve financial stability,
it may also bring the cost in term of reducing the level of financial inclusion. Nonetheless, theliterature on the relationship between MPs and financial inclusion is limited. To contribute to
the existing literature, we attempt to examine the relationship between the adoption of MP
instruments on financial inclusion in Chapter 4. Our empirical results suggest that the adoptionof macroprudential policy cause negative consequence on financial inclusion, particularly theuse of financial service in developing countries. We find the adverse effects are asymmetricin developed and developing countries. The dissertation is organized as follow. Chapter 2

3provides some insightful finding on the effects of a banking crisis on some macroeconomic
conditions i.e., “the effects of a banking crisis on the credit growth in developing countries”.
Chapter 3 presents the analysis on the benefit of macroprudential policy titled“Macroprudential policy and banking crises: Targeting borrowers or financial institutions?”.
Chapter 4 discusses the cost of the adoption of macroprudential policy i.e., “Macroprudential
policy and financial inclusion: Any difference between developed and developing countries?”.
The last Chapter provides some conclusions.

4

Chapter 2: Effects of a banking crisis on credit growth in developing
countries

2.1 Introduction
Banking crises often impose tremendous economic costs on the financial system as a whole,
one of which is the shrinking of credit. Most related studies have focused on the effect of abanking crisis on output or growth (Demirguc-Kun et al., 2006; Dell’Ariccia et al., 2008;
Furceri & Zdzienicka, 2012; Fernandez et al., 2013), but its impact on credit growth has notbeen examined extensively.1 Furthermore, while financial conditions such as financial cycles
and financial development are highly related to a banking crisis, the existing literature has not
fully addressed how financial conditions alter the impact of a banking crisis.2
This study empirically examines how a banking crisis affects credit growth while
controlling for financial cycles and financial development. For this purpose, we estimate thedynamic effect of a banking crisis on credit growth by using the local projection (LP) methodof Jordà (2005). This empirical method is flexible in estimating state-dependence impulse
responses and allows us to examine how states of financial conditions alter the effects ofbanking crises. We are particularly interested in developing countries, as financial
development is more relevant to developing countries than developed countries. Financial

An exception may be Demirguc-Kun et al. (2006), which examines the crisis effects on various macroeconomic
variables, including output and credit growth. However, their study does not consider how the effects rely on
financial conditions, such as financial cycles and financial development, in developing countries.
2
Although several studies such as Babecký et al. (2014) suggest that banking crises occur during financial booms,
a significant portion of banking crises have occurred even during financial slumps. In our dataset, approximately
38% of banking crises occurred during financial slumps. In addition, some studies such as Mathonnat and Minea(2018) and Naceur et al. (2019) have emphasized that financial development is closely associated with banking
crises. These arguments demonstrate the possible important roles of financial cycles and financial developmentin the discussion of the crisis effects.
15development is an important policy agenda in developing countries, while developed countries
generally have a mature financial system with a relatively high level of financial development.
Our empirical analysis finds that the banking crisis has an adverse impact on credit
growth, and this negative effect depends on financial conditions. We demonstrate that thenegative effect of a banking crisis during a financial boom is more substantial than that during
a financial slump. We also find that the negative effect of a banking crisis is more substantial
for countries with a high level of financial development than for those with a low level offinancial development. Several sensitivity analyses confirm the empirical validity of ourfindings.

2.2 Methodology and data
To examine the dynamic effects of a banking crisis on credit growth, this study employs the
LP method proposed by Jordà (2005) with panel data of 109 developing countries during
1993–2017. 3 As the LP method is flexible and can easily be extended to estimate statedependent effects, it is suitable for our analysis. This study uses financial cycles and financial
development as the state variables of financial conditions. First, we estimate the following
baseline model:ℎ𝑌𝑖,𝑡+ℎ − 𝑌𝑖,𝑡 = 𝛽 ℎ 𝑆ℎ𝑜𝑐𝑘𝑖,𝑡+1 + 𝜃 ℎ 𝑋𝑖,𝑡 + 𝜆ℎ𝑖 + 𝛿𝑡ℎ + 𝜀𝑖,𝑡,(1)where 𝑌𝑖,𝑡 is the log of credits to the private sector in country 𝑖 in period 𝑡; 𝑆ℎ𝑜𝑐𝑘𝑖,𝑡 is abanking crisis dummy assumed to be exogenous,4 𝑋𝑖,𝑡 is the set of control variables, including

The LP method has been a popular tool in applied macroeconomic research. Starting with Jordà (2005), thismethod has been used by researchers as an alternative to other time series methods such as vector autoregressive(VAR) models. The LP method is more robust to omitted variables and misspecification and easily handles
nonlinearity. As discussed by Auerbach and Gorodnichenko (2012) and Jordà (2005), the LP technique can easily
adapt non-linearly and thus estimate state-dependent models and compute impulse response functions. Moreover,the method does not constrain the shape of the impulse response function, so it is less sensitive to misspecificationof the standard VAR models.
4
In the robustness check, we also check the sensitivity of our finding by estimating a model treating a banking
crisis as nonrandom.
3

6

lags of the dependent variable, output gap, log of real GDP per capita, and inflation; 𝜆ℎ𝑖ℎcaptures the country fixed effects; 𝛿𝑡ℎ captures time fixed effects; and 𝜀𝑖,𝑡is the disturbanceterm with zero mean and constant variance. We estimate Equation (1) for each horizon h = 1,
2, …, 5. We follow the common practice of using the medium-term 5-year horizon, where h =1
is the year of the shock. Impulse response functions are computed using the estimatedcoefficients 𝛽 ℎ , and the confidence bands associated with the estimated impulse response
functions are obtained using the estimated standard errors of the coefficients 𝛽 ℎ .
We extend our analysis to investigate whether the responses of credit growth to thebanking crisis depend on financial cycles and financial development. First, we introduce adummy variable that indicates the state of financial cycles (boom versus slump) and financial
development (high versus low). Specifically, we estimate the following nonlinear equation:ℎ𝑌𝑖,𝑡+ℎ − 𝑌𝑖,𝑡 = 𝛽𝑎ℎ 𝐼𝑖,𝑡 𝑆ℎ𝑜𝑐𝑘𝑖,𝑡+1 + 𝛽𝑏ℎ (1 − 𝐼𝑖,𝑡 )𝑆ℎ𝑜𝑐𝑘𝑖,𝑡+1 + 𝜃 ℎ 𝑋𝑖,𝑡 + 𝜆ℎ𝑖 + 𝛿𝑡ℎ + 𝜀𝑖,𝑡,(2)where 𝐼𝑖,𝑡 is a dummy variable that captures the state of financial cycles or financial
development. The dummy variable takes a value of 1 if the status of the country is a financial
boom or a high level of financial development, and 0 otherwise. We use credit gap, measured
by the cyclical component of credit, to capture the state of financial cycles. If the credit gap isabove (below) 0, the state of the country is classified as being in a financial boom (slump).5
To capture the state of financial development, we use the Financial Development Index
constructed by Sahay et al. (2015). If the index is above (below) the mean, the state of thecountry is classified as being in a high (low) level of financial development.
We then examine how the two states of financial cycles and financial development
alter the effect of a banking crisis on credit growth. We consider the following specification:

There are several types of financial cycles (Borio, 2014; Strohsal, 2019). The cyclical component of credit isderived from the Hodrick-Prescott (HP) filter of the log of credit in a real term and is normalized to have zeromean and unit variance.
5

7
𝐹𝐶 ℎ 𝐹𝐷
𝐹𝐷𝐹𝐶
𝐹𝐷
𝑌𝑖,𝑡+ℎ − 𝑌𝑖,𝑡 = 𝐼𝑖,𝑡[𝛽𝑎 𝐼𝑖,𝑡 𝑆ℎ𝑜𝑐𝑘𝑖,𝑡+1 + 𝛽𝑏ℎ (1 − 𝐼𝑖,𝑡𝑆ℎ𝑜𝑐𝑘𝑖,𝑡+1 +)𝑆ℎ𝑜𝑐𝑘𝑖,𝑡+1 ] + (1 − 𝐼𝑖,𝑡)[𝛽𝐴ℎ 𝐼𝑖,𝑡ℎ𝐹𝐷ℎ𝛽𝐵ℎ (1 − 𝐼𝑖,𝑡)𝑆ℎ𝑜𝑐𝑘𝑖,𝑡+1 ] + 𝜃 𝑋𝑖,𝑡 + 𝜆ℎ𝑖 + 𝛿𝑡ℎ + 𝜀𝑖,𝑡
,

(3)

𝐹𝐶
𝐹𝐷
where 𝐼𝑖,𝑡and 𝐼𝑖,𝑡
are dummy variables that take values of 1 if the status of the country is in afinancial boom and a high level of financial development, respectively.
We use panel data from 109 developing countries from 1993 to 2017.6 The World Bank
country classification is used to identify developing countries. In this study, data on credit
growth, our dependent variable, are taken from the World Bank’s Global Financial
Development Database.7 This study uses the binary index of banking crises constructed by
Laeven and Valencia (2018). We choose this database because it is updated and covers abroader sample of countries and periods. The Financial Development Index is taken from the
Financial Development Index Database of the International Monetary Fund (IMF) (Sahay etal., 2015). It captures broad aspects of the development of financial institutions and financial
markets—depth, access, and efficiency. Data on the control variables used in this study aretaken from various sources.

Although this study focuses on developing countries, we also estimate the models for developed countries forreference. We use panel data of 55 developed countries.
7
We also employ IMF economies groupings to classify developed and developing countries in the robustness
check.
6

8

Table 2-A1. Data description and sources of variables
Variable
CreditDefinition
Private credit by deposit money banks

Banking crisis

A crisis dummy of 1 in the year of banking crisis episode and0 otherwise.
Difference between the log of credit to private sector and its
long-run trend derived from the Hodrick-Prescott (HP) filter

Credit gap
Financial
development index

Trade to GDP

A ranking of countries on the depth, access, and efficiency of
their financial institutions and financial markets. It is anaggregate of the Financial Institutions index and the Financial
Markets index
Difference between log of actual real GDP and its long-run
trend
Ratio of trade flow to GDP

Real GDP per capita

Log of real GDP per capita

Bank concentration

Assets of three largest commercial banks as a share of total
commercial banking assets.

Inflation

Change in consumer price index (2010=100)

MPI

Overall macroprudential policy instruments

Public debt to GDP

The ratio of public debts to GDP

Output gap

SourceWorld Bank’s Global
Financial Development
Database (2019)
Laeven and Valencia (2018)
World Bank’s Global
Financial Development
Database (2019)
Financial Development Index
Database-IMF
Penn World Table 10
World Development
Indicators
World Bank’s Global
Financial Development
Database (2019)
World Bank’s Global
Financial Development
Database (2019)
World Bank’s Global
Financial Development
Database (2019)
Authors’ calculation based on
Alam et al. (2019)
World Economic Outlook
database 2019 (IMF)

2.3 Results
Figure 1 shows the estimated impulse responses of credit growth to a banking crisis, obtainedby estimating Equation (1). The results confirm the conventional argument that a banking
crisis has an adverse impact on credit and demonstrate that the impact for developing countries
does not differ from that for developed countries. In developing countries, a banking crisis
reduces the credit growth rate by 0.07 in the first year, and the cumulative effect is 0.32 over
five years after the crisis (Table A2). These results are consistent with those of Demirguc-Kun
et al. (2006), which indicates a substantial slowdown in credit growth during the post-crisis
period. The banking system plays an important role in credit markets, and hence, a sudden
shock to financial intermediaries in the system would have a disproportionately contractionary
impact on credit growth.

9Figure 2-1: Effects of banking crisis
Developed countries
0.2

0

1

2

3

Developing countries
4

5

0.2

0

0

-0.2

-0.2

-0.4

-0.4

-0.6

-0.6

-0.8

-0.8

0

1

2

3

4

5

Note: t=1 is the year of the shock. Blue solid line is the impulse response of credit growth to banking crisis.
Dotted red lines are the 95 percent confidence bands.

Table 2-A2. The effect of banking crisis on credit growth (baseline), developed anddeveloping countries(1)
Year 1(2)
Year 2(3)
Year 3(4)
Year 4(5)
Year 5-0.0577***(0.0118)
1,175-0.1402***(0.0260)
1,120-0.2151***(0.0410)1,065-0.2802***(0.0538)
1,010-0.3400***(0.0660)955-0.0727***(0.0130)2,356-0.1990***(0.0321)2,250-0.2852***(0.0512)2,142-0.3121***(0.0595)
2,034-0.3199***(0.0625)
1,925-0.0570***(0.0133)788-0.1341***(0.0276)752-0.2125***(0.0407)716-0.2936***(0.0548)680-0.3672***(0.0697)644-0.0804***(0.0129)
Observations
2,677
Notes: *** p<0.01, ** p<0.05, * p<0.1-0.2205***(0.0324)2,556-0.3186***(0.0518)2,433-0.3535***(0.0606)
2,310-0.3695***(0.0644)2,186

World Bank’s classification
Developed countries
Banking crisis
Observations
Developing countries
Banking crisis
Observations
IMF’s classification
Developed countries
Banking crisis
Observations
Developing countries
Banking crisis

Figure 2 shows the state-dependent impulse responses of credit growth to a banking
crisis, using financial cycles as the state. A banking crisis during a financial boom has a larger
negative impact on credit growth, particularly in the medium term, than that during a financial
slump. A banking crisis during a financial boom (slump) decreases the credit growth rate by10

0.45 (0.15) over five years after the crisis (Table A3). This result is intuitive. During a financial
boom, we typically observe the rapid expansion of economic and financial activities, often
associated with excessive credit growth. An unanticipated shock of a banking crisis would
inevitably interrupt these activities. Thus, the impact of a banking crisis during a financial
boom is more substantial than that during a financial slump.
Figure 2-2: Financial cycles
Financial boom
0.2

0

1

2

3

Financial slump
4

5
0.2

0

0

-0.2

-0.2

-0.4

-0.4

-0.6

-0.6

-0.8

-0.8

0

1

2

3

4

5

Note: t=1 is the year of the shock. Blue solid line is the impulse response of credit growth to banking crisis.
Dotted red lines are the 95 percent confidence bands.

11

Table 2-A3. The effect of banking crisis on credit growth, financial cycles, developed anddeveloping countries(1)
Year 1(2)
Year 2(3)
Year 3(4)
Year 4(5)
Year 5-0.0447***(0.0098)-0.0945***(0.0290)
1,175-0.1283***(0.0258)-0.1733***(0.0422)1,120-0.2130***(0.0433)-0.2209***(0.0503)
1,065-0.2913***(0.0574)-0.2498***(0.0642)1,010-0.3614***(0.0687)-0.2813***(0.0850)955-0.0417**(0.0205)-0.1089***(0.0219)
2,356-0.2040***(0.0413)-0.1929***(0.0462)2,250-0.3561***(0.0611)-0.1934***(0.0618)2,142-0.4243***(0.0711)-0.1640**(0.0735)
2,034-0.4504***(0.0676)-0.1572*(0.0827)
1,925-0.0499***(0.0111)-0.0803**(0.0310)788-0.1324***(0.0282)-0.1394***(0.0405)752-0.2182***(0.0428)-0.1944***(0.0473)716-0.3101***(0.0561)-0.2417***(0.0668)680-0.3958***(0.0681)-0.2781***(0.0937)644-0.0474**(0.0186)
Banking crisis-slump-0.1192***(0.0227)
Observations
2,677
Notes: *** p<0.01, ** p<0.05, * p<0.1-0.2229***(0.0392)-0.2176***(0.0471)2,556-0.3896***(0.0603)-0.2274***(0.0630)
2,433-0.4682***(0.0709)-0.2033***(0.0741)2,310-0.5006***(0.0698)-0.2061**(0.0824)
2,186

World Bank’s classification
Developed countries
Banking crisis-boom
Banking crisis-slump
Observations
Developing countries
Banking crisis-boom
Banking crisis-slump
Observations
IMF’s classification
Developed countries
Banking crisis-boom
Banking crisis-slump
Observations
Developing countries
Banking crisis-boom

Concerning the state of financial development, Figure 3 presents the impulse responsesof credit growth to a banking crisis. The negative effects of a banking crisis in countries witha high level of financial development are more substantial than those with a low level offinancial development. A banking crisis decreases the credit growth rate by 0.43 (0.24) over
five years for countries with a high (low) level of financial development (Table A4). A
possible explanation may be related to the fragile financial systems in developing countries.
Some developing countries have enjoyed a high degree of financial depth and rapid economic
growth. However, their financial systems are often more vulnerable to an unanticipated shock
due to financial fragility, given weak institutions and less effective financial policies; thus, abanking crisis tends to have a more substantial adverse effect in these countries.

12

Figure 2-3: Financial development
Low level of financial
development

High level of financial
development
0.2

0

1

2

3

4

5
0.2

0

0

1

2

3

4

5

0

-0.2

-0.2

-0.4

-0.4

-0.6

-0.6

-0.8

-0.8

Note: t=1 is the year of the shock. Blue solid line is the impulse response of credit growth to banking crisis.
Dotted red lines are the 95 percent confidence bands.

Table 2-A4. The effect of banking crisis on credit growth, financial development, developing
countries

World Bank’s classification
Banking crisis-HFD
Banking crisis-LFD
Observations
IMF’s classification
Banking crisis-HFD(1)Year 1(2)
Year 2(3)
Year 3(4)
Year 4(5)
Year 5-0.0759***(0.0208)-0.0751***(0.0218)2290-0.2412***(0.0523)-0.1730***(0.0456)2188-0.3795***(0.0784)-0.2131***(0.0694)2084-0.4245***(0.0829)-0.2239***(0.0850)1980-0.4296***(0.0795)-0.2391**(0.0961)
1875-0.0719***-0.2338***-0.3702***-0.4176***-0.4230***(0.0183)(0.0467)(0.0695)(0.0739)(0.0740)
Banking crisis-LFD-0.0896***-0.2063***-0.2646***-0.2878***-0.3159***(0.0218)(0.0496)(0.0781)(0.0939)(0.1025)
Observations
2,677
2,556
2,433
2,310
2,186
Notes: (1) HFD: high financial development and LFD: low financial development. (2) *** p<0.01, ** p<0.05,* p<0.1

Figure 4 shows the impulse responses derived from Equation (3) with the two states offinancial cycles and financial development simultaneously. The results confirm our previous
findings on the state-dependent crisis effects (Table A5). The negative crisis effect is mostsubstantial when a developing country has a high level of financial development, and a crisis
occurs during a financial boom (upper-left panel in Figure 4).

13

Figure 2-4: Financial cycles and financial development

00.2

Financial boom and the high levelof financial development
1

2

3

4

5

Financial slump and the high levelof financial development

0.2

0

0

-0.2

-0.2

-0.4

-0.4

-0.6

-0.6

-0.8

-0.8

Financial boom and the low levelof financial development

0.2

0

1

2

3

4

5

0

1

2

3

4

Financial slump and the low levelof financial development

0.2

0

0

-0.2

-0.2

-0.4

-0.4

-0.6

-0.6

-0.8

-0.8

0

1

2

3

4

5

5

Note: t=1 is the year of the shock. Blue solid line is the impulse response of credit growth to banking crisis.
Dotted red lines are the 95 percent confidence bands.

Table 2-A5. The effect of banking crisis on credit growth, financial cycles and financial
development, developing countries

World Bank’s classification
Banking crisis-boom-HFD
Banking crisis-slump-HFD
Banking crisis-boom-LFD
Banking crisis-slump-LFD
Observations
IMF’s classification
Banking crisis-boom-HFD(1)
Year 1(2)
Year 2(3)
Year 3(4)
Year 4(5)
Year 5-0.0405(0.0284)-0.1581***(0.0504)-0.0469*(0.0252)-0.0918***(0.0313)2,290-0.2100***(0.0530)-0.3228**(0.1236)-0.2119***(0.0622)-0.1485***(0.0563)2,188-0.4047***(0.0811)-0.3063**(0.1383)-0.3049***(0.0856)-0.1554*(0.0815)2,084-0.4920***(0.0961)-0.2178*(0.1251)-0.3537***(0.0899)(0.1411)(0.1006)
1,980-0.5028***(0.0935)-0.2194*(0.1122)-0.4079***(0.0906)(0.1360)(0.1166)
1,875-0.0439*-0.2112***-0.3973***-0.4819***-0.4979***(0.0242)(0.0465)(0.0728)(0.0873)(0.0875)
Banking crisis-slump-HFD-0.1335***-0.2880***-0.2979***-0.2399**-0.2266**(0.0434)(0.1028)(0.1129)(0.1063)(0.0999)
Banking crisis-boom-LFD-0.0543**-0.2457***-0.3760***-0.4438***-0.5057***(0.0228)(0.0620)(0.0975)(0.1090)(0.1076)
Banking crisis-slump-LFD-0.1116***-0.1822***-0.1935**-0.1857*-0.1969*(0.0322)(0.0590)(0.0825)(0.0989)(0.1131)
Observations
2,677
2,556
2,433
2,310
2,186
Notes: (1) HFD: high financial development and LFD: low financial development. (2) *** p<0.01, ** p<0.05,* p<0.1

14

2.4 Robustness checks
In this section, we perform sensitivity analyses to check the empirical validity of our baseline
finding. First, we apply a smooth transition function to generate states of financial conditions.
Second, we use an alternative measure of financial development. Third, we employ theaugmented regression-adjusted estimation approach (AIPW), proposed by Jordà and Taylor(2015), to mitigate possible endogeneity problems. Last, we use the IMF economies groupings
to classify countries into developed and developing countries.

2.4.1 Smooth transition function approach
We applied binary or dummy variables to capture the states of financial cycles and financial
development. This section differentiates the state dependence of financial cycles and financial
development by applying a smooth transition function approach, instead of a dummy variableapproach, to the estimation of Equation (2). In contrast to the dummy variable, the smoothtransition function models the state as a continuous process, allowing to incorporate stateswitching behaviors. The smooth transition function is described by:exp (−γz )F(zi,t ) = 1+exp (−γzi,t ),i,tand γ > 0, where zi,t is an indicator of financial cycles and financial development normalized
to have zero mean and unit variance. The smooth transition function F(zi,t ) varies between 0
and 1. F = 0 corresponds to a situation where the countries experience a financial boom orhave a high level of financial development, while F = 1 corresponds to a situation where thecountries experience a financial slump or have a low level of financial development. We set
the γ = 1.0. The estimated results confirm our findings (Table A6).

15

Table 2-A6. The effect of banking crisis on credit growth, financial cycles and financial
development, developing countries (smooth transition function)

World Bank’s classification
Financial cycles
Banking crisis-boom
Banking crisis-slump
Observations
Financial development
Banking crisis-HFD
Banking crisis-LFD
Observations
IMF’s classification
Financial cycles
Banking crisis-boom
Banking crisis-slump
Observations
Financial development
Banking crisis-HFD(1)Year 1(2)
Year 2(3)
Year 3(4)
Year 4(5)
Year 50.0512(0.0392)-0.2089***(0.0384)2,356-0.1545**(0.0677)-0.2494***(0.0776)
2,250-0.4142***(0.1045)-0.1353(0.1077)
2,142-0.5607***(0.1209)-0.0219(0.1345)2,034-0.6019***(0.1171)0.0041(0.1588)
1,925-0.0385(0.0276)-0.1224***(0.0370)2,290-0.2267***(0.0800)-0.1820*(0.0927)2,188-0.4150***(0.1186)-0.1430(0.1407)2,084-0.4767***(0.1215)-0.1239(0.1620)1,980-0.4670***(0.1188)-0.1566(0.1772)
1,875

0.0359(0.0336)-0.2105***(0.0362)2,677-0.1833***(0.0597)-0.2632***(0.0730)
2,556-0.4571***(0.0945)-0.1552(0.0990)2,433-0.6126***(0.1097)-0.0465(0.1220)2,310-0.6621***(0.1050)-0.0277(0.1429)2,186-0.0320-0.2070***-0.3882***-0.4566***-0.4629***(0.0244)(0.0729)(0.1078)(0.1111)(0.1141)
Banking crisis-LFD-0.1369***-0.2362***-0.2369*-0.232-0.2591(0.0345)(0.0884)(0.1370)(0.1586)(0.1725)
Observations
2,677
2,556
2,433
2,310
2,186
Notes: (1) HFD: high financial development and LFD: low financial development. (2) *** p<0.01, ** p<0.05,* p<0.1

2.4.2 Alternative measure of financial development
To capture the level of financial development, we employed the Financial Development Index,
taken from the Financial Development Index Database of the IMF (Sahay et al., 2015).
Although this index has been used in many studies, several studies, such as Mathonnat andMinea (2018), employ the credit-to-GDP ratio to measure the level of financial development.
Thus, to ensure the validity of our findings, we also use credit-to-GDP as an alternative
measure of financial development. The estimated results are consistent with our findings(Table A7).

16

Table 2-A7. The effect of banking crisis on credit growth, financial development (based on
the credit to GDP ratio), developing countries

World Bank’s classification
Banking crisis-HFD
Banking crisis-LFD
Observations
IMF’s classification
Banking crisis-HFD(1)Year 1(2)
Year 2(3)
Year 3(4)
Year 4(5)
Year 5-0.0334(0.0321)-0.0939***(0.0181)2,356-0.1846***(0.0633)-0.2068***(0.0411)2,250-0.3503***(0.0943)-0.2496***(0.0598)
2,142-0.4153***(0.1066)-0.2578***(0.0715)2,034-0.4265***(0.1026)-0.2666***(0.0797)
1,925-0.0528**-0.2059***-0.3625***-0.4449***-0.4638***(0.0255)(0.0614)(0.0944)(0.1104)(0.1118)
Banking crisis-LFD-0.0963***-0.2290***-0.2930***-0.3020***-0.3184***(0.0165)(0.0395)(0.0591)(0.0674)(0.0743)
Observations
2,677
2,556
2,433
2,310
2,186
Notes: (1) HFD: high financial development and LFD: low financial development. (2) *** p<0.01, ** p<0.05,* p<0.1

2.4.3 Augmented regression-adjusted estimation approach
In the previous section, we assumed that a banking crisis is exogenous. However, several
studies have emphasized that the occurrence of a banking crisis is endogenously determinedby various macroeconomic and financial conditions (Mathonnat & Minea, 2018; Babecký etal., 2014). Thus, the models treating a banking crisis as exogenous may suffer fromendogeneity issues. To mitigate this, we apply the augmented regression-adjusted estimation
approach (AIPW) proposed by Jordà and Taylor (2015). The logic behind this approach is to
apply local projections to a sample that has been re-randomized using propensity scores. The
AIPW estimator has the doubly robust property that the consistency of the estimated averagetreatment effect (ATE) can be proved in the special case where either the probit model or theconditional mean model is correctly specified. Following this approach, we first estimate thepropensity scores deriving the probability with which a banking crisis is expected byregressing it on its predictors.8 We then use the propensity scores to derive an ATE based on

We use credit gap, output gap, log of real GDP per capita, public debts to GDP, and bank concentration as
covariates in the probit estimation. The definitions of the variables are provided in Table A1 in the appendix.
8

17

inverse probability weighting. The estimated results confirm our findings (Table A8).
Table 2-A8. The effect of banking crisis on credit growth, financial cycles and financial
development (AIPW), developing countries

World Bank’s classification
Financial cycles
ATE_banking crisis_boom
ATE_banking crisis_slump
Observations
Financial development
ATE_banking crisis_HFD
ATE_banking crisis_LFD
Observations
IMF’s classification
Financial cycles
ATE_banking crisis_boom
ATE_banking crisis_slump
Observations
Financial development
ATE_banking crisis_HFD(1)Year 1(2)
Year 2(3)
Year 3(4)
Year 4(5)
Year 5-0.0808***(0.0137)-0.1475***(0.0091)279-0.2085***(0.0221)-0.2733***(0.0086)264-0.3152***(0.0313)-0.2140***(0.0106)249-0.3089***(0.0430)-0.1211***(0.0154)234-0.2594***(0.0526)-0.0673***(0.0179)
219-0.1122***(0.0074)
0.0288***(0.0090)279-0.2282***(0.0188)-0.1450***(0.0121)264-0.3327***(0.0254)-0.2335***(0.0107)249-0.3745***(0.0256)-0.1617***(0.0158)234-0.3811***(0.0240)-0.0671***(0.0192)219-0.0631***(0.0118)-0.1587***(0.0104)354-0.1811***(0.0183)-0.2689***(0.0100)335-0.2731***(0.0251)-0.2889***(0.0153)316-0.2522***(0.0359)-0.2481***(0.0170)297-0.1900***(0.0455)-0.1553***(0.0152)278-0.1007*** -0.1945*** -0.2683*** -0.2723*** -0.2460***(0.0082)(0.0158)(0.0183)(0.0202)(0.0282)
ATE_banking crisis_LFD
0.0535***-0.1040*** -0.1985*** -0.1460*** -0.0687***(0.0123)(0.0166)(0.0101)(0.0146)(0.0169)
Observations
354
335
316
297
278
Notes: (1) HFD: high financial development and LFD: low financial development. (2) *** p<0.01, ** p<0.05,* p<0.1

2.4.4 International Monetary Fund (IMF) economies groupings
We used the World Bank country classification by income level to categorize developed anddeveloping countries. To ensure the validity of the baseline finding, we also use IMF
economies groupings to group developed and developing countries. Developed countries inthis study are equivalent to advanced economies in the IMF economies groupings. Developing
countries consist of emerging market and middle-income economies, and low-income
developing countries in the IMF classification. The estimated results generally confirm our

18findings. The estimated results using the IMF economies groupings are presented along withthe table results based on the World Bank county classification.9

2.5 Conclusion
Our empirical results demonstrated that the effects of a banking crisis on credit growth arehighly dependent on financial conditions in developing countries. First, the adverse effects ofa banking crisis would become more substantial, particularly when a banking crisis occurs
during a financial boom. Second, the adverse effects of a banking crisis would be intensified
by financial development, which is recognized as a crucial determinant of efficient resource
allocation.
The results of this study led to two policy implications for financial regulators. First,
to mitigate the negative crisis effect, especially during booms, financial regulators should
introduce or establish effective early warning schemes, including the monitoring of excess
credit (credit gap), property price gap, and debt-service ratios, which could ease excessive
credit and reduce the likelihood of a banking crisis. In addition, the adoption ofmacroprudential policies, such as countercyclical capital buffer and loan-to-value ratio, helps
mitigate the excess boom of credit and systemic risk associated with a banking crisis(Dell’Ariccia et al., 2016; Cerutti et al., 2017).
Second, our results revealed the relatively large negative crisis effects in countries witha high level of financial development. Financial development is widely considered a driving
force for economic growth. However, financial authorities in developing countries should
realize that once their countries encounter a banking crisis, particularly during a financial

In addition, we conduct two additional robustness checks: (i) adding more control variables (bank concentration,
real GDP per capita, macroprudential policy index, monetary and fiscal policy stances) into our baseline
specification and (ii) changing the numbers of lags in our baseline specification. The results are generally
consistent with our main findings. The results are available upon request.
9

19boom, a high level of financial development could have an unfavorable side effect. Financial
regulators should not only emphasize financial development but also enhance the resilience ofthe financial system. Thus, countries should continuously strive to enhance institutional
quality and financial architecture for financial regulators to accommodate the instantaneousdevelopment of the financial system.
Our study examined the effects of a banking crisis on credit. However, certain otherfinancial crises, such as currency crises and debt crises, also affect macroeconomic andfinancial conditions. In addition, the recent COVID-19 pandemic outbreak is another type ofcrisis that has caused all countries around the world to experience a turbulent period with highuncertainty. More careful studies are required to achieve a comprehensive understanding of
the crisis effects.

20

References
Auerbach, A.J., Gorodnichenko, Y., 2012. Measuring the output responses to fiscal policy.
American Economic Journal: Economic Policy 4(2), 1–27.
Babecký, J., Havránek, T., Matějů, J., Rusnák, M., Šmídková, K., Vašíček, B., 2014. Banking,debt, and currency crises in developed countries: Stylized facts and early warning
indicators. Journal of Financial Stability 15, 1–17.
Borio, C., 2014. The financial cycle and macroeconomics: What have we learnt? Journal of
Banking and Finance 45, 182-198.
Cerutti, E., Claessens, S., Laeven, L., 2017. The use and effectiveness of macroprudential
policies: New evidence. Journal of Financial Stability. 28, 203–224.
Dell’Ariccia, G., Detragiache, E., Rajan, R., 2008. The real effect of banking crises. Journalof Financial Intermediation 17, 89-112.
Dell’Ariccia, G., Igan, D., Laeven, L., Tong, H., 2016. Macrofinancial Stability. Economic
Policy. Economic Policy. 31 (86), 299–355.
Demirgüç-Kunt, A., Detragiache, E., Gupta, P., 2006. Inside the crisis: An empirical analysis
of banking system in distress. Journal of International Money and Finance 25, 702-718.
Fernandez A. I., González, F., Suárez, N., 2013. The real effect of banking crises: Finance orasset allocation effects? Some international evidence. Journal of Banking and Finance 37,
2419-2433.
Furceri, D., Zdzienicka, A., 2012. Banking crises and short and medium term output losses inemerging and developing countries: The role of structural and policy variables. World
Development 40, (12) 2369-2378.
Jordà, O., 2005. Estimation and inference of impulse responses by local projections. American
Economic Review 95, (1) 161-182.
Jordà, O., Talylor, A. M., 2016. The time for austerity: Estimating the average treatment effectof fiscal policy. The Economic Journal 126, 219-255.
Mathonnat, C., Minea, A., 2018. Financial development and the occurrence of banking crises.
Journal of Banking and Finance 96, 344–354.
Naceur, S. B., Candelon, B., Lajaunie, Q., 2019. Taming Financial Development to Reduce
Crises. IMF Working Paper 19/94.
Sahay, R., Čihák, M., N’diaye, P., Barajas, A., Bi, R., Ayala, D., Gao, Y., Kyobe, A., Nguyen,
L., Saborowski, C., Svirydzenka, K., Yousefi, S. R., 2015. Rethinking financial
deepening: stability and growth in emerging markets. IMF Staff Discussion Note,21

SDN/15/08.
Strohsal, T., Proaño, C. R., Wolters, J., 2019. Characterizing the financial cycle: Evidence
from a frequency domain analysis. Journal of Banking and Finance 106, 568-591.

22

Chapter 3: Macroprudential policy and banking crises: Targetingborrowers or financial institutions?

3.1 Introduction
The macroprudential objective can be defined as limiting the costs to the economy of financial
distress, including limiting the likelihood of the failure, and the corresponding costs tosignificant portions of the financial system, which is often loosely referred to as limiting“systemic risk” (Crockett, 2000).10 Over the past two decades, macroprudential policy (MP)has increasingly become widespread among financial regulators. Nonetheless, banking crises
have not stopped happening, and their forms have kept changing over time. This would cast
doubts on the roles of MP in limiting the likelihood of financial failure. Many studies
demonstrate that MP is effective in limiting the level of financial indicators associated withfinancial failure, such as credit growth and housing prices (Alam et al., 2019; Olszak et al.,2018; Cerutti et al., 2017; Fendoğlu, 2017; Bruno et al., 2017; Lim et al., 2011). Credit andasset price booms, however, are not the only driving forces that increase systemic risk andinduce banking crises.11
On the other hand, there is very limited evidence on the effect of MP on various factors,
such as the externalities generated from the strategy of financial institutions, which are crucial
in intensifying systemic risk. 12 One possible reason that these factors are abstract in theliterature is the difficulty in measuring them accurately. In contrast to previous studies, ourstudy attempts to evaluate the MP role in limiting systemic risk in the financial system by

Crockett (2000) also mentions that the micro-prudential objective can be seen as limiting the likelihood of
failure of individual institutions, which means limiting “idiosyncratic risk.”
11
Dell’Ariccia et al. (2016) show that a significant portion of credit booms are not followed by banking crises.
12
The externality includes “fire sales”, “strategic complementarities” and “contagion”. MP instruments aredesigned to eliminate these externalities. See the discussion of De Nicolò et al. (2014) in details.
10

23

taking the occurrences of banking crises as the proxy for systemic risk.13 Systemic risk can beunderstood as market failure in the financial system or financial crises (Butzbach, 2016).
Moreover, we evaluate the effects of the major components of MP, which share the same
purpose or target. MP consists of various policy instruments, and every instrument may notachieve the expected effects.14 This exploration is particularly relevant for financial regulators
because these instruments also come with costs to economic performance.15
We employ the most recent integrated macroprudential policies (iMaPP) database
constructed by Alam et al. (2019), which consists of 17 types of MP tools over 134 countries
from 1990 to 2016. We group all MP tools into seven types of policy instruments, among
which three main groups are (i) overall macroprudential policy (MPI), (ii) financial institutiontargeted policy (FINANCIAL) and (iii) borrower-targeted policy (BORROWER). The latter
two are subsets of the first. Moreover, since FINANCIAL consists of a large variety of tools,
we extend our analysis by constructing another three subgroups of financial institutiontargeted policy instructions: (a) capital-targeted policy (CAPITAL), (b) loan supply-targeted
policy (LOAN-SUPPLY), and (c) general-targeted policy (GENERAL). In addition, we
consider the loan-targeted policy (LOAN-TARGET), which consists of all policy tools(belonging to both FINANCIAL and BORROWER) related to loan policies.
Given the occurrences of banking crises as our binary dependent variable, theestimation is based on a logistic model with time and country fixed effects. As some of MP
tools can be triggered in reaction to credit conditions, we also examine the models with the

Although some risk measures, such as the expected default frequency (EDF) and the Z-score, have beendeveloped, the measurement of systemic risk in a comprehensive manner is still rudimentary (Altunbas et al.,2018).
14
Based on the objective it targets, MP is usually categorized as BORROWER or FINANCIAL (demand- andsupply-side MP) (Cerutti et al., 2017; Fendoğlu, 2017; Alam et al., 2019). BORROWER, such as caps on debtto-income and loan-to-value ratio, may reduce the risks of loans by directing credit flow to borrowers who areless likely to default when an adverse shock arrives. FINANCIAL aims to prevent fire-sales and the propagationof shocks by influencing banks’ management strategy.
15
Richter et al. (2019) show that over a four-year horizon, a 10 percentage point decrease in the maximum LTV
ratio leads to a 1.1% reduction in output.
13

24

interaction term of MP measures and credit variables. To ensure the robustness of the results,
we also conduct several sensitivity analyses. First, we apply factor analysis to categorize thegroups of MP instruments. Second, we attempt to mitigate endogeneity problems by
employing a linear probability model with instrumental variables. Last, we incorporate
monetary and fiscal policy stances into our empirical analysis to control the influence of thesepolices on the occurrence of banking crises, given the crucial roles of monetary and fiscal
policies in mitigating the impact of a shock on economic conditions. The inclusion of thesetwo police stances also lessens the concern of some omitted variable bias.
In line with our arguments, our results show that the MPI (overall index) is effective
in reducing the likelihood of banking crises. For the subgroups, BORROWER reduces thepossibility of banking crises. However, the effect of BORROWER on the occurrence ofbanking crises is reduced when credit gap is large. On the other hand, the effect of
FINANCIAL is less clear. Nonetheless, two subgroups of FINANCIAL, namely, CAPITAL
and LOAN-SUPPLY, have significant influences on reducing the likelihood of banking crises.
The other subgroup GENERAL does not have a clear result. The last subgroup, LOANTARGET, which includes tools from BORROWER and LOAN-SUPPLY, is negatively
correlated with the occurrence of banking crises. Our sensitivity analyses generally confirm
the empirical validity of our baseline results.
This study contributes to the literature by evaluating the effectiveness of various
instruments of MP in reducing systemic risks. Altunbas et al. (2018) examine the MP effects
on banks’ risks, which is measured by the expected default frequency (EDF). Although our
research topic seems similar, we emphasize the influence of MP on aggregate or systemic risk
under our argument that a banking crisis serves as the measurement of risks. In addition,
another related study, Dell’Ariccia et al. (2016), explores the effects of MP on credit booms
and shows that MP has proven effective in containing booms and in reducing the probability

25that booms end badly (with busts or recession). If we take these bad booms as another measureof systemic risks, our findings are in line with theirs. Nonetheless, it is important to note thata large proportion of banking crises are not initiated by credit booms, i.e., a large proportionof the observations of banking crises in our sample are not in their observations. Thus, ourfindings complement theirs, supporting the effectiveness of MP in reducing systemic risks.
The rest of the study is organized as follows. Section 2 presents a selective literature
review on financial crises and macroprudential policies. Section 3 describes the data andmethodology in this study. Section 4 presents the empirical results and their implications, andsection 5 shows several robustness checks of our baseline results. The last section provides
some conclusions.

3.2 Literature review
To better understand the possible impacts of macroprudential policies and their relationshipwith financial crises, this section overviews past studies on macroprudential policies andfinancial crises.

3.2.1 Financial crises
Many studies have attempted to identify and classify the types of financial crises (Laeven &Valencia, 2008, 2013, 2018,; Reinhart & Rogoff, 2011; Pescatori & Sy, 2007). Financial crises
are classified into several categories, such as banking, currency, inflation, debt, and capital
market crises, based on either a certain threshold or chronological events. Moreover, many
works have dedicated efforts to identifying possible determinants, including macroeconomic
and financial conditions, that give rise to financial crises (Hasanov & Bhattacharya, 2018;
Mathonnat & Minea, 2018; Caprio et al., 2014; Feldkircher, 2014; Babecký et al., 2014;
Berkmen et al., 2012; Bordo & Meissner, 2012; Baier et al., 2012; Shehzad & De Haan, 2009;
Haile & Pozo, 2008; Beck et al., 2006; Kaminsky, 1999). Among them, some studies show a26

clear relationship between the likelihood of a banking crisis and the financial structure, suchas the concentration of the banking system (Caprio et al., 2014; Beck et al., 2006), and others
focus on the role of political channels that may trigger credit booms and banking crises(Hasanov & Bhattacharya, 2018).
In addition, several studies emphasize the role of credit conditions in financial crises.
Many of these studies show that the development of credit affects the likelihood of a financial
crisis and suggest that financial authorities should monitor credit-related indicators in theframework of the early warning system for financial crises (Borio & Lowe, 2002, 2004; Borio& Drehmann, 2009; BCBS, 2010; Babecký et al., 2014). Hasanov and Bhattacharya (2018)
point out that household credit growth has a greater effect on the incidence of a banking crisis.
Mathonnat and Minea (2018) reveal that the ratio of money supply to GDP and the ratio ofcredit to deposits have a positive effect on the prospect of banking crises, but they fail to finda clear relationship between the ratio of credit to GDP and the likelihood of a banking crisis.
Although some studies show a positive relationship between credit development, or boom,
and the likelihood of a banking crisis, several works, such as Dell’Ariccia et al. (2016), showthat banking crises do not always follow credit booms. Using the data of 170 countries from1970 to 2010, they show that only one-third of credit boom episodes are followed by a banking
crisis.16

Following the idea of Dell’Ariccia et al. (2016), we attempt to identify the relationship between credit booms
and banking crises using banking crisis years in our dataset and credit boom episodes in the dataset ofDell’Ariccia et al. (2016). We find that among 200 banking crises in our sample, there are 66 starting years ofbanking crises, and 134 are the subsequent periods. Among the 66 starting years of banking crises, 23 banking
crises follow credit boom episodes within three years, while 34 banking crises do not follow credit boom episodes
within three years. Due to the data unavailability, we cannot identify whether or not the remaining 9 banking
crises follow credit boom episodes. This implies that approximately 35% of banking crises follow credit boom
episodes. Our analysis might be consistent with the argument in Dell’Ariccia et al. (2016) that not all credit boom
episodes are followed by banking crises. Table A1 shows the details of credit boom episodes and banking crises.
16

27

3.2.2 Financial crisis and regulatory frameworks
The discussion about how financial regulatory frameworks relate to the likelihood of a banking
crisis has triggered a debate among researchers. Kim et al. (2013) show that policies that
restrict bank activities and entry requirements mitigate the likelihood of a banking crisis, but
those that allow banks to become involved in nontraditional or risky activities fuel thepossibility of a banking crisis. Demirgüç-Kunt and Detragiache (2011) examine therelationship between banking soundness and Basel core principles compliance, but they failto find clear conclusions on the relationship between Basel core principles and bank risk.
Caprio et al. (2014) state that the Basel III advisory to increase banks’ capital puts morepressure on banks since banks need to find more sources of funds, which may lead to anincrease in lending rates and financial instability. However, they also argue that some policies
that restrict the risk activities of banks, including real estate and insurance, reduce thelikelihood of a banking crisis.
In addition, Lee et al. (2016) show that financial liberalization decreases the probabilityof a banking crisis, while Chen et al. (2019) argue that a higher level of financial liberalization
increases the chance of a banking crisis. Klomp and De Haan (2012, 2015) use seven measuresof financial policy and supervision to examine the effects on banks’ risk behaviors. They
conclude that most of the measures have a negative effect on bank risk, but the effectivenessof the measures depends on banks’ structure, such as ownership, size, and level of bank risk.
Similarly, Laeven and Levine (2009) analyze the relationship between financial policy andbank risk behavior with the consideration of different structures of bank ownership andindicate that financial policy, such as capital requirements and restrictions on banking
activities, has a significant effect on bank risk, although the direction of the effect varies
depending on the structure of bank ownership.

28

3.2.3 Macroprudential policies
The aftermath of the Global Financial Crisis in the late 2000s brings about more debate on theresilience of the international financial system and the roles of MP in mitigating theaccumulation of financial vulnerabilities and safeguarding financial stability. Many studies
have analyzed the use and effectiveness of MP to manage various financial indicators,
including boom-and-bust of credit, property cycles, and international financial flows. Amongthem, several works, such as Alam et al. (2019), Carreras et al. (2018), Cerutti et al. (2017),
Bruno et al. (2017), Dell’Ariccia et al. (2016), and Lim et al. (2011), find that MP is effective
in containing credit, particularly household credit.
Although MP is acknowledged as important, Bruno et al. (2017) and Lim et al. (2011) argue
that one cannot deny the roles of other financial policies and policies, such as monetary policy.
They suggest that the efficacy of MP is more auspicious in reducing credit growth if it iscomplementarily implemented with monetary policy.
There are plenty of empirical studies examining the effect of MP by its type or target.
In particular, the effectiveness of borrower-targeted MP (BORROWER) have been widely
explored. For instance, Claessens et al. (2013) demonstrate that the instruments of
BORROWER, such as caps on debt-to-income, loan-to-value ratios, limits on credit growthand foreign currency lending, reduce bank risks, particularly in boom periods. Furthermore,
several studies, such as Richter et al. (2019) and Kuttner and Shim (2016), show that the useof BORROWER (i.e., loan-to-value ratio and debt-to-income ratio) has a negative effect on
housing credit and house prices. Morgan et al. (2019) confirm the effectiveness of demandside-targeted MP (equivalent to BORROWER) by explaining that the use of the loan-to-value
ratio is effective in coping with excessive mortgage loans, especially for small banks and bankswith a low level of non-performing loans. As for the effect of financial institution-targeted
policy (FINANCIAL), Vandenbussche et al. (2015) find that the instruments of FINANCIAL,29

such as capital adequacy ratio and marginal reserve requirements on foreign funding, areeffective in curbing credit growth and housing prices in 16 European countries. Garbers andLiu (2018) argue that the adoption of the loan-to-value policy in conjunction with capitalrequirement is an effective method to deal with foreign interest rate shocks. Moreover, whencomparing the effectiveness of BOROWER and FINANCIAL, Olszak et al. (2018) andFendoğlu (2017) show that borrower-based instruments outperform financial institution-based
ones in mitigating the procyclicality of loan loss provisions and in limiting excessive credit
cycles.
Some studies examine the effect of MP based on country specific evidence. Igan andKang (2011) on the case of Korea points out that the implementation of the policy on the loanto-value ratio is more effective than the debt-to-income ratio in stabilizing housing prices
through curbing expectations and speculative incentives. Liao et al. (2015) uses a simulation
approach based on Northland’s data, showing that macroprudential capital requirements can
reduce individual and multiple banks default probabilities.

3.3 Data and empirical specification
In this section, we first describe how we construct the measures of macroprudential policy
instruments. We also document key features of these instruments. Next, we introduce our
empirical model and its specification.

3.3.1 The database of macroprudential policies
Several studies have constructed databases of macroprudential policies.17 This study employs
the most recent integrated macroprudential policies (iMaPP) database constructed by Alam et

Shim et al. (2013) construct a database based primarily on official sources of publications and press releasesof the financial authorities over 60 countries during the period from January 1990 to June 2012. Cerutti et al.
1730

al. (2019).18 It provides the most comprehensive coverage in terms of the number of countries(134), the length of the period covered (January 1990 to December 2016), and the types ofpolicy tools (17). These 17 tools include countercyclical buffer (CCB), capital conservationbuffer (Conservation), capital requirement for banks (Capital), limit on leverage of banks(LVR), loan loss provision (LLP), limits on growth or the volume of aggregate credit (LCG),
loan restrictions (LoanR), limits on foreign currency lending (LFC), limits of the loan to value
ratio (LTV), limits on the debt service to income ratio (DSTI), taxes and levies applied to
specified transactions (Tax), measures taken to mitigate systemic liquidity (Liquidity), limits
to the loan to deposit ratio (LTD), limits on net or gross open foreign exchange positions(LFX), measures taken to mitigate risks from global and domestic systemically importantfinancial institutions (SIFI), reserve requirements (RR), and other macroprudential measures(OT). Table 1 presents the detailed explanations of each macroprudential policy tool.

(2017) employ a database of macroprudential policies on an annual basis over 119 countries during the period
from 2000 to 2013. Instead of referring to official publication sources, their method is based mainly on a survey
project with various questionnaires, the Global Macroprudential Policy Instruments (GMPI) survey, conductedby the International Monetary Fund (IMF) during the period of 2013-2014.
18
It integrates a wide range of previously constructed datasets (i) Lim et al. (2011), (ii) Lim et al. (2013), (iii)
Global Macroprudential Policy Instruments (2013), (iv) Shim et al. (2013), (v) European Systemic Risk Board(ESRB) database, (vi) the Bank of International Settlement (BIS) and Financial Stability Board website and
IMF official documents, and (vii) the IMF’s annual macroprudential policy survey.

31

Table 3-1. Definitions of macroprudential policy tools
Individual policy tools
1. CCB, A requirement for banks to maintain a countercyclical capital buffer. Implementations
at 0% are not considered as a tightening in dummy-type indicators.
2. Conservation, Requirements for banks to maintain a capital conservation buffer, including theone established under Basel III.
3. Capital, Capital requirements for banks, which include risk weights, systemic risk buffers,
and minimum capital requirements. Countercyclical capital buffers and capital conservationbuffers are captured in their sheets respectively and thus not included here. Subcategories ofcapital measures are also provided, classifying them into household sector targeted (HH),
corporate sector targeted (Corp), broad-based (Gen), and FX-loan targeted (FX) measures.
4. LVR, A limit on leverage of banks, calculated by dividing a measure of capital by the bank’s
non-risk-weighted exposures (e.g., Basel III leverage ratio).
5. LLP, Loan loss provision requirements for macroprudential purposes, which include dynamic
provisioning and sectoral provisions (e.g. housing loans).
6. LCG, Limits on growth or the volume of aggregate credit, the household-sector credit, or the
corporate-sector credit by banks, and penalties for high credit growth. Subcategories of limits
to credit growth are also provided, classifying them into household sector targeted (HH),
corporate sector targeted (Corp), and broad-based (Gen) measures.
7. LoanR, Loan restrictions, that are more tailored than those captured in "LCG". They include
loan limits and prohibitions, which may be conditioned on loan characteristics (e.g., thematurity, the size, the LTV ratio and the type of interest rate of loans), bank characteristics(e.g., mortgage banks), and other factors. Subcategories of loan restrictions are also provided,classifying them into household sector targeted (HH), and corporate sector targeted (Corp)
measures. Restrictions on foreign currency lending are captured in "LFC".
8. LFC, Limits on foreign currency (FC) lending, and rules or recommendations on FC loans.
9. LTV, Limit on the loan to value ratio including those mostly targeted at housing loans, butalso includes those targeted at automobile loans, and commercial real estate loans.
10. DSTI, Limit on the debt service to income ratio and the loan-to-income ratio, which restrict
the size of debt services or debt relative to income. They include those targeted at housing
loans, consumer loans, and commercial real estate loans.
11. Tax, Taxes and levies applied to specified transactions, assets, or liabilities, which include
stamp duties, and capital gain taxes.
12. Liquidity, Measures taken to mitigate systemic liquidity and funding risks, including
minimum requirements for liquidity coverage ratios, liquid asset ratios, net stable funding
ratios, core funding ratios and external debt restrictions that do not distinguish currencies.
13. LTD, Limits to the loan-to-deposit (LTD) ratio and penalties for high LTD ratios.
14. LFX, Limits on net or gross open foreign exchange (FX) positions, limits on FX exposures
and FX funding, and currency mismatch policies.
15. SIFI, Measures taken to mitigate risks from global and domestic systemically importantfinancial institutions (SIFIs), which includes capital and liquidity surcharges.
16. RR, Reserve requirements (domestic or foreign currency) for macroprudential purposes.
Please note that this category may currently include those for monetary policy as
distinguishing those for macroprudential or monetary policy purposes is often not clear-cut. A
subcategory of reserve requirements is provided for those differentiated by currency (FCD),
as they are typically used for macroprudential purposes.
17. OT, Macroprudential measures not captured in the above categories—e.g., stress testing,restrictions on profit distribution, and structural measures (e.g., limits on exposures between
financial institutions).

Notes: The definition of individual instrument used in our analyses are based mainly on iMaPP constructed byAlam et al. (2019).

32

3.3.2 Measuring macroprudential policy instruments
In the iMaPP database, similar to all other databases, each tool in a given year is recorded as“dummy-type tightening and loosening indices.” The index takes the value of 1 when the tool
is being tightened, the value of -1 when the tool is being loosened, and zero otherwise. Using
monthly indicators, we construct panel data covering the “state” of each macroprudential
policy tool for each country in each year. Specifically, the state of macroprudential policy tool
i of country j in year t is described by:
MPijt = MPijt−1 + ∆MPijt ,where ∆MPijt is the indicator of annual policy changes and MPijt−1 is the indicator of the stateof macroprudential policy tool i in the previous year t − 1. We set the initial value of theindicator representing the state of the macroprudential policy tool in the initial year 1990 aszero and use the above formula to construct the state of each policy tool for all years. 19 Notethat in our empirical investigation, we use the “state of policy” instead of the “change of policy”
as the independent variable. By doing so, the value of the independent variable is not measured
as the change of policy across a period for a given country (i.e., we implicitly take into accountthe “level” of MP in each country). The estimated “coefficient” in the empirical model can beinterpreted as the influence of tightening (or loosening) one unit of the measure of a state of
MP on the occurrence of a banking crisis.
One main objective of this study is to explore the effectiveness of different groups of
MP instruments for a similar target. We follow the literature and construct several measuresof MP instruments using a total of 17 state of policy tools.20 The details of each measure ofpolicy instruments are presented in Table 2. Here, we briefly discuss how we construct the

One may be concerned about the initial value (annual indicators of the state of the macroprudential policy
instruments), since our dataset is constructed from the indicators representing annual policy changes. However,this issue can be solved by applying the fixed effects estimation in our analysis.
20
In this study, policy “tool” refers to each of 17 individual policy tools, while policy “instrument” means agroup consisting of several policy tools that share the same target or purpose.
19

33

measures of different groups of MP instruments. First, the overall macroprudential policy
index (MPI) is the sum of the 17 states of policy tools. Then, based on the policy target related
to the demand or supply side of credit markets, we categorize all 17 MP tools into two groups:
borrower-targeted

policy(BORROWER)

andfinancial

institution-targeted

policy(FINANCIAL) instruments.21 The measure of BORROWER is the sum of two states of policy
tools: LTV and DSTI, while the measure of FINANCIAL is the sum of the other 15 states ofpolicy tools. Next, we group the MP tools by their policy purposes, which gives us another 4
new subgroups of MP instruments: (i) capital-targeted policy instruments (CAPITAL),
focusing on the restriction on bank capital; (ii) general-targeted policy instruments(GENERAL), including policy tools to secure systemic liquidity; (iii) loan supply-targeted
policy instruments (LOAN-SUPPLY) imposing direct restrictions on financial institutions’loans; and (iv) loan-targeted policy instruments (LOAN-TARGET) covering overall loanrelated policy tools. FINANCIAL contains a large variety of tools, and the tools in CAPITAL,GENERAL, and LOAN-SUPPLY are the subsets of FINANCIAL. However, LOANTARGET is different and contains all loan-related tools of both financial institutions andborrowers.22
Table 3-2. Groups of macroprudential policy instruments
Macroprudential policy indicators
Overall macroprudential policy instruments
Borrower-targeted policy instruments
Financial institutions- targeted policy instruments
Capital-targeted policy instruments
General-targeted policy instruments
Loans supply-targeted policy instruments
Loan-targeted policy instruments

MPI
BORROWER
FINANCIALCAPITAL
GENERAL
LOAN-SUPPLY
LOAN-TARGET

All 17 policy instruments
LTV and DSTI
15 instruments beside LTV and DSTICCB, Conservation, Capital and LVR
Liquidity, LFX, RR
LCG, LLP, LoanR, LFC and LTD
LCG, LLP, LoanR, LFC, LTD, LTV andDSTI

Notes: The group and subgroup of macroprudential policy classification used in our analyses are based mainly on iMaPP and Cerutti et al. (2017). Macroprudential
policy indicators (MPI, BORROWER, FINANCIAL, CAPITAL, GENERAL, LOAN-SUPPLY, and LOAN-TARGET) are constructed by using the data of
iMaPP. Each macroprudential policy instrument is normalized by dividing it by the corresponding number of policy instruments.

The classification of MP instruments into BORROWER and FINANCIAL basically follows the categories in
Cerutti et al. (2017).
22
The detailed classification of MP instruments generally follows the work of Alam et al. (2019).
21

34

3.3.3 Observations on the MP instruments
Figure 1 presents the evolution of our 7 MP instruments (MPI, FINANCIAL, BORROWER,CAPITAL, GENERAL, LOAN-SUPPLY, and LOAN-TARGET). Each graph shows theaverage measure of the state of each group of MP instruments over all sample countries foreach year. All measures share an increasing trend, which implies that policymakers graduallyadopted more stringent macroprudential policies. For most of the instruments, the increase
pattern becomes more significant starting from 2003, and this increasing pattern continues
until the 2008-2009 global financial crisis, at which time MP shows a reversal or pause. After
2010, all measures increase again.
Figure 3-1. States of MP instruments(A) Overall macroprudential policy instruments (MPI)(B) Financial institutions-targeted policy instruments (FINANCIAL)

35(C) Borrowers-targeted policy instruments (BORROWER)(D) Capital-targeted policy instruments (CAPITAL)(E) General-targeted policy instruments (GENERAL)

36(F) Loans supply-targeted policy instruments (LOAN-SUPPLY)(G) Loans-targeted policy instruments (LOAN-TARGET)S

Figure 2 presents the annual change of each measure of MP instruments, which shows moredetails of the MP instruments. The adaptation of MP starts as early as 1994-95, approximately
3 years immediately before the Asian financial crisis. After the crisis, some instruments, suchas FINANCIAL and GENERAL, show a reversal. After 2008, there is an initial reversal—
several measures of MP are loosened in 2009. In 2010, due to the introduction of Basel III,
most of these measures are tightened on a large scale. It should be noted that the trend
described above seems slightly counterintuitive: MP started to be intensified before thefinancial crisis and was loosened after the crisis. Based on the simple observations of the trend,
we cannot conclude whether MP is effective in reducing systemic risk.

37

Figure 3-2. Changes of MP instruments(A) Overall macroprudential policy instruments (MPI)(B) Financial institutions-targeted policy instruments (FINANCIAL)(C) Borrowers-targeted policy instruments (BORROWER)38(D) Capital-targeted policy instruments (CAPITAL)(E) General-targeted policy instruments (GENERAL)(F) Loans supply-targeted policy instruments (LOAN-SUPPLY)(G) Loans-targeted policy instruments (LOAN-TARGET)39

To discuss a clearer relationship between banking crises and the usage of MP, we
provide an additional simple examination. Table 3 reports the mean of “the change of MP
instruments” over all sample countries during three types of periods: (i) three years before abanking crisis (pre-crisis periods), (ii) three years after a banking crisis (post-crisis periods),and (iii) years without a banking crisis (non-crisis periods).23 We observe a similar pattern asin the previous discussion: the average change of overall MP (MPI) is larger during the precrisis period (10.18) than during the post-crisis period (2.29). In addition, the average change
in MPI is the largest during the non-crisis period (28.64).24 Countries tend to upgrade MP
aggressively during the non-crisis periods and upgrade MP less aggressively during the precrisis periods. Once countries experience a banking crisis, they tend to significantly reduce thespeed of the upgrading of MP. Given that MP has been upgraded during our sample period,
this simple analysis appears to coincide with the conjecture that MP may help reduce thelikelihood of a banking crisis and systemic risk in the banking system, although more careful
examination is required.

For instance, a country experiences a banking crisis in 2004. In this case, the pre- and post-crisis periods are
2001-2003 and 2004-2006, respectively.
24
Given the fact that MP has been adapted particularly since around 2010, these figures may be biased. Thus, we
compute the average change in MP for a restricted sample period of 1995-2010. Table A2 in the appendix presents
a similar pattern to the case of the whole sample period of 1993-2015.
23

40

Table 3-3. MP instruments and banking crises (1993-2015)
Variable

Full sample 1993 to 2015
Pre-crisis period Post-crisis period Non-crisis period
Obs MeanObs MeanObs MeanCCB
129 0.0000
315 0.0000
784 0.2126
Conservation
127 0.0000
311 0.2680
764 1.8543
Capital125 2.0000
307 4.0717
750 5.8889
LVR
128 0.0000
314 0.7962
783 1.1707
LLP
123 0.0000
310 2.4194
746 2.2341
LCG
124 0.0000
306 0.0000
749 0.4450
LoanR
124 0.6720
306 -0.2723
749 2.3364
LFC
126 0.6614
316 1.8460
783 1.0643
LTV
128 0.6510
316 0.7911
784 3.9328
DSTI
128 0.6510
322 1.5528
798 1.4620
TAX
125 0.0000
307 0.0000
750 1.3333
Liquidity
128 0.0000
323 -0.2580
820 2.6423
LTD
126 0.0000
316 0.0000
783 0.3193
LFX
128 0.6510
323 1.0320
820 1.8293
RR
128 5.2083
317 -10.7781 806 2.6882
SIFI
129 0.0000
315 0.0000
784 0.2126
MPI
131 10.1781 328 2.2866
835 28.6427
FINANCIAL
131 8.9059
328 0.0000
835 23.5529
BORROWER
131 1.2723
328 2.2866
835 5.0898
CAPITAL
131 1.9084
328 4.8272
835 8.2834
GENERAL
131 5.7252
328 -9.6545
835 6.9860
LOAN-SUPPLY 131 1.2723
328 3.8110
835 5.7884
LOAN-TARGET 131 2.5445
328 6.0976
835 10.8782

Note: We report annual changes of all the values at unit of 1.e-3.

Table 4 reports the correlation matrix among MP instruments. These instruments arein general positively correlated, while the magnitudes are not very large. For instance, thecorrelations of CAPITAL with BORROWER, GENERAL, LOAN-SUPPLY, and LOANTARGETED are 0.26, 0.17, 0.32, and 0.33, respectively. We also notice that the correlationsof some instruments seem large. For instance, the correlation between FINANCIAL and
BORROWER is 0.47. Nonetheless, once examining the subcomponents of FINANCIAL(CAPITAL, GENERAL, and LOAN-SUPPLY), we find that only one subcomponent, LOANSUPPLY, is (relatively) highly correlated with BORROWER (0.50), but other components

41(CAPITAL and GENERAL) are not.25 The above observations imply that the adaption of each
instrument is not simultaneous. Therefore, given the low correlations among the instruments,
we may not need to “control” for other MP instruments when examining the effect of aparticular MP instrument.26

Table 3-4. Correlation matrix of MP instruments
FINANCIALBORROWERCAPTIAL
GENERAL
LOAN-SUPPLY
LOAN-TARGET

FINANCIAL
1.000
0.473
0.497
0.870
0.610
0.629

BORROWER CAPTIAL GENERAL LOAN-SUPPLY
1.000
0.257
0.264
0.499
0.850

1.000
0.174
0.321
0.335

1.000
0.252
0.297

1.000
0.881

3.3.4 Empirical specification
Since the outcome variable is a binary measure of the occurrence of a banking crisis, weemploy conditional logit/fixed effects logit. The baseline estimated regression model takes thefollowing form:
Prob(BCi,t = 1|MPi,t−1 , Xi,t−1 ) = F(βMPi,t−1 + γXi,t−1 + αi + λt ),(1)

where BCi,t is a dummy variable that takes the value one if country i experiences a banking
crisis in period t and zero otherwise; MPi,t is the measure of MP instruments, which include
MPI, BORROWER, FINANCIAL, CAPITAL, GENERAL, LOAN-SUPPLY, and LOANTARGET; Xi,t is a set of control variables; and F is the logistic distribution function. The
model includes the country fixed effects, αi , and the year effects, λt , to control country- andyear-specific effects. The year effects are used to control the implicit common trend of the
The correlation of LOAN-SUPPLY and LOAN-TARGET seems to be very high, but this is because both
instruments share several common tools. The same reason also explains the high correlations between MPI and
FINANCIAL and between MPI and BORROWER.
26
In fact, most of the existing studies do not control for the influence of other instruments.
25

42

adaption of MP. We use the one-year lag of MP since it may take some time to realize theimpacts of policy adoption.27 Similarly, we also use the one-year lag of other control variables
to mitigate some problems of simultaneity.
Several studies have constructed a database of banking crises. We use the one
constructed by Laeven and Valencia (2018), which defines the occurrence of a banking crisisby two conditions: (i) significant signs of financial distress in the banking system, as indicatedby significant bank runs, losses in the banking system, and/or bank liquidations, and (ii)significant banking policy intervention in response to significant losses in the banking system.
We choose this database because it is updated and covers a broader sample of countries.
Some empirical works show that several macroeconomic variables are highly related
to the occurrence of a banking crisis. These are selected as our control variables. First, we
include the credit gap to control credit health and the accumulation of financial vulnerabilities
because it is widely acknowledged as a signal of financial vulnerabilities and a useful early
warning indicator for banking crises (Borio & Lowe, 2002, 2004; Borio & Drehmann, 2009;
BCBS, 2010; Drehmann & Tsatsaronis, 2014). Moreover, the relationship between MP andcredit conditions would vary by the intensity and phase of financial cycle (Cerutti et al., 2017),
and some of MP tools can be triggered in reaction to credit conditions, which would call forthe interaction term of MP measures and credit variables. Thus, we also examine an additionalmodel that considers the effects of MP conditional on credit condition.
We also incorporate the ratio of credit to GDP, which has a positive relationship withthe likelihood of a banking crisis (Demirgüç-Kunt & Detragiache, 1998; Bekaert et al., 2005;
Babecký et al., 2014; Hasanov & Bhattacharya, 2019). This variable can also be considered tocontrol the level of financial development (Beck et al., 2014). In addition, we further include
the output gap because it has been shown to have a clear relationship with ﬁnancial crises

27This setting is common in related studies. See, e.g., Cerutti et al. (2017) and Altunbas et al. (2018).

43(Feldkircher, 2014; Lane & Milesi-Ferretti, 2011). Moreover, we include real GDP per capita
and real GDP to capture the income level and size of the economy for each country,
respectively. These two variables are also found to be related to banking crises. Rose andSpiegel (2009) show that countries with a low level of income or per capita GDP tend to
experience crises. Several studies show that optimistic economic growth is likely to reduce
vulnerability to a banking crisis (Davis & Karim, 2008; Hasanov & Bhattacharya, 2019).
Furthermore, we include trade openness, measured by the ratio of trade to GDP, tocontrol the degree of trade integration, given that trade integration might be able to intensify
the severity of a financial crisis (Feldkircher, 2014). We also control regional banking crises
to capture the contagion or spillover effects of banking crises at the regional level. Mathonnat
and Minea (2018) mention that banking crises can be viral events that can propagate at theinternational or regional level and emphasize the role of spillovers of a crisis from a country
to its neighboring countries. Following the idea in Mathonnat and Minea (2018), we construct
the variable of regional crises by the number of countries experiencing banking crises in theregion to which a country belongs during the past three years.28 Last, we follow the literature
to include the presence of IMF programs (Jensen, 2004; Barro & Lee, 2005; Conway, 2006;
Abbott et al., 2010). Table 5 presents the list of countries in our baseline sample. Tables 6 and7 show the descriptions of variables and the descriptive statistics, respectively.

28

We use the World Bank regional classification.

44

Table 3-5. List of countries (60 countries)
Algeria
Argentina
Armenia
Austria
Azerbaijan
BelarusBelgiumBrazilBulgaria
BurundiChina
Colombia
Costa Rica
Croatia
CyprusCzech Republic
Dominican Republic
EcuadorFinlandFranceGermany
Haiti
Hungary
Iceland
India
Indonesia
Ireland
Italy
JamaicaJapan

Kazakhstan
Kenya
Korea, Rep.
Kyrgyz Republic
LatviaLebanonLithuania
Macedonia, FYR
Malaysia
MexicoMoldova
Mongolia
Netherlands
NorwayParaguay
Philippines
PolandPortugal
Russian Federation
Slovak Republic
Slovenia
Spain
SwedenSwitzerland
ThailandTurkeyUgandaUkraineUnited Kingdom
United States

45

Table 3-6. Data description and sources of variables
Variable
Banking crisis
MPI

Definition
A crisis dummy of 1 in the year of crisis episode and 0
otherwise.
Overall macroprudential policy instruments

BORROWER

Borrower-targeted policy instruments

FINANCIAL

Financial institutions- targeted policy instruments

CAPITAL

Capital-targeted policy instruments

GENERAL

General-targeted policy instruments

LOAN-SUPPLY

Loans supply-targeted policy instruments

LOAN-TARGET

Loan-targeted policy instruments

Credit gap

Difference between the ratio of bank credit private sector
credit to GDP and its long-run trend

Credit to GDP

Bank credit to private sector credit as percentage of GDP

Output gap
Real GDP per capita

Difference between log of actual real GDP and its long-run
trend
Log of real GDP per capita

Real GDP
Trade to GDP

Log of real GDP
Ratio of trade flow to GDP

IMF program

A dummy variable of 1 if there is a present of IMF programand 0 otherwise

Regional banking
crisisThe number of countries experiencing banking crises in the region towhich a country belongs during the past three years

SourceLaeven and Valencia (2008,2013, 2018)
Authors’ calculation based on
Alam et al. (2019)
Authors’ calculation based on
Alam et al. (2019)
Authors’ calculation based on
Alam et al. (2019)
Authors’ calculation based on
Alam et al. (2019)
Authors’ calculation based on
Alam et al. (2019)
Authors’ calculation based on
Alam et al. (2019)
Authors’ calculation based on
Alam et al. (2019)
World Bank’s Global
Financial Development
Database (2018)
World Bank’s Global
Financial Development
Database (2018)
Penn World Table 9
World Bank’s Global
Financial Development
Database (2018)
Penn World Table 9
World Development
Indicators
IMF-Monitoring of Fund
Arrangements (MONA
Database)
Authors’ calculation based on
Mathonnat and Minea (2018)

Table 3-7. Descriptive of Statistics
Variables
Bank crisis
Overall macroprudential policy instruments
Financial institutions-targeted policy instruments
Borrowers-targeted policy instruments
Capital-targeted policy instruments
General-targeted policy instruments
Loan supply-targeted policy instruments
Loan-targeted policy instruments
Credit gap
Output gap
Trade to GDP
Log per capita GDP
Log real GDP
IMF program
Credit to GDP

No of obs.
1294
1294
1294
1294
1294
1294
1294
1294
1294
1294
1294
1294
1294
1294
1294

Mean
0.155
0.067
0.056
0.156
0.085
-0.013
0.085
0.106
0.122
-0.00279.3488.961
12.2230.185
53.757

Std
0.362
0.304
0.294
0.638
0.216
1.133
0.269
0.330
7.539
0.055
37.886
1.390
1.900
0.388
45.661Min
0-0.647-0.733-1.500-0.750-3.667-0.600-0.857-51.078-0.29614.731
5.389
8.247
0
0.983

Max
1
3.118
2.867
5.500
1.750
8.667
2.400
3.286
85.8300.330
220.40711.42516.6531
263.268

46

3.4 Empirical results
Tables 8 and 9 show the estimation results of our baseline models without and with theinteraction term of each MP measure and credit gap. The numbers reported are the estimatesof the average semi-elasticities (i.e., when the MP instrument increases by one unit, on average,
how much the probability of a banking crisis changes).29 The results of the models without theinteraction term, shown in Table 8, reveal that MPI is negatively associated with the likelihoodof a banking crisis, so MP could generally reduce the likelihood of a banking crisis.
Specifically, when MPI increases by 0.1 points, the probability of a banking crisis is reducedby 14.9 percent.30 Once we consider the subcomponents FINANCIAL and BORROWER, theinfluence appears to reveal some difference. The results of the models without the interaction
term show a significantly negative coefficient of the indicator of BORROWER but fail topresent a significant coefficient of the indicator of FINANCIAL. 31 When BORROWER
increases by 0.1 points, the probability of a banking crisis is reduced by 14.5 percent.32 Table
8 also presents that the results of the models with the interaction term generally coincide withKitazawa (2012) presents the calculation formula of the average elasticity of the logit probability with respect
to the exponential function of the explanatory variable for the fixed effects logit model.
30
Our measure of MPI is constructed by the sum of the measures of all 17 tools and normalized by dividing it by
the number of tools (17). Thus, a one-unit increase in the use of a tool means that the average over our measuresof 17 tools increases by one point. Since the estimated coefficient of MPI in column (1) of Table 8 is -1.4890, a
0.1 point increase in MPI is associated with a 14.9 percent decrease in the probability of a banking crisis.
31
We also examine the relationship between MP and the likelihood of a banking crisis using the level indicatorof MP constructed by Cerutti et al. (2017), although the database of iMaPP (Alam et al., 2019) covers moredetailed MP components, more countries, and longer periods than the database of Cerutti et al. (2017). This data
set is one of the databases integrated into iMaPP. These two databases provide different features of policy tools.
The database of iMaPP provides dummy-type policy actions that a country has taken, while Cerutti et al. (2017)indicate the number of tools a country has adopted in a given year. The database of Cerutti et al. (2017) consistsof 12 tools over the period from 2000 to 2013. The measure of overall macroprudential policy instruments takes
a value of 0 to 12, which denotes the number of tools a country uses in a given period. The measure of
BORROWER takes a value of 0 to 2, which denotes the number of BORROWER measures a country uses. The
measure of FINANCIAL takes a value of 0 to 10, which denotes the number of FINANCIAL measures a country
uses. Table A3 in the appendix indicates the estimated results of the models using the database of Cerutti et al.
(2017). The results show a negative relationship between the measure of BORROWER and the likelihood of abanking crisis but an insignificant relationship between the measure of FINANCIAL and the likelihood of abanking crisis, which are consistent with our baseline results.
32
Similar to the case of MPI, our measure of BORROWER is constructed by the average over the measures of
two borrower-targeted MPs. Since the estimated coefficient of BORROWER in column (5) of Table 8 is -1.4480,
a 0.1 point increase in BORROWER is associated with a 14.5 percent decrease in the probability of a banking
crisis.
29

47

those of the models without the interaction terms. The inclusion of the interaction termpresents the insignificant coefficients in most models, except for the model with
BORROWER.33 The significantly positive coefficient of the interaction term of BORROWER
and credit gap indicates that as credit gap becomes larger, perhaps in credit boom periods, thefavorable effect of BORROWER on the occurrence of banking crises would be reduced.
BORROWER aims to control the demand side of the credit market by imposing
financial policies on credit provisions, such as the maximum limits on the loan-to-value ratio
and on the debt-service-to-income ratio. Previous studies emphasize the role of BORROWER
in mitigating financial system vulnerability by showing that these instruments could reduce
excessive movements of financial indicators particularly related to credit conditions, such as
household credit, housing credit, leverage and assets, and the non-core to core liabilities ratio
in the banking system (Alam et al., Claessens et al., 2013; Kuttner & Shim, 2016; Cerutti etal., 2017; Fendoğlu, 2017; Richter et al., 2019; Carreras et al., 2018). Our results complement
the previous findings in the sense that BORROWER helps reduce the probability of a banking
crisis, i.e., our proxy for systemic risk, even though some credit-related variables, such as
credit gap and credit-to-GDP, are controlled in our models. By imposing policies on the
demand side of the credit market, financial regulators help improve the quality of loans andreduce the risk of loans by directing credit to borrowers who are less likely to default, which
could mitigate systemic risk in the banking system.34

Most models with the interaction terms show the statistically insignificant coefficients of the interaction terms.
In addition, the goodness-of-fit tests based on Bayesian Information Criterion (BIC) support the models without
interactions term over those with the interaction term. Thus, our explanation is based mainly on the results of themodels without the interaction terms.
34
In addition to adopting dummy-type policy indicators, Alam et al. (2019) construct the average LTV limit toquantify the impact of the policy change. Using the quantitative LTV limit, they find that changes in LTV are
effective in coping with household credit, although the impact is less significant in the case that current policy
has already been strongly tightened. Following the work of Alam et al. (2019), we also examine the relationshipbetween the average LTV limit and the occurrence of banking crises by constructing the data of the average LTV
limit in the empirical model. The estimated results in Table A4 in the appendix show that the average LTV limit
has a positive relationship with the likelihood of a banking crisis. A high average LTV limit indicates policy
loosening. Thus, the result suggests that the tightening of LTV limits ratio is associated with less likelihood of abanking crisis, which is consistent with the finding of our baseline model.
33

48

On the other hand, the insignificant coefficient of FINANCIAL in Table 8 shows lesseffectiveness of FINANCIAL, which seems to suggest that an attempt to regulate financial
institutions, or the supply side of the credit market, would not help mitigate systemic risk.
However, once MP are classified into four subgroups, (i) CAPITAL, (ii) GENERAL, (iii)
LOAN-SUPPLY, and (iv) LOAN-TARGET, we can identify the policy-crisis relationship ina deeper sense, particularly from the perspective of the supply side of the credit market. Table
9 indicates the results of the models for the indicators of CAPITAL, GENERAL, LOANSUPPLY, and LOAN-TARGET, respectively. 35 First, column (1) shows the significantly
negative coefficient of CAPITAL, which consists of MP requiring banks to build up more
capital buffer with the systemic risk profile of the banks (BCBS, 2012). A tightening of
CAPITAL would reduce the possibility of a banking crisis. Previous works reveal theimportant role of bank capital in enhancing bank performance, measured by bank survival andmarket share, during banking crisis periods (Berger & Bouwman, 2013). In addition, several
studies, such as Laeven and Levine (2009), emphasize stringent capital requirements tomaintain the stability of the banking system. Our results related to CAPITAL also strengthen
the importance of capital-related policies and their favorable effects on the likelihood of abanking crisis. Second, the result in column (3) fails to provide clear evidence on how
GENERAL relates to the likelihood of a banking crisis, which suggests that GENERAL,
whose targets include systemic liquidity, may not help reduce systemic risk.
Third, column (5) presents a significantly negative coefficient of LOAN-SUPPLY,
which indicates that LOAN-SUPPLY is negatively associated with the occurrence of banking
crises. Unlike BORROWER, LOAN-SUPPLY aims at curbing credit expansion from financial
institutions or credit suppliers in the credit market. Once banks are restricted, for example, by

Similar to Table 8, Table 9 shows that all models with the interaction terms show the statistically insignificant
coefficients of the interaction terms, and the goodness-of-fit tests based on Bayesian Information Criterion (BIC)
support the models without interactions term.
35

49

policies requiring limits on aggregate credit volume or loan-to-deposit ratios, banks tend to
become more cautious about the quality of loan disbursements and seek a better way to acquire
more sources of funds, including deposits, to ensure that they can comply with the policies.
Consequently, banks tend to be more resilient to risk, so the vulnerability of the overall
financial system, i.e., systemic risk, can be mitigated. Alam et al. (2019) find the crucial roleof MP targeting the supply side by empirically revealing the negative association withhousehold credit growth. Our result supports the argument that LOAN-SUPPLY also plays acrucial role in reducing the likelihood of a banking crisis and in mitigating systemic risk.
Fourth, similar to the result related to LOAN-SUPPLY, column (7) shows the negative
coefficient of LOAN-TARGET. Given that LOAN-TARGET covers overall loan-related
policy instruments, which consist of BORROWER and LOAN-SUPPLY, this result confirmsthat loan-related MP targeting borrowers and financial institutions could help reduce thelikelihood of a banking crisis.

50

Table 3-8. Macroprudential policy instruments and banking crises

MPI
MPI*Credit gap

Model 1(1)
-1.4890**(0.7097)

FINANCIAL

Model 2(2)
-1.7722**(0.7744)0.1037(0.0853)

FINANCIAL*Credit gap

Model 3(3)Model 4
(4)-0.8687(0.6874)-1.0676(0.7389)
0.0824(0.0863)

BORROWERBORROWER*Credit gap
Credit gap
Credit to GDP
Output gap
Real GDP per capita
Real GDP
Trade to GDP
IMF program
Regional crises
Observations
Number of countries
Log likelihood
McFadden's R-squared
BIC

0.0351(0.0240)0.0617***(0.0101)-1.6605(2.1445)-0.1957(1.0496)-0.8660(0.8473)-0.0040(0.0076)
0.5861**(0.2751)
1.1715***(0.3357)1294
60-249.475
0.436-259.329

0.0331(0.0237)
0.0622***(0.0101)-1.6023(2.1464)-0.4362(1.0743)-0.7377(0.8559)-0.0038(0.0077)0.6201**(0.2769)
1.2003***(0.3370)1294
60-248.735
0.438-256.714

0.0372(0.0239)0.0600***(0.0099)-1.5174(2.1295)-0.4070(1.0404)-0.8074(0.8381)-0.0047(0.0076)
0.6066**(0.2751)
1.1919***(0.3359)1294
60-251.012
0.433-256.254

0.0354(0.0238)0.0604***(0.0099)-1.5073(2.1316)-0.5851(1.0627)-0.7033(0.8473)-0.0045(0.0076)
0.6365**(0.2768)
1.2117***(0.3363)1294
60-250.549
0.434-253.087

Model 5(5)

-1.4483***(0.5263)
0.0271(0.0237)
0.0688***(0.0106)-1.9808(2.1482)-0.4287(1.0130)-0.8187(0.8521)0.0004(0.0078)
0.6021**(0.2770)
1.1921***(0.3396)1294
60-244.611
0.447-269.056

Model 6
(6)

-1.7434***(0.5155)
0.0729**(0.0351)
0.0333(0.0222)0.0693***(0.0107)-1.9368(2.1530)-0.5602(1.0188)-0.7675(0.8542)-0.0002(0.0079)
0.6268**(0.2786)
1.1999***(0.3427)1294
60-243.016
0.451-268.152

Model 7
(7)

Model 8(8)-0.1646(0.7509)-0.3332(0.7970)0.0554(0.1013)-1.7380***(0.5300)
0.0662*(0.0382)
0.0320(0.0224)0.0695***(0.0108)-1.9172(2.1548)-0.6077(1.0677)-0.7374(0.8646)-0.0001(0.0079)
0.6427**(0.2814)1.1969***(0.3433)1294
60-242.829
0.451-260.337-1.4306***(0.5327)0.0272(0.0237)
0.0687***(0.0106)-1.9815(2.1496)-0.3803(1.0402)-0.8336(0.8563)0.0003(0.0078)
0.5987**(0.2774)
1.1849***(0.3409)1294
60-244.587
0.447-265.010

Notes: The banking crisis dummy is the dependent variable used in the regression. We estimated the regressions using the logistic regression with the country and period fixed effects. The table reports the estimates of
the average elasticities of the conditional probability with respect to the regressors and the corresponding standard errors.
*** p<0.01, ** p<0.05, * p<0.1

51

The estimated results in Table 9 provide additional implications about the effectivenessof each subgroup of FINANCIAL. It should be noted that the measure of each subgroup(CAPITAL, GENERAL, LOAN-SUPPLY, and LOAN-TARGET) is normalized by using the
average over the measures of MP in the corresponding subgroup. This allows us to evaluate
which subgroup of policy targeting is most effective in reducing the likelihood of a banking
crisis.36 A 0.1 point increase in CAPITAL and LOAN-SUPPLY reduces the probability of abanking crisis by 36.2 percent and 13.9 percent, respectively.37 This result suggests that among
the three subgroups of FINANCIAL, CAPITAL has the most substantial effect on reducing
the occurrence of a banking crisis.38

Alam et al. (2019) mention that their dummy-type data of MP only indicate the direction of a policy change
but lack information about the intensity of the change, which implies that large changes in their index do notnecessarily mean large changes in the policy instruments. Although we admit that our data based on iMaPP
also cannot fully capture the intensity of each policy change, we attempt to compare the effectiveness among
the subgroups of MP as a reference.
37
In addition, a 0.1 point increase in LOAN-TARGET, which is the measure combining BORROWER and
LOAN-SUPPLY, reduces the probability of a banking crisis by 24.4 percent.
38
In the baseline models we use the data of Laeven and Valencia (2018) to capture banking crises at the country
level. We also examine the effect of MP on the likelihood of banking crises using alternative data sources ofbanking crises constructed by Reinhart and Rogoff (2011). Reinhart and Rogoff (2011) build the data of banking
crises based on the two types of events: (i) bank runs leading to the closure, merging, or takeover by the public
sector of one or more financial institutions, or (ii) if there are no runs, the closure, merging, takeover, or largescale government assistance of an important financial institution (or group of institutions) that marks the start ofa string of similar outcomes for other financial institutions. The data of Reinhart and Rogoff (2011) cover a
relatively small numbers of countries (70 countries) compared to the data of Laeven and Valencia (2018). We
estimate the logit model with country and period fixed effects by replacing the crisis dummy based on the dataof Laeven and Valencia (2018) with that based on the data of Reinhart and Rogoff (2011). Table A5 shows theestimated results, which also coincide with the findings of our baseline analysis.
36

52

Table 3-9. Subgroup of financial institutions- and loan-targeted policy instruments and banking crises

CAPITALCAPITAL*credit gap
GENERAL
GENERAL*credit gap
LOAN-SUPPLY
LOAN-SUPPLY*credit gap
LOAN-TARGET

Model 1
(1)
-3.6228***(1.0897)

Model 2(2)
-4.2503***(1.3044)0.1496(0.1527)

Model 3(3)Model 4(4)0.0858(0.1516)0.0536(0.1581)0.0156(0.0183)

Model 5(5)Model 6
(6)

-1.3902**(0.6478)-1.4699**(0.6925)
0.0368(0.0974)

Model 7
(7)

-2.4429***(0.8548)

Model 8(8)-2.8230***(0.8967)
LOAN-TARGET*credit gap
0.1113(0.0798)
Credit gap
0.03230.02930.03790.03720.03570.03510.03100.0329(0.0244)(0.0244)(0.0240)(0.0239)(0.0241)(0.0241)(0.0239)(0.0230)
Credit to GDP
0.0653***0.0639***0.0597***0.0603***0.0621***
0.0620***0.0667***
0.0666***(0.0106)(0.0106)(0.0100)(0.0100)(0.0103)(0.0103)(0.0106)(0.0107)
Output gap-1.2197-1.0813-1.2635-1.3166-1.3545-1.3431-1.6215-1.5763(2.1703)(2.1766)(2.1058)(2.1064)(2.1288)(2.1307)(2.1537)(2.1581)
Real GDP per capita-0.5077-0.5590-0.9657-1.0665-0.4565-0.4659-0.2462-0.3332(1.0058)(1.0068)(1.0078)(1.0179)(1.0042)(1.0060)(1.0165)(1.0225)
Real GDP-1.0632-1.0498-0.7039-0.6331-0.8493-0.8443-0.9359-0.8941(0.8751)(0.8753)(0.8247)(0.8320)(0.8279)(0.8278)(0.8376)(0.8386)
Trade to GDP-0.0038-0.0036-0.0042-0.0039-0.0009-0.00100.00220.0019(0.0078)(0.0078)(0.0077)(0.0077)(0.0078)(0.0078)(0.0080)(0.0080)
IMF program
0.5461**
0.5385**0.6272**
0.6628**0.6195**0.6157**0.5986**0.6041**(0.2734)(0.2738)(0.2755)(0.2781)(0.2746)(0.2750)(0.2755)(0.2765)
Regional crises
1.1825***
1.1899***1.2549***1.2600***1.2747***1.2839***1.2671***1.2840***(0.3389)(0.3383)(0.3384)(0.3380)(0.3386)(0.3395)(0.3407)(0.3426)
Observations
1294
1294
1294
1294
1294
1294
1294
1294
Number of countries
60
60
60
60
60
60
60
60
Log likelihood-244.600-244.093-251.699-251.324-249.388-249.318-245.249-244.500
McFadden's R-squared
0.447
0.449
0.431
0.432
0.437
0.437
0.446
0.448
BIC
-269.079-265.999-254.880-251.537-259.502-255.548-267.781-265.185
Notes: The banking crisis dummy is the dependent variable used in the regression. We estimated the regressions using the logistic regression with the country and period fixed effects. The table reports the estimates of
the average elasticities of the conditional probability with respect to the regressors and the corresponding standard errors.
*** p<0.01, ** p<0.05, * p<0.1

53

Concerning other control variables, the results show that the coefficients of the ratioof credit to GDP are significantly positive, indicating that a high level of the credit-to-GDP
ratio is likely to increase the probability of a banking crisis. This result is consistent with theprevious findings that excessive credit expansion intensifies risks and accumulates
vulnerabilities to the banking system (Demirgüç-Kunt & Detragiache, 1998; Bekaert et al.,2005; Babecký et al., 2014; Hasanov & Bhattacharya, 2018). In addition, the coefficients of
the regional banking crisis variable are significantly positive, so a regional spillover effect of
the crisis event exists, i.e., once a banking crisis occurs in a country, the crisis tends to spreadout regionally and to increase the likelihood of a banking crisis in the neighboring countries
in the same region. The evidence of the geographical spillover effects is consistent with thefindings of Mathonnat and Minea (2018). Moreover, the estimations reveal that the presenceof an IMF program is positively associated with the likelihood of a banking crisis. IMF
programs are often conducted during periods of macroeconomic instability, such as financial
crisis periods. The drawbacks of IMF programs have been mentioned in several studies(Oberdabernig, 2013; Marchesi & Sirtori, 2011; Przeworski & Vreeland, 2000).39 Finally, theanalysis generally fails to show clear results related to credit gap, output gap, real GDP, percapita GDP, and the ratio of trade to GDP.

3.5 Robustness checks
We perform several sensitivity analyses to check the empirical validity of our baseline resultsin the previous section. First, we apply factor analysis to construct an alternative measure of

The moral hazard hypothesis, initially proposed by Vaubel (1983), suggests that IMF lending may beinterpreted as a (subsidized) income insurance against adverse shocks. The insurance could induce potential
recipients to excessively lower their precautions against future damages or even to intentionally generate a crisis(Dreher & Vaubel, 2001). Moreover, critics point out that the IMF might give misguided policy advice, whichmight in fact increase the risk of crises (Stiglitz, 2002; Dreher & Walter, 2010).
39

54

MP. Second, to mitigate possible endogeneity issues, we estimate the binary choice models
using linear two-stage least squares (2SLS), which corresponds to the estimation of a linear
probability model with instrumental variables (Mylonidis et al., 2019). As a final robustness
check, we incorporate monetary and fiscal policy stances into the models.

3.5.1 Factor analysis
To check the robustness of our findings in the previous section, we re-examine how MP relates
to the likelihood of a banking crisis by using an alternative continuous indicator of MP that isconstructed through factor analysis with various MP. Several studies apply factor analysis toconstruct indicators of interest from the original sets of data in the field of financial economics.
As examples, Quinn et al. (2011) generate de jure and de facto indicators of the capital accountand financial account openness of 187 countries over the period from 1950 to 2007, and Klomp
and De Haan, (2009) construct a financial instability indicator of 60 countries over the period
from 1985 to 2005. Gilbert and Meijer (2006) use Canadian data to generate money and credit
indictors over the period from 1981 to 2004, and Stock and Watson (2002) construct andforecast the Federal Reserve Board’s Index of Industrial Production from 1970 to 1997.
Factor analysis is a statistical method to explain variability among observed, correlated
variables in terms of a potentially fewer number of unobserved random variables, which arecalled factors (Kim & Mueller, 1978). The method models the observed variables as linear
combinations of the potential factors plus error terms. Each factor captures a certain amountof the overall variance in the observed variables. The eigenvalue for a given factor measures
how much of the variance of the observed variables the particular factor explains. A factor
with a low eigenvalue may be ignored since other factors are more important in explaining thevariance. According to the scree test based on the visualization of a scree plot showing a line
plot of the eigenvalues of factors, we find the “elbow” point of the graph, where the

55

eigenvalues level off, and then retain factors to the left of this point for the subsequent analysis(Cattell, 1966). In this study, we apply factor analysis to each of the following seven datasetsof indicators: (i) MPI, (ii) FINANCIAL, (iii) BORROWER, (iv) CAPITAL, (v) GENERAL,(vi) LOAN-SUPPLY, and (vii) LOAN-TARGET. The scree tests suggest that each group or
subgroup of MP can be represented as a one-dimensional construct. Thus, we apply a onefactor model in the analysis, i.e., one factor is retained, and the rest of the factors are dropped
off. Table 10 reports the estimations of the models with the indicators based on factor analysis.
The estimated results are consistent with the baseline findings in the previous section.

56

Table 3-10. Factor analysis of macroprudential policy instruments
MPI
MPI*Credit gap
FINANCIALFINANCIAL*Credit gap
BORROWERBORROWER*Credit gap
CAPITALCAPITAL*credit gap
GENERAL
GENERAL*credit gap
LOAN-SUPPLY
LOAN-SUPPLY*credit gap
LOAN-TARGET

Model 1
(1)-0.4609***(0.1768)

Model 2
(2)
-0.5842***(0.2134)
0.0322(0.0235)

Model 3(3)Model 4(4)-0.0819(0.1916)-0.1192(0.2049)
0.0262(0.0300)

Model 5
(5)-0.6326**(0.2522)

Model 6
(6)

-0.8196***(0.2670)0.0403*(0.0211)

Model 7
(7)

Model 8(8)0.0475(0.2502)0.0793(0.2689)0.0053(0.0308)-0.8263***(0.2817)0.0401*(0.0238)-0.6415**(0.2583)

Model 9(9)Model 10(10)

-1.1565***(0.3669)-1.1529***(0.3673)-0.0072(0.0389)

Model 11(11)

Model 12(12)0.0643(0.1577)

0.0663(0.1677)-0.0006(0.0182)

Model 13
(13)

Model 14(14)-0.3509**(0.1782)-0.3498*(0.1915)-0.0006(0.0417)

Model 15
(15)

-0.5756***(0.2149)

Model 16(16)-0.6258***(0.2238)
LOAN-TARGET*credit gap
0.0223(0.0184)
Credit gap
0.03080.03710.0376
0.0415*0.02860.0426*0.0286
0.0430*0.03390.03170.03770.0376
0.03470.03460.03220.0396*(0.0241)(0.0237)(0.0239)(0.0244)(0.0238)(0.0240)(0.0239)(0.0242)(0.0242)(0.0268)(0.0240)(0.0242)(0.0239)(0.0253)(0.0234)(0.0238)
Credit to GDP
0.0659***0.0656***
0.0596***0.0602***
0.0667***0.0673***0.0667***0.0674***0.0647***0.0651***0.0597***0.0597***
0.0614***0.0614***0.0652***0.0653***(0.0105)(0.0105)(0.0099)(0.0099)(0.0104)(0.0105)(0.0104)(0.0106)(0.0105)(0.0108)(0.0100)(0.0100)(0.0101)(0.0101)(0.0103)(0.0104)
Output gap-2.4894-2.5428-1.4221-1.4372-2.0244-1.9712-2.0337-1.9915-1.7379-1.7252-1.3309-1.3279-0.6737-0.6739-2.0195-1.9577(2.1499)(2.1556)(2.1159)(2.1171)(2.1423)(2.1466)(2.1410)(2.1445)(2.1480)(2.1497)(2.1035)(2.1053)(2.1461)(2.1461)(2.1496)(2.1521)
Real GDP per capita-0.4052-0.4161-0.6747-0.7787-0.4702-0.6084-0.5059-0.6903-0.4413-0.4415-0.9207-0.9177-0.5543-0.5547-0.5330-0.6283(1.0025)(1.0080)(1.0192)(1.0310)(1.0069)(1.0141)(1.0218)(1.0296)(1.0105)(1.0114)(1.0076)(1.0114)(0.9998)(1.0000)(1.0237)(1.0291)
Real GDP
-0.6661-0.6055-0.7422-0.6679-0.7603-0.6971-0.7425-0.6512-0.9144-0.9260-0.7001-0.7017-1.0002-0.9999-0.7336-0.6887(0.8271)(0.8261)(0.8308)(0.8359)(0.8441)(0.8475)(0.8478)(0.8524)(0.8594)(0.8625)(0.8269)(0.8281)(0.8398)(0.8400)(0.8460)(0.8462)
Trade to GDP-0.0001-0.0007-0.0046-0.00430.0002-0.00020.0002-0.0001-0.0043-0.0043-0.0044-0.0044-0.0003-0.0003-0.0017-0.0019(0.0078)(0.0079)(0.0076)(0.0076)(0.0078)(0.0078)(0.0078)(0.0079)(0.0077)(0.0077)(0.0076)(0.0076)(0.0079)(0.0079)(0.0078)(0.0078)
IMF program
0.5898**0.5798**0.6235**0.6522**0.5998**0.6265**0.6023**0.6361**0.5442**0.5449**0.6229**0.6213**0.6296**0.6297**0.6110**0.6283**(0.2750)(0.2758)(0.2753)(0.2770)(0.2764)(0.2782)(0.2767)(0.2798)(0.2737)(0.2737)(0.2752)(0.2793)(0.2766)(0.2766)(0.2757)(0.2772)
Regional crises
1.2968***1.3190***1.2216***
1.2292***
1.2046***1.2088***1.2113***
1.2212***
1.1912***
1.1919***1.2605***1.2607***
1.2134***
1.2131***
1.1643***
1.1740***(0.3393)(0.3412)(0.3361)(0.3362)(0.3389)(0.3414)(0.3411)(0.3434)(0.3394)(0.3396)(0.3423)(0.3424)(0.3346)(0.3353)(0.3361)(0.3378)
Observations
1294
1294
1294
1294
1294
1294
1294
1294
1294
1294
1294
1294
1294
1294
1294
1294
Number of countries
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
Log likelihood-247.373-246.580-251.757-251.357-245.715-244.356-245.697-244.273-244.949-244.932-251.775-251.775-249.352-249.352-247.402-246.786
McFadden's R-squared
0.441
0.443
0.431
0.432
0.445
0.448
0.445
0.448
0.447
0.447
0.431
0.431
0.437
0.437
0.441
0.442
BIC
-263.532-261.024-254.765-251.471-266.850-265.472-262.791-257.451-268.381-264.321-254.728-250.635-259.575-255.480-263.475-260.612
Notes: The banking crisis dummy is the dependent variable used in the regression. We estimated the regressions using the logistic regression with the country and period fixed effects. The table reports the estimates of the average elasticities of the conditional probability with respect to theregressors and the corresponding standard errors.
*** p<0.01, ** p<0.05, * p<0.1

57

3.5.2 Instrumental variables estimation
One crucial concern is that our empirical model may suffer from possible endogeneity issues.
For instance, the occurrence of a banking crisis influences regulators’ decision to adopt the
specific dimensions of MP. Several past studies suggest the crisis-begets-reform hypothesis(Hlaing & Kakinaka, 2018; Waelti, 2015; Abiad & Mody, 2005; Lora & Olivera, 2004). The
origin of a ﬁnancial crisis is a key factor that drives policy reforms and influences regulators’decisions regarding regulatory and supervisory measures in the banking sector (Waelti, 2015).
Financial liberalization normally hastens when an economy comes across ﬁnancial crises,
including banking crises (Hlaing & Kakinaka, 2018). We attempt to mitigate potential
endogeneity issues by applying an instrumental variable (IV) approach. It is a difficult
challenge to find valid IVs that satisfy the two main conditions: (i) the instrument must be
closely related to the endogenous explanatory variables, MP in our study, conditional on theother covariates, and (ii) this instrument must not be correlated with the error term in theequation, conditional on the other covariates (exclusion restriction).
We employ a country’s MP adopted by other countries in the same geographical region
that the country belongs to (regional MP) as an instrument of the country’s MP. Specifically,
we construct the indicator of regional MP by using the number of countries adopting
macroprudential policy instruments in the region. Past studies emphasize the presence ofpolicy diffusion, especially among geographically neighboring countries or countries withsimilar ideological positions (Simmons & Elkins, 2004; Volden et al., 2008; Hlaing &Kakinaka, 2018). Simmons and Elkins (2004) show that domestic governments tend to adopt
policies that were successfully implemented by other countries; i.e., international economic
policy relates to domestic policy. Thus, regional MP may be closely related to a country’s MP.
There may be some concerns about the violation of the exclusion restriction. Since we control
financial and economic conditions, including regional crises, in our empirical models, this58

problem may be mitigated so that the likelihood of a banking crisis in a country may not bedirectly associated with the adoption of MP in neighboring countries. This study estimates theempirical binary choice models using 2SLS, which corresponds to the estimation of a linear
probability model (LPM) with instrumental variables (Mylonidis et al., 2019). 40 Table 11
reports the results of IV estimations, which generally confirms our baseline findings in theprevious section.

Although there are some issues in using LPMs, such as the heteroscedasticity and the non-zero one predictedprobabilities, LPMs often provide good estimates of underlying non-linear models (Wooldridge, 2016; Angrist& Pischke, 2009). Our purpose for the use of the LPM estimation is to check the sensitivity of the baseline results
about the relationship between the occurrence of banking crises and MP. In addition, it may be noticed that oursamples of the IV estimations cover 107 countries (Table A6 in the appendix), which are larger than those of thebaseline logistic regression models with fixed effects in the previous section, since fixed effects models look at
the elements of within-subject variability. Some sampled countries in our analysis did not experience any banking
crises during the sample period, so we can regain many observations by employing the LPMs with an
instrumental variable.
40

59

Table 3-11. Macroprudential policies and banking crises (Linear probability model)
MPI
MPI*Credit gap
FINANCIALFINANCIAL*Credit gap
BORROWERBORROWER*Credit gap
CAPITALCAPITAL*credit gap
GENERAL
GENERAL*credit gap
LOAN-SUPPLY
LOAN-SUPPLY*credit gap
LOAN-TARGET

Model 1(1)-0.3839**(0.1908)

Model 2(2)-0.3536*(0.1977)-0.0263(0.0465)

Model 3(3)Model 4
(4)

-0.3228(0.2258)-0.3069(0.2264)-0.0195(0.0418)

Model 5(5)-0.2073***(0.0605)

Model 6
(6)

-0.1923***(0.0647)-0.0084(0.0124)

Model 7
(7)

Model 8(8)0.2100(0.2821)0.2381(0.2807)-0.0124(0.0325)-0.2558***(0.0680)-0.0012(0.0063)-0.2514***(0.0659)

Model 9(9)Model 10(10)-0.3441**(0.1352)-0.3430**(0.1351)
0.0069(0.0317)

Model 11(11)

Model 12(12)0.0512(0.0652)

0.0346(0.0700)-0.0149(0.0161)

Model 13
(13)

Model 14(14)-0.5556(0.3902)-0.3609(0.5445)-0.0614(0.1291)

Model 15(15)-0.4166**(0.1639)

Model 16(16)-0.3473*(0.2043)
LOAN-TARGET*credit gap-0.0313(0.0534)
Credit gap
0.0041**0.0047***0.0043**0.0047***0.0036**0.0040**0.0036**0.0039**0.0041**0.0038**0.0044***0.0038*0.0041**0.00620.0038**0.0049**(0.0017)(0.0017)(0.0017)(0.0017)(0.0017)(0.0019)(0.0017)(0.0017)(0.0017)(0.0018)(0.0017)(0.0021)(0.0016)(0.0041)(0.0017)(0.0021)
Credit to GDP
0.0048***0.0047***0.0047***0.0046***0.0051***0.0050***0.0051***0.0051***0.0050***0.0049***0.0046***0.0045***0.0047***0.0044***0.0049***0.0047***(0.0006)(0.0006)(0.0006)(0.0006)(0.0006)(0.0007)(0.0006)(0.0006)(0.0006)(0.0006)(0.0006)(0.0006)(0.0006)(0.0008)(0.0006)(0.0007)
Output gap
0.2258*0.2170*0.2616**
0.2580**0.16660.15450.17130.16550.2485**0.2469**0.3266***0.3332***0.2218*0.19460.17950.1560(0.1221)(0.1225)(0.1203)(0.1202)(0.1184)(0.1196)(0.1215)(0.1216)(0.1133)(0.1139)(0.1126)(0.1152)(0.1327)(0.1468)(0.1235)(0.1299)
Real GDP per capita
0.0636
0.0566
0.03740.03750.01820.0044-0.0355-0.0408-0.0390-0.0410-0.1259-0.08690.08410.01430.06390.0282(0.0809)(0.0814)(0.0876)(0.0868)(0.0570)(0.0606)(0.0968)(0.0965)(0.0474)(0.0474)(0.0771)(0.0899)(0.1190)(0.1835)(0.0725)(0.0957)
Real GDP-0.0782**-0.0830**-0.0836***-0.0882***-0.0695**-0.0679**-0.0704**-0.0729**-0.0591*-0.0579*-0.0904***-0.1073***-0.0764**-0.0746**-0.0707**-0.0681**(0.0325)(0.0332)(0.0318)(0.0330)(0.0325)(0.0325)(0.0327)(0.0335)(0.0333)(0.0338)(0.0302)(0.0356)(0.0339)(0.0343)(0.0330)(0.0333)
Trade to GDP-0.0009***-0.0008**-0.0010***-0.0010***-0.0003-0.0001
0.00000.0000-0.0007**-0.0007**-0.0005-0.0007-0.0007**-0.0007**-0.0004-0.0003(0.0003)(0.0004)(0.0004)(0.0004)(0.0004)(0.0004)(0.0005)(0.0005)(0.0003)(0.0003)(0.0004)(0.0005)(0.0003)(0.0003)(0.0003)(0.0004)
IMF program
0.0649***0.0560**0.0676***0.0602**0.0672***
0.0666***
0.0714***0.0668**0.0620***0.0631***0.0811***
0.05370.0680***0.0651***
0.0665***0.0647***(0.0233)(0.0276)(0.0234)(0.0279)(0.0224)(0.0225)(0.0236)(0.0266)(0.0229)(0.0233)(0.0228)(0.0367)(0.0240)(0.0251)(0.0228)(0.0232)
Regional crises
0.0524***0.0497***0.0477***0.0457***0.0595***0.0587***0.0587***0.0575***0.0469***0.0473***0.0382***0.0350**0.0553***0.0486**
0.0594***0.0563***(0.0166)(0.0170)(0.0161)(0.0165)(0.0164)(0.0164)(0.0167)(0.0168)(0.0146)(0.0147)(0.0148)(0.0152)(0.0195)(0.0231)(0.0176)(0.0180)
Observations
2250
2250
2250
2250
2250
2250
2250
2250
2250
2250
2250
2250
2250
2250
2250
2250
Number of iso3
107
107
107
107
107
107
107
107
107
107
107
107
107
107
107
107
R-squared
0.091
0.096
0.103
0.109
0.081
0.078
0.072
0.073
0.133
0.135
0.133
0.070
0.041
0.041
0.075
0.067
Note: The banking crisis dummy is the dependent variable used in the regression. For the instrumental variables, we construct the regional macroprudential policy instruments by summing up all macroprudential policy instruments adopted by countries in the same geographical region as the
instrument for country i’s overall macroprudential policy instruments and other group of policies. We estimate by using linear probability regression with robust standard errors in brackets.
*** p<0.01, ** p<0.05, * p<0.1

60

3.5.3 Monetary and fiscal policy stances
Several studies have pointed out that the adoption of MP instruments in conjunction with
monetary and fiscal policy tools can be more effective than the stand-alone policies (Bruno etal., 2017; Lim et al., 2011). The combination of policies helps achieve the objectives of MP
in mitigating systemic risk, particularly during the periods when risks in financial sectors areclosely related to those in other sectors or when the financial cycle is closely related to thebusiness cycle (Lim et al., 2011). Given these arguments, we estimate an empirical model that
includes the measures of monetary and fiscal policy stances. This step seems necessary
because it helps the estimation to identify the direct effects of MP. But the drawback ofincorporating the measures of monetary and fiscal policy stances into the model is that the
sample size is reduced largely. 41 Thus, we regard this estimation as a robustness check.
Following the work of Dell’Ariccia et al. (2016), we construct the measure of monetary policy
stance by computing the error term by which the money market rate exceeds its predicted levelbased on a regression of money market rate on inflation and real GDP growth. We also
construct the measure of fiscal policy stance by computing the error term by which the general
government net lending (revenue minus expenditure) in percent of GDP deviates from its
predicted level based on a regression of the net lending on real GDP growth. Table 12 showsthe estimated results, which generally supports the findings of our baseline analysis.

Our sample of the estimation models with monetary and fiscal policy stances cover only 30 countries. One
possible reason for the large drop of the sample is that many developing countries have many missing data ofmoney market rates due to less mature short-term markets. Table A7 in the appendix shows the list of thesample countries for the models with monetary and fiscal policy stances.
41

61

Table 3-12. Macroprudential policies and banking crises with monetary and fiscal policy stances
MPI
MPI*Credit gap
FINANCIALFINANCIAL*Credit gap
BORROWERBORROWER*Credit gap
CAPITALCAPITAL*credit gap
GENERAL
GENERAL*credit gap
LOAN-SUPPLY
LOAN-SUPPLY*credit gap
LOAN-TARGET

Model 1(1)
-3.2021**(1.6173)

Model 2(2)
-3.9684**(1.7291)0.2325(0.1639)

Model 3(3)Model 4(4)

-2.4979*(1.5075)-2.6518*(1.5787)
0.0604(0.1549)

Model 5(5)-2.8190***(1.0622)

Model 6
(6)

-2.6846***(0.9532)
0.1260***(0.0394)

Model 7
(7)

Model 8(8)-0.8975(1.5722)-1.1227(1.6905)-0.0242(0.1754)-2.4826**(1.0147)0.1268***(0.0398)-2.6635**(1.0911)

Model 9(9)Model 10(10)

-2.4096(1.7553)-3.6772*(2.1064)0.4323(0.2944)

Model 11(11)

Model 12(12)-0.4053(0.3213)-0.4191(0.3306)
0.0056(0.0289)

Model 13
(13)

Model 14
(14)-0.5729(0.9598)-0.5612(1.0048)-0.0047(0.1230)

Model 15
(15)

-3.2659**(1.3908)0.2212**(0.0957)
Credit gap
0.00540.01250.00740.0081-0.0060
0.0318-0.00400.03400.0028-0.00410.00720.00760.00690.00690.00600.0231(0.0307)(0.0298)(0.0313)(0.0313)(0.0278)(0.0324)(0.0281)(0.0326)(0.0320)(0.0309)(0.0315)(0.0316)(0.0313)(0.0313)(0.0299)(0.0297)
Credit to GDP
0.0808***0.0802***0.0780***0.0777***
0.0941***
0.0926***
0.0931***
0.0918***0.0793***0.0756***0.0771***0.0770***0.0759***0.0759***0.0788***0.0800***(0.0165)(0.0166)(0.0162)(0.0162)(0.0183)(0.0191)(0.0183)(0.0192)(0.0166)(0.0173)(0.0159)(0.0158)(0.0160)(0.0161)(0.0165)(0.0172)
Output gap
8.3836*9.3020**8.7858**8.9560**9.2658**9.4148**9.0019**9.0091**
8.8313**9.6326**9.4022**9.4699**9.7994**9.8041**
9.3191**9.5920**(4.4994)(4.5410)(4.4643)(4.4747)(4.3373)(4.4032)(4.4226)(4.5529)(4.3689)(4.4444)(4.3353)(4.3441)(4.2195)(4.2216)(4.2758)(4.3201)
Real GDP per capita-0.8858-1.3398-0.9995-1.0870-1.2559-1.9120-1.0649-1.7260-2.2742-2.7105-1.1601-1.1640-1.3656-1.3633-0.9154-1.3399(2.7906)(2.8314)(2.7662)(2.7774)(2.7194)(2.7881)(2.7785)(2.8518)(2.6339)(2.7256)(2.7250)(2.7257)(2.6069)(2.6062)(2.6697)(2.7218)
Real GDP-0.6981-0.4142-0.7736-0.7290-1.4299-1.1205-1.2092-0.7613-0.6703-0.5690-1.1175-1.1185-1.2649-1.2693-1.1867-0.8282(2.4662)(2.5874)(2.4529)(2.4772)(2.3129)(2.4336)(2.3986)(2.5596)(2.4385)(2.5079)(2.3446)(2.3504)(2.3083)(2.3113)(2.3495)(2.4489)
Trade to GDP-0.0252-0.0288-0.0252-0.0259-0.0285-0.0349*-0.0281-0.0343*-0.0254-0.0259-0.0277*-0.0282*-0.0245-0.0246-0.0218-0.0249(0.0174)(0.0180)(0.0171)(0.0173)(0.0174)(0.0183)(0.0175)(0.0185)(0.0171)(0.0171)(0.0167)(0.0169)(0.0173)(0.0174)(0.0173)(0.0180)
IMF program
2.0290***2.1822***2.0221***2.0449***2.1828***2.1449***2.2130***2.1677***1.8185***1.8851***
2.1072***2.1199***1.9939***1.9947***1.9831***2.0719***(0.6284)(0.6455)(0.6133)(0.6155)(0.6505)(0.6445)(0.6608)(0.6641)(0.6045)(0.6198)(0.6127)(0.6159)(0.5947)(0.5949)(0.6173)(0.6336)
Regional crises
1.2469**1.3262**
1.2773**1.3013**1.2830**1.3138**1.2529**1.2740*1.3290**1.4507**1.3016**
1.3115**1.4087**1.4078**1.4108**1.4373**(0.6033)(0.6052)(0.5966)(0.5992)(0.6335)(0.6531)(0.6342)(0.6549)(0.5911)(0.6114)(0.5934)(0.5953)(0.5921)(0.5926)(0.6020)(0.6141)
Monetary policy stance
0.04660.04780.04760.04780.04610.04940.04500.04810.04960.05000.04870.04880.04840.04840.04580.0452(0.0331)(0.0338)(0.0329)(0.0331)(0.0329)(0.0339)(0.0330)(0.0340)(0.0330)(0.0330)(0.0325)(0.0326)(0.0320)(0.0320)(0.0319)(0.0317)
Fiscal policy stance-0.1090*-0.1149*-0.1098*-0.1121*-0.0997-0.0782-0.0972-0.0756-0.1139*-0.1115*-0.1114*-0.1123*-0.1105*-0.1106*-0.1043*-0.0945(0.0643)(0.0646)(0.0643)(0.0648)(0.0635)(0.0658)(0.0640)(0.0664)(0.0638)(0.0640)(0.0641)(0.0643)(0.0633)(0.0633)(0.0629)(0.0611)
Observations
591
591
591
591
591
591
591
591
591
591
591
591
591
591
591
591
Number of countries
30
30
30
30
30
30
30
30
30
30
30
30
30
30
30
30
Log likelihood-82.889-81.882-84.028-83.951-80.254-76.636-80.083-76.363-84.553-83.305-84.794-84.775-85.467-85.467-83.848-81.802
McFadden's R-squared
0.624
0.628
0.619
0.619
0.636
0.652
0.637
0.654
0.616
0.622
0.615
0.615
0.612
0.612
0.620
0.629
BIC
-162.761-161.375-160.484-157.237-168.032-171.868-164.974-165.610-159.433-158.529-158.953-155.588-157.605-154.205-160.844-161.535
Notes: The banking crisis dummy is the dependent variable used in the regression. We estimated the regressions using the logistic regression with the country and period fixed effects. The table reports the estimates of the average elasticities of the conditional probability with respect tothe regressors and the corresponding standard errors.
*** p<0.01, ** p<0.05, * p<0.1
LOAN-TARGET*credit gap-1.9660(1.3574)

Model 16(16)

62

3.6 Conclusion
The adverse consequences arising from the occurrences of a banking crisis are costly andhighly disruptive, leading to the failure of economic activities and a substantial decline insocial welfare. For this reason, academicians and policymakers dedicate much effort tomodeling and designing sound policies to mitigate such unfavorable impacts on the economy.
In this study, we utilized the newly constructed macroprudential policy instrument database(iMaPP) to investigate the effectiveness of MP in reducing systemic risk. We used theoccurrence of a banking crisis to measure systemic risk. This approach differs from most of
the existing studies, which mostly focus on MP’s effect on the “observable” channel offinancial failure, such as credit growth and the boom of asset prices. By contrast, our focus is
MP’s effect on unobservable factors, such as externalities generated by financial sectors. Thus,
our findings could help answer the direct question “would these policy tools be useful?”,although we may not be able to answer questions such as “why are they useful?”.
Our analysis contributes to the literature by discussing crucial insights into the efficacy
roles of overall MP and different subgroups of MP instruments in mitigating systemic risk or
the occurrence of a banking crisis while controlling for various macroeconomic conditions,such as credit variables. Several previous studies have focused mostly on examining the roleof MP in mitigating credit growth or credit-related issues. Most of them argue that MP has
proven effective in containing booms and in decreasing the probability that booms end badly.
Although excessive credit booms could be considered a measure of systemic risk, a large
proportion of banking crises have not followed credit booms (Dell’Ariccia et al., 2016). Thissuggests that the use of credit variables as a risk measure may not be comprehensive to discuss
the link of MP to systemic risk. Rather than focusing on the MP effects on credit conditions,this study has emphasized the link of MP to the likelihood of a banking crisis under the

63

argument that the occurrence of a banking crisis can be considered the actual realization ofsystemic risk. Our empirical results, suggesting the favorable role of MP, could complement
the findings in previous studies and confirm the effectiveness of MP. Our analysis has also
revealed that several specific objective-oriented MP instruments, CAPITAL, LOAN-SUPPLY
and LOAN, have a significant negative relationship with the likelihood of a banking crisis.
Moreover, we have confirmed our empirical results by conducting various sensitivity analyses,
including using different measures of crises, instrumental variables, and a different way toconstruct the MP instruments, such as factor analysis.

References
Abbott, P., Andersen, T. B., Tarp, F., 2010. IMF and economic reform in developing countries.
Quarterly Review of Economics and Finance 50 (1), 17–26.
Abiad, A., Mody, A., 2005. Financial reform: what shakes it? What shapes it? American
Economic Review 95 (1), 66–88.
Alam, Z., Alter, A., Eiseman, J., Gelos, G., Kang, H., Narita, M., Nier, E., Wang, N., 2019.
Digging Deeper--Evidence on the Effects of Macroprudential Policies from a New
Database. IMF Working Papers 19/66.
Altunbas, Y., Binici, M., Gambacorta, L., 2018. Macroprudential policy and bank risk. Journalof International Money and Finance 81, 203–220.
Angrist, J.D., Pischke, J.S., 2008. Mostly Harmless Econometrics: An Empiricist’sCompanion. Princeton University Press.
Babecký, J., Havránek, T., Matějů, J., Rusnák, M., Šmídková, K., Vašíček, B., 2014. Banking,debt, and currency crises in developed countries: Stylized facts and early warning
indicators. Journal of Financial Stability 15, 1–17.
Baier, S. L., Clance, M., Dwyer, G. P., 2012. Banking crises and economic
freedom. Forthcoming in Economic Freedom of the World: 2012 Annual Report 201–
217
Barro, R. J., Lee, J. W., 2005. IMF programs: Who is chosen and what are the effects? Journalof Monetary Economics 52 (7), 1245–1269.
Basel Committee on Banking Supervision, 2010. Guidance for national authorities operating
the countercyclical capital buffer. Basel, Bank for International Settlements.
Basel Committee on Banking Supervision, 2012. Instructions for calculating capitalrequirements for bank default fund exposures to central counterparties. Basel, Bank forInternational Settlements.

64

Beck, T., Degryse, H., Kneer, C., 2014. Is more finance better? Disentangling intermediationand size effects of financial systems. Journal of Financial Stability 10 (1), 50–64.
Beck, T., Demirgüç-Kunt, A., Levine, R., 2006. Bank concentration, competition, and crises:
First results. Journal of Banking and Finance 30 (5), 1581–1603.
Bekaert, G., Harvey, C. R., Lundblad, C., 2005. Does financial liberalization spur growth?
Journal of Financial Economics 77(1), 3–55.
Berger, A. N., Bouwman, C. H., 2013. How does capital affect bank performance during
financial crises. Journal of Financial Economics 109 (1), 146–176.
Berkmen, S. P., Gelos, G., Rennhack, R., Walsh, J. P., 2012. The global financial crisis:
Explaining cross-country differences in the output impact. Journal of International
Money and Finance 31 (1), 42–59.
Bordo, M. D., Meissner, C. M., 2012. Does inequality lead to a financial crisis? Journal ofInternational Money and Finance 31 (8), 2147–2161.
Borio, C., Drehmann, M., 2009. Assessing the risk of banking crises–revisited. BIS Quarterly
Review 29-46.
Borio, C., Lowe, P., 2002. Assessing the risk of banking crises. BIS Quartlerly Review 43-54.
Borio, C., Lowe, P., 2004. Securing sustainable price stability: should credit come back fromthe wilderness? BIS Working Paper 175.
Bruno, V., Shim, I., Shin, H. S., 2017. Comparative assessment of macroprudential policies.
Journal of Financial Stability 28, 183–202.
Butzbach, O., 2016. Systemic risk, macro-prudential policy and organizational diversity inbanking. Policy and Society 35 (3), 239-251.
Caprio, G., D’Apice, V., Ferri, G., Puopolo, G. W., 2014. Macro-financial determinants of thegreat financial crisis: Implications for financial policy. Journal of Banking and Finance
44 (1), 114–129.
Carreras, O., Davis, E. P., Piggott, R., 2018. Assessing macroprudential tools in OECD
countries within a cointegration framework. Journal of Financial Stability 37, 112–130.
Cattell, R. B., 1966. The Scree Plot Test for the Number of Factors. Multivariate Behavioral
Research 1 (2), 140-161.
Cerutti, E., Claessens, S., Laeven, L., 2017. The use and effectiveness of macroprudential
policies: New evidence. Journal of Financial Stability 28, 203–224.
Chen, P. F., Lin, C. W., Lee, C. C., 2019. Financial crises, globalization, and insurer
performance: Some international evidence. North American Journal of Economics andFinance 48, 835–856.
Claessens, S., Ghosh, S. R., Mihet, R., 2013. Macro-prudential policies to mitigate financial
system vulnerabilities. Journal of International Money and Finance 39, 153–185.
Conway, P., 2006. The international monetary fund in a time of Crisis: A review of Stanley
Fischer’s IMF essays from a time of crisis: The international financial system,stabilization, and development. Journal of Economic Literature 44 (1), 115–144.
Crockett, A., 2000. Marrying the Micro- and Macro-prudential Dimensions of Financial

65

Stability. BIS Speeches. September 21.
Davis, E. P., Karim, D., 2008. Comparing early warning systems for banking crises. Journalof Financial Stability 4 (2), 89–120.
Dell’Ariccia, G., Igan, D., Laeven, L., Tong, H., 2016. Macrofinancial Stability. Economic
Policy 31 (86), 299–355.
Demirgüç-Kunt, A., Detragiache, E., 1998. Financial Liberalization and Financial Fragility.
The World Bank.
Demirgüç-Kunt, A., Detragiache, E., 2011. Basel Core Principles and bank soundness: Does
compliance matter? Journal of Financial Stability 7 (4), 179–190.
De Nicolo, G., Favara, G., Ratnovski, L., 2014. Externalities and Macroprudential Policy.
Journal of Financial Perspectives 2 (1), 2014.
Dreher, A., Vaubel, R., 2001. Does the IMF Cause Moral Hazard and Political Business
Cycles? Evidence from Panel Data (2001). Institut für Volkswirtschaftslehre undStatistik; Department of Economics, Universität Mannheim. Working Paper 598/01.
Dreher A., Walter S., 2010. Does the IMF Help or Hurt? The Effect of IMF Programs on the
Likelihood and Outcome of Currency Crises. World Development 38 (1), 1-18.
Drehmann, M., Tsatsaronis, K., 2014. The Credit-to-GDP Gap and Countercyclical Capital
Buffers: Questions and Answers. BIS Quarterly Review 55–73.
Feldkircher, M., 2014. The determinants of vulnerability to the global financial crisis 2008 to
2009: Credit growth and other sources of risk. Journal of International Money and
Finance 43, 19–49.
Fendoğlu, S., 2017. Credit cycles and capital flows: Effectiveness of the macroprudential
policy framework in emerging market economies. Journal of Banking and Finance 79,
110–128.
Garbers, C., Liu, G., 2018. Macroprudential policy and foreign interest rate shocks: A
comparison of loan-to-value and capital requirements. International Review of
Economics and Finance 58, 683-698.
Gilbert, P., Meijer, E., 2006. Money and credit factors. Bank of Canada. Working Paper
2006/3.
Haile, F., Pozo, S., 2008. Currency crisis contagion and the identification of transmission
channels. International Review of Economics and Finance 17 (4), 572–588.
Hasanov, R., Bhattacharya, P. S., 2018. Do political factors influence banking crisis?
Economic Modelling 76, 305–318.
Hlaing, S. W., Kakinaka, M., 2018. Financial crisis and financial policy reform: Crisis origins
and policy dimensions. European Journal of Political Economy 55, 224–243.
Igan, D., Kang, H., 2011. Do Loan-to-Value and Debt-to-Income Limits Work? Evidence
from Korea. IMF Working Paper 11/297
Jensen, N. M., 2004. Crisis, Conditions, and Capital: The effect of International Monetary
Fund agreements on foreign direct investment inflows. Journal of Conflict Resolution 48(2), 194–210.

66

Kaminsky, G. L., 1999. Currency and Banking Crises: The Early Warning of Distress. IMF
Working Paper 99/178.
Kim, J.O., Mueller, C.,W., 1978. Factor Analysis. Statistical Methods and Practical Issues.
Beverly Hills, CA: Sage.
Kim, T., Koo, B., Park, M., 2013. Role of financial policy and innovation in the financial crisis.
Journal of Financial Stability 9 (4), 662–672.
Kitazawa, Y., 2012. Hyperbolic Transformation and Average Elasticity in the Framework of
the Fixed Effects Logit Model. Theoretical Economics Letters 2 (2), 192-199.
Klomp, J., De Haan, J., 2009. Central bank independence and financial instability. Journal of
Financial Stability 5 (4), 321–338.
Klomp, J., De Haan, J., 2012. Banking risk and policy: Does one size fit all? Journal of
Banking and Finance 36 (12), 3197–3212.
Klomp, J., De Haan, J., 2015. Bank policy and financial fragility in developing countries: Doesbank structure matter? Review of Development Finance 5 (2), 82–90.
Kuttner, K. N., Shim, I., 2016. Can non-interest rate policies stabilize housing markets?
Evidence from a panel of 57 economies. Journal of Financial Stability 26, 31–44.
Laeven, L., Levine, R., 2009. Bank governance, policy and risk taking. Journal of Financial
Economics 93 (2), 259–275.
Laeven, L., Valencia, F., 2008. Systemic Banking Crises : A New Database. IMF Working
Papers 08/224.
Laeven, L., Valencia, F., 2013. Systemic banking crises database. IMF Economic Review 61(2), 225–270.
Laeven, L., Valencia, F., 2018. Systemic Banking Crises Revisited. IMF Working Paper
18/206.
Lane, P. R., Milesi-Ferretti, G. M., 2011. The cross-country incidence of the global crisis. IMF
Economic Review 59 (1), 77–110.
Lee, C. C., Lin, C. W., Zeng, J. H., 2016. Financial liberalization, insurance market, and thelikelihood of financial crises. Journal of International Money and Finance 62, 25–51.
Liao, S., Sojli, E. and Tham, W.W., 2015. Managing systemic risk in The
Netherlands. International Review of Economics and Finance, 40, 231-245.
Lim, C., Columba, F., Costa, A., Kongsamut, P., Otani, A., Saiyid, M., Wezel, T., Wu, X.,
2011. Macroprudential policy: What instruments and how to use them? IMF Working
Paper 11/238.
Lim, C. H., Krznar, I., Lipinsky, F., Otani, A., Wu, X., 2013. The Macroprudential
Framework: Policy Responsiveness and Institutional Arrangements. IMF Working
Papers 13/166.
Lora, E., Olivera, M., 2004. What Makes Reforms Likely: Political Economy Determinants of
Reforms in Latin America. Journal of Applied Economics 7 (1), 99–135.
Marchesi, S., Sirtori, E., 2011. Is two better than one? The effects of IMF and World Bank
interaction on growth. Review of International Organizations 6 (3), 287–306.

67

Mathonnat, C., Minea, A., 2018. Financial development and the occurrence of banking crises.
Journal of Banking and Finance 96, 344–354.
Morgan, P. J., Regis, P. J., Salike, N., 2019. LTV policy as a macroprudential tool and its
effects on residential mortgage loans. Journal of Financial Intermediation 37, 89–103.
Mylonidis, N., Chletsos, M., Barbagianni, V., 2019. Financial exclusion in the USA: Looking
beyond demographics. Journal of Financial Stability 40, 144–158.
Oberdabernig, D. A., 2013. Revisiting the Effects of IMF Programs on Poverty and Inequality.
World Development 46, 113–142.
Olszak, M., Roszkowska, S., Kowalska, I., 2018. Macroprudential policy instruments andprocyclicality of loan-loss provisions – Cross-country evidence. Journal of International
Financial Markets, Institutions and Money 54, 228–257.
Pescatori, A., Sy, A. N. R., 2007. Are debt crises adequately defined? IMF Staff Papers 54,
306–337.
Przeworski, A., Vreeland, J. R., 2000. The effect of IMF programs on economic growth.
Journal of Development Economics 62, 385–421.
Quinn, D., Schindler, M., Toyoda, A. M., 2011. Assessing measures of financial openness andintegration. IMF Economic Review 59 (3), 488–522.
Reinhart, C. M., Rogoff, K. S., 2011. From financial crash to debt crisis. American Economic
Review 101 (5), 1676–1706.
Richter, B., Schularick, M., Shim, I., 2019. The Costs of Macroprudential Policy. Journal ofInternational Economics 118, 263-282.
Rose, A. K., Spiegel, M. M., 2012. Cross-country Cause and Consequences of the 2008 Crisis:
Early Warning. Japan and the World Economy 24 (1), 1-16.
Shehzad, C. T. T., De Haan, J., 2009. Financial Reform and Banking Crises. CESIFO Working
Paper 2870.
Shim, I., Bogdanova, B., Shek, J., Subelyte, A., 2013. Database for policy actions on housing
markets. BIS Quarterly Review 83-95.
Simmons, B. A., Elkins, Z. (2004). The globalization of liberalization: Policy diffusion in theinternational political economy. American political science review 98 (1), 171-189.
Stiglitz, J., 2002. Globalization and its discontents. London: Penguin Books. Thacker, S. C.
(1999). The high politics of IMF lending. World Politics 52, 38–75.
Stock, J., Watson, M., 2002. Forecasting using principal components from a large number ofpredictors. Journal of the American Statistical Association 97 (460), 1167–1179.
Vandenbussche, J., Vogel, U., Detragiache, E., 2015. Macroprudential policies and housing
prices: A new database and empirical evidence for central, Eastern, and Southeastern
Europe. Journal of Money, Credit and Banking 47 (S1), 343–377.
Vaubel, R. (1983). The moral hazard of IMF lending. World Economy 6 (3), 291-304.
Volden, C., Ting, M. M., Carpenter, D. P., 2008. A formal model of learning and policy
diffusion. American Political Science Review 102 (3), 319–332.

68

Waelti, S., 2015. Financial crisis begets ﬁnancial reform? The origin of the crisis matters.
European Journal of Political Economy 40, 1–15
Wooldridge, J.M., 2016. Introductory Econometrics: A Modern Approach. Cengage Learning,
Boston.
Yoshino, N., Morgan, P., J. 2017. Financial Inclusion, Policy, and Education: Asian
Perspectives. Asian Development Bank Institute.

69

Appendix
Table 3-A1. Credit booms and banking crises
Country
Algeria
Argentina
Argentina
Armenia
Austria
Azerbaijan
BelarusBelgiumBrazilBulgaria
BurundiChina
Colombia
Costa Rica
Croatia
CyprusCzech Republic
Dominican Republic
EcuadorFinlandFranceGermany
Haiti
Hungary
Hungary
Iceland
India
Indonesia
Ireland
Italy
JamaicaJapan
Kazakhstan
Kazakhstan
Kenya
Korea, Rep.
LatviaLebanonLithuania
Macedonia, FYR
Malaysia
MexicoMoldova
Mongolia
Netherlands
NorwayParaguay
Philippines
PolandPortugal
Russian Federation
Russian Federation
Slovak Republic
Slovenia
Spain
SwedenSwedenSwitzerland
ThailandTurkeyUgandaUkraineUkraineUkraineUnited Kingdom
United States

Banking crisis years(Laeven & Valencia, 2018)
1993-1994
1995
2001-2003
1994
2008-2011
1995
1995
2008-20111993-1998
1996
1994-1998
1998
1998-2000
1994-1995
1998-1999
2011-2015
1996-2000
2003-2004
1998-2002
1993-1995
2008-20112008-20111997
1993-1995
2008-20112008-20111993
1997-2001
2008-20112008-2011
1996-1998
1997-2001
1996
2008-20111993-1994
1997-1998
2011
1993
1996
1994-1995
1997-1999
1994-1996
2014-2015
2008-20112008-2011
1993
1995
1997-2001
1993-1994
2008-20111998
2008-2011
1998-2002
2008-20112008-20111993-1995
2008-20112008-2011
1997-2000
2000-2001
1994
1998-1999
2008-20102014
2007-20112007-2011

Credit boom episodes (Dell’Ariccia et al., 2016):
3 years before crisis
1992-1994
No data
No data
No data
2006-2007
1992-1994
No data
1992-1993
2006-2008
1999-2000
2000-2007
2003-2006
2004-2006
No data
1996-2002
2002-2006
No data
2003-2007
No data
1995-1997
1989-1994
No data
1991-1994
1992-1997
2003-2007
1996-1997
2004-2007
2003-2007
2002-2006
1987-1995
1995-1997
2002-2007
No data
2006-2008-

Does a crisis follow a credit boom?
No
Yes
No
Not identifiedNo
Not identified
Not identified
Yes
Yes
Not identified
No
No
No
Yes
No
Yes
No
Yes
No
No
No
No
No
No
Yes
Yes
No
No
Yes
No
No
No
Not identified
No
No
Yes
No
Not identifiedNo
Not identified
Yes
Yes
Not identified
No
No
No
Yes
Yes
No
No
No
Yes
Yes
Yes
Yes
No
No
Yes
Yes
Yes
No
No
Yes
Not identified
Yes
No

Notes: The information of credit boom episodes is obtained from Dell’Ariccia et al. (2016). For the details about the definition of credit boom episodes and their characteristics, see
Dell’Ariccia et al. (2016). Among 200 banking crises in our sample, there are 66 starting years of banking crises and 134 are the subsequent periods. Among the 66 starting years ofbanking crises, 23 banking crises follow credit boom episodes within three years, while 34 banking crises do not follow credit boom episodes within three years. Due the data
unavailability, we cannot identify whether or not the remaining 9 banking crises follow credit boom episodes.

70

Table 3-A2. MP instruments and banking crises (1993-2010)
Variable

1993 to 2010
Pre-crisis period Post-crisis period
Obs MeanObs Mean
CCB
124
0.0000224 0.0000
Conservation
122
0.0000220 0.0000
Capital121
2.0661
221 2.2624
LVR
124
0.0000228 1.0965
LLP
119
0.0000224 2.2321
LCG
120
0.0000220 0.0000
LoanR
120
0.0000220 -0.7576
LFC
122
0.6831
230 1.0870
LTV
123
0.6775225 -1.1111
DSTI
123
0.6775231 0.7215
TAX
121
0.0000221 0.0000
Liquidity
124
0.0000237 -0.7032
LTD
122
0.0000230 0.0000
LFX
124
0.6720237 1.0549
RR
124
5.3763231 -12.9870
SIFI
124
0.0000224 0.0000
MPI
126
9.9206237 -7.0323
FINANCIAL
126
8.5979237 -6.6807
BORROWER
126
1.3228237 -0.3516
CAPITAL
126
1.9841
237 3.1646
GENERAL126
5.9524237 -12.3066
LOAN-SUPPLY 126
0.6614237 2.4613
LOAN-TARGET 126
1.9841
237 2.1097

Note: We report annual changes of all the values at unit of 1.e-3.

Non-crisis period
Obs Mean
545 0.0000
531 0.0000
517 2.0954
538 0.3098
513 2.1118
516 0.3230
516 1.2920
538 0.7745
545 2.7523
553 1.3562
517 0.8059
563 0.5921
538 0.1549
563 1.6282
555 0.1502
545 0.0000
578 13.9850
578 10.0923
578 3.8927
578 2.1626
578 2.3068
578 4.1811
578 8.0738

71

Table 3-A3. Macroprudential policy (Cerutti et al., 2017) and banking crises
Variables
MPI
FINANCIAL

Model 1
(1)
-7.0250(4.5860)

BORROWER
Credit gap
Credit to GDP
Output gap
Real GDP per capita
Real GDP
Trade to GDP
IMF program
Regional crises
Observations
Number of countries
Log likelihood
McFadden's R-squared-0.1800***(0.0613)0.1910***(0.0442)-3.1290(7.0290)-8.1130(5.3910)0.9840(3.6250)-0.0091(0.0247)
1.3670*(0.7770)
3.7670*(1.9320)424
31-45.560
0.748

Model 2(2)
-3.2050(5.7740)-0.1740***(0.0597)0.1860***(0.0436)-4.8260(7.0160)-6.2020(5.1700)
0.3880(3.5150)-0.0086(0.0246)
1.4720*(0.7570)3.8690*(1.9810)424
31-46.6070.742

Model 3(3)Model 4(4)

-3.5430***(1.0530)-0.2100***(0.0691)0.2170***(0.0491)-1.1790(7.4620)-12.1000**(6.1250)
2.9870(4.1980)-0.0034(0.0263)1.6430**(0.7910)
3.9470*(2.1440)424
31-41.4750.770-3.6170(6.4500)-3.4930***(1.0410)-0.2070***(0.0682)
0.2160***(0.0483)-1.1290(7.3330)-12.5300**(6.0950)
3.1980(4.2090)-0.0030(0.0261)1.5940**(0.7990)
3.8920*(2.1360)424
31-41.320
0.771

Notes: The banking crisis dummy is the dependent variable used in the regression. We estimated the regressions
using the logistic regression with the country and period fixed effects. The table reports the estimates of theaverage elasticities of the conditional probability with respect to the regressors and the corresponding standard
errors.
*** p<0.01, ** p<0.05, * p<0.1

72

Table 3-A4. Quantitative LTV_average and banking crises
Variables
LTV_average
Credit gap
Credit to GDP
Output gap
Real GDP per capita
Real GDP
Trade to GDP
IMF program
Regional crises
Observations
Number of countries
Log likelihood
McFadden's R-squared

Model 1(1)0.12900**(0.0610)-0.3280***(0.0920)0.2330***(0.0552)-6.0940(8.8360)-17.2000**(8.6760)
2.0220(6.5490)-0.0797*(0.0429)2.4070**(1.0140)
3.7990(4.0220)395
27-24.970
0.854

Notes: The banking crisis dummy is the dependent variable used in the regression. We estimated the regressions
using the logistic regression with the country and period fixed effects. The table reports the estimates of theaverage elasticities of the conditional probability with respect to the regressors and the corresponding standard
errors.
*** p<0.01, ** p<0.05, * p<0.1

73

Table 3-A5. Macroprudential policies and banking crises (Reinhart and Rogoff, 2011)
Variables
MPI
FINANCIAL

Model 1(1)
-1.2060(0.8830)

BORROWER

Model 2
(2)-0.5540(0.9350)

CAPITAL

Model 3(3)Model 4
(4)-0.9270**(0.4670)-0.0050(0.9720)-0.9260**(0.4710)

GENERAL

Model 5(5)

1.3830(0.9270)

LOAN-SUPPLY

Model 6
(6)0.0547(0.2130)

LOAN-TARGET
Credit gap
Credit to GDP
Output gap
Real GDP per capita
Real GDP
Trade to GDP
IMF program
Regional crises
Observations
Number of iso3
Log likelihood
McFadden's R-squared

0.1190***(0.0335)
0.0498***(0.0102)2.9490(2.8960)-1.4600(1.4850)-3.8790***(1.4350)0.0170(0.0114)0.5900*(0.3410)2.1680***(0.4180)879
42-170.028
0.531

0.1180***(0.0333)
0.0497***(0.0101)3.4900(2.9030)-1.7190(1.4710)-4.1060***(1.4450)
0.0166(0.0114)0.6040*(0.3430)
2.2010***(0.4190)879
42-170.809
0.529

0.1190***(0.0333)
0.0509***(0.0104)
3.3560(2.8150)-1.3640(1.4750)-4.1650***(1.4040)0.0205*(0.0116)0.6460*(0.3460)
2.1990***(0.4200)879
42-167.811
0.537

0.1190***(0.0333)
0.0509***(0.0104)
3.3530(2.9000)-1.3630(1.4930)-4.1630***(1.4530)0.0204*(0.0117)0.6460*(0.3470)
2.1990***(0.4220)879
42-167.811
0.537

0.1190***(0.0333)
0.0499***(0.0102)
5.006*(2.8920)-2.2430(1.4820)-4.8240***(1.4520)0.0184(0.0112)0.6440*(0.3490)
2.2820***(0.4260)879
42-169.891
0.531

0.1180***(0.0332)
0.0498***(0.0101)
4.0630(2.8380)-1.8960(1.4440)-4.3840***(1.4140)0.0175(0.0114)0.6320*(0.3450)2.2330***(0.4190)879
42-170.951
0.528

Model 7
(7)

-1.7340**(0.7910)0.1250***(0.0340)0.0484***(0.0103)
3.2090(2.8240)-1.1500(1.4940)-3.950***(1.4220)0.0217*(0.0118)
0.6030*(0.3430)
2.2290***(0.4200)879
42-168.327
0.536

Model 8(8)-2.2740**(0.9680)
0.1240***(0.0337)0.0495***(0.0104)
2.9690(2.8250)-0.9300(1.5100)-3.8750***(1.4270)0.0230*(0.0120)0.6270*(0.3450)
2.2190***(0.4210)879
42-166.685
0.540

Notes: The banking crisis dummy is the dependent variable used in the regression. We estimated the regressions using the logistic regression with the country and period fixed effects. The table reports the estimates of the average elasticities of theconditional probability with respect to the regressors and the corresponding standard errors.
*** p<0.01, ** p<0.05, * p<0.1

74

Table 3-A6. List of countries (107 countries)
Albania
Algeria
AngolaArgentina
Armenia
Australia
Austria
Azerbaijan
Bahamas, The
Bahrain
Bangladesh
BelarusBelgium
BhutanBosnia and Herzegovina
Botswana
BrazilBrunei Darussalam
Bulgaria
BurundiCabo VerdeCambodia
CanadaChile
China
Colombia
Costa Rica
Croatia
CyprusCzech Republic

Dominican Republic
EcuadorEl Salvador
EstoniaFiji
FinlandFranceGambia, The
Georgia
Germany
Ghana
Haiti
HondurasHong Kong SAR, China
Hungary
Iceland
India
Indonesia
IrelandIsraelItaly
JamaicaJapan
JordanKazakhstan
Kenya
Korea, Rep.
KuwaitKyrgyz Republic
Lao PDR

Table 3-A7. List of countries (30 countries)
BrazilChina
Colombia
Croatia
Czech Republic
Dominican Republic
Finland
Iceland
India
Indonesia
Ireland
Italy
Japan
Korea, Rep.
Kyrgyz Republic

Lithuania
Malaysia
MexicoMoldova
Mongolia
Paraguay
Philippines
Russian Federation
Slovenia
Spain
SwedenThailandUkraineUnited Kingdom
United States

LatviaLebanonLesotho
Lithuania
Macedonia, FYR
Malaysia
Malta
Mauritius
MexicoMoldova
Mongolia
Montenegro
MoroccoMozambique
Nepal
Netherlands
New Zealand
NorwayPakistan
Paraguay
Peru
Philippines
PolandPortugal
Russian Federation
Saudi Arabia
SerbiaSingapore
Slovak Republic
Slovenia

South Africa
Spain
Sri Lanka
St. Kitts and Nevis
Sudan
SwedenSwitzerland
Tajikistan
ThailandTunisia
TurkeyUgandaUkraineUnited Arab Emirates
United Kingdom
United States
Zambia

75

Chapter 4: Macroprudential policy and financial inclusion: Any difference
between developed and developing countries?

4.1 Introduction
Since the onset of the 2008 financial crisis, many countries have adopted macroprudential
policies (MPs) as a novel means to increase the resilience of their financial sectors. By
controlling the banking sector’s risks in aggregate, this approach has been effective in curbing
the growth of credit and reducing the possibility of financial crises. However, similar to any
other policies, MP may discourage financial development while achieving stability. One
possible pitfall for policymakers is that these newly developed instruments might jeopardize
financial inclusion, which has been recognized as one of the most important development
targets. This study aims to provide evidence of this issue. Specifically, we explore the extent
to which MP may affect financial inclusion. How do the influences differ across different typesof policies? Does the effect differ by country groups?
MP might deteriorate the degree of financial inclusion by reducing the opportunity toreceive a loan. For instance, the limit on loan-to-value (LTV) ratio and the loan-to-income
ratio reduces the accessibility of credit, particularly for the economically disadvantaged(Akinci & Olmstead-Rumsey, 2018, De Araujo et al., 2020). Moreover, some MP tools requirebanks to prepare additional capital (e.g., countercyclical capital and capital conservationbuffers). The additional requirement of capital is based on banks' risk exposure. This type ofpolicy is equivalent to levying costs on bank loans, which allows banks to raise the credit bar.
The above examples show how MP could result in fewer people accessing the credit market.
Such adverse effects may disproportionately hurt the poor and those living in rural areas who
have difficulty accessing financial services.

76

MP might also exert a positive influence on financial inclusion. It is perhaps inevitablethat MP harms banks' profits – it increases their costs by requiring them to preserve more
capital and reduces revenues due to restrictions imposed on leverage ratios and credit supply.
In response to these negative impacts, banks adopt several strategies including geographicalexpansion to increase productivity by diversifying their risks (Goetz et al., 2016; Levine et al.,2016). Moreover, by expanding service coverage, banks increase profits by engaging in
nontraditional banking activities or noninterest, income-based fees and services (Allen &Santomero, 2001; Elsas et al., 2010; Demirgüç-Kunt & Huizinga, 2010; Goddard et al., 2013;
Saunders et al., 2020). These responses increase access to banking services and reduce theextent of the unbanked population.
In this study, we also explore the influence of MP on mobile banking services. Mobilebanking has played a critical role in promoting financial inclusion. In particular, it provides a
cost-efficient way to reach the unbanked poor in remote areas. One feature of the mobilebanking industry is its economies of scale due to its high fixed costs. The break-eventransaction, as estimated by Osafo-Kwaako et al. (2018), is around US$ 2–3 billion in annualtransactions. Therefore, if transaction volumes are restricted by financial policy, eveninadvertently, this may harm the development of mobile banking. We discuss the possible
negative link between MP and credit supply in developing countries, which also results inimpediments to mobile banking services.
In our empirical investigation, we first concentrate on the case of developing countries,
using a penal data set consisting of 71 countries over the period 2004 to 2015. We then change
the measure of financial inclusion, from the conventional indicators (geographical access tobanking outlets) to measures based on mobile banking. Lastly, we examine the case ofdeveloped countries. The developed and developing countries are separately investigatedbecause the problems of financial inclusion they face, and the implementation of MP are very

77

different. These differences are related to the forces at work discussed earlier. And in fact, as
later shown in our empirical results, we find the influences in developed and developing
countries are significantly different.
Following the work of Beck et al. (2007), we use eight indicators of financial inclusion,
divided equally between access to and use of banking services. To measure MP we adapted
the latest integrated MP database (iMaPP) constructed by Alam et al. (2019), in which MP isclassified into 17 types of policy tools. We construct an aggregate measure of overallmacroprudential policy (MPI). Moreover, we evaluate the effects of several major componentsof MP, which share a common purpose or target which clarifies the means by which MP affects
financial inclusion. The two major indices are financial institution-targeted policy(FINANCIAL) and borrower-targeted policy (BORROWER), which represent the policy on
the supply and demand sides of the financial markets, respectively. Since FINANCIAL
consists of a relatively large number of tools for a variety of purposes, we consider two
additional subgroups indexes: capital-targeted policy (CAPITAL) and loans supply-targeted
policy (LOAN-SUPPLY). The former includes policies on banks’ capital requirements, while
the latter represents the policy on banks’ credit supply. As explained earlier, policies on these
two aspects have direct impacts on financial inclusion.
Our empirical results suggest that for developing countries, MP deteriorates financial
inclusion in terms of the use of banking services. Among all the categories, BORROWER,
which consists of all restrictions for borrowers on acquiring credit, has a significantly negative
influence on several measures of financial inclusion. This conclusion also holds for the caseof mobile banking. This finding is in line with the earlier argument that MP reduces theopportunity of receiving a loan. This seemingly intuitive finding, however, does notnecessarily hold for developed countries. MPs (BORROWER, LOAN-SUPPLY) also have anegative influence on the use of banking services, but some tools (FINANCIAL, CAPITAL,

78

LOAN-SUPPLY) positively influence access to banking services. That is, more stringent
policies decrease the unbanked population. This finding seems puzzling at first, but it isentirely possible to give the arguments and evidence provided earlier, in response to thepolicies, banks increase the geographical convergence and conduct more nontraditionalbanking activities.
We provide policy implications in two crucial aspects. First, the macroprudential
literature mainly focuses on its beneficial effect—the effectiveness in reducing the systemicrisks. In comparison, this study provides evidence on its costs, i.e., potential adverse influences
on financial inclusion. This information is crucial for financial regulators, given that financial
inclusion is the top priority policy agenda of many countries. Second, our results show that
MP leads to asymmetric effects in developed and developing countries, highlighting the factthat the problems of financial inclusion in different countries are not identical. The rest of thisstudy is organized as follow. Section 2 presents selective literature review on financial
inclusion and MP. Section 3 provides details on the data and explains the econometricspecification. Section 4 presents the empirical findings of the cases of developing countries,
including the mobile banking, and developed countries. The last section provides conclusionand discussion.

4.2 Related Literature
Several studies have shown that MPs reduce excessive credit growth for both intensive andextensive margins (Alam et al., 2019; Cerutti et al., 2017; Bruno et al., 2017; Lim et al., 2011),
which prevents borrowers from accessing and using financial services. This is achieved bypolicies using two different approaches—the credit demand side (borrowers) and the supply
side (financial intermediaries) of the credit market. The policies targeting the borrowers’ side

79

include limits on the LTV ratio and the debt-service-to-income ratio (DSTI). Several empiricalstudies have provided evidence of their negative influences on housing prices and mortgage
demand. Kuttner and Shim (2016) show that a reduction of the DSTI has significant negative
effects on housing credit, with a typical tightening action lowering the real credit growth rateby 4–6 percentage points. Allen et al. (2020) use a calibrated model based on the Canadiandataset to evaluate policy effects. The simulation results indicate that a tightening of the loanto-value ratio (from 100% to 95%) has a significant negative impact on mortgage demand(8.2%), the number of first-time house buyers (7.9%), and number of households qualified formortgages (51.5%). Other important studies include Richter et al. (2019) and Morgan et al.
(2019), which provide similar evidence.
The policies on the supply side include restrictions on the capital requirement ratio andrestrictions on credit supply. The effects of the capital requirement are less clear. Gambacortaand Murcia (2020) used loan-level data for five Latin American countries (Argentina, Brazil,
Colombia, Mexico, and Peru) to evaluate the effects of various types of MP. They conclude
that policies aimed at curbing the cycle and increasing capital requirements are both effective
in reducing credit growth. Alternatively, Basten (2020) find that countercyclical capital
buffers (CCBs) have a larger impact on the composition of credit suppliers; but the influences
on market-wide mortgage growth is not significant. Fang et al. (2018) find that an increase incapital requirements lead to a short-term, negative impact on bank credit in Peru, but this effectbecomes statistically insignificant after about six months. Stringent capital requirements as apart of MP deteriorates the banking sector’s performance, which may lead to the fragility of
the financial system (Vinhado & Divino, 2019).
Imposing MPs that aim to constrain excessive credit growth, targeting either the supply
or demand side of the credit markets, would unavoidably affect the credit disbursements offinancial institutions. One possible way to deal with restrictive policies is for banks to change

80

their management strategies, including geographic and service coverage expansion. Goetz etal. (2016) examine the net effect of geographic expansion on bank holding companies
throughout US metropolitan areas and indicate that geographic expansion decreases bank risk.
Similarly, Levine et al. (2016) indicate that geographic expansion would provide banks withthe chance to diversify risk thereby lowering their funding costs. Geographical expansion
offers opportunities for banks to diversify their risks, while engaging in more nontraditionalbanking or noninterest generating activities. Saunders et al. (2020) claim that a higher
proportion of noninterest income is correlated with a bank’s higher profitability.
The literature on the relationship between MPs and financial inclusion is limited.
Sarma and Pais (2011) provide indirect evidence that a healthy banking system (represented
by a low capital asset ratio or low nonperforming assets) is associated with low ﬁnancial
inclusion. Since stringent financial policy may contribute to a healthy banking system, thisevidence implies a negative relationship between MP and financial inclusion. Anarfo et al.
(2020) examine the impact of financial policy on financial inclusion in sub-Saharan Africa
and find that stringent policies negatively impact access to financial services. Other studies
use a single country dataset to discuss this relationship. Using data from Kenya, Kodongo(2018) indicates that capital and liquidity MPs could harm financial inclusion. Yoshino andMorgan (2017) present descriptive evidence on financial policies and the degree of financial
inclusion in eight countries.

4.3 Methodology
This study examines how the adoption of MP relates to financial inclusion. Developed anddeveloping countries are separately investigated because the problems of financial inclusion
and the implementation of MP differ substantially between them. Our empirical analysis first
concentrates on the case of developing countries given that financial inclusion is a priority.

81

We use panel data from 71 developing countries from 2004 to 2015. We then change themeasure of financial inclusion from conventional indicators (geographical access to banking
outlets) to those based on mobile banking as this is widely acknowledged to be important indeveloping countries. We evaluate how MP relates to mobile money accounts as a measure offinancial inclusion, using panel data from 24 developing countries over the period 2009 to
2015.42 Lastly, we examine the case of developed countries, covering 44 countries over theperiod 2004 to 2015, which enables us to compare the roles of MP between developing anddeveloped countries. We classify developing and developed countries based on the WorldBank income classification.43

4.3.1 Empirical model
To examine the relationship between MPs and financial inclusion, we estimate the following
empirical model:
FIi,t = βMPi,t−1 + γXi,t−1 + λi + δt + εi,t ,where FIi,t is the indicator of financial inclusion, MPi,t is the indicator of MP, Xi,t is a set ofcontrol variables, and εi,t is the error term. The model includes the country fixed effects, λi ,and the year fixed effects, δt , to control country and year specific effects. The year fixed
effects are used to control the implicit common trend of the adaption of MPs. We use the oneyear lag of MP since it may take some time to realize the impacts of policy adoption. 44
Similarly, we also use the one-year lag of other control variables to mitigate some problemsof simultaneity. In this study, we estimate the empirical model for developing and developed
countries separately.

The sample in the analysis of the mobile banking in developing countries is reduced due to the data limitation.
The group of developing countries consist of three income groups—low, lower-middle, and upper-middle
income countries—while the group of developed countries consists of high-income countries.
44
This setting is common in related studies (Cerutti et al., 2017; Altunbas et al., 2018).
42
43

82

4.3.2 Financial inclusion
We employ eight indicators of financial inclusion for our dependent variables, following thework of Beck et al. (2007). Four indicators measure access to banking services: (i)demographic branch penetration—branches per 100,000 adults (Access 1), (ii) geographic
branch penetration—bank branches per 1,000 km2 (Access 2), (iii) demographic ATM
penetration—ATMs per 100,000 adults (Access 3), and (iv) geographic ATM penetration—
ATMs per 1,000 km2 (Access 4). The other four indicators measure the use of banking
services: (i) loan accounts per capita—loan accounts per 1,000 adults (Use 1), (ii) deposit
accounts per capita—deposit accounts per 1,000 adults (Use 2), (iii) number of borrowers per
capita—borrowers per 1,000 adults (Use 3), and (iv) deposits per capita— deposits per 1,000
adults (Use 4).45 The latter two indicators are reasonable measures for the banked population
as one person may have more than one account. All eight indicators of financial inclusion aretaken from the IMF’s Financial Access Survey database. Our choice of indicators is commonlyused by international organizations such as the World Bank, IMF, and G20.46 We apply thelog transformation to all indicators of financial inclusion as they are distributed with fat tails,
following the work of Beck et al. (2007).
The eight indicators of financial inclusion may not fully capture an important aspectof the recent development of mobile banking. The innovation of financial products, such as
mobile money or mobile banking, has grown rapidly over several decades.47 Several studies

The last two indicators were absent in the past (Beck et al., 2007; Sarma, 2008). Although the two indicators
are now available from the IMF’s Financial Access Survey (FAS), the number of observations is relatively
limited compared to the other six indicators.
46
There are several other indicators that measure the level of financial inclusion, e.g., policies holders withinsurance cooperation, number of debit/credit cards, disputes resolution, which are listed in the G20 Financial
Inclusion Indicators. Although the adoption of MP may affect other financial institutions, such as insurancecompanies, this study focuses on the indicators of financial inclusion that are primarily related to banking
activities.
47
Many studies suggest that mobile money has significantly contributed to poverty alleviation in developing
countries. For example, M-PESA, a mobile money service in Kenya, has lifted 194,000 household or around 2%of poor Kenyans out of poverty (Suri & Jack, 2016). Similarly, Blauw and Franses (2016) and Aker et al. (2016)claim that mobile money is positively associated with income levels.
45

83

have shown the essential role of mobile money or mobile banking in promoting financial
inclusion (Jack & Suri, 2011; Demirgüç-Kunt & Kapper, 2012; Mbiti & Weil, 2013;
Munyegera & Matsumoto, 2016; Suri & Jack, 2016; Lashitew et al., 2018; Kochar, 2011. In
developing countries, commercial banks tend to cover mainly urban or populated areas while
excluding a large portion of the population in rural areas due to the lack of infrastructure andhigh operational costs. Mobile money enables previously unbanked customers to accessvarious financial services, such as utilities and bill payments, money transfers, savings, andeven borrowing. This important role encourages financial intermediaries, including banks, toswitch their outreach from physical branches and ATMs to more cost-effective businessstrategies focusing on mobile banking. Thus, examining the effect of MP on the prevalence ofthe mobile banking should be required to discuss its effect on financial inclusion, particularly
in developing countries.
To complement to the eight indicators of financial inclusion, our study employs
alternative measures based on mobile banking, in the case of developing countries.
Specifically, we use four indicators of mobile banking, two of which capture access to banking
services: (i) mobile money agent outlets active per 1,000 km2 and (ii) mobile money agent
outlets active per 100,000 adults, and the other two indicators measure the use of banking
services, (iii) mobile money accounts active per 1,000 adults, and (iv) mobile moneytransactions number per 1,000 adults. The data on these mobile money accounts are also
sourced from the IMF Financial Access Survey.48

Although the mobile banking plays an important role in promoting financial inclusion, the country-level data
is very limited. We apply the log transformation to all indicators of mobile banking.
48

84

4.3.3 Macroprudential policy
We construct the measures of MP using the most recent integrated macroprudential policy(iMaPP) database constructed by Alam et al. (2019). 49 Although several studies haveconstructed databases of MPs,50 iMaPP provides the most comprehensive coverage in termsof the number of countries (134), the length of the period covered (January 1990 to December2016), and the types of policy tools (17). These 17 tools include countercyclical buffer (CCB),
capital conservation buffer (Conservation), capital requirement for banks (Capital), limit onleverage of banks (LVR), loan loss provision (LLP), limits on growth or the volume ofaggregate credit (LCG), loan restrictions (LoanR), limits on foreign currency lending (LFC),
limits of the loan-to-value ratio (LTV), limits on the debt-service-to-income ratio (DSTI),
taxes and levies applied to specified transactions (Tax), measures taken to mitigate systemic
liquidity (Liquidity), limits to the loan-to-deposit ratio (LTD), limits on net or gross open
foreign exchange positions (LFX), measures taken to mitigate risks from global and domestic
systemically important financial institutions (SIFI), reserve requirements (RR), and other MP
tools (OT). Table 1 presents the detailed explanations of each MP tool.

It integrates a wide range of previously constructed datasets (i) Lim et al. (2011), (ii) Lim et al. (2013), (iii)
Global MP Instruments (2013), (iv) Shim et al. (2013), (v) European Systemic Risk Board (ESRB) database, (vi)the Bank of International Settlement (BIS) and Financial Stability Board website and IMF official documents,and (vii) the IMF’s annual MP survey.
50
Shim et al. (2013) construct a database based primarily on official sources of publications and press releasesof the financial authorities over 60 countries during the period from January 1990 to June 2012. Cerutti et al.
(2017) employ a database of MPs on an annual basis over 119 countries during the period from 2000 to 2013.
Instead of referring to official publication sources, their method is based mainly on a survey project with various
questionnaires, the Global Macroprudential Policy Instruments (GMPI) survey, conducted by the International
Monetary Fund (IMF) during the period of 2013 to 2014.
49

85

Table 4-1. Definitions of macroprudential policy tools
Individual policy tools
1. CCB, A requirement for banks to maintain a countercyclical capital buffer. Implementations at
0% are not considered as a tightening in dummy-type indicators.
2. Conservation, Requirements for banks to maintain a capital conservation buffer, including theone established under Basel III.
3. Capital, Capital requirements for banks, which include risk weights, systemic risk buffers, andminimum capital requirements. Countercyclical capital buffers and capital conservation buffers
are captured in their sheets respectively and thus not included here. Subcategories of capital
measures are also provided, classifying them into household sector targeted (HH), corporate
sector targeted (Corp), broad-based (Gen), and FX-loan targeted (FX) measures.
4. LVR, A limit on leverage of banks, calculated by dividing a measure of capital by the bank’s
non-risk-weighted exposures (e.g., Basel III leverage ratio).
5. LLP, Loan loss provision requirements for macroprudential purposes, which include dynamic
provisioning and sectoral provisions (e.g. housing loans).
6. LCG, Limits on growth or the volume of aggregate credit, the household-sector credit, or the
corporate-sector credit by banks, and penalties for high credit growth. Subcategories of limits tocredit growth are also provided, classifying them into household sector targeted (HH), corporate
sector targeted (Corp), and broad-based (Gen) measures.
7. LoanR, Loan restrictions, that are more tailored than those captured in "LCG". They include loan
limits and prohibitions, which may be conditioned on loan characteristics (e.g., the maturity, thesize, the LTV ratio and the type of interest rate of loans), bank characteristics (e.g., mortgage
banks), and other factors. Subcategories of loan restrictions are also provided, classifying theminto household sector targeted (HH), and corporate sector targeted (Corp) measures. Restrictions
on foreign currency lending are captured in "LFC".
8. LFC, Limits on foreign currency (FC) lending, and rules or recommendations on FC loans.
9. LTV, Limit on the loan to value ratio including those mostly targeted at housing loans, but also
includes those targeted at automobile loans, and commercial real estate loans.
10. DSTI, Limit on the debt service to income ratio and the loan-to-income ratio, which restrict thesize of debt services or debt relative to income. They include those targeted at housing loans,consumer loans, and commercial real estate loans.
11. Tax, Taxes and levies applied to specified transactions, assets, or liabilities, which include stamp
duties, and capital gain taxes.
12. Liquidity, Measures taken to mitigate systemic liquidity and funding risks, including minimum
requirements for liquidity coverage ratios, liquid asset ratios, net stable funding ratios, corefunding ratios and external debt restrictions that do not distinguish currencies.
13. LTD, Limits to the loan-to-deposit (LTD) ratio and penalties for high LTD ratios.
14. LFX, Limits on net or gross open foreign exchange (FX) positions, limits on FX exposures and
FX funding, and currency mismatch policies.
15. SIFI, Measures taken to mitigate risks from global and domestic systemically important financial
institutions (SIFIs), which includes capital and liquidity surcharges.
16. RR, Reserve requirements (domestic or foreign currency) for macroprudential purposes. Please
note that this category may currently include those for monetary policy as distinguishing thosefor macroprudential or monetary policy purposes is often not clear-cut. A subcategory of reserve
requirements is provided for those differentiated by currency (FCD), as they are typically used
for macroprudential purposes.
17. OT, Macroprudential measures not captured in the above categories—e.g., stress testing,restrictions on profit distribution, and structural measures (e.g., limits on exposures between
financial institutions).
Notes: (1) The definition of individual instrument used in our analyses are based mainly on iMaPP constructed by Alam etal. (2019). (2) Each MP indicator is normalized by dividing it by the corresponding number of policy instruments.

86

In the iMaPP database, similar to other databases, each tool in a given year is recorded
as dummy-type tightening and loosening indices. The index takes the value of 1 when the tool
is being tightened, −1 when the tool is being loosened, and zero otherwise. Using monthly
indicators, we construct the panel data of the state of each MP tool for each country in eachyear. Specifically, the state of the MP tool i of country j in year t is described by
MPijt = MPijt−1 + ∆MPijt

where ∆MPijt is the indicator of annual policy changes of the MP tool i from year t − 1 to yeart, and MPijt−1 is the indicator of the state of the MP tool i in the previous year t − 1. We set
the initial value of the indicator representing the state of the MP tool in the initial year 1990
as zero to construct the state of each MP tool for all years.51 We use the state of policy insteadof the change of policy as the independent variable so that its value is not measured as thechange in policy across a period for a given country—we implicitly take into account the levelof MP in each country. The estimated coefficient of MP, β, in the empirical model can beinterpreted as the influence of tightening (or loosening) one unit of the measure of a state of
MP on the level of financial inclusion.
One main objective of this study is to explore the effectiveness of different groups of
MP tools for a similar target. We follow the literature and construct several MP instrument
measures using a total of 17 MP tools.52 The details are presented in Table 2. The overall MP
index (MPI) is the sum of the 17 states of the MP tools. Based on the policy targets of thedemand or supply side of credit markets, we categorize these tools into two groups: borrower-

One may be concerned about the initial value (annual indicators of the state of the MP instruments), since ourdataset is constructed from the indicators representing annual policy changes. However, this issue can be solved
by applying the fixed effects estimation in our analysis.
52
In this study, “tool” refers to each of 17 individual MP tools, while “instrument” means a group consisting ofseveral MP tools that share the same target or purpose.
51

87

targeted MP (BORROWER) and financial institution-targeted MP (FINANCIAL)
instruments.53 The measure of BORROWER is the sum of two states of MP tools: LTV and
DSTI, while the measure of FINANCIAL is the sum of the other 15 states of MP tools. We
then divide the financial institution-targeted MP (FINANCIAL) instrument into two new
subgroups based on policy purpose: (i) capital-targeted MP instruments (CAPITAL), focusing
on the restrictions on bank capital and (ii) loan supply-targeted MP instruments (LOANSUPPLY) imposing direct restrictions on the loans of financial institutions.54
Table 4-2. Groups of macroprudential policy (MP) instruments
Macroprudential policy indicators
Overall MP instruments
MPI
All 17 policy instruments
Borrower-targeted MP instruments
BORROWER
LTV and DSTI
Financial institutions- targeted MP instruments
FINANCIAL
15 instruments beside LTV and DSTICapital-targeted MP instruments
CAPITAL
CCB, Conservation, Capital and LVR
Loans supply-targeted MP instruments
LOAN-SUPPLYLCG, LLP, LoanR, LFC and LTD
Notes: The group and subgroup of MP classification used in our analyses are based mainly on iMaPP and Cerutti et al. (2017).
MP indicators (MPI, BORROWER, FINANCIAL, CAPITAL, and LOAN-SUPPLY) are constructed by using the data of
iMaPP.

4.3.4 Other control variables
Our choice of control variables reflects the relationship between the level of financial inclusion
and macroeconomic conditions in past studies. Our study includes real GDP and real per capita
GDP to capture the size of the economy and the income level of the country, respectively.
Lashitew et al. (2019) state that GDP growth drives greater financial inclusion. Numerous
studies have discussed the relationship between real per capita GDP and ﬁnancial inclusion,indicating that a higher level of real per capita GDP is associated with greater financial
inclusion (Lashitew et al., 2019; Grohmann et al., 2018; Owen & Pereira, 2018; Kumar et al.,2018; Chakravarty & Pal, 2013; Sarma & Pais, 2011; Beck et al., 2007). Following past works
such as Allen et al. (2016), Kumar et al. (2018), and Sarma and Pais (2011), which claim that
The classification of MP instruments into BORROWER and FINANCIAL basically follows the categories in
Cerutti et al. (2017).
54
The detailed classification of MP instruments generally follows the work of Alam et al. (2019).
53

88

unemployment rate plays an important role for financial inclusion, we also incoporate theunemployment rate in the model. In addition, we include the bank cost-to-income ratio
variable to capture the association of banking activities with the level of financial inclusion.
A higher cost of financial intermediation is associated with low financial inclusion (Allen etal., 2016; Nurbekyan & Hovanessian, 2018).
We include secondary and tertiary education levels in the model. Higher education isassociated with higher financial inclusion (Grohmann et al., 2018; Allen et al., 2016; Zins &Weill, 2016; Fungáčová & Weill, 2014). Following studies on the role of institutional quality
and political risk in promoting financial inclusion (Lashitew et al., 2019; Kabakova &Plaksenkov, 2018; Allen et al., 2016), including Allen et al. (2016) who show that a morestable political environment encourages greater financial inclusion, we also incorporate
political stability into the model. Table 3 provides a brief description of the definitions andsources of all variables used in our analysis; Table 4 is the list of 71 developing and 24
developed countries in the empirical model; and Table 5 presents a summary of the statistics
for all variables used in our study.

89

Table 4-3. Data description and sources of variables
Variable
Dependent variable

Definition

SourceDemographic branchpenetration
Geographic branchpenetration
Demographic ATM
penetration
Geographic ATM
penetration
Loan account per capita

Number of branches per 100,000 adults

Deposit account percapitaBorrower per capita

Number of deposit account per 1,000 adults

Depositor per capita

Number of depositors per 1,000 adults

Financial Access SurveyIMF
Financial Access SurveyIMF
Financial Access SurveyIMF
Financial Access SurveyIMF
Financial Access SurveyIMF
Financial Access SurveyIMF
Financial Access SurveyIMF
Financial Access SurveyIMF

Independent variable

Number of branches per 1,000 km2
Number of ATMs per 100,000 adults
Number of ATMs per 1,000 km2
Number of loan account per 1,000 adults

Number of borrowers per 1,000 adults

MPI

Overall MP instruments

BORROWER

Borrower-targeted MP instruments

FINANCIAL

Financial institutions- targeted MP instruments

CAPITAL

Capital-targeted MP instruments

LOAN-SUPPLY

Loans supply-targeted MP instruments

Real GDP per capita

Log of real GDP per capita

Real GDP
Unemployment

Log of real GDP
Unemployment refers to the share of the labor force that iswithout work but available for and seeking employment.
Unemployment, total (% of total labor force) (modeled ILO
estimate).
Operating expenses of a bank as a share of sum of netinterest revenue and other operating income.

Bank cost to income
ratio
Secondary school
education

Tertiary school
education

Political stability

Gross enrollment ratio is the ratio of total enrollment,
regardless of age, to the population of the age group that
officially corresponds to the level of education shown.
Secondary education completes the provision of basic
education that began at the primary level and aims at laying
the foundations for lifelong learning and humandevelopment, by offering more subject- or skill-oriented
instruction using more specialized teachers.
Gross enrollment ratio is the ratio of total enrollment,
regardless of age, to the population of the age group that
officially corresponds to the level of education shown.
Tertiary education, whether or not to an advanced research
qualification, normally requires, as a minimum condition ofadmission, the successful completion of education at thesecondary level.
Political Stability and Absence of Violence/Terrorism
measures perceptions of the likelihood of politicalinstability and/or politically motivated violence, including
terrorism.

Authors’ calculation basedon Alam et al. (2019)
Authors’ calculation basedon Alam et al. (2019)
Authors’ calculation basedon Alam et al. (2019)
Authors’ calculation basedon Alam et al. (2019)
Authors’ calculation basedon Alam et al. (2019)
World Bank’s Global
Financial Development
Database (2018)
Penn World Table9
The World Bank Data

World Bank’s Global
Financial Development
Database (2018)
World Bank Data

World Bank Data

Worldwide GovernanceIndicators

90

Table 4-4. List of developing and developed countries
Developing countries
Albania
Algeria
AngolaArmenia
Bangladesh
BelarusBenin
BhutanBotswana
BrazilBulgaria
Burkina Faso
BurundiCabo VerdeCambodia
China
Colombia
Congo, Dem. Rep.
Costa Rica
Côte d'Ivoire
Dominican Republic
EcuadorEl Salvador
Ethiopia
Gambia, The

Georgia
Ghana
Honduras
India
Indonesia
Jamaica
JordanKazakhstan
Kenya
Kyrgyz Republic
Lao PDR
LebanonLesothoMacedonia, FYR
Malaysia
Mali
Mauritania
Mauritius
MexicoMoldova
Mongolia
Montenegro
MoroccoMozambique
Nepal

Niger
Nigeria
Pakistan
Paraguay
Peru
Philippines
Romania
Russian Federation
SenegalSerbiaSouth Africa
Sri Lanka
Sudan
Tajikistan
Thailand
Togo
Tunisia
TurkeyUgandaUkraine
Yemen, Rep.

Developed countries
Argentina
Austria
Belgium
Brunei Darussalam
CanadaChile
Croatia
CyprusCzech Republic
Denmark
Estonia
Finland
Korea, Rep.
KuwaitLatviaLithuania
Luxembourg
Malta
Netherlands
New Zealand
NorwayOman
PolandPortugal

91

Table 4-5. Descriptive of Statistics
Variables
Access to financial service indicators
Demographic branch penetration
Geographic branch penetration
Demographic ATM penetration
Geographic ATM penetration
Use of financial service indicators
Loan account per capita
Deposit account per capita
Borrower per capita
Depositor per capita
Indicator of Macroprudential policy
instruments
Overall MP instruments
Financial institutions-targeted MP
instruments
Borrowers-targeted MP instruments
Capital-targeted MP instruments
Loan supply-targeted MP instruments
Control variables
Real GDP
Real GDP per capita
Unemployment
Bank cost to income ratio
Secondary school education
Tertiary school education
Political stability

No of Observations

Mean

Std.Dev.

Min

Max

549
549
518
518

2.168
1.413
2.632
1.987

1.075
1.835
1.597
2.041

-.908
-3.848-4.425-5.173

4.524
4.717
5.222
5.173

260
358
296
288

5.203
6.426
4.408
5.665

1.259
1.208
1.450
1.387

.351
2.476
-.676
1.581

7.741
8.453
6.771
8.126

549
549

3.122
2.652

5.505
4.71

-9
-9

53
42

549
549
549

.47
.548
.696

1.17
1.036
1.636

-1
0
-3

11
6
12

549
549
549
549
549
549
549

11.33
7.764
8.844
56.61769.85628.445
11.33

1.859
1.051
6.774
12.39526.07420.741
1.859

7.885
5.268
.32
23.377
7.228
.898
7.885

16.5829.496
37.25
103.786120.328
92.512
16.582

4.4 Empirical results
This section discusses the results of the relationship between MP instruments and financial
inclusion of each subsample for developing and developed countries. Table 6 summarizes themain results of our estimations.
Table 4-6. Macroprudential policy and financial inclusion
Developing countries
Access to
Use ofbankingbankingServices

services

Mobile banking

Developed countries
Access to
Use ofbankingbanking

Access to
Use ofservices
servicesbankingbanking
services
services
MPI
–
–
–
–
Positive–
BORROWER–
Negative
Negative
Negative–
Negative
FINANCIAL––––
Positive–
CAPITAL––––
Positive–
LOAN-SUPPLY –Negative––
Positive
Negative
Notes: “Negative” (“positive”) implies the significantly negative (positive) relationship between MP instrument
and financial inclusion, and “–“ implies no significant relationship between them.

92

4.1 Developing countries
Table 7 shows the estimated results of overall MP (MPI) for access to (columns 1–4) and useof (columns 5–8) banking services, and Table 8 shows the estimated results of BORROWER
and FINANCIAL. First, we do not find any significant relationship between the aggregate
measure of MP (MPI) and financial inclusion. Second, Table 8 presents that the coefficientsof BORROWER are significantly negative for the use of banking services, but not significant
for access to banking services. This finding suggests that demand side MP discourage people’s
use of banking services. For instance, with a lower maximum loan-to-value ratio, somepotential customers who do not have enough income and collateral may not be able to open
bank accounts to obtain loans, including mortgage loans. The primary objective of borrowertargeted MP is to prevent rapid credit expansion from the demand side of the credit market
and hence to reduce the systemic risk and the accumulation of the vulnerabilities of thefinancial system. This favorable effect is generally substantial in developing countries(Certutti et al., 2017; Ayyagari et al., 2017; Fendoğlu, 2017; Alam et al., 2019). Our results
show the presence of the costs of MP. Borrower-targeted MPs would bring about a negativeside effect on the use of banking services, that is, the reduction of systemic risk at the expenseof financial inclusion in developing countries.

93

Table 4-7. Overall measure of MP (developing countries)

MPI
Real GDP
Real GDP per capita
Unemployment
Bank cost to income ratio
Secondary school education
Tertiary school education
Political stability
Observations
Number of countries
R-squared

Access to the banking services
Access 1
Access 2
0.0028-0.0008(0.0085)(0.0087)0.24970.3957**(0.1524)(0.1524)
0.25840.0176(0.2948)(0.3016)-0.0045-0.0060(0.0097)(0.0100)-0.0047**-0.0057***(0.0019)(0.0019)0.0185***0.0197***(0.0049)(0.0054)-0.0085*-0.0097*(0.0044)(0.0049)
0.08690.0598(0.0613)(0.0654)
549
549
71
71
0.45470.5429

Access 3-0.0026(0.0117)0.9143***(0.3388)2.0603**(0.8879)
0.0035(0.0122)0.0062*(0.0036)0.0201**(0.0098)-0.0183***(0.0059)-0.0310(0.1040)518
70
0.7067

Access 4-0.0044(0.0115)
1.0243***(0.3527)
1.8954**(0.9317)0.0008(0.0122)0.0051(0.0037)0.0205**(0.0097)-0.0196***(0.0062)-0.0570(0.1077)518
70
0.7288Use of banking services
Use 1
Use 2
-0.0072-0.0020(0.0134)(0.0068)0.01420.3692(0.3677)(0.3347)
1.2943
1.1736**(0.7729)(0.4430)0.04020.0204(0.0245)(0.0137)-0.0035-0.0016(0.0034)(0.0026)
0.00460.0047(0.0060)(0.0048)-0.0115-0.0031(0.0135)(0.0055)
0.2280*-0.0400(0.1155)(0.0637)
260
358
37
48
0.49010.5705

Use 3-0.0338*(0.0181)0.3238(0.6203)
1.0954(0.8502)0.0232(0.0212)0.0001(0.0036)0.0069(0.0097)-0.0039(0.0052)
0.2917(0.1916)296
41
0.6063Use 4-0.0043(0.0043)0.5949(0.3538)0.7009*(0.4018)0.0157(0.0101)-0.0013(0.0034)
0.0135**(0.0055)-0.0143***(0.0053)-0.0275(0.0619)288
430.6004

Notes: Eight indicators of financial inclusion of which four indicators measure access to and four indicators measure use of financial services are the dependent variables usedin the regression. We estimated the regressions using the fixed effects estimation with robust standard errors in brackets.
*** p<0.01, ** p<0.05, * p<0.1

94

Table 4-8. Borrower- and financial institution-targeted MP (developing countries)
BORROWER
FINANCIALReal GDP
Real GDP per capita
Unemployment
Bank cost to income ratio
Secondary school education
Tertiary school education
Political stability
Observations
Number of countries
R-squared

BORROWER
Access 1
Access 2-0.0420-0.0394(0.0377)(0.0392)

Access 3-0.0544(0.0475)

Access 4-0.0493(0.0474)

Use 1-0.0734(0.0697)

Use 2-0.0705**(0.0270)

Use 3-0.1937***(0.0637)

Use 4-0.0406(0.0289)0.2578*(0.1500)
0.2755(0.2839)-0.0052(0.0092)-0.0045**(0.0019)0.0184***(0.0049)-0.0079*(0.0045)0.0876(0.0612)549
71
0.45860.9113***(0.3383)
2.0980**(0.8491)
0.0030(0.0123)
0.0062*(0.0036)0.0204**(0.0097)-0.0180***(0.0059)-0.0373(0.0995)518
70
0.70811.0237***(0.3522)1.9015**(0.8954)0.0005(0.0123)0.0051(0.0037)0.0208**(0.0097)-0.0194***(0.0062)-0.0611(0.1035)518
70
0.72970.0078(0.3645)
1.2255(0.7544)0.0400(0.0278)-0.0027(0.0031)0.0045(0.0058)-0.0112(0.0130)0.2102*(0.1233)260
370.49710.3729(0.3166)
1.2736***(0.4129)
0.0160(0.0133)-0.0014(0.0027)0.0051(0.0044)-0.0023(0.0056)-0.0620(0.0664)358
480.5846

0.2720(0.6095)
0.7798(0.7776)
0.0257(0.0214)0.0016(0.0033)
0.0071(0.0090)-0.0047(0.0046)
0.2791(0.1866)296
41
0.62210.5969*(0.3479)
0.7338*(0.3908)0.0151(0.0103)-0.0014(0.0034)
0.0137**(0.0053)-0.0139**(0.0054)-0.0334(0.0643)288
430.60280.4029***(0.1502)0.0145(0.2858)-0.0064(0.0096)-0.0056***(0.0020)0.0197***(0.0054)-0.0094*(0.0050)0.0608(0.0649)549
71
0.5456

FINANCIAL
Access 1
Access 2

Access 3

Access 4

Use 1

Use 2

Use 3

Use 4

0.0050(0.0095)
0.2504(0.1520)0.2461(0.2958)-0.0043(0.0096)-0.0048**(0.0019)0.0185***(0.0049)-0.0086*(0.0043)0.0871(0.0611)549
71
0.4556-0.0007(0.0133)
0.9164***(0.3386)
2.0334**(0.8950)
0.0037(0.0122)0.0061*(0.0036)0.0200**(0.0098)-0.0184***(0.0059)-0.0293(0.1039)518
70
0.7067-0.0032(0.0130)
1.0260***(0.3526)
1.8736**(0.9383)0.0009(0.0121)0.0051(0.0037)0.0205**(0.0097)-0.0197***(0.0063)-0.0555(0.1077)518
70
0.7287-0.0038(0.0154)0.0155(0.3705)
1.2436(0.7706)
0.0417*(0.0246)-0.0036(0.0035)0.0046(0.0060)-0.0115(0.0135)0.2313*(0.1167)260
370.48890.0009(0.0086)
0.3742(0.3352)
1.1204**(0.4388)0.0207(0.0135)-0.0017(0.0026)
0.0047(0.0047)-0.0033(0.0055)-0.0379(0.0635)358
480.5703-0.0230(0.0212)0.3065(0.6496)
1.0513(0.8644)0.0247(0.0212)0.0003(0.0035)
0.0068(0.0098)-0.0047(0.0055)
0.3063(0.1928)296
41
0.5997-0.0037(0.0055)
0.5972*(0.3546)
0.6762*(0.4013)0.0156(0.0099)-0.0013(0.0034)
0.0134**(0.0055)-0.0145***(0.0053)-0.0253(0.0614)288
430.60000.0009(0.0099)
0.3957**(0.1522)0.0079(0.3053)-0.0058(0.0100)-0.0058***(0.0019)0.0197***(0.0054)-0.0098**(0.0049)
0.0599(0.0651)549
71
0.5429Notes: Eight indicators of financial inclusion of which four indicators measure access to and four indicators measure use of financial services are the dependent variables used in the regression. We estimated theregressions using the fixed effects estimation with robust standard errors in brackets.
*** p<0.01, ** p<0.05, * p<0.1

95
Third, for all indicators of access to and use of banking services, the coefficients of
FINANCIAL are insignificant. Financial institution-targeted MP are composed of several policy tools
with different objectives, so that each tool could have different effects on financial inclusion. Thus, we
extend our analysis to the efficacy of the two subgroups of FINANCIAL (CAPITAL and LOANSUPPLY). Table 9 reports the estimated results. The estimations show that CAPITAL fails to show aclear relationship with any indicators of financial inclusion. 55 Conversely, LOAN-SUPPLY is
negatively correlated with the use of banking services (loan accounts and the number of borrowers per
capita). Once banks’ loan supply activities are restricted by policies, such as limits on aggregate credit
volume, they become more cautious about the quality of loan disbursements. To comply with suchpoliciess, banks need to limit loan applications, which consequently deteriorates the use of banking
services. Accounting for the results of the negative relationship between the demand side MP
instruments (BORROWER) and the use of banking services, our analysis indicates that both demandand supply side loan-related MP instruments (BORROWER and LOAN-SUPPLY) hinder financial
inclusion, particularly the use of banking services in developing countries. Loan-related MPs incur
costs in the context of financial inclusion, although they mitigate rapid credit expansion and its
associated systemic risk (Aiyar et al., 2014; Cerutti et al., 2017; Fendoğlu, 2017; Richter et al., 2019;
Alam et al., 2019).

Anarfo et al. (2020) and Sarma and Pais (2011) show that capital adequacy ratio (CAR) is negatively associatedwith financial inclusion. There are some possible reasons for this difference. The previous studies use only single
tool, CAR, while our study considers a group of tools which share similar policy targets. In addition, we employ
different measures of financial inclusion indicators.
55

96

Table 4-9. Capital- and loan supply-targeted MP (developing countries)
CAPITAL
LOAN-SUPPLYReal GDP
Real GDP per capita
Unemployment
Bank cost to income ratio
Secondary school education
Tertiary school education
Political stability
Observations
Number of countries
R-squared

CAPITAL
Access 1
0.0012(0.0256)

Access 2-0.0138(0.0286)

Access 3-0.0514(0.0399)

Access 4-0.0642(0.0399)

Use 1-0.0151(0.0456)

Use 2
0.0175(0.0363)

Use 3-0.0799(0.0516)

Use 4-0.0221(0.0369)

0.2504(0.1541)0.2727(0.2831)-0.0047(0.0099)-0.0046**(0.0019)0.0184***(0.0049)-0.0084*(0.0045)0.0868(0.0625)549
71
0.45430.3923**(0.1531)0.0280(0.2848)-0.0068(0.0104)-0.0056***(0.0020)0.0198***(0.0053)-0.0096*(0.0050)
0.0580(0.0670)549
710.54360.9058**(0.3433)
2.0714**(0.8640)0.0008(0.0123)
0.0068*(0.0037)0.0204**(0.0097)-0.0176***(0.0056)-0.0398(0.1025)518
70
0.70871.0148***(0.3577)
1.8936**(0.9042)-0.0025(0.0123)0.0059(0.0038)
0.0210**(0.0096)-0.0187***(0.0059)-0.0670(0.1065)518
70
0.73150.0034(0.3799)1.2275(0.7525)
0.0415(0.0248)-0.0035(0.0036)0.0048(0.0063)-0.0118(0.0136)0.2262*(0.1130)260
370.4891

0.3828(0.3325)
1.1062***(0.4067)
0.0216(0.0132)-0.0018(0.0025)0.0047(0.0047)-0.0037(0.0054)-0.0348(0.0630)358
480.57140.2618(0.6550)
1.0176(0.8721)0.0253(0.0218)0.0007(0.0033)
0.0081(0.0102)-0.0050(0.0053)
0.2838(0.2028)296
41
0.60290.6029*(0.3519)0.6440*(0.3821)0.0145(0.0103)-0.0012(0.0034)
0.0134**(0.0053)-0.0138**(0.0053)-0.0251(0.0653)
288
43
0.601

LOAN-SUPPLY
Access 1
Access 2

Access 3

Access 4

Use 1

Use 2

Use 3

Use 4

-0.0064(0.0238)0.2537(0.1552)
0.2767(0.2916)-0.0051(0.0098)-0.0046**(0.0019)0.0184***(0.0049)-0.0083*(0.0044)0.0848(0.0603)549
71
0.45460.0456(0.0518)0.8948**(0.3436)
1.9189**(0.8786)
0.0055(0.0121)0.0065*(0.0037)0.0197**(0.0097)-0.0190***(0.0060)-0.0026(0.1087)518
70
0.71020.0443(0.0539)
1.0073***(0.3560)
1.7324*(0.9208)
0.0029(0.0119)0.0054(0.0038)0.0202**(0.0096)-0.0203***(0.0063)-0.0279(0.1122)518
70
0.7316-0.1248***(0.0235)0.1678(0.3182)
1.4423**(0.6294)0.0206(0.0228)-0.0046(0.0031)0.0072(0.0055)-0.0154(0.0118)0.1340(0.1005)260
370.57160.0044(0.0216)0.3709(0.3361)
1.1219**(0.4198)
0.0209(0.0133)-0.0017(0.0025)0.0046(0.0048)-0.0034(0.0054)-0.0355(0.0615)358
480.5704-0.1225***(0.0381)0.3943(0.5698)
0.9577(0.7538)
0.0211(0.0195)-0.0007(0.0038)
0.0066(0.0098)-0.0044(0.0048)0.2225(0.1805)296
41
0.6254-0.0066(0.0226)
0.6036*(0.3520)0.6379(0.3798)
0.0151(0.0099)-0.0013(0.0035)
0.0133**(0.0055)-0.0146***(0.0054)-0.0237(0.0634)288
43
0.5997-0.0057(0.0271)
0.3989**(0.1542)0.0155(0.2935)-0.0063(0.0101)-0.0058***(0.0020)0.0197***(0.0053)-0.0097*(0.0049)
0.0582(0.0635)549
71
0.5431

Notes: Eight indicators of financial inclusion of which four indicators measure access to and four indicators measure use of financial services are the dependent variables used in the regression. We estimated the regressions using the fixed effects
estimation with robust standard errors in brackets.
*** p<0.01, ** p<0.05, * p<0.1

97

To ensure the robustness of our baseline findings, we also conduct several sensitivity
analyses by (i) employing the system GMM estimator, (ii) estimating the relationship between
MPs and financial inclusion with a constructed multidimensional index of financial inclusion,and (iii) estimating the models with additional control variables.56 The results are generally
consistent with our baseline findings.

4.4.2 Mobile banking
Given that mobile banking plays a crucial role in developing countries, we extend our analysis
by using the four indicators of mobile banking as alternative measures of financial inclusion
in developing countries. 57 Tables 10 and 11 report the estimated results. The results are
generally in line with the results based on the conventional measures—BORROWER is
negatively associated with the use of banking services. Interestingly, BORROWER is also
negatively associated with access to banking services. This finding implies that as the MP
become more stringent, fewer loans are provided. Since more people are excluded from theuse of banking services, less money agents’ outlets are required. The results provide further
evidence that mobile banking has become important compared with traditional banks’
outreach, such as branches and ATMs, in developing countries.

See the appendix for the discussions and the results of the sensitivity analyses for developing countries (Tables
A1–A7).
57
Mobile banking becomes more important for both developing and developed countries. However, due to thedata limitation for developed countries, we can only estimate the relationship between MP and mobile banking
in developing countries. Thus, the relationship between all MP instruments and mobile banking in developed
countries is still an open discussion for future research.
56

98

Table 4-10. Mobile money agent outlets and MP in developing countries (access to banking services)
MPI
BORROWER
FINANCIALCAPITAL
LOAN-SUPPLY

Mobile money agent outlets: active per 1,000 km2-0.0556(0.0985)-1.4709***(0.4137)-0.0190(0.1164)-0.4324(0.3547)

Mobile money agent outlets: active per 100,000 adults-0.0538(0.0980)-1.4628***(0.4138)-0.0174(0.1159)-0.4262(0.3537)

0.51810.5186(0.3877)(0.3875)
Real GDP
10.9824***
11.2489***
11.1054***
11.0629***
10.5853***
10.8101***
11.0701***
10.9324***
10.8861***
10.4064***(3.4175)(3.1256)(3.4523)(3.2891)(3.3657)(3.3805)(3.0887)(3.4151)(3.2514)(3.3271)
Real GDP per capita
1.31582.60231.02480.95103.7241
1.4973
2.7874
1.20951.14493.9214(7.7802)(7.5821)(7.6599)(7.8754)(6.4935)(7.7542)(7.5671)(7.6330)(7.8510)(6.4728)
Unemployment
0.13470.14510.13150.15320.02700.13830.14860.13500.15660.0306(0.1405)(0.1367)(0.1433)(0.1378)(0.1514)(0.1403)(0.1364)(0.1430)(0.1375)(0.1507)
Observations
70
70
70
70
70
70
70
70
70
70
R-squared
0.74450.76770.74320.7492
0.78270.73120.75560.72990.73610.7719Number of countries
23
23
23
23
23
23
23
23
23
23
Notes: Eight indicators of financial inclusion of which two indicators measure access to the banking services are the dependent variables used in the regression. We estimated the regressions using the fixed effects
estimation with robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1

99

Table 4-11. Mobile money agent outlets and MP in developing countries (use of banking services)
MPI
BORROWER
FINANCIALCAPITAL
LOAN-SUPPLY

Mobile money accounts: active per 1,000 adults
0.1474(0.1266)-2.0208***(0.5611)0.1941(0.1316)-0.4208(0.6005)

Mobile money transactions: number per 1,000 adults-0.1905**(0.0816)-0.8446***(0.2180)-0.1869(0.1290)-0.7456(0.4482)

0.6563-0.0306(0.6369)(0.4417)
Real GDP
5.3703*4.51725.6081*4.4632
3.86194.8545*3.81045.1101*4.29185.0393(2.8812)(2.8324)(2.8511)(2.8622)(2.9688)(2.6647)(2.5265)(2.9540)(3.0471)(4.0600)
Real GDP per capita-4.7855-2.7728-4.9012-3.9605-1.2304
9.921610.9273*
9.33498.43148.3545(7.1402)(8.0180)(7.0942)(7.7403)(8.1149)(6.7175)(6.3568)(6.8270)(6.6670)(7.3375)
Unemployment
0.17450.18630.1756
0.19410.05990.3208*0.2930*0.3332*0.3892**
0.3488*(0.1922)(0.1890)(0.1911)(0.1890)(0.1409)(0.1706)(0.1605)(0.1736)(0.1771)(0.1750)
Observations
87
87
87
87
87
131
131
131
131
131
R-squared
0.53140.5526
0.53630.52830.56410.68630.70470.6752
0.67320.6571
Number of countries
24
24
24
24
24
32
32
32
32
32
Notes: Eight indicators of financial inclusion of which two indicators measure use of banking services are the dependent variables used in the regression. We estimated the regressions using the fixed effects estimation
with robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1

100

4.4.3 Developed countries
Financial inclusion or exclusion from financial services is a crucial agenda for developing countries,
but this issue is also a challenge for developed countries (Mylonidis et al., 2019; Sarma & Pais, 2011;
Simpson & Buckland, 2009; Carbo et al., 2007; Devlin, 2005; Kempson et al., 2004). The nature of
the MP effects are different, depending on their developmental stages. Thus, we extend our analysis todeveloped countries and compare our results to developing countries with those in developed countries.
Tables 12, 13, and 14 present the estimated results of the models using MPI, BORROWER,FINANCIAL, CAPITAL, and LOAN-SUPPLY in developed countries, which indicate similarities anddifferences in the effects of MPs on financial inclusion between the two country groups.58

See the appendix for the discussions and the results of the sensitivity analyses for developed countries(Tables A8–A14).
58

101

Table 4-12. Overall measure of MP (developed countries)

MPI
Real GDP
Real GDP per capita
Unemployment
Bank cost to income ratio
Secondary school education
Tertiary school education
Political stability
Observations
Number of countries
R-squared

Access to banking services
Access 1
Access 2
0.0137**0.0181***(0.0054)(0.0054)0.05070.3928***(0.1290)(0.1441)-0.2115-0.7712***(0.2142)(0.2365)-0.0162***-0.0224***(0.0054)(0.0056)-0.0003-0.0001(0.0007)(0.0007)-0.0024-0.0030(0.0019)(0.0020)0.0057***0.0073***(0.0021)(0.0022)0.03480.0134(0.0679)(0.0653)
437
437
44
44
0.46940.4693

Access 3
0.0104(0.0075)
0.5042***(0.1537)
0.2293(0.3540)-0.0088(0.0063)-0.0006(0.0008)
0.0025(0.0019)0.0032(0.0020)0.0523(0.0619)417
420.5603

Access 4
0.0144*(0.0072)
0.8377***(0.1714)-0.3290(0.3449)-0.0146**(0.0060)-0.0004(0.0008)0.0018(0.0020)0.0047**(0.0020)0.0414(0.0583)417
420.6667

Use of banking services
Use 1
Use 2
-0.0171-0.0003(0.0240)(0.0059)
0.48680.3322**(0.3436)(0.1258)
0.98330.8117***(0.8899)(0.2774)
0.00540.0022(0.0130)(0.0068)-0.0003-0.0009(0.0023)(0.0007)-0.00660.0011(0.0077)(0.0015)0.0046-0.0003(0.0039)(0.0021)0.1352-0.1226*(0.1342)(0.0653)
163
247
18
27
0.50810.5645

Use 3-0.0150(0.0097)
0.1650(0.1314)0.7148**(0.3181)0.0013(0.0087)-0.0033*(0.0017)-0.0032(0.0028)
0.0026(0.0028)
0.0524(0.0635)131
160.7163Use 4-0.0140*(0.0067)
0.0592(0.1025)1.2266***(0.2703)
0.0076(0.0097)-0.0004(0.0019)-0.0014(0.0037)
0.0087**(0.0031)-0.0386(0.0956)93
13
0.7842

Notes: Eight indicators of financial inclusion of which four indicators measure access to and four indicators measure use of financial services are the dependent variables used in the regression. We estimated the regressions using the fixed
effects estimation with robust standard errors in brackets.
*** p<0.01, ** p<0.05, * p<0.1

102Table 4-13. Borrower- and financial institution-targeted MP (developed countries)
BORROWER
FINANCIALReal GDP
Real GDP per capita
Unemployment
Bank cost to income ratio
Secondary school education
Tertiary school education
Political stability
Observations
Number of countries
R-squared

BORROWER
Access 1
Access 2
0.01400.0171(0.0133)(0.0121)

Access 3
0.0162(0.0156)

Access 4
0.0172(0.0140)

Use 1-0.0477*(0.0256)Use 2
0.0000(0.0116)

Use 3-0.0378(0.0395)

Use 4-0.0538**(0.0235)0.0848(0.1293)-0.1725(0.2235)-0.0175***(0.0054)-0.0002(0.0008)-0.0024(0.0020)0.0056**(0.0021)0.0488(0.0696)437
440.44410.5039***(0.1597)0.2753(0.3465)-0.0096(0.0062)-0.0005(0.0008)
0.0024(0.0019)0.0032(0.0020)0.0640(0.0618)417
420.55150.8347***(0.1799)-0.2590(0.3512)-0.0158**(0.0060)-0.0003(0.0009)
0.0016(0.0019)0.0046**(0.0020)0.0584(0.0569)417
420.64940.5052(0.3504)
0.8601(0.8946)
0.0037(0.0113)-0.0006(0.0024)-0.0069(0.0081)0.0037(0.0037)
0.1321(0.1313)163
180.51770.3312***(0.1108)
0.8110***(0.2761)
0.0022(0.0068)-0.0010(0.0007)0.0011(0.0014)-0.0003(0.0021)-0.1222*(0.0675)247
270.56450.1580(0.1364)0.6471*(0.3270)0.0013(0.0094)-0.0034*(0.0017)-0.0029(0.0030)
0.0022(0.0028)0.0672(0.0710)131
160.70250.0348(0.1351)
1.2421***(0.2838)0.0103(0.0111)-0.0008(0.0019)-0.0006(0.0039)0.0080**(0.0034)-0.0677(0.1094)93
130.77990.4362***(0.1412)-0.7172***(0.2560)-0.0242***(0.0058)
0.0001(0.0008)-0.0030(0.0021)0.0071***(0.0024)0.0316(0.0668)437
440.4251FINANCIAL
Access 1
Access 2

Access 3

Access 4

Use 1

Use 2

Use 3

Use 4

0.0184***(0.0064)
0.0234(0.1292)-0.1993(0.2072)-0.0165***(0.0051)-0.0002(0.0007)-0.0022(0.0019)0.0056***(0.0020)0.0269(0.0686)437
440.47150.0118(0.0094)
0.4994***(0.1530)
0.2349(0.3552)-0.0090(0.0065)-0.0005(0.0008)
0.0025(0.0020)0.0029(0.0020)0.0524(0.0638)417
420.55720.0178*(0.0094)
0.8316***(0.1696)-0.3292(0.3440)-0.0147**(0.0063)-0.0003(0.0008)0.0019(0.0020)0.0043**(0.0020)0.0396(0.0608)417
420.6659-0.0103(0.0294)
0.5048(0.3415)
1.0194(0.8919)0.0071(0.0129)-0.0002(0.0021)-0.0073(0.0078)0.0050(0.0039)0.1422(0.1370)163
180.4965-0.0004(0.0068)
0.3328**(0.1297)
0.8119***(0.2773)
0.0022(0.0067)-0.0010(0.0007)0.0011(0.0015)-0.0003(0.0020)-0.1227*(0.0653)247
270.5645-0.0160(0.0106)
0.1658(0.1348)0.7323**(0.3181)0.0019(0.0086)-0.0033*(0.0017)-0.0034(0.0027)
0.0028(0.0029)0.0501(0.0634)131
160.7136-0.0137(0.0079)
0.0703(0.0992)
1.2055***(0.2740)0.0079(0.0093)-0.0005(0.0020)-0.0012(0.0038)
0.0087**(0.0031)-0.0265(0.0931)93
130.77840.0247***(0.0071)
0.3557**(0.1478)-0.7564***(0.2270)-0.0228***(0.0053)0.0002(0.0007)-0.0026(0.0019)0.0072***(0.0021)0.0025(0.0658)
437
44
0.475

Notes: Eight indicators of financial inclusion of which four indicators measure access to and four indicators measure use of financial services are the dependent variables used in the regression. We estimated the regressions using the fixed effects
estimation with robust standard errors in brackets.
*** p<0.01, ** p<0.05, * p<0.1

Table 4-14. Capital- and loan supply-targeted MP (developed countries)
CAPITAL
LOAN-SUPPLYReal GDP
Real GDP per capita
Unemployment
Bank cost to income ratio
Secondary school
education
Tertiary school education
Political stability
Observations
Number of countries
R-squared

CAPITAL
Access 1
0.0300**(0.0117)

Access 2
0.0344***(0.0124)

Access 3
0.0089(0.0169)

Access 4
0.0137(0.0171)

Use 1
0.0116(0.0347)

Use 2-0.0143(0.0107)

Use 3-0.0253(0.0200)

Use 4-0.0269(0.0154)

LOAN-SUPPLY
Access 1
Access 2

Access 3

Access 4

Use 1

Use 2

Use 3

Use 4

0.0928(0.1057)
1.0760***(0.2749)
0.0074(0.0088)-0.0012(0.0020)0.0363**(0.0174)
0.0237(0.1285)-0.1841(0.2236)-0.0172***(0.0050)-0.0001(0.0007)0.0405**(0.0156)
0.3664**(0.1490)-0.7275***(0.2493)-0.0239***(0.0053)0.0003(0.0008)0.0527**(0.0235)
0.5212***(0.1409)0.1503(0.3140)-0.0086(0.0056)-0.0006(0.0008)0.0545**(0.0213)0.8524***(0.1640)-0.3879(0.3125)-0.0148***(0.0053)-0.0003(0.0008)-0.0945*(0.0453)
0.4673(0.3545)
1.0838(0.8495)
0.0064(0.0111)0.0002(0.0021)-0.0087(0.0158)
0.3517**(0.1375)
0.8345***(0.2810)0.0024(0.0069)-0.0009(0.0007)-0.1043***(0.0237)
0.0621(0.1155)
0.8170**(0.2788)
0.0023(0.0080)-0.0034**(0.0013)-0.0506**(0.0198)
0.0114(0.1054)
1.3206***(0.2801)
0.0111(0.0103)-0.0006(0.0018)

0.0454(0.1248)-0.1324(0.2313)-0.0182***(0.0053)0.0001(0.0007)0.3899***(0.1428)-0.6693**(0.2646)-0.0250***(0.0055)
0.0005(0.0008)

0.4925***(0.1598)
0.2970(0.3486)-0.0100(0.0068)-0.0003(0.0008)

0.8209***(0.1815)-0.2350(0.3567)-0.0162**(0.0067)-0.0001(0.0009)0.5079(0.3558)
1.0223(0.9425)
0.0075(0.0121)-0.0003(0.0022)0.3487***(0.1121)0.7411***(0.2319)0.0019(0.0061)-0.0010(0.0007)0.1603(0.1541)0.6595*(0.3613)0.0017(0.0091)-0.0037*(0.0018)-0.0019(0.0019)0.0050**(0.0020)0.0149(0.0687)437
440.4543-0.0023(0.0020)0.0064***(0.0022)-0.0075(0.0667)437
440.43690.0025(0.0019)0.0026(0.0020)0.0581(0.0600)417
42
0.54570.0018(0.0019)0.0038*(0.0020)0.0481(0.0570)417
420.6457-0.0081(0.0081)0.0048(0.0039)0.1450(0.1371)163
18
0.49390.0012(0.0014)0.0001(0.0018)-0.1200*(0.0627)247
270.5696-0.0038(0.0026)
0.0029(0.0031)0.0594(0.0624)131
160.7035-0.0016(0.0042)0.0085**(0.0031)0.0060(0.0875)93
130.7749-0.0021(0.0020)0.0061***(0.0022)0.0338(0.0661)437
440.4652-0.0026(0.0020)0.0077***(0.0025)0.0145(0.0638)437
440.44890.0028(0.0019)0.0036*(0.0019)0.0419(0.0592)417
420.59390.0020(0.0019)0.0049**(0.0020)0.0356(0.0565)417
420.6843-0.0087(0.0076)0.0040(0.0040)0.1423(0.1474)163
18
0.52090.0010(0.0014)-0.0005(0.0021)-0.1272**(0.0615)247
270.5663-0.0040(0.0026)0.0014(0.0025)
0.0122(0.0653)131
160.7445-0.0013(0.0037)
0.0061*(0.0030)-0.0685(0.0876)93
130.7809Notes: Eight indicators of financial inclusion of which four indicators measure access to and four indicators measure use of financial services are the dependent variables used in the regression. We estimated the regressions using the fixed effects
estimation with robust standard errors in brackets.
*** p<0.01, ** p<0.05, * p<0.1

103Similarly, the use of banking services is negatively associated with BORROWER and LOANSUPPLY, but it has an insignificant relationship with FINANCIAL and CAPITAL. As in developing
countries, loan-related MP limit the use of banking services, so that they achieve the goal of mitigating
systemic risk at the expense of financial inclusion, particularly the use of banking services, indeveloped countries. In contrast to the findings in developing countries, access to banking services ispositively correlated with FINANCIAL and its two subsets, CAPITAL and LOAN-SUPPLY, indeveloped countries, while it has insignificant relationships with any type of MP instrument indeveloping countries—financial institution-targeted MPs promote access to banking services indeveloped countries, but not in developing countries.
Since FINANCIAL corresponds to MP targeting the supply side of credit markets, they restrict
the credit expansion of financial institutions. To comply with the restrictive effects of MP, banks may
choose to promote coverage to seek other sources of income. By expanding the service coverage andestablishing more branches and ATMs, banks can mobilize more deposits and diversify their activities
to nontraditional banking activities or noninterest income-based fees and services. 59 These results

support the discussions in several studies on banks’ income diversification and the increasing
trend toward their noninterest income activities, such as investment banking, venture capital,
security brokerage, insurance underwriting, and asset securitization (Allen & Santomero,
2001; Elsas et al., 2010; Demirgüç-Kunt & Huizinga, 2010; Goddard et al., 2013; Saunders etal., 2020).60 The increasing trend of banks’ income diversification overlaps with their adoptionof MP in our sample, which could justify our findings that banks attempt to look for noninterest
income once they are constrained by the adoption of MP, and particularly financial institution-

An article published by The Financial Brand in 2018 state that even the giant financial institutions like Bankof America and JP Morgan Chase, which have rapidly expanded their non-branch channels, still realize that they
will not effectively grow without the appropriate establishment of physical branches. It also emphasizes that JP
Morgan Chase and Bank of America plan to build 400 to 500 new branches across states and nationwide toprovide financial services in new domestic markets.
59

The proportion of service charges, including overdraft fee, ATM fees, and maintenance charges to total
noninterest income has significantly increased from 14 % in 2001 to 25 % in 2018 (Haubrich & Young, 2019).
They also find that banks have increased their income from services charges to compensate for lost interest
income.
60

104targeted MP. This management strategy is only possible in developed countries that generally
enjoy a mature financial system, but not in developing countries.61

4.4.4 Other control variables
Concerning other control variables, our analysis shows some findings, although someestimated results are less clear depending on the model specifications. For developing
countries, access to banking services is positively associated with the size of the economy(measured by real GDP), income level (measured by real GDP per capita), and secondary
school education, but it is negatively associated with the bank cost-to-income ratio and tertairy
school education. In addition, the use of banking services is positively correlated with income
level (measured by real GDP per capita). For developed countries, access to banking services
is positively associated with real GDP and tertiary school education and is negatively
associated with the unemployment rate. In addition, the use of banking services is positively
associated with real GDP per capita.

4.5 Conclusion
This study explored how MP relates to financial inclusion. Our contention is that MP
influences financial inclusion through two channels: (1) credit contracts and (2) cost expansion.
The former happens because the more prudential policies tend to restrict banks’ credit supply
and reduce the number of qualifying borrowers. As a result, less people are able to accessbanks’ (credit) services. As for the second channel, a more stringent MP requires banks to
prepare more capital and implicitly impose the costs of risky capital. These increase banks’operational costs, in response to which banks may broaden their scope of service and acquire

The results of the sensitivity analysis for developed countries in this study is shown in Appendix. In general,
the results confirm with our baseline findings.
61

105

revenues from noninterest income activities. This requires banks’ geographical expansion,
which leads to an increased access to banking services.
Our empirical findings are generally consistent with our argument. The conclusion,
however, is not identical for developing and developed countries. In the case of developing
countries, some instruments of MP such as those targeting borrowers (BORRROWER) andthose targeting financial institutions related to loan supply (LOAN-SUPPLY)) are negatively
related to the use of banking services. This result remains robust when we change the measureusing the indicators of mobile banking, which plays a more important role in most developing
countries. In the case of developed countries, these instruments (BORRROWER and LOANSUPPLY) are still negatively related to the use of banking services. However, instruments
targeting financial institutions are positively associated with access to banking services. Thisimplies that the channel of cost-expansion only holds for developed countries. This conclusion
is not counter-intuitive because the bank’s geographic expansion depends on the country’s
degree of financial development.
Our results remain robust after being tested by various methods, including (i) applying
the system GMM estimator, (ii) replacing the index of financial inclusion with a constructed
multidimensional index, and (iii) incorporating more controls (including bank concentration).
Thus, we have concluded that stringent MP harms financial inclusion in developing countries,
perhaps by preventing people from accessing the credit markets, although MP might bringabout beneficial effects in developed countries. Given that in recent years MP has been
regarded as a new policy tool towards financial stability, this study contributes by warning
policymakers of the cons of these favorable policies.
We posit two areas affecting policy. First, the macroprudential literature mainly
focuses on its beneficial effect— its effectiveness in reducing systemic risks. In comparison,
this study provides evidence of its costs, that is, potential adverse influences on financial

106

inclusion. This information is crucial for financial regulators, given that financial inclusion is
the top priority policy agenda of many countries. Second, our results show that MP leads toasymmetric effects in developed and developing countries, highlighting the fact that the
problems of financial inclusion in different countries are not identical.

References
Ahamed, M. M., Mallick, S. K., 2017. Is financial inclusion good for bank stability?
International evidence. Journal of Economic Behavior and Organization 157, 403-427.
Aiyar, S., Calomiris, C. W., Wieladek, T., 2014. Does macro-prudential regulation leak?
Evidence from a UK policy experiment. Journal of Money, Credit and Banking, 46 (S1),
181–214.
Aker. J. C., Boumnijel, R., McClelland, A., Tierney, N., 2016. Payment mechanisms andantipoverty programs: evidence from a mobile money cash transfer experiment in Niger.
Economic Dvelopment and Cultural Change 65 (1), 1-37.
Akinci, O., Olmstead-Rumsey, J., 2018. How effective are macroprudential policies? An
empirical investigation. Journal of Financial Intermediation 33, 33-57.
Alam, Z., Alter, A., Eiseman, J., Gelos, G., Kang, H., Narita, M., Nier, E., Wang, N., 2019.
Digging Deeper--Evidence on the Effects of Macroprudential Policies from a New
Database. IMF Working Papers 19/66.
Allen, F., Demirgüç-Kunt, A., Klapper, L., Martinez Peria, M. S., 2016. The foundations offinancial inclusion: Understanding ownership and use of formal accounts. Journal of
Financial Intermediation 27, 1–30.
Allen, F., Santomero, A. M., 2001. What do financial intermediaries do? Journal of Banking
and Finance 25, 271-294.
Allen, J., Grieder, T., Peterson, B., Roberts, T., 2020. The impact of macroprudential housingﬁnance tools in Canada. Journal of Financial Intermediation 42, 100761.
Altunbas, Y., Binici, M., Gambacorta, L., 2018. Macroprudential policy and bank risk. Journalof International Money and Finance 81, 203–220.

107

Anarfo, E. B., Abor, J. Y., Osei, K. A., 2020. Financial regulation and financial inclusion in
Sub-Saharan Africa: Does financial stability play a moderating role? Research inInternational Business and Finance 51, 101070.
Arellano, M., Bond, S., 1991. Some tests of specification for panel data: Monte Carlo evidence
and an application to employment equations. The review of economic studies, 58 (2),277-297
Arellano, M., Bover O., 1995. Another look at the instrumental variable estimation of errorcomponents models. Journal of Econometrics 1995, 68 (1), 29-51.
Ayyagari, M., Beck, T., Peria, M. S. M., 2017. Credit growth and macroprudential policies:
preliminary evidence on the firm level. Bank for International Settlement Working Paper
91.
Baltagi, B., 2008. Econometric analysis of panel data. John Wiley & Sons.
Basten, C., 2020. Higher Bank Capital Requirements and Mortgage Pricing: Evidence fromthe Counter-Cyclical Capital Buffer. Review of Finance, 24 (2), 453–495.
Beck, T., Demirguc-Kunt, A., Martinez-Peria, M. S., 2007. Reaching out: Access to and useof banking services across countries. Journal of Financial Economics, 85 (1), 234–266.
Beck, T., Levine, R., Levkov, A., 2010. Big bad banks? The winners and losers from bankderegulation in the United States. The Journal of Finance, 65 (5), 1637-1667
Blauw, S., Franses, P.H., 2016. Off the hook: measuring the impact of mobile telephones use
on economic development of households in Uganda using coplus. Journal of
Development Studies 52 (3), 315-330.
Bound, J., Jaeger, D. A., Baker, R. M., 1995. Problems with instrumental variables estimation
when the correlation between the instruments and the endogenous explanatory variable
is weak. Journal of the American statistical association, 90 (430), 443-450
Bruno, V., Shim, I., Shin, H. S., 2017. Comparative assessment of macroprudential policies.
Journal of Financial Stability, 28, 183–202.
Blundell, R., Bond, S., 1998. Initial conditions and moment restrictions in dynamic panel data
models. Journal of Econometric 1998, 87 (1), 115-43.
Carbo, S., Gardener, E.P., Molyneux, P., 2007. Financial exclusion in Europe. Public Money

108

and Management 27 (1), 21–27.
Carbo-Valverde, S., Rodriguez-Fernandez, F., Udell, G. F., 2009. Bank market power and
SME financing constraints. Review of Finance, 13 (2), 309-340.
Cattell, R. B., 1966. The Scree Plot Test for the Number of Factors. Multivariate Behavioral
Research 1 (2), 140-161.
Cerutti, E., Claessens, S., Laeven, L., 2017. The use and effectiveness of macroprudential
policies: New evidence. Journal of Financial Stability 28, 203–224.
Cetorelli, N., Gambera, M., 2001. Banking market structure, financial dependence and growth:International evidence from industry data. The Journal of Finance, 56 (2), 617-648
Chakravarty, S. R., Pal, R., 2013. Financial inclusion in India: An axiomatic approach. Journalof Policy Modeling 35 (5), 813–837.
Chauvet, L., Jacolin, L. (2017). Financial Inclusion, Bank Concentration, and Firm
Performance. World Development, 97, 1–13.
Claessens, S., Van Horen, N., 2014. Foreign banks: Trends and impact. Journal of Money,
Credit and Banking, 46 (s1), 295-326.
Clarke, G. R., Cull, R., Peria, M. S. M., 2006. Foreign bank participation and access to credit
across firms in developing countries. Journal of comparative economics, 34 (4), 774-795.
Cull, R., & Soledad Martinez Peria, M. (2010). Foreign bank participation in developing
countries: what do we know about the drivers and consequences of this phenomenon?
The World Bank Policy Research Working Paper 5398.
De Araujo, D. K. G., Barroso, J. B. R. B., Gonzalez, R. B., 2020. Loan-to-value policy andhousingfinance:

Effects

onconstrained

borrowers. JournalofFinancial

Intermediation, 42, 100830.
Demirgüç-Kunt, A., Huizinga, H., 2010. Bank activity and funding strategies: the impact onrisk and returns. Journal of Financial Economics 98, 626-650.
Demirgüç-Kunt, A., Klapper, L., 2012. Measuring financial inclusion: The global findex
database. The World Bank Working Paper No. 6025.
Detragiache, E., Tressel, T., Gupta, P., 2008. Foreign banks in poor countries: theory andevidence. The Journal of Finance, 63(5), 2123-2160.

109

Devlin, J.F., 2005. A detailed study of financial exclusion in the UK. Journal of Consumer
Policy 28, 75-108.
Elsas, R., Hackethal, A., Holzhaeuser, M., 2010. The anatomy of bank diversification. Journalof Banking and Finance 34, 1274-1287.
Fang, X., Jutrsa, D., Martinez Peria, M., Presbitero, A., Ratnovski, L., Vardy, F., 2018. The
Effects of Higher Bank Capital Requirements on Credit in Peru. IMF Working Papers,
18 (222), 1.
Fendoğlu, S., 2017. Credit cycles and capital flows: Effectiveness of the macroprudential
policy framework in emerging market economies. Journal of Banking and Finance, 79,
110–128.
Fungáčová, Z., Weill, L., 2014. Understanding financial inclusion in China. China Economic
Review 34, 196–206.
Gambacorta, L., Murcia, A., 2020. The impact of macroprudential policies in Latin America:
An empirical analysis using credit registry data. Journal of Financial Intermediation, 42,
100828.
Gilbert, P., Meijer, E., 2006. Money and credit factors. Bank of Canada. Working Paper
2006/3.
Goddard, J., Liu, H., Molyneux, P., Wilson, J., 2013., Do bank profits converge? European
Financial Management 19 (2), 345-365.
Goetz, M., Laeven, L., Levine, R., 2016. Does the geographic expansion of banks reduce risk?
Journal of Financial Economics 120, 346-362.
Gormley, T. A., 2010. The impact of foreign bank entry in emerging markets: Evidence from
India. Journal of Financial Intermediation, 19 (1), 26-51.
Grohmann, A., Klühs, T., Menkhoff, L., 2018. Does financial literacy improve financial
inclusion? Cross country evidence. World Development 111, 84–96.
Haubrich, J. G., Young, T., 2019. Trends in the Noninterest Income of Banks. Economic
Commentary, (2019-4).
Jack, W., Suri, T., 2011. Mobile Money: The Economics of M-PESA. National Bureau of
Economic Research Working Paper 16721.

110

Kabakova, O., Plaksenkov, E., 2018. Analysis of factors affecting financial inclusion:
Ecosystem view. Journal of Business Research 89, 198–205.
Kempson, E., Atkinson, A., Pilley, O., 2004. Policy level response to ﬁnancial exclusion indeveloped economies: lessons for developing countries. Report of Personal FinanceResearch Centre, University of Bristol.
Kim, J.O., Mueller, C.,W., 1978. Factor Analysis. Statistical Methods and Practical Issues.
Beverly Hills, CA: Sage.
Klomp, J., de Haan, J., 2009. Central bank independence and financial instability. Journal of
Financial Stability, 5 (4), 321–338.
Kochar, A., 2011. The distributive consequences of social banking: a micro-empirical analysisof the indian experience. Economic Development and Cultural Change 59 (2), 251-280.
Kodongo, O., 2018. Financial Regulations, Financial Literacy, and Financial Inclusion:
Insights from Kenya, Emerging Markets Finance and Trade 54 (12), 2851-2873.
Kumar, A., Pal, R., Pal, R., 2018. Usage of formal financial services in India: Demand barriers
or supply constraints? Economic Modelling 80, 244-259.
Lashitew, A. A., van Tulder, R., Liasse, Y., 2019. Mobile phones for financial inclusion: What
explains the diffusion of mobile money innovations? Research Policy 48 (5), 1201–1215.
Leon, F., 2015. Does bank competition alleviate credit constraints in developing countries?
Journal of Banking & Finance 57, 130–142.
Levine, R., Lin, C., Xie, W., 2016. Geographic diversification and banks' funding costs.
National Bureau of Economic Research Working Paper 22544.
Lim, C., Columba, F., Costa, A., Kongsamut, P., Otani, A., Saiyid, M., Wezel, T., Wu, X.,
2011. Macroprudential policy: What instruments and how to use them? IMF Working
Paper 11/238.
Lim, C. H., Krznar, I., Lipinsky, F., Otani, A., Wu, X., 2013. The Macroprudential
Framework: Policy Responsiveness and Institutional Arrangements. IMF Working
Papers 13/166.
Mbiti, I., Weil, D.N., 2013. The home economics of E-money: velocity, cash management,
and discount rates of M-Pesa users. American Economic Review 103 (3), 369-374.

111

Morgan, P. J., Regis, P. J., Salike, N. 2019. LTV policy as a macroprudential tool and its
effects on residential mortgage loans. Journal of Financial Intermediation 37, 89–103.
Munyegera, G. K., Matumoto, T., 2016. Mobile money, remittances, and household welfare:
panel evidence from rural Uganda. World Development 79, 127-137.
Mylonidis, N., Chletsos, M., Barbagianni, V., 2019. Financial exclusion in the USA: Looking
beyond demographics. Journal of Financial Stability 40, 144–158.
Nurbekyan, A., Hovanessian, N., 2018. Financial inclusion, regulation, financial literatcy, andfinancial education in Armenia. Asian Development Bank Institute Working Paper 843.
Osafo-Kwaako, P., Singer, M., White, O., Zouaoui, Y., 2018. Mobile money in emerging
markets: The business case for financial inclusion. McKinsey Global Institute, March.
Owen, A. L., Pereira, J. M., 2018. Bank concentration, competition, and financial inclusion.
Review of Development Finance 8 (1), 1–17.
Quinn, D., Schindler, M., Toyoda, A. M., 2011. Assessing measures of financial openness andintegration. IMF Economic Review, 59 (3), 488–522.
Richter, B., Schularick, M., Shim, I., 2019. The Costs of Macroprudential Policy. Journal ofInternational Economics 118, 263-282.
Sarma, M., 2008. Index of Financial Inclusion. Indian Council for Research on International
Economic Relations Working Paper 215.
Sarma, M., Pais, J., 2011. Financial Inclusion and Development. Journal of International
Development 23, 613–628.
Saunders, A., Schmid, M., Walter, I., 2020. Strategic scope and bank performance. Journal of
Financial Stability 46, 100715.
Shim, I., Bogdanova, B., Shek, J., Subelyte, A., 2013. Database for policy actions on housing
markets. Bank for International Settlements Quarterly Review 83-95.
Simpson, W., Buckland, J., 2009. Examining evidence of financial and credit exclusion in
Canada from 1999 to 2005, The Journal of Socio-Economics 38, 966-976.
Stock, J., Watson, M., 2002. Forecasting using principal components from a large number ofpredictors. Journal of the American Statistical Association 97 (460), 1167–1179.

112

Suri, T., Jack, W., 2016. The long-run poverty and gender impacts of mobile money. Science
354 (6317), 1288-1292.
Vinhado, F. D. S., Divino, J. A., 2019. Interactions between monetary and macroprudential
policies in the transmission of discretionary shocks. North American Journal of
Economics and Finance 50, 101020.
Wang, X., Han, L., Huang, X. 2020. Bank Competition, concentration and EU SME cost ofdebt. Internatinal Review of Financial Analysis 71, 101534.
Yoshino, N., Morgan, P. J., Trinh, L. Q., 2017. Financial literacy in Japan: Determinants andimpacts (No. 796). Asian Development Bank Institute Working Paper 796.
Zins, A., Weill, L., 2016. The determinants of financial inclusion in Africa. Review ofDevelopment Finance 6, 46-57.

Appendix
In our baseline analysis, we have examined the relationship between the adoption of MP
instruments and various indicators of financial inclusion, the access to and the use of banking
services, using the fixed effects estimation. This appendix first provides further analysis of therelationship by employing the system GMM estimator. Second, we construct a
multidimensional index of financial inclusion using factor analysis to ensure the sensitivity of
the results of our baseline model. Last, we add two more control variables related to thebanking system structure to ensure the robustness of our baseline findings.
A1. System GMM estimator
Dynamic panel data models include some lags of the dependent variable as covariates, so thatthe model allows for a partial adjustment mechanism, and they also contain unobserved panel
level fixed or random effects. By construction, the lagged dependent variables are correlatedwith the unobserved panel level effects. In addition, explanatory variables are not strictly

113

exogenous, so that they are correlated with past and possibly current realizations of the error.
Moreover, the model may contain heteroskedasticity and autocorrelation within individual
units’ errors, but not across them. In such cases, standard estimators become inconsistent.
Arellano and Bond (1991) develop a consistent GMM estimator for the model. However, theArellano-Bond estimator performs poorly if the autoregressive parameters or the ratio of thevariance of the panel level effect to the variance of idiosyncratic error are relatively large. Poor
instruments in the difference GMM estimator cause the inefficient and biased coefficientestimates (Arellano and Bond, 1991; Bound et al., 1995; Baltagi, 2008).
Following the work of Arellano and Bover (1995), Blundell and Bond (1998) develop
a system GMM estimator that includes additional moment conditions, which is designed forpanel data with many panels and short periods, under the assumption that there is no
autocorrelation in the idiosyncratic errors and the panel level effects are uncorrelated with thefirst difference of the first observation of the dependent variable. The system GMM estimator
combines the use of lagged levels of the series as instruments for the pre-determined andendogenous variables in equations in first differences and the use of lagged differences of thedependent variable as instruments for equations in levels. The system GMM estimator derives
more efficient results. Thus, this study employs the two-step system GMM estimator toestimate the empirical model.
Lacking valid instruments for the indicators of MP instruments, we cannot claim tohave fully resolved all endogeneity issues, but the system GMM estimator mitigates some ofthem since this methodology is suitable for the adjustment process of the dependent variablewith independent variables that are not strictly exogenous. Tables A1-A3 and A8-A10 present
the estimated results of the two-step system GMM estimators for the models with all MP
instruments in our study for developing and developed countries, respectively (due to data
limitation, we do not estimate the models with Use 4 for developed countries). When applying

114

the system GMM estimators, we need to confirm first- but not for second-order serial
correlation. As the tests for the specification for the absence of serial correlation, the AR(1)and AR(2) statistics show that in every model, the null hypothesis of no second-order serial
correlation cannot be rejected, but the null hypothesis of no first-order serial correlation canbe rejected, as required by the specification. In addition, the Hansen tests for the exogeneity
show that the J-statistic has a p-value greater than 0.10 in all models, so that we cannot reject
the null hypothesis that instruments as a group are exogenous in the system GMM estimation,
as required by the specification. The estimated results are generally consistent with thebaseline findings.
A2. Alternative index of financial inclusion
To check the robustness of our findings, we re-examine how MP instruments relate to the levelof financial inclusion by applying an alternative continuous indicator of financial inclusion
which is constructed by using factor analysis. There are several studies in the field of financial
economics, which utilize factor analysis to construct indicators of their interests from theoriginal sets of the data. For example, Anarfo et al. (2020) use six variables of financial
inclusion of 217 countries from 1990 to 2014 to construct the index of financial inclusion.
Similarly, using the data for 86 countries for the period 2004 to 2012, Ahamed and Mallick(2017) generate the financial inclusion index by using two dimensions, financial outreach andusage. Quinn et al. (2011) construct de jure and de facto indicators of capital account andfinancial account openness of 187 countries over the period from 1950 to 2007, and Klomp
and de Haan, (2009) construct the financial instability indicator of 60 countries over the period
from 1985 to 2005. Using the Canadian data, Gilbert and Meijer (2006) generate money andcredit indictors over the period from 1981 to 2004, and Stock and Watson (2002) construct theFederal Reserve Board’s Index of Industrial Production from 1970 to 1997.

115

Factor analysis is a statistical method to explain variability among observed, correlated
variables in terms of a potentially fewer number of unobserved random variables, which arecalled factors (Kim & Mueller, 1978). The method models the observed variables as linear
combinations of the potential factors plus error terms. Each factor represents a specific amountof the overall variance in the observed variables. The eigenvalue for a given factor measures
how much of the variance of the observed variables the particular factor explains. A low
eigenvalue factor is ignored, since other factors are more significant in explaining the variance.
According to the eyeballing of a scree plot that showing a line plot of the eigenvalues of factors,
we notify the ‘elbow’ point of the graph, where the eigenvalues level off, and then retain
factors to the left of this point for the subsequent analysis (Cattell, 1966). In this study, weapply factor analysis to generate the index of financial inclusion by using all the eight variables,
demographic and geographic bank branches and ATMs penetration, loan and deposit accounts
per capita and number of borrowers and depositors per capita. Based on the scree tests, theindex of financial inclusion can be represented as a one-dimensional construct. Thus, this study
applies one-factor model in the analysis. Tables A4 and A11 show the estimations of themodels with the index of financial inclusion based on factor analysis for developing anddeveloped countries, respectively. The results generally confirm our baseline findings.
A3. Additional controls
Several studies have discussed about the roles of the banking system structure, such as bankconcentration (Cetorelli & Gambera, 2001; Carbo-Valverde et al., 2009; Beck et al., 2010;
Leon, 2015; Chauvet & Jacolin, 2017; Owen & Pereira, 2018; Wang et al., 2020) and thepresence of foreign banks (Clarke et al., 2006; Detragaiche et al., 2008; Gormley, 2010; Cull
& Peria 2010; Claessens & Horen, 2014), and their effects on bank’s risk-taking behavior andfinancial services. Among them, Wang et al. (2020) indicate that banking concentration isnegatively associated with credit availability, i.e., Small and medium enterprises (SMEs)

116credit availability is more constrained, while Cetorelli and Gambera (2001) find that firms’
financial access is positively correlated with the concentrated banking system. On the other
hand, the presence of foreign banks could also constrain credit provision in the market,
particularly risky firms or companies, since foreign banks may target only large and low-riskones which results in forcing local banks out of the market (Detragaiche et al. 2008).
Nonetheless, Claessens and Horen (2014) show that the influence of foreign banks isdependent on host countries’ characteristics, such as regulatory framework quality, thenationality of ownership, and information asymmetries. For low-income countries, where
there is a limited market share and enormous information asymmetries, the presence of foreignbanks has a negative effect on credit (Claessens & Horen, 2014). Taking all the discussion
together, we extend our analysis by adding two additional control variables related to thebanking system structure, bank concentration and the share of foreign banks to total banks,
into our models. Tables A5-A7 and A12-A14 present the estimations of the models withadditional control variables for developing and developed countries, respectively. The results
are generally consistent with our baseline findings.

117

Table 4-A1. Overall measure of MPs (developing countries): system-GMM
Access to banking services
Use of banking services
Access 1
Access 2
Access 3
Access 4
Use 1
Use 2
Use 3
Use 4
Lag dependent
0.9962***0.9990***0.7567***0.8645***0.7058***0.9274***0.6878***0.7839***(0.0404)(0.0296)(0.0902)(0.0517)(0.1882)(0.0659)(0.1246)(0.0991)
MPI-0.0050-0.0018-0.0164-0.0051-0.0151
0.0051-0.0143*-0.0048(0.0041)(0.0043)(0.0171)(0.0079)(0.0160)(0.0088)(0.0080)(0.0069)
Real GDP
0.0520*0.0562**0.1895*0.1233**0.0850*-0.06420.0713-0.0967(0.0304)(0.0239)(0.1123)(0.0588)(0.0496)(0.0470)(0.0544)(0.0783)
Real GDP per capita-0.0633-0.0828-0.0483-0.0455
0.5383*0.1660**0.15020.2033*(0.0593)(0.0667)(0.2376)(0.1781)(0.3226)(0.0837)(0.1722)(0.1219)
Unemployment
0.00770.00600.00630.00850.02220.0066-0.00930.0022(0.0099)(0.0085)(0.0120)(0.0108)(0.0175)(0.0193)(0.0165)(0.0262)
Bank cost to income ratio-0.0025*-0.0021*0.0074*0.0066*0.00080.00290.0008-0.0049(0.0015)(0.0011)(0.0038)(0.0038)(0.0082)(0.0024)(0.0035)(0.0056)
Secondary school education
0.00070.00000.00890.0020-0.00080.00450.01080.0005(0.0019)(0.0025)(0.0078)(0.0073)(0.0109)(0.0048)(0.0078)(0.0059)
Tertiary school education-0.0034-0.0026-0.0036-0.0002-0.0114-0.00380.00010.0012(0.0026)(0.0019)(0.0049)(0.0043)(0.0089)(0.0052)(0.0049)(0.0053)
Political stability
0.1094***0.0890**0.1576**
0.1562**-0.0714-0.0446-0.1351
0.1533**(0.0402)(0.0372)(0.0715)(0.0656)(0.1413)(0.0988)(0.1137)(0.0759)
Observations
505
505
472
472
235
328
268
261
Number of countries
71
71
70
70
36
48
41
42
Number of instruments
46
50
45
43
35
40
39
40
AR2
0.464
0.454
0.477
0.336
0.67
0.173
0.247
0.285
Hansen test
0.451
0.630
0.415
0.207
0.612
0.308
0.857
0.373
Notes: Eight indicators of financial inclusion of which four indicators measure access to and four indicators measure use of financial
services are the dependent variables used in the regression. We estimated the regressions using the system GMM estimator with robust
standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1

118Table 4-A2. Borrower- and financial institution-targeted MPs (developing countries): system-GMM
Lag dependent
BORROWER
FINANCIALReal GDP
Real GDP per capita
Unemployment
Bank cost to income ratio
Secondary school education
Tertiary school education
Political stability
Observations
Number of countries
Number of instruments
AR2
Hansen test

BORROWER
Access 1
0.9981***(0.0320)-0.0074(0.0215)

Access 2
0.9963***(0.0311)-0.0013(0.0170)

Access 30.7008***(0.0731)-0.0440(0.0278)

Access 4
0.8715***(0.0550)-0.0166(0.0319)

Use 1
0.4082*(0.2335)-0.1518(0.1235)

Use 2
0.5796***(0.1974)-0.1199**(0.0521)

Use 3
0.5779***(0.1137)-0.0952**(0.0484)

Use 4
0.7790***(0.0716)-0.0163(0.0290)0.0540*(0.0308)-0.0485(0.0613)0.0065(0.0078)-0.0016*(0.0009)-0.0008(0.0019)-0.0023(0.0021)0.0842**(0.0334)
505
71
43
0.469
0.526

0.0418**(0.0209)-0.0694(0.0541)0.0033(0.0074)-0.0019**(0.0009)-0.0013(0.0024)-0.0016(0.0024)
0.0913**(0.0377)
505
71
49
0.436
0.520

0.1350*(0.0707)0.0451(0.2119)0.0036(0.0106)
0.0069**(0.0034)
0.0078(0.0063)-0.0001(0.0033)
0.1520**(0.0663)
472
70
45
0.34
0.311

0.1408**(0.0583)-0.0390(0.1686)
0.0046(0.0089)0.0082**(0.0040)0.0025(0.0065)-0.0008(0.0035)
0.1590**(0.0712)
472
70
43
0.268
0.175

0.0814(0.0909)1.0095**(0.4641)0.0223(0.0418)-0.0066(0.0099)-0.0030(0.0078)-0.0114(0.0089)-0.1609(0.1438)
235
36
33
0.369
0.522

-0.0476(0.1270)0.3836*(0.2315)-0.0061(0.0131)-0.0028(0.0069)
0.0091(0.0057)
0.0027(0.0109)-0.0116(0.0992)
328
48
40
0.319
0.196

0.1435**(0.0663)0.3247*(0.1802)0.0030(0.0228)
0.0028(0.0060)
0.0086(0.0057)-0.0009(0.0054)-0.0726(0.1223)
268
41
35
0.133
0.871

-0.1057(0.0731)0.2451*(0.1328)0.0058(0.0162)-0.0038(0.0046)0.0004(0.0049)0.0007(0.0052)
0.1145*(0.0605)
261
42
41
0.251
0.693

FINANCIAL
Access 1
Access 2
0.9962***
0.9714***(0.0404)(0.0360)

Access 3
0.7836***(0.1090)

Access 4
0.8679***(0.0499)

Use 1
0.7483***(0.1159)

Use 2
0.9215***(0.0594)

Use 3
0.6474***(0.1695)

Use 4
0.7842***(0.0914)-0.0056(0.0044)0.0484*(0.0282)-0.0570(0.0594)0.0076(0.0095)-0.0025*(0.0015)0.0008(0.0020)-0.0035(0.0027)
0.1071***(0.0415)
505
71
46
0.464
0.472

-0.0236(0.0207)0.2016*(0.1086)-0.1119(0.2652)
0.0047(0.0124)0.0077**(0.0039)0.0089(0.0069)-0.0030(0.0045)0.1513**(0.0744)
472
70
45
0.479
0.475

-0.0064(0.0107)0.1186*(0.0644)-0.0447(0.1768)
0.0088(0.0114)0.0063*(0.0038)0.0016(0.0076)0.0001(0.0046)
0.1586***(0.0610)
472
70
43
0.341
0.214

-0.0102(0.0186)
0.0441(0.0470)0.4909**(0.2376)0.0186(0.0153)
0.0036(0.0060)
0.0019(0.0071)-0.0133(0.0083)-0.1353(0.1348)
235
36
35
0.826
0.605

0.0072(0.0114)-0.0588(0.0486)0.1861*(0.0990)0.0081(0.0213)0.0033(0.0030)
0.0040(0.0048)-0.0041(0.0052)-0.0508(0.0985)
328
48
40
0.173
0.293

0.0073(0.0184)-0.0198(0.0732)
0.3660*(0.2086)-0.0172(0.0354)0.0010(0.0078)0.0042(0.0170)0.0002(0.0057)-0.1532(0.1396)
268
41
36
0.382
0.526

-0.0053(0.0071)-0.1109(0.0746)
0.2228*(0.1315)
0.0033(0.0257)-0.0058(0.0063)-0.0007(0.0059)
0.0022(0.0046)
0.1389*(0.0752)
261
42
39
0.279
0.312

0.0002(0.0053)0.0408*(0.0220)-0.0853(0.0590)0.0064(0.0095)-0.0027**(0.0013)0.0002(0.0029)-0.0011(0.0020)0.0723*(0.0394)
505
71
50
0.499
0.752

Notes: Eight indicators of financial inclusion of which four indicators measure access to and four indicators measure use of financial services are the dependent variables used in the regression. We estimated the regressions using the system
GMM estimator with robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1

119Table 4-A3. Capital- and loan supply-targeted MPs (developing countries): system-GMM
Lag dependent
CAPITAL
LOAN-SUPPLYReal GDP
Real GDP per capita
Unemployment
Bank cost to income ratio
Secondary school education
Tertiary school education
Political stability
Observations
Number of countries
Number of instruments
AR2
Hansen test

CAPITAL
Access 1
0.9966***(0.0423)0.0051(0.0142)

Access 2
0.9987***(0.0313)0.0019(0.0174)

Access 3
0.7131***(0.0703)-0.0271(0.0341)

Access 4
0.8817***(0.0508)-0.0091(0.0300)

Use 1
0.6900***(0.1635)-0.0030(0.0636)

Use 2
0.7893***(0.1323)-0.0303(0.0365)

Use 3
0.5979***(0.1069)-0.0023(0.0489)

Use 4
0.8103***(0.0980)0.0210(0.0344)0.0526*(0.0302)-0.0730(0.0558)
0.0080(0.0081)-0.0020**(0.0010)-0.0009(0.0020)-0.0016(0.0021)0.0714**(0.0343)
505
71
49
0.496
0.671

0.0380*(0.0230)-0.0593(0.0571)0.0040(0.0091)-0.0020*(0.0010)-0.0017(0.0026)-0.0016(0.0024)0.1007**(0.0425)
505
71
50
0.421
0.432

0.1169**(0.0542)0.0775(0.1527)-0.0014(0.0148)0.0085**(0.0037)
0.0072(0.0054)0.0000(0.0031)0.1148*(0.0631)
472
70
47
0.268
0.528

0.1693**(0.0827)-0.0211(0.1728)0.0126(0.0093)0.0082**(0.0041)0.0006(0.0074)-0.0015(0.0044)
0.1895***(0.0671)
472
70
43
0.231
0.224

-0.0055(0.1387)
0.6348**(0.3013)0.0107(0.0236)-0.0018(0.0043)-0.0050(0.0106)-0.0074(0.0099)-0.0130(0.1340)
235
36
35
0.655
0.345

-0.0534(0.0733)
0.2975**(0.1495)
0.0061(0.0169)
0.0058(0.0059)
0.0063(0.0043)-0.0032(0.0056)-0.0506(0.1073)
328
48
47
0.199
0.229

-0.0301(0.0646)
0.4182**(0.1934)-0.0248(0.0162)
0.0023(0.0051)
0.0040(0.0076)0.0015(0.0062)-0.1269(0.0953)
268
41
34
0.350
0.891

-0.0574(0.0815)0.2653*(0.1586)-0.0133(0.0171)-0.0043(0.0057)-0.0020(0.0054)-0.0023(0.0036)0.1113*(0.0676)
261
42
41
0.360
0.662

LOAN-SUPPLY
Access 1
Access 2
0.9916***
0.9928***(0.0407)(0.0402)

Access 3
0.7824***(0.0656)

Access 40.8500***(0.0586)

Use 1
0.5363***(0.2028)

Use 2
0.7760***(0.1221)

Use 3
0.6377***(0.1319)

Use 4
0.8248***(0.0816)-0.0004(0.0094)
0.0495*(0.0262)-0.0652(0.0612)0.0086(0.0068)-0.0017*(0.0010)0.0000(0.0024)-0.0026(0.0025)0.0847**(0.0389)
505
71
49
0.453
0.433

0.0204(0.0295)
0.0964*(0.0584)
0.1742(0.2622)0.0121(0.0126)
0.0070*(0.0036)0.0009(0.0061)-0.0019(0.0051)
0.1379**(0.0648)
472
70
45
0.219
0.232

0.0212(0.0351)0.1462**(0.0719)0.0244(0.2046)
0.0134(0.0105)
0.0084**(0.0043)0.0032(0.0076)-0.0023(0.0044)0.1486*(0.0874)
472
70
46
0.246
0.315

-0.0650*(0.0356)
0.0922*(0.0557)
0.7601**(0.3620)0.0228(0.0198)-0.0087(0.0079)-0.0008(0.0106)-0.0159(0.0141)-0.1128(0.1553)
235
36
35
0.304
0.531

0.0143(0.0359)-0.0575(0.0731)0.2958*(0.1721)0.0090(0.0163)
0.0067(0.0054)
0.0071(0.0053)-0.0031(0.0057)-0.0198(0.1301)
328
48
45
0.215
0.115

-0.0661**(0.0276)
0.1231*(0.0683)
0.3272*(0.1756)
0.0130(0.0225)
0.0029(0.0052)
0.0083(0.0074)-0.0043(0.0068)-0.1789(0.1139)
268
41
33
0.252
0.745

-0.0155(0.0278)-0.0863(0.0670)
0.2165*(0.1280)-0.0008(0.0200)-0.0036(0.0059)0.0014(0.0046)0.0005(0.0042)0.0847***(0.0308)
261
42
41
0.360
0.662

-0.0025(0.0096)
0.0568**(0.0281)-0.0731(0.0670)0.0080(0.0068)-0.0021**(0.0010)-0.0002(0.0024)-0.0026(0.0024)0.0851*(0.0441)
505
71
49
0.472
0.428

Notes: Eight indicators of financial inclusion of which four indicators measure access to and four indicators measure use of financial services are the dependent variables used in the regression. We estimated the regressions using the system
GMM estimator with robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1

120

Table 4-A4. Factor analysis (developing countries)
MPI
BORROWER
FINANCIAL

Access to banking services(1)(2)-0.0018(0.0016)-0.0020(0.0018)

CAPITAL(3)0.0003(0.0079)

LOAN-SUPPLYReal GDP
Real GDP per capita
Unemployment
Bank cost-income ratio
Secondary education
Tertiary education
Political stability
Observations
Number of countries
R-square0.1348***(0.0306)-0.0645(0.0687)-0.0027*(0.0014)-0.0009***(0.0003)0.0019**(0.0009)-0.0015(0.0010)-0.0067(0.0108)499
70
0.69590.1345***(0.0305)-0.0633(0.0690)-0.0026*(0.0014)-0.0009***(0.0003)0.0019**(0.0009)-0.0015(0.0009)-0.0066(0.0108)499
70
0.69630.1332***(0.0304)-0.0719(0.0702)-0.0024(0.0014)-0.0009**(0.0004)
0.0019**(0.0009)-0.0016*(0.0009)-0.0060(0.0110)499
70
0.6924(4)-0.0082(0.0055)
0.1320***(0.0303)-0.0663(0.0657)-0.0029*(0.0015)-0.0008**(0.0003)
0.0020**(0.0009)-0.0014(0.0010)-0.0076(0.0115)499
70
0.6996(5)0.0027(0.0047)0.1309***(0.0297)-0.0712(0.0700)-0.0022(0.0013)-0.0009**(0.0004)
0.0019**(0.0010)-0.0016*(0.0009)-0.0049(0.0100)
499
70
0.6936

Use of banking services(1)(2)-0.0107(0.0117)-0.0056(0.0145)0.2898(0.2228)0.7238(0.6718)0.0409***(0.0135)-0.0022(0.0028)0.0061(0.0040)-0.0001(0.0078)
0.1162**(0.0551)167
25
0.63270.3009(0.2326)
0.6499(0.6755)
0.0426***(0.0140)-0.0020(0.0028)0.0058(0.0040)-0.0003(0.0078)
0.1266**(0.0576)167
25
0.6284(3)-0.0671(0.0426)0.2303(0.2274)
0.6357(0.6107)0.0450***(0.0148)-0.0016(0.0024)0.0058(0.0037)0.0008(0.0073)
0.0973(0.0650)167
250.6464(4)-0.0297(0.0312)0.2608(0.2415)
0.6961(0.6883)
0.0424***(0.0125)-0.0020(0.0027)
0.0065(0.0039)-0.0009(0.0079)
0.1076*(0.0594)167
25
0.6324(5)-0.0864***(0.0208)0.4596***(0.1502)
0.6184(0.5550)0.0273**(0.0116)-0.0041(0.0025)
0.0070(0.0041)-0.0039(0.0067)
0.0513(0.0569)167
25
0.7185

Index of financial inclusion(1)(2)
0.0083(0.0387)
0.0315(0.0411)0.3940**(0.1863)0.9216(0.8321)0.0882(0.0564)-0.0071(0.0103)0.0008(0.0076)-0.0040(0.0249)
0.1806(0.1142)
103
18
0.488

0.4400**(0.1697)
0.6823(0.8675)
0.0909(0.0529)-0.0077(0.0101)-0.0006(0.0074)-0.0022(0.0224)
0.1996*(0.1038)103
180.5017(3)-0.2665**(0.1018)

0.2531(0.1836)
1.1974**(0.5509)
0.0619(0.0504)-0.0088(0.0095)
0.0017(0.0055)-0.0078(0.0203)
0.0650(0.0975)103
18
0.5768(4)-0.0322(0.0709)0.3068***(0.1021)1.1300(0.8051)0.0841(0.0502)-0.0068(0.0108)
0.0029(0.0061)-0.0062(0.0219)0.1464(0.0982)10318
0.4895(5)-0.1716***(0.0459)
0.5386**(0.2110)
1.0076*(0.5024)0.0375(0.0457)-0.0056(0.0056)
0.0040(0.0059)-0.0062(0.0213)0.0767(0.1041)103
180.6076

Notes: Access, Use and Index of financial inclusion constructed by using factor analysis are the dependent variables used in the regression. We estimated the regressions using the fixed effects estimation with robust standard errors in brackets.
*** p<0.01, ** p<0.05, * p<0.1

121Table 4-A5. Overall measure of MPs with additional controls (developing countries)

MPI
Real GDP
Real GDP per capita
Unemployment
Bank cost to income ratio
Secondary school education
Tertiary school education
Political stability
Bank concentration
Foreign banks to total banks
Observations
R-squared
Number of countries

Access to banking services
Access 1
Access 2
0.0018-0.0003(0.0096)(0.0098)
0.1366
0.3162(0.1930)(0.1996)0.46050.1693(0.2955)(0.3265)-0.0024-0.0040(0.0112)(0.0113)-0.0052*** -0.0062***(0.0017)(0.0017)0.0223***0.0237***(0.0053)(0.0056)-0.0093*-0.0110**(0.0047)(0.0052)
0.06160.0307(0.0527)(0.0539)0.00260.0024(0.0019)(0.0021)0.0004-0.0006(0.0024)(0.0025)455
455
0.50920.5914
62
62

Access 3-0.0064(0.0119)0.7473(0.4911)1.9184**(0.9220)0.0027(0.0126)
0.0065(0.0048)0.0227**(0.0100)-0.0155**(0.0059)-0.1059(0.1063)-0.0012(0.0037)0.0197**(0.0089)422
0.717862

Access 4-0.0071(0.0119)0.8964*(0.5043)1.7108*(0.9659)0.0000(0.0125)0.0054(0.0048)0.0234**(0.0098)-0.0172***(0.0062)-0.1334(0.1062)-0.0013(0.0037)
0.0182*(0.0093)
422
0.738
62

Use of banking services
Use 1
Use 2
0.0044-0.0032(0.0120)(0.0065)-0.6795** -0.0872(0.2614)(0.3458)
1.3639*** 1.3101***(0.4639)(0.4719)0.03900.0162(0.0276)(0.0147)-0.0061*-0.0016(0.0030)(0.0028)0.00040.0056(0.0084)(0.0058)-0.0103-0.0019(0.0120)(0.0063)
0.1988-0.0667(0.1414)(0.0539)0.0100*** 0.0007(0.0036)(0.0015)0.0133*** 0.0032(0.0047)(0.0037)210
311
0.6258
0.576
31
43

Use 3
-0.0275(0.0180)-1.3296*(0.7539)
1.9938**(0.7622)0.0200(0.0243)-0.0003(0.0036)
0.0075(0.0125)0.0012(0.0059)
0.2365(0.1901)
0.0021(0.0029)0.0218**(0.0094)235
0.646935

Use 4-0.0021(0.0042)0.1220(0.2739)0.7645**(0.3439)0.0016(0.0114)-0.0032(0.0025)0.0175***(0.0050)-0.0157**(0.0062)-0.0192(0.0530)
0.0025(0.0024)-0.0025(0.0034)232
0.647836

Notes: Eight indicators of financial inclusion of which four indicators measure access to and four indicators measure use of financial services are the dependent variables
used in the regression. We estimated the regressions using the fixed effects estimation with robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1

122Table 4-A6. Borrower- and financial institution-targeted MPs with additional controls (developing countries)
BORROWER
FINANCIALBORROWER
Access 1
Access 2-0.0309-0.0234(0.0440)(0.0464)

Access 3-0.0496(0.0575)

Access 4-0.0411(0.0581)

Use 1
0.0226(0.0554)

Use 2-0.0677**(0.0274)

Use 3-0.1387**(0.0590)

Use 4-0.0179(0.0205)

FINANCIAL
Access 1
Access 2

Access 3

Access 4

Use 1

Use 2

Use 3

0.00340.0008-0.0056-0.0068
0.0036-0.0008-0.0192(0.0107)(0.0109)(0.0133)(0.0133)(0.0131)(0.0082)(0.0182)
Real GDP
0.13900.31940.74940.9038*-0.6796**-0.0851-1.3961*0.12260.13830.31720.75180.8998*-0.6801**-0.0783-1.3623*(0.1889)(0.1961)(0.4947)(0.5079)(0.2543)(0.3197)(0.7371)(0.2700)(0.1929)(0.1997)(0.4903)(0.5039)(0.2617)(0.3484)(0.7727)
Real GDP per capita
0.4788*0.17201.9030**1.6672*
1.4084***
1.4121***
1.8317**0.7841**0.45040.1624
1.8943**
1.6927*1.3819***1.2536**
1.9839**(0.2804)(0.3084)(0.8923)(0.9413)(0.4520)(0.4153)(0.7206)(0.3135)(0.3001)(0.3330)(0.9284)(0.9716)(0.4565)(0.4698)(0.7867)
Unemployment-0.0033-0.00450.0021-0.0003
0.03840.01160.02070.0010-0.0023-0.00390.00300.00020.03850.01660.0217(0.0107)(0.0109)(0.0128)(0.0128)(0.0274)(0.0137)(0.0236)(0.0111)(0.0112)(0.0113)(0.0126)(0.0124)(0.0281)(0.0146)(0.0242)
Bank cost to income ratio-0.0050***-0.0062***0.00650.0053-0.0064**-0.00150.0014-0.0033-0.0052***-0.0062***0.00650.0053-0.0060*-0.0016-0.0001(0.0017)(0.0017)(0.0047)(0.0047)(0.0024)(0.0029)(0.0035)(0.0024)(0.0017)(0.0017)(0.0048)(0.0048)(0.0030)(0.0027)(0.0035)
Secondary school education
0.0222***0.0236***0.0229**0.0235**0.00070.00590.00680.0175***0.0224***0.0237***0.0226**0.0233**0.00040.00550.0078(0.0053)(0.0057)(0.0099)(0.0098)(0.0080)(0.0053)(0.0124)(0.0050)(0.0052)(0.0056)(0.0099)(0.0098)(0.0085)(0.0057)(0.0127)
Tertiary school education-0.0089*-0.0108**-0.0156**-0.0173***-0.0107-0.0015-0.0006-0.0157**-0.0093**-0.0111**-0.0156**-0.0172***-0.0103-0.00210.0006(0.0047)(0.0052)(0.0060)(0.0063)(0.0120)(0.0061)(0.0066)(0.0062)(0.0046)(0.0052)(0.0059)(0.0062)(0.0121)(0.0062)(0.0063)
Political stability
0.05900.0288-0.1139-0.1380
0.2050-0.0994*
0.2332-0.02390.06140.0307-0.1032-0.1309
0.1968-0.06310.2515(0.0536)(0.0548)(0.1019)(0.1031)(0.1422)(0.0550)(0.1900)(0.0547)(0.0527)(0.0539)(0.1062)(0.1062)(0.1415)(0.0541)(0.1928)
Bank concentration
0.00240.0023-0.0014-0.00150.0104***0.00030.00110.00240.00250.0024-0.0011-0.00120.0099***0.00070.0024(0.0020)(0.0022)(0.0036)(0.0036)(0.0036)(0.0014)(0.0027)(0.0024)(0.0019)(0.0021)(0.0037)(0.0037)(0.0034)(0.0015)(0.0030)
Foreign banks to total banks
0.0008-0.0005
0.0196**0.0180*0.0132***0.00340.0199**-0.00260.0004-0.00070.0196**0.0181*0.0133***0.0031
0.0213**(0.0026)(0.0028)(0.0089)(0.0093)(0.0048)(0.0036)(0.0090)(0.0034)(0.0023)(0.0024)(0.0089)(0.0093)(0.0047)(0.0038)(0.0093)
Observations
455
455
422
422
210
311
235
232
455
455
422
422
210
311
235
R-squared
0.51140.59240.71840.73810.62590.59020.65420.64840.50970.59150.71760.73780.6256
0.57520.6429Number of countries
62
62
62
62
31
43
35
36
62
62
62
62
31
43
35
Notes: Eight indicators of financial inclusion of which four indicators measure access to and four indicators measure use of financial services are the dependent variables used in the regression. We estimated the regressions using the fixed effects estimation withrobust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1

Use 4-0.0018(0.0056)
0.1245(0.2749)0.7515**(0.3483)0.0017(0.0114)-0.0032(0.0025)0.0174***(0.0050)-0.0158**(0.0062)-0.0178(0.0527)
0.0025(0.0024)-0.0025(0.0034)232
0.647736

123

Table 4-A7. Capital- and loan supply-targeted MPs with additional controls (developing countries)
CAPITAL
LOAN-SUPPLYCAPITAL
Access 1
0.0007(0.0260)

Access 2-0.0107(0.0288)

Access 3-0.0644(0.0491)

Access 4-0.0733(0.0486)

Use 1-0.0453(0.0332)

Use 2
0.0092(0.0362)

Use 3-0.1113***(0.0359)

Use 4-0.0146(0.0320)

LOAN-SUPPLY
Access 1
Access 2

Access 3

Access 4

Use 1

Use 2

Use 3

0.00280.0051
0.0581
0.0618-0.0664**0.0111-0.0622*(0.0268)(0.0287)(0.0515)(0.0523)(0.0272)(0.0233)(0.0321)
Real GDP
0.13560.30900.71140.8550-0.7533***-0.0643-1.4592*
0.12560.13290.31250.74690.8969*-0.5330**-0.0803-1.2411(0.1952)(0.2015)(0.5011)(0.5146)(0.2690)(0.3440)(0.7577)(0.2737)(0.1904)(0.1959)(0.4929)(0.5047)(0.2472)(0.3448)(0.7871)
Real GDP per capita
0.4715*0.1874
1.9278**1.7244*
1.5969***1.2124***2.0949***0.7437**0.47210.1662
1.6416*1.41091.5994***
1.1913***
1.8857**(0.2718)(0.2956)(0.9124)(0.9538)(0.3901)(0.4176)(0.7523)(0.3016)(0.2913)(0.3189)(0.9191)(0.9646)(0.3895)(0.4352)(0.7789)
Unemployment-0.0026-0.0049-0.0018-0.0052
0.03160.01720.01830.0005-0.0025-0.0037
0.00650.00400.02680.01680.0209(0.0120)(0.0123)(0.0124)(0.0123)(0.0284)(0.0146)(0.0250)(0.0116)(0.0113)(0.0113)(0.0127)(0.0125)(0.0257)(0.0142)(0.0227)
Bank cost to income ratio-0.0052***-0.0061***
0.00700.0059-0.0058**-0.00160.0004-0.0031-0.0051***-0.0062***0.00700.0059-0.0060**-0.0016-0.0007(0.0017)(0.0017)(0.0049)(0.0049)(0.0028)(0.0027)(0.0035)(0.0025)(0.0018)(0.0018)(0.0049)(0.0049)(0.0027)(0.0027)(0.0035)
Secondary school education
0.0223***0.0236***0.0218**0.0224**0.00090.00570.00760.0171***
0.0223***0.0237***0.0219**0.0226**0.00320.00530.0076(0.0053)(0.0057)(0.0097)(0.0095)(0.0079)(0.0057)(0.0128)(0.0053)(0.0053)(0.0056)(0.0098)(0.0097)(0.0077)(0.0057)(0.0128)
Tertiary school education-0.0092*-0.0109**-0.0151***-0.0166***-0.0118-0.00240.0003-0.0154**-0.0092**-0.0111**-0.0164***-0.0181***-0.0119-0.00240.0008(0.0046)(0.0052)(0.0055)(0.0059)(0.0124)(0.0062)(0.0061)(0.0060)(0.0046)(0.0051)(0.0059)(0.0061)(0.0120)(0.0061)(0.0070)
Political stability
0.06170.0291-0.1185-0.14800.1770-0.0589
0.1925-0.01960.06250.0326-0.0552-0.0792
0.1419-0.0533
0.2129(0.0528)(0.0539)(0.1085)(0.1079)(0.1415)(0.0543)(0.1829)(0.0540)(0.0534)(0.0542)(0.1119)(0.1110)(0.1195)(0.0513)(0.1928)
Bank concentration
0.00260.0025-0.0003-0.00030.0102***0.00060.00320.00270.00260.0024-0.0004-0.0005
0.0086**0.00080.0016(0.0018)(0.0020)(0.0037)(0.0037)(0.0034)(0.0015)(0.0030)(0.0024)(0.0018)(0.0019)(0.0036)(0.0036)(0.0037)(0.0015)(0.0029)
Foreign banks to total banks
0.0005-0.0005
0.0204**0.0189**0.0140***0.00280.0229**-0.00230.0005-0.00070.0184**0.0168*0.0120**0.00300.0200**(0.0025)(0.0026)(0.0090)(0.0093)(0.0046)(0.0037)(0.0091)(0.0033)(0.0023)(0.0024)(0.0087)(0.0091)(0.0047)(0.0038)(0.0093)
Observations
455
455
422
422
210
311
235
232
455
455
422
422
210
311
235
R-squared
0.509
0.59180.72
0.74060.6306
0.57540.65240.64820.50910.59160.72260.74280.64610.57610.6461
Number of countries
62
62
62
62
31
43
35
36
62
62
62
62
31
43
35
Notes: Eight indicators of financial inclusion of which four indicators measure access to and four indicators measure use of financial services are the dependent variables used in the regression. We estimated the regressions using the fixed effects estimation withrobust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1

Use 4
0.0072(0.0202)0.1410(0.2768)
0.6736*(0.3363)0.0016(0.0113)-0.0032(0.0025)0.0173***(0.0050)-0.0163**(0.0062)-0.0097(0.0519)0.0026(0.0023)-0.0027(0.0034)232
0.647936

124

Table 4-A8. Overall measure of MPs (developed countries): system-GMM
Access to banking services
Use of banking services
Access 1
Access 2
Access 3
Access 4
Use 1
Use 2
Lag dependent
0.9731***
0.9933***0.9337***0.9834***0.8940***0.9509***(0.0729)(0.0159)(0.0448)(0.0114)(0.2324)(0.0865)
MPI
0.0064**0.0091*-0.0003
0.0047*-0.0095
0.0104(0.0027)(0.0053)(0.0034)(0.0026)(0.0587)(0.0103)
Real GDP
0.0429*0.0603***0.0697**0.0229*-0.01290.0417*(0.0257)(0.0225)(0.0314)(0.0136)(0.0790)(0.0216)
Real GDP per capita-0.0811-0.1116***-0.1088**-0.0588***
0.3419*-0.0798(0.0594)(0.0429)(0.0491)(0.0223)(0.1752)(0.1102)
Unemployment
0.00530.00810.00150.0007-0.01240.0042(0.0065)(0.0077)(0.0030)(0.0028)(0.0342)(0.0074)
Bank cost to income ratio
0.00110.00120.00040.0018-0.0037-0.0005(0.0017)(0.0011)(0.0023)(0.0014)(0.0107)(0.0023)
Secondary school education
0.00380.00530.0024*-0.0011-0.0197
0.0025(0.0026)(0.0036)(0.0013)(0.0011)(0.0169)(0.0022)
Tertiary school education-0.0008-0.0016-0.0014-0.0003-0.0005
0.0020(0.0012)(0.0015)(0.0012)(0.0009)(0.0061)(0.0014)
Political stability
0.05900.09690.08680.0561-0.2057
0.0767(0.0809)(0.0683)(0.0822)(0.0593)(0.3171)(0.0931)
Observations
400
400
379
379
147
224
Number of iso3
44
44
42
42
18
27
Number of instruments
38
42
38
39
24
26
AR2
0.147
0.132
0.369
0.513
0.669
0.755
Hansen test
0.424
0.442
0.325
0.347
1.000
0.110
Notes: Eight indicators of financial inclusion of which four indicators measure access to and four indicators measure useof financial services are the dependent variables used in the regression. We estimated the regressions using the system
GMM estimator with robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1

Use 3
0.7727***(0.1015)0.0137(0.0118)-0.0765(0.0562)0.1399*(0.0832)-0.0072(0.0207)-0.0039(0.0041)0.0147(0.0101)-0.0053(0.0076)-0.0649(0.2844)
116
16
26
0.665
1.000

125

Table 4-A9. Borrower- and financial institution-targeted MPs (developed countries): system-GMM
Lag dependent
BORROWER
FINANCIALReal GDP
Real GDP per capita
Unemployment
Bank cost to income ratio
Secondary school education
Tertiary school education
Political stability
Observations
Number of iso3
Number of instruments
AR2
Hansen test

Access 1
0.9960***(0.0622)0.0035(0.0080)

Access 2
0.9973***(0.0091)
0.0033(0.0079)

Access 3
0.9381***(0.0497)
0.0026(0.0080)

BORROWER
Access 4
0.9972***(0.0214)-0.0076(0.0069)

Use 1
0.8757***(0.0915)-0.1441**(0.0703)Use 20.8704***(0.1866)-0.0362(0.0389)

Use 3
0.8198***(0.3011)0.0433(0.0494)

0.0413*(0.0245)-0.0982**(0.0499)
0.0029(0.0059)0.0007(0.0009)
0.0023(0.0024)-0.0009(0.0009)
0.0553(0.0754)
400
44
36
0.204
0.224

0.0416*(0.0233)-0.0619**(0.0308)
0.0009(0.0048)0.0006(0.0008)0.0014(0.0026)-0.0006(0.0016)0.0339(0.0866)
400
44
41
0.259
0.603

0.0585***(0.0214)-0.1135**(0.0449)0.0041(0.0035)0.0009(0.0049)0.0010(0.0020)-0.0012(0.0015)0.1046*(0.0616)
379
42
36
0.452
0.275

0.0510***(0.0177)-0.1004**(0.0393)-0.0012(0.0037)-0.0009(0.0013)0.0006(0.0023)-0.0011(0.0013)0.0388(0.0899)
379
42
40
0.422
0.126

-0.0091(0.0740)0.4692**(0.2048)0.0067(0.0122)-0.0080(0.0051)-0.0326(0.0203)0.0000(0.0045)0.0221(0.2876)
147
18
26
0.105
1.000

0.0903*(0.0526)-0.0343(0.2698)
0.0045(0.0087)-0.0072(0.0057)-0.0015(0.0029)0.0009(0.0025)
0.2787(0.2309)
224
27
26
0.308
0.179

-0.1090(0.2437)
0.2620*(0.1564)-0.0387(0.0505)
0.0035(0.0162)0.0005(0.0432)
0.0001(0.0056)-0.1471(0.6681)
116
16
28
0.608
1.000

Access 1
0.9965***(0.0735)

Access 2
0.9985***(0.0148)

Access 3
0.9296***(0.0386)

FINANCIAL
Access 4
0.9797***(0.0105)

Use 1
0.9008***(0.2556)

Use 2
0.9633***(0.0591)

Use 3
0.8734***(0.1524)0.0081**(0.0039)0.0411*(0.0221)-0.0783(0.0563)0.0040(0.0063)0.0007(0.0015)
0.0038(0.0027)-0.0007(0.0013)0.0446(0.0618)
400
44
37
0.158
0.448

0.0111**(0.0052)
0.0561***(0.0216)-0.1075***(0.0403)
0.0071(0.0065)0.0005(0.0008)0.0055(0.0038)-0.0015(0.0015)0.1005(0.0719)
400
44
42
0.18
0.419

0.0004(0.0055)
0.0565**(0.0278)-0.0887*(0.0527)-0.0001(0.0026)-0.0004(0.0020)0.0018*(0.0010)-0.0007(0.0010)0.0477(0.0674)
379
42
41
0.356
0.434

0.0069***(0.0025)
0.0238*(0.0143)-0.0398(0.0253)-0.0003(0.0034)0.0014(0.0010)-0.0004(0.0012)-0.0005(0.0008)0.0095(0.0829)
379
42
39
0.356
0.232

-0.0102(0.0624)-0.0162(0.0641)
0.3416*(0.1756)-0.0146(0.0407)-0.0036(0.0104)-0.0197(0.0173)0.0000(0.0063)-0.2042(0.2972)
147
18
24
0.671
1.000

0.0105(0.0082)0.0405*(0.0239)-0.0856(0.0935)-0.0022(0.0064)0.0000(0.0019)0.0056(0.0037)0.0012(0.0020)0.0612(0.0601)
224
27
26
0.647
0.386

-0.0219(0.0324)0.0063(0.1608)
0.2183*(0.1167)
0.0286(0.0435)-0.0032(0.0052)-0.0155(0.0140)0.0038(0.0108)-0.6457*(0.3325)
116
16
26
0.771
1.000

Notes: Eight indicators of financial inclusion of which four indicators measure access to and four indicators measure use of financial services are the dependent variables used in the regression. We estimated the regressions using the system
GMM estimator with robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1

125

126

Table 4-A10. Capital- and loan supply-targeted MPs (developed countries): system-GMM
Lag dependent
CAPITAL
LOAN-SUPPLY

Access 1
0.9660***(0.0606)
0.0206*(0.0124)

Access 2
0.9989***(0.0086)
0.0130*(0.0074)

Access 3
0.9448***(0.0232)
0.0006(0.0121)

CAPITAL
Access 4
0.9946***(0.0169)-0.0050(0.0137)

Use 1
0.8279***(0.1056)-0.0151(0.0396)

Use 2
0.9931***(0.0940)-0.0093(0.0197)

Use 3
0.8856***(0.1425)0.1703(0.1300)

Access 1
0.9935***(0.0339)

Access 2
0.9953***(0.0151)

Access 3
0.9217***(0.0455)

LOAN-SUPPLY
Access 4
Use 1
0.9886***0.7081***(0.0096)(0.2664)

Use 2
0.9896***(0.1381)

Use 3
0.8867***(0.1510)0.0064*0.0139*0.0127**0.0124**-0.1948*0.0134-0.0987**(0.0039)(0.0075)(0.0058)(0.0062)(0.1154)(0.0278)(0.0428)
Real GDP
0.0243*0.0456**0.0496**0.0470***-0.1167
0.0285*-0.21210.0351**0.0507**0.0265*0.0296*-0.00240.0389**-0.0019(0.0142)(0.0218)(0.0193)(0.0178)(0.1685)(0.0173)(0.1564)(0.0169)(0.0247)(0.0148)(0.0165)(0.0724)(0.0186)(0.0638)
Real GDP per capita-0.0400-0.0955***-0.0789**-0.06250.4402**-0.0583
0.3668*-0.0861***-0.1100**-0.0215-0.0466-0.0024-0.0592
0.0048(0.0478)(0.0358)(0.0326)(0.0407)(0.2155)(0.0735)(0.1939)(0.0299)(0.0548)(0.0242)(0.0287)(0.2001)(0.0743)(0.1134)
Unemployment-0.0003
0.00350.0000-0.0014-0.0084-0.00280.01280.00480.00620.0005-0.0043*-0.0247-0.0018-0.0163(0.0035)(0.0038)(0.0023)(0.0025)(0.0226)(0.0066)(0.0291)(0.0059)(0.0079)(0.0038)(0.0026)(0.0227)(0.0100)(0.0149)
Bank cost to income ratio-0.0001
0.0003-0.0006
0.0001-0.0004-0.0023-0.00680.00020.0008-0.00210.0000-0.0066-0.00160.0017(0.0018)(0.0013)(0.0022)(0.0012)(0.0127)(0.0018)(0.0072)(0.0007)(0.0019)(0.0016)(0.0013)(0.0090)(0.0033)(0.0084)
Secondary school education
0.00190.00170.0009-0.0006-0.01650.00060.00390.00230.00330.00040.00040.02050.00520.0107(0.0014)(0.0018)(0.0019)(0.0021)(0.0134)(0.0063)(0.0095)(0.0020)(0.0023)(0.0020)(0.0007)(0.0162)(0.0038)(0.0081)
Tertiary school education
0.0008-0.0010-0.0007-0.0009-0.0005
0.0007-0.0046-0.0009-0.0016-0.0010-0.00130.00790.0006-0.0029(0.0007)(0.0010)(0.0010)(0.0009)(0.0055)(0.0019)(0.0048)(0.0010)(0.0014)(0.0011)(0.0010)(0.0053)(0.0020)(0.0052)
Political stability-0.01880.07990.0327-0.0272-0.28640.1005-0.37810.0681
0.0524-0.0136-0.0301-0.0257
0.0259-0.3390(0.0607)(0.0670)(0.0442)(0.0666)(0.2569)(0.0948)(0.2866)(0.0720)(0.0841)(0.0542)(0.0542)(0.4092)(0.0893)(0.5162)
Observations
400
400
379
379
147
224
116
400
400
379
379
147
224
116
Number of iso3
44
44
42
42
18
27
16
44
44
42
42
18
27
16
Number of instruments
43
40
41
40
24
26
26
41
40
38
41
26
26
25
AR2
0.269
0.36
0.368
0.341
0.467
0.414
0.647
0.33
0.315
0.436
0.312
0.686
0.536
0.888
Hansen test
0.523
0.491
0.431
0.154
1.000
0.15
1.000
0.357
0.671
0.556
0.326
1.000
0.225
1.000
Notes: Eight indicators of financial inclusion of which four indicators measure access to and four indicators measure use of financial services are the dependent variables used in the regression. We estimated the regressions using the system
GMM estimator with robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1

126

127

Table 4-A11. Factor analysis (developed countries)
MPI
FINANCIALBORROWER

Access to banking services(1)(2)
0.0041***(0.0012)0.0059***(0.0017)

CAPITAL(3)

0.0023(0.0020)

LOAN-SUPPLYReal GDP
Real GDP per capita
Unemployment
Bank cost-income ratio
Secondary education
Tertiary education
Political stability
Observations
Number of countries
R-square0.2385***(0.0540)-0.3844***(0.0620)-0.0052***(0.0010)0.0001(0.0002)-0.0007*(0.0004)
0.0015***(0.0005)-0.0047(0.0125)411
420.6777

0.2365***(0.0535)-0.3873***(0.0592)-0.0052***(0.0009)
0.0001(0.0001)-0.0007*(0.0004)
0.0014***(0.0004)-0.0062(0.0124)411
420.69340.2358***(0.0544)-0.3633***(0.0675)-0.0057***(0.0011)0.0001(0.0002)-0.0008**(0.0004)
0.0014***(0.0005)
0.0010(0.0110)411
420.6255(4)0.0059*(0.0031)0.2319***(0.0559)-0.3585***(0.0691)-0.0057***(0.0010)0.0002(0.0002)-0.0007**(0.0004)
0.0012***(0.0005)-0.0044(0.0112)411
420.6359(5)0.0054(0.0042)0.2368***(0.0543)-0.3748***(0.0627)-0.0056***(0.0011)0.0001(0.0002)-0.0007*(0.0004)
0.0014***(0.0005)-0.0012(0.0119)411
420.6341

Use of banking services(1)(2)-0.0107(0.0098)-0.0103(0.0117)0.3689*(0.1850)-0.1208(0.2556)-0.0031(0.0059)-0.0013(0.0014)-0.0003(0.0033)
0.0028(0.0020)0.0537(0.0495)100
120.66030.3733*(0.1913)-0.1136(0.2657)-0.0026(0.0063)-0.0013(0.0014)-0.0004(0.0035)
0.0029(0.0021)0.0518(0.0504)100
120.6494(3)-0.0380**(0.0154)0.3825*(0.1945)-0.1312(0.3015)-0.0024(0.0058)-0.0012(0.0011)-0.0003(0.0035)
0.0022(0.0019)0.0740(0.0561)100
120.6595(4)-0.0227(0.0176)0.4408*(0.2142)-0.2332(0.3441)-0.0029(0.0066)-0.0016(0.0016)-0.0011(0.0036)0.0027(0.0022)0.0626(0.0526)100
120.6469(5)-0.0724**(0.0233)
0.3186(0.1848)0.0114(0.2564)-0.0019(0.0051)-0.0008(0.0010)-0.0010(0.0030)0.0017(0.0020)0.0152(0.0430)100
120.7011

Index of financial inclusion(1)(2)-0.0008(0.0145)-0.0005(0.0170)0.3313(0.4380)0.7527(0.6412)0.0176(0.0128)-0.0029(0.0022)-0.0060(0.0065)-0.0024(0.0056)
0.0645(0.1009)56
80.72250.3328(0.4390)0.7610(0.6839)0.0178(0.0131)-0.0029(0.0023)-0.0060(0.0069)-0.0024(0.0057)
0.0650(0.1026)56
80.7225(3)-0.0045(0.0245)0.3213(0.4361)0.7803(0.4853)0.0179(0.0096)-0.0028(0.0021)-0.0062(0.0066)-0.0022(0.0065)0.0601(0.1106)56
80.7226(4)-0.0051(0.0261)
0.3412(0.4367)
0.7028(0.6112)0.0169(0.0115)-0.0030(0.0026)-0.0059(0.0067)-0.0025(0.0059)
0.0692(0.1086)56
80.7231(5)-0.0779*(0.0408)0.2808(0.4411)0.6232(0.5049)
0.0124(0.0099)-0.0022(0.0021)-0.0039(0.0064)-0.0033(0.0064)-0.0083(0.0672)56
80.7525

Notes: Access, Use and Index of financial inclusion constructed by using factor analysis are the dependent variables used in the regression. We estimated the regressions using the fixed effects estimation with robust standard errors in brackets.
*** p<0.01, ** p<0.05, * p<0.1

127

128

Table 4-A12. Overall measure of MPs with additional controls (developed countries)
Access to banking services
Use of banking services
Access 1
Access 2
Access 3
Access 4
Use 1
Use 2
Use 3
Use 4
MPI
0.0114**0.0157***0.00360.0075-0.01210.0019-0.0160-0.0048(0.0047)(0.0049)(0.0060)(0.0061)(0.0221)(0.0051)(0.0104)(0.0050)
Real GDP-0.01790.3604**0.3677** 0.7577*** 0.5406
0.3560*** 0.1583
0.1684**(0.1156)(0.1355)(0.1594)(0.1999)(0.3224)(0.1211)(0.1315)(0.0678)
Real GDP per capita-0.2304-0.8273*** 0.6225** 0.0127
0.59681.0624*** 0.8384** 1.3485***(0.2201)(0.2411)(0.2593)(0.2961)(0.7222)(0.2659)(0.3221)(0.2235)
Unemployment-0.0173*** -0.0233*** -0.0050-0.0101** 0.0026
0.00670.00010.0237***(0.0049)(0.0051)(0.0051)(0.0047)(0.0095)(0.0061)(0.0097)(0.0049)
Bank cost to income ratio-0.0001
0.0001-0.0004-0.00030.0009-0.0014** -0.0020-0.0023*(0.0006)(0.0006)(0.0006)(0.0007)(0.0020)(0.0006)(0.0016)(0.0013)
Secondary school education -0.0011-0.00150.00150.00070.00220.0000-0.00240.0020(0.0017)(0.0017)(0.0020)(0.0021)(0.0048)(0.0018)(0.0029)(0.0022)
Tertiary school education
0.0056**0.0069***0.0034*0.0045**0.0055*-0.00160.00190.0056(0.0021)(0.0022)(0.0020)(0.0020)(0.0026)(0.0026)(0.0028)(0.0033)
Political stability
0.02430.00530.05480.04940.2095** -0.0925
0.0642-0.0827(0.0731)(0.0688)(0.0620)(0.0549)(0.0779)(0.0555)(0.0708)(0.0836)
Bank concentration
0.00060.00020.00110.0006-0.0030
0.0021*0.00090.0024**(0.0010)(0.0010)(0.0011)(0.0010)(0.0022)(0.0012)(0.0017)(0.0010)Foreign banks to total banks 0.0028
0.00220.00320.00270.00610.00160.0003-0.0124**(0.0019)(0.0018)(0.0025)(0.0025)(0.0038)(0.0032)(0.0046)(0.0043)
Observations
375
375
355
355
131
205
111
71
R-squared
0.43630.46040.62790.70540.61780.62940.74220.9033
Number of countries
42
42
40
40
16
25
15
11
Notes: Eight indicators of financial inclusion of which four indicators measure access to and four indicators measure use of financial services are thedependent variables used in the regression. We estimated the regressions using the fixed effects estimation with robust standard errors in brackets.
*** p<0.01, ** p<0.05, * p<0.1

128

129

Table 4-A13. Borrower- and financial institution-targeted MPs with additional controls (developed countries)
BORROWER
FINANCIALReal GDP
Real GDP per capita
Unemployment
Bank cost to income ratio
Secondary school education
Tertiary school education
Political stability
Bank concentration
Foreign banks to total banks
Observations
R-squared
Number of countries

BORROWER
Access 1
Access 2
0.00910.0130(0.0125)(0.0117)

Access 3
0.0083(0.0142)

Access 4
0.0091(0.0131)

Use 1-0.0349(0.0267)

Use 2
0.0020(0.0103)

Use 3-0.0793(0.0463)

Use 4-0.0253(0.0197)

0.0134(0.1086)-0.1825(0.2173)-0.0183***(0.0048)0.0001(0.0007)-0.0012(0.0017)0.0054**(0.0022)0.0275(0.0753)0.0000(0.0012)0.0022(0.0020)375
0.4148420.3699**(0.1622)0.6343**(0.2517)-0.0051(0.0050)-0.0004(0.0007)0.0014(0.0020)0.0036*(0.0020)0.0577(0.0624)0.0010(0.0013)0.0031(0.0025)355
0.6279400.7558***(0.2062)
0.0584(0.2898)-0.0105**(0.0046)-0.0002(0.0007)0.0005(0.0021)0.0046**(0.0020)0.0540(0.0557)
0.0002(0.0013)0.0024(0.0025)355
0.701140

0.5813(0.3620)0.4603(0.8041)0.0011(0.0094)0.0004(0.0022)0.0024(0.0049)0.0045(0.0027)
0.2197**(0.0797)-0.0030(0.0021)0.0045(0.0041)131
0.6238160.3641***(0.1094)
1.0613***(0.2631)0.0066(0.0061)-0.0014*(0.0007)0.0000(0.0017)-0.0016(0.0026)-0.0954*(0.0545)0.0020*(0.0011)0.0015(0.0033)205
0.628825

0.1937(0.1323)0.8490**(0.2962)0.0017(0.0089)-0.0019(0.0016)-0.0019(0.0028)-0.0006(0.0026)
0.0966(0.0817)0.0002(0.0020)0.0011(0.0048)111
0.738115

0.1904**(0.0723)
1.3635***(0.2025)
0.0254***(0.0054)-0.0026*(0.0014)0.0021(0.0022)0.0045(0.0034)-0.0700(0.0778)0.0021**(0.0008)-0.0119**(0.0043)71
0.903111

0.4040***(0.1245)-0.7626***(0.2416)-0.0247***(0.0051)0.0004(0.0007)-0.0017(0.0017)0.0066***(0.0023)0.0100(0.0710)-0.0007(0.0013)0.0014(0.0019)375
0.423142

FINANCIAL
Access 1

Access 2

Access 3

Access 4

Use 1

Use 2

Use 3

Use 4

0.0158**(0.0059)-0.0446(0.1162)-0.2130(0.2164)-0.0175***(0.0047)0.0000(0.0006)-0.0009(0.0016)0.0054**(0.0021)0.0155(0.0734)0.0006(0.0009)
0.0028(0.0020)
375
0.44
42

0.0215***(0.0065)
0.3241**(0.1402)-0.8028***(0.2378)-0.0236***(0.0048)0.0003(0.0006)-0.0012(0.0017)0.0066***(0.0021)-0.0068(0.0694)0.0001(0.0009)
0.0024(0.0019)375
0.4661420.0030(0.0077)0.3646**(0.1595)
0.6355**(0.2521)-0.0051(0.0051)-0.0004(0.0006)
0.0014(0.0020)0.0033(0.0020)0.0544(0.0625)0.0010(0.0010)0.0031(0.0025)
355
0.627
40

0.0088(0.0082)
0.7523***(0.1999)
0.0238(0.2872)-0.0101**(0.0048)-0.0002(0.0007)0.0007(0.0021)0.0042**(0.0020)0.0473(0.0563)0.0004(0.0010)0.0027(0.0026)355
0.704740-0.0081(0.0270)0.5560*(0.3113)0.6047(0.7046)
0.0034(0.0093)0.0010(0.0017)0.0018(0.0051)
0.0056**(0.0026)
0.2088**(0.0826)-0.0031(0.0023)
0.0063(0.0038)131
0.6105160.0023(0.0061)
0.3535***(0.1246)
1.0662***(0.2629)
0.0066(0.0061)-0.0014*(0.0007)-0.0001(0.0018)-0.0017(0.0025)-0.0924(0.0567)
0.0021*(0.0011)0.0016(0.0033)205
0.629425-0.0161(0.0115)0.1507(0.1358)
0.8201**(0.3403)0.0000(0.0101)-0.0021(0.0016)-0.0025(0.0029)0.0025(0.0030)
0.0588(0.0722)0.0009(0.0017)-0.0002(0.0047)111
0.738115-0.0048(0.0062)0.1652**(0.0667)
1.3451***(0.2262)0.0238***(0.0051)-0.0023(0.0013)0.0021(0.0023)0.0058*(0.0032)-0.0876(0.0818)
0.0024**(0.0009)-0.0128**(0.0043)71
0.9028
11

Notes: Eight indicators of financial inclusion of which four indicators measure access to and four indicators measure use of financial services are the dependent variables used in the regression. We estimated the regressions using the fixed effects
estimation with robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1

129

130

Table 4-A14. Capital- and loan supply-targeted MPs with additional controls (developed countries)
CAPITAL
LOAN-SUPPLY

Access 1
0.0249**(0.0123)

Access 2
0.0283*(0.0143)

Access 3
0.0044(0.0172)

CAPITAL
Access 4
Use 1
0.0086-0.0097(0.0181)(0.0385)

Use 20.0009(0.0119)

Use 3-0.0274(0.0264)

Use 4-0.0019(0.0197)

Access 1

Access 2

Access 3

LOAN-SUPPLY
Access 4
Use 1

Use 2

Use 3

Use 4

0.0297**0.0336**
0.0336*
0.0346*-0.1028**-0.0044-0.1296***-0.0214(0.0142)(0.0133)(0.0177)(0.0172)(0.0455)(0.0150)(0.0219)(0.0224)-0.0191
0.3640***0.3615**0.7451***0.59340.3626***0.13850.1717**-0.04030.3402**0.3902**0.7763***0.51180.3758***0.03680.1633**(0.1094)(0.1308)(0.1636)(0.2090)(0.3985)(0.1081)(0.1556)(0.0743)(0.1196)(0.1435)(0.1534)(0.1965)(0.3405)(0.1342)(0.0905)(0.0678)
Real GDP per capita-0.1335-0.6994***
0.6585***0.08730.53591.0702***0.7439*1.3379***-0.2032-0.7782***0.5348**-0.0427
0.66111.0708***0.9681***1.3578***(0.2311)(0.2581)(0.2357)(0.2776)(0.8615)(0.2758)(0.3893)(0.2484)(0.2307)(0.2501)(0.2456)(0.2824)(0.6964)(0.2677)(0.3086)(0.2183)
Unemployment-0.0187***-0.0252***-0.0053-0.0107**0.00350.0065-0.00130.0254***-0.0181***-0.0246***-0.0047-0.0101**
0.00300.00650.00300.0244***(0.0049)(0.0051)(0.0051)(0.0049)(0.0095)(0.0061)(0.0113)(0.0066)(0.0046)(0.0049)(0.0046)(0.0042)(0.0086)(0.0061)(0.0089)(0.0052)
Bank cost to income ratio
0.00020.0006-0.0003-0.0001
0.0009-0.0013*-0.0023-0.00240.00000.0004-0.0006-0.0004
0.0013-0.0013*-0.0022*-0.0025*(0.0006)(0.0007)(0.0006)(0.0007)(0.0018)(0.0007)(0.0017)(0.0014)(0.0006)(0.0006)(0.0006)(0.0007)(0.0018)(0.0006)(0.0012)(0.0013)
Secondary school education-0.0010-0.00130.00130.00040.0018-0.0001-0.0026
0.0025-0.0008-0.00120.00200.0011-0.0012-0.0002-0.00390.0020(0.0017)(0.0018)(0.0020)(0.0021)(0.0054)(0.0017)(0.0030)(0.0029)(0.0016)(0.0017)(0.0020)(0.0021)(0.0047)(0.0017)(0.0026)(0.0023)
Tertiary school education
0.0047**0.0057**0.00310.0040*0.0055*-0.0018
0.00230.00540.0058**0.0069***0.0037*0.0046**0.0051-0.00190.00090.0044(0.0021)(0.0021)(0.0020)(0.0020)(0.0027)(0.0025)(0.0031)(0.0031)(0.0022)(0.0023)(0.0019)(0.0020)(0.0031)(0.0025)(0.0027)(0.0037)
Political stability-0.0012-0.02430.05250.04520.2114**-0.0962*
0.0653-0.09510.0144-0.0065
0.04320.03890.2239**-0.0993*0.0548-0.0682(0.0749)(0.0716)(0.0610)(0.0563)(0.0850)(0.0552)(0.0722)(0.0759)(0.0724)(0.0685)(0.0578)(0.0529)(0.0910)(0.0525)(0.0715)(0.1002)
Bank concentration
0.0000-0.00070.00090.0000-0.0033
0.0020*0.00070.0025**0.0006-0.0001
0.0018*0.0010-0.00280.0018-0.00030.0018(0.0011)(0.0013)(0.0011)(0.0012)(0.0023)(0.0011)(0.0020)(0.0009)(0.0011)(0.0011)(0.0009)(0.0009)(0.0019)(0.0011)(0.0019)(0.0012)
Foreign banks to total banks
0.00250.00180.00310.00240.00630.00150.0003-0.0139***0.00170.00090.00260.00180.0073*0.00150.0017-0.0107(0.0021)(0.0020)(0.0026)(0.0026)(0.0037)(0.0032)(0.0050)(0.0040)(0.0019)(0.0018)(0.0023)(0.0023)(0.0036)(0.0034)(0.0038)(0.0062)
Observations
375
375
355
355
131
205
111
71
375
375
355
355
131
205
111
71
R-squared
0.424
0.43180.62650.70040.60840.62870.72790.90040.43250.44160.64430.71450.64210.62910.77580.9028
Number of countries
42
42
40
40
16
25
15
11
42
42
40
40
16
25
15
11
Notes: Eight indicators of financial inclusion of which four indicators measure access to and four indicators measure use of financial services are the dependent variables used in the regression. We estimated the regressions using the fixed effects estimation with robust standard errors inbrackets. *** p<0.01, ** p<0.05, * p<0.1
Real GDP

130

131

Chapter 5: Conclusion

Financial crises, particularly banking crisese bring about numerous costs to economic
development such as decline of output or growth and the shrinking of credit. Several studies
show that banking crises have a negative impact on output or growth (Demirguc-Kun et al.,2006; Dell’Ariccia et al., 2008; Furceri & Zdzienicka, 2012; Fernandez et al., 2013). Laeven
and Valencia (2013) estimate that a median output loss caused by banking crises amounts to19.74 percent of trend GDP. Our empirical finding on the effect of banking crises on credit
growth in developing countries indicates that a banking crisis during a financial boom has
more substantial effects on credit growth than that of a banking crisis during financial slump.
The effects of a banking crisis on credit growth are more magnified in the countries with highfinancial development than that of low financial development.

To mitigate the likelihood of banking crises, financial regulators have introduced
macroprudential policy which aim at mitigating the accumulation of the vulnerability in thefinancial system. The primary objective of macroprudential policies, although there is noformally agreed definition, is to reduce the accumulation of financial imbalance and systemicrisk and their consequence on the real economy. Some macroprudential policy tools have been
introduced to target financial institutions, the supply side of credit market, such as counter
cyclical buffer, capital requirement, reserve requirement, while other tools have been adopted
to target borrowers, the demand side of credit market including maximum loan to value ratio
and debts to income ratio. The adoption of macroprudential policy would help mitigate thevulnerability in the financial system and achieve financial stability, the implementation of

131

132

these polices could also bring along the unfavorable consequence or cost for the wholefinancial system, particularly the decline of the level of financial inclusion.
Our empirical results suggest that the adoption of macroprudential policy instruments,
borrower-targeted and some financial institution-targeted instruments help lessen thelikelihood of banking crises. However, the adoption of macroprudential policy also bringabout the negative consequence on financial inclusion, particularly the use of banking service
in, particularly developing countries. Drawing upon these finding, we posit two areas affecting
policy. First, the macroprudential literature mainly focuses on its beneficial effect— its
effectiveness in reducing systemic risks. Meanwhile, empirical study on the adoption ofmacroprudential policy also provides evidence of its costs, that is, potential adverse influences
on financial inclusion. This information is crucial for financial regulators, given that financial
inclusion is the top priority policy agenda of many countries. Our finding suggest that once
financial regulators impose specific type of policy instruments which intended to reduce thesystemic risk or the accumulation of the vulnerabilities of financial system, they would also
induce negative consequences on the level of financial inclusion, the access to and the use offinancial services. Therefore, when a country faces speciﬁc type of risks or crises, ﬁnancial
regulators or policy makers should evaluate the cost and benefits of each policy instruments
and choose the optimal combination of policy dimensions to preserve ﬁnancial stability andto lessen the adverse effects on other policy goals, i.e., financial inclusion.

132

133

Reference
Babecký, J., Havránek, T., Matějů, J., Rusnák, M., Šmídková, K., Vašíček, B., 2014.
Banking, debt, and currency crises in developed countries: Stylized facts and early warning
indicators. Journal of Financial Stability 15, 1–17.
Borio, C., Drehmann, M., 2009. Assessing the risk of banking crises–revisited. BIS Quarterly
Review 29-46.
Borio, C., Lowe, P., 2002. Assessing the risk of banking crises. BIS Quartlerly Review 43-54.
Borio, C., Lowe, P., 2004. Securing sustainable price stability: should credit come back fromthe wilderness? BIS Working Paper 175.
Butzbach, O., 2016. Systemic risk, macro-prudential regulation and organizational diversity
in banking. Policy and Society 35 (3), 239-251.
Caprio, G., D’Apice, V., Ferri, G., Puopolo, G. W., 2014. Macro-financial determinants of thegreat financial crisis: Implications for financial regulation. Journal of Banking and Finance 44(1), 114–129.
Crockett, A., 2000. Marrying the Micro- and Macro-prudential Dimensions of Financial
Stability. BIS Speeches. September 21.
Hasanov, R., Bhattacharya, P. S., 2018. Do political factors influence banking crisis?
Economic Modelling 76, 305–318.

133