Financial development and the occurrence of banking crises

Accepted Manuscript

Financial development and the occurrence of banking crises Clement Mathonnat , Alexandru Minea ´ PII: DOI: Reference:

S0378-4266(18)30192-4 https://doi.org/10.1016/j.jbankfin.2018.09.005 JBF 5413

To appear in:

Journal of Banking and Finance

Received date: Revised date: Accepted date:

17 August 2015 30 August 2018 10 September 2018

Please cite this article as: Clement Mathonnat , Alexandru Minea , Financial development ´ and the occurrence of banking crises, Journal of Banking and Finance (2018), doi: https://doi.org/10.1016/j.jbankfin.2018.09.005

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT Financial development and the occurrence of banking crises Clément Mathonnat  and Alexandru Minea*

CR IP T

Abstract: We perform an in-depth analysis of the effect of different dimensions of financial development on the occurrence of banking crises. Horse-race estimations carried out on a large dataset of 113 banking crises in 112 countries reveal that the growth of M3/GDP and the level of banks’ Credits/Deposits increase the occurrence of banking crises, while the growth and sometimes the volatility of the ratio of banks’ assets to the sum of banks’ and the Central

AN US

Bank’s assets decrease it. In addition, we do not find a significant effect of banks’ Credits/GDP. Finally, we unveil heterogeneities related to nonlinearities in the effect of financial development, the time span for the pre-crisis dynamics of financial development, and the level of economic development. Our results suggest that only some dimensions of

M

financial development are significantly associated with the occurrence of banking crises.

ED

Keywords: Financial development; Banking crises; Economic development.

PT

JEL codes: F30; G01; O11.

Acknowledgment: We are particularly indebted to the Editor (Geert Bekaert), and to an

CE

anonymous referee for their numerous and extremely helpful comments. Data and Stata dofiles are available upon request. We thank the ANR (Agence Nationale de la Recherche) for

AC

their financial support through the "Grand Emprunt" and the LABEX IDGM+ (ANR-10LABX-14-01) mechanism. Usual disclaimers apply. 

CERDI and CRCGM, University Clermont Auvergne, 65 Bd. Fr. Mitterrand, 63009 Clermont-Ferrand, France. Email: [email protected]. * Corresponding author: School of Economics & CERDI, University Clermont Auvergne, 65 Bd. Fr. Mitterrand, 63009 Clermont-Ferrand, France; and the Department of Economics, Carleton University, C-870 Loeb Building, 1125 Colonel By Drive, Ottawa, Ontario, Canada, K1S 5B6. Phone: +33.4.73.17.75.00. Email: [email protected]. We are extremely indebted to the Editor (Geert Bekaert), and to an anonymous referee for their very helpful comments. We thank the ANR (Agence Nationale de la Recherche) for their financial support through the “Grand Emprunt” and the LABEX IDGM+ (ANR-10-LABX-14-01) mechanism. Data and Stata do-files are available upon request. Usual disclaimers apply.

1

ACCEPTED MANUSCRIPT I. Introduction Do more developed financial systems have an increased risk of banking crises? This question came back in the spotlight following the 2007-2008 global financial crisis. Indeed, during the last four decades, financial systems experienced extensive development related to the adoption of financial liberalization policies and financial innovations (Claessens et al., 2013; Beck et al., 2016). By better mobilizing funds for investment, managing risk, and fostering an efficient allocation of resources, financial development can have positive effects on the

CR IP T

economy. However, a large expansion of financial systems may weaken their ability to manage information asymmetries, to reduce risk, and to allocate funds efficiently, which can increase banking sector vulnerability and exposure to liquidity risks. 1 From this perspective, according to Laeven & Valencia (2013), the last decades witnessed increases in the number of banking crises.

AN US

Consequently, a large and increasing body of literature aims to analyze the role of financial development in the occurrence of banking crises. Using data for 31 episodes of banking crises in a sample of up to 65 countries during the 1980-1994 period, Demirguc-Kunt & Detragiache (1998) notably find that the level of banks’ credit-to-GDP ratio and the growth of banks’ credit significantly increase the occurrence of banking crises only sometimes.

M

Drawing upon different measures of banks’ credit, which is the usual variable to capture financial development in the literature (see Beck et al., 2014), subsequent studies confirm the

ED

existence of such conflicting effects (see Kauko, 2014, for a survey). For example, when extending their database to include 77 crises for 94 countries, Demirguc-Kunt & Detragiache

PT

(2005) reveal a positive effect of the level of the credit-to-GDP ratio and of the growth of credit on the occurrence of banking crises, irrespective of the considered specification (see

CE

also, e.g., Eichengreen & Arteta, 2000, or Bekaert et al., 2011). In contrast, when revisiting Demirguc-Kunt & Detragiache (2005), Davis & Karim (2008a) fail to find a robust effect of

AC

either the credit-to-GDP ratio or the growth of credit in a sample of 105 countries. In particular, the effect of the growth of credit is even negative in some specifications, particularly when using Caprio & Klingebiel (2003) definition of banking crises (see also e.g. Hardy & Pazarbasioglu, 1999, or Beck et al., 2006). In this paper, motivated by these conflicting findings, we revisit the link between financial development and the occurrence of banking crises. Compared with the existing literature, our approach differs mainly on three grounds.

1

See Rajan (2005) and Beck (2012) for discussions on the benefits and risks of financial development.

2

ACCEPTED MANUSCRIPT First, the existing literature mainly focuses on banks’ credit. Our paper adopts a wider perspective and considers five financial development variables, namely, M3/GDP, banks’ assets/GDP, Bank ratio (the ratio of banks’ assets to the sum of banks’ and the Central Bank’s assets), banks’ Credits/GDP, and Credits/Deposits (the ratio of credits to the private sector by banks, and their deposits). These variables were selected to capture different dimensions of banking sector size (the first three variables) and activity (the last two variables). Second, the existing literature suggests that the measure of financial development

CR IP T

variables might be important. For example, the level of Credits/GDP is found to be a significant determinant of banking crises in the work of Bekaert et al. (2011), while for Jorda et al. (2016), it is the growth of Credits/GDP that influences the probability of banking crises. For this reason, we consider in our analysis several alternative measures, namely, the level, growth rate, and volatility, of each of our five variables of financial development.

AN US

Third, two issues arise when going beyond the existing literature by jointly considering several financial development variables with several measures. On the one hand, if these variables are correlated, the efficiency of the estimators might be affected. To deal with this issue, we supplement our analysis based on individual financial development variables (i.e., a disaggregated analysis) with estimations performed on aggregated indexes of

M

financial development, built using a principal component analysis (PCA). On the other hand, more importantly, the results in the existing literature might be affected by an omitted-

ED

variable bias that arises either from not accounting for several dimensions of financial development (exceptions include Betz et al., 2014, who jointly account for the total assets-to-

PT

GDP ratio and the private sector credit flow-to-GDP ratio, and Caggiano et al., 2016, who look at the credits-to-deposits ratio and the change in the credit-to-GDP ratio) or from not

CE

considering different measures (for example, in Jorda et al., 2016, only the growth of Credits/GDP significantly affects the probability of banking crises when included jointly with

AC

the level of Credits/GDP). In our analysis, we address this issue through the use of two-stage horse-race regressions designed to put in competition different financial development variables (with different measures), while conserving the same vector of control variables.2 Specifically, for each of the five financial development variables we account for, we jointly consider its level, growth rate, and volatility. Then, all significant variables are included in a

2

Alternatively, Barrell et al. (2010) perform a general-to-specific horse race on a set of potential determinants of banking crises by sequentially omitting the variable with the lowest significance (the growth of domestic credit does not make it to their final model specification)

3

ACCEPTED MANUSCRIPT second horse-race regression. Only those that remain significant in the second stage are considered to be robustly associated with the occurrence of banking crises. Estimations from a multivariate logit model applied to 113 banking crises in a large panel of 112 countries during the 1980-2009 period reveal the following. From an aggregated perspective (i.e., when using aggregated financial development indexes computed using a PCA), the level of activity of the banking sector and, to some extent, the growth of its size, are associated with a significant increase in the probability of banking crises. From a

CR IP T

disaggregated perspective, the growth of M3/GDP, the level of Credits/Deposits, and the growth and sometimes the volatility of the Bank ratio significantly affect the occurrence of banking crises. In addition, Credits/GDP does not appear to significantly affect the probability of banking crises when we jointly introduce its level, growth, and volatility. Instead, the significant influence of the level of Credits/Deposits suggests that the liquidity risk faced by

AN US

banks, due to a large credit supply relative to deposits, may be of importance when evaluating the occurrence of banking crises.

Finally, using these results as a benchmark, we explore three potential sources of heterogeneity in the relationship between financial development and banking crises. First, when we divide each financial development variable into quintiles to check for nonlinearities,

M

we find that the significant variables from our benchmark estimations still affect the occurrence of banking crises. In particular, the size of the significant effect of the growth of

ED

M3/GDP and of the level of Credits/Deposits (the growth of the Bank ratio) increases (decreases) in absolute value across quintiles, while the volatility of the Bank ratio

PT

significantly affects the occurrence of banking crises only for countries in the top two quintiles. These results should, however, be considered with caution, given the difficulty of

CE

performing horse-race regressions on quintiles. Second, the time span used for computing the dynamics of financial development variables seems relevant. Estimations performed on a

AC

yearly panel (a short-term perspective) are fairly close to those obtained in our benchmark analysis carried out on a 3-year period panel, while only the level of banking sector activity, and particularly the level of Credits/Deposits, is significantly associated with banking crises when using a 5-year period panel (a medium-term perspective). Third, such heterogeneity also arises when our sample is divided into low-, intermediate-, and high-income countries to account for the level of economic development, since the financial development variables that exert a significant effect are the volatility of the Bank ratio and the level of Credits/Deposits (in low-income countries), the growth of M3/GDP and of the Bank ratio (in intermediateincome countries), and the level of M3/GDP and of Credits/GDP (in high-income countries). 4

ACCEPTED MANUSCRIPT Nevertheless, these findings should be considered with caution, given the small number of observations, particularly in the group of high-income countries. The remainder of this paper is organized as follows. Section II describes the database and the methodology, section III presents the baseline results, section IV assesses their robustness, section V explores potential heterogeneities in the relationship between financial development and the occurrence of banking crises, and section VI concludes the paper.

CR IP T

II. Data and Methodology 2.1. Data

Our database covers 113 banking crises observed in 112 countries over the 1980-2009 period. This period was selected to capture both the important changes experienced by the financial system since the 1980s and the increase in financial instability. We divide this time span into

AN US

ten non-overlapping 3-year periods, as a compromise between two issues. First, we want to preserve a sufficient time dimension that allows us to study the dynamic interaction between financial development and banking crises. Second, the estimation of a panel logit model requires the cross-section dimension to be significantly greater than the time dimension.

M

2.1.1. Banking crises

We define a binary variable (Crisis) that is equal to 1 if a country experiences a banking crisis

ED

during a given 3-year period, and to 0 otherwise. Following, e.g., Demirguc-Kunt & Detragiache (1998) and Davis & Karim (2008a), since our focus is on the outbreak of banking

PT

crises, the coding of this binary variable accounts exclusively for the starting year of the banking crisis (the following 3-year periods are coded 0 regardless of the duration of the

CE

banking crisis). The raw banking crises data come from Laeven & Valencia’s (2013) database, which provides the widest coverage of banking crises for the period we study. None

AC

of the 112 countries in our sample experienced more than one banking crisis during each 3year period (see the Online Appendix for descriptive statistics).

2.1.2. Financial development In this study, we focus on the size and activity of the banking sector. By considering these two features of the banking system, our analysis follows the majority of studies devoted to the macroeconomic consequences of financial development (see, e.g., Samargandi et al., 2015). Moreover, in developing countries (approximately three-fourths of our sample) and even in

5

ACCEPTED MANUSCRIPT some developed countries, the banking sector is the main source of external financing (see Mishkin, 2015). We draw upon five variables from the World Bank’s Global Financial Development Database (2016), each of them averaged by 3-year period. We capture the size of the banking sector using three variables: (i) M3/GDP, as a measure of the size of financial intermediaries’ liabilities, which evaluates the overall amount of liquidity in the economy to assess the liquidity risk faced by banks; (ii) Bank Assets/GDP, as a measure of the size of financial

CR IP T

intermediaries’ assets, which indicates the importance of commercial banks, in terms of their role in savings allocation and of risk-taking, because of the too-big-to-fail moral hazard problem. These two variables are intended to also account for contagion and exposure-toshock risks, following an increase in banking sector’s size; and (iii) Bank ratio, computed as the ratio of commercial banks’ assets to the sum of assets of commercial banks’ and the

AN US

Central Bank’s, which evaluates the risk of banking crises coming from changes in the relative share of assets held by banks and the Central Bank due to variations in their respective balance sheets.

We capture the activity of the banking sector using two variables: (iv) Credits/GDP (defined as the ratio between credits to the private sector by commercial banks, and GDP)

M

measures banks’ activity from the perspective of their crucial function of canalizing savings towards investment in the private sector, which evaluates the credit risk related to an increase

ED

in financial intermediation; and (v) Credits/Deposits (defined as the ratio between credits to the private sector by commercial banks and their deposits) measures the banking system’s

PT

intermediation capacity, which evaluates the risk-taking behavior of banks and the liquidity risk arising from a bank run.

CE

To mitigate the influence of large positive or negative values, we follow Kumar et al.

x  signxlog1  x  . In (2003) and transform each financial development variable x into ~

AC

addition to preserving negative x values compared to those in a logarithmic transformation, the use of ~ x is appropriate when estimating a logit model, which may be sensitive to the presence of outliers (see, e.g., Congdon, 2003). Conversely, the outliers of financial development variables may be associated with banking crises. Given that transformations in financial development variables may not be neutral regarding their effects on the occurrence of banking crises (see Davis & Karim, 2008a, for credit variables), we discuss this issue in section IV.

6

ACCEPTED MANUSCRIPT Finally, in addition to the level of financial development variables, we consider their growth (computed as the average growth rate) and their volatility (computed as the average standard deviation) for each 3-year period. Thus, we investigate in detail the precise importance of the level, growth, and volatility of the size and activity of the banking sector for the occurrence of banking crises.

2.2. Methodology

CR IP T

2.2.1. Model specification and estimation

To estimate the relationship between financial development and banking crises, we follow e.g. Demirguc-Kunt & Detragiache (1998, 2005) and Davis & Karim (2008a), and resort to a commonly used econometric model in the literature on the determinants of banking crises,

PCrisis it  1  F X  

e 'X . 1  e 'X

AN US

namely, a pooled logit model3

(1)

PCrisis it  1 is the probability that country i experiences a banking crisis during the 3-year period t (i.e., the probability that the dummy variable Crisis equals 1), and F X  stands for

M

the cumulative logistic distribution, with X the vector of banking crises determinants and  the vector of coefficients. In vector X we include our financial development variables and

ED

control variables. To account for potential correlations between observations over time at the country level, the variance-covariance matrix of estimated coefficients is robust to within-

PT

country correlations. In addition, consistent with the discussion in Davis & Karim (2008a) and Caggiano et al. (2016), we follow the related literature and do not include country fixed effects in our baseline specification to avoid a selection bias, i.e., we conserve in our sample

CE

the countries that did not experience banking crises over the considered period, since they represent an important counterfactual. Finally, in the robustness analysis (see section IV), we

AC

will consider a random-effects logit model and a population-averaged logit model to account for country-level unobserved heterogeneity and potential within-country autocorrelation of errors, respectively.

3

Alternative methods to assess the determinants of banking crises include (i) the signal approach, coined by Kaminsky & Reinhart (1999) and used more recently by Davis & Karim (2008a), Drehmann et al. (2011), or Lo Duca & Peltonen (2013), or (ii) the binary classification tree approach, used by e.g. Davis & Karim (2008b), Duttagupta & Cashin (2011), or Alessi & Detken (2018). Alessi et al. (2015) perform a comprehensive comparison of concurrent methods and provide support for the use of multivariate methods, as the one used in our analysis.

7

ACCEPTED MANUSCRIPT 2.2.2. Control variables We consider three sets of control variables (definitions, sources, and descriptive statistics are detailed in the Online Appendix). First, four variables account for the contemporaneous contagion of banking crises, and for their correlations with other types of crises. Since banking crises can be viral events that propagate at the international or regional level, we control for their contagion using the number of banking crises worldwide (Global crises) and in the region to which a country belongs (Regional crises). Moreover, since banking crises

CR IP T

may be linked to the outbreak of currency crises (Kaminsky & Reinhart, 1999) and sovereign debt crises (Reinhart & Rogoff, 2011), the variables Currency crises and Debt crises account for their contemporaneous occurrence, using data from Laeven & Valencia (2013).

Second, four variables are used to control for lagged macroeconomic and financial conditions. The first three variables are inspired by the work of Demirguc-Kunt &

AN US

Detragiache (1998, 2005) and come from the World Bank’s World Development Indicators (2016). The level of economic development (GDP/capita) may influence the occurrence of banking crises through its effect on e.g. countries’ production structure, the quality of institutions, or implemented policies. Next, since banking crises tend to be preceded by a growth slowdown that may increase credit risk and by high inflation rates that may increase

M

the interest rate and the exchange rate risks, we consider the variables GDP growth and Inflation. The fourth variable is related to financial liberalization. Although financial openness

ED

has not been found to directly affect banking crises so far (Bekaert et al., 2011), it may exert indirect effects, given its significant impact on investment and factor productivity (Bekaert et

PT

al., 2011), economic growth (Bekaert et al., 2005), or growth volatility (Bekaert et al., 2006). We proxy for financial liberalization using the de jure index of capital account openness from

CE

Chinn & Ito (2011).4 Similar to financial development variables, these variables are transformed following Kumar et al. (2003).

AC

Finally, we introduce a time trend to capture common time-varying unobserved factors that may be correlated with both financial development and the occurrence of banking crises.

2.2.3. The sequencing of estimations To analyze the effect of financial development on banking crises, we adopt a two-step strategy, from general (aggregated level) to specific (disaggregated level).

4

Although Chinn & Ito’s (2011) index does not allow capturing the precise dimensions of financial openness, its wide coverage allows preserving the size of our sample. Quinn et al. (2011) and Bekaert et al. (2016) discuss alternative measures.

8

ACCEPTED MANUSCRIPT First, following, e.g. Samargandi et al. (2015), because financial development variables are correlated (see bilateral correlations in the Online Appendix), we draw upon a global (aggregated) composite index of financial development (FD index) equal to the first factor of a PCA applied to the five variables of financial development presented in section 2.1.2. The level, growth, and volatility of the FD index are first separately introduced in the model, and then jointly introduced. Next, using the same logic, we compute two additional composite indexes, for the size (FD size) and the activity (FD activity) of the banking system,

CR IP T

equal to the first factor of a PCA performed on the three (two) variables for the size (activity) of the banking sector.5 For each of these two indexes, we look at the effect of their level, growth, and volatility, both separately and jointly. Finally, we perform a horse-race estimation that jointly includes only the significant measures of the FD size and FD activity indexes. Second, we deepen the analysis based on composite indexes by investigating the

AN US

influence of each of our five financial development variables (disaggregated level). We begin by considering the level, growth, and volatility of each variable, and then we consider them jointly. Finally, we perform a horse-race estimation that includes all significant measures of the five financial development variables.

Using this two-step strategy, we sequentially investigate which specific dimensions

M

and measures of financial development are robustly correlated with banking crises.

ED

III. Results

Tables 1, 2, and 3 report our main results. The pseudo-R2 and Wald tests of the joint

PT

significance of covariates support the explanatory power of our model. Relatedly, when comparing the banking crises predicted by our model with those observed in our sample,

CE

using a 10% threshold of predicted probabilities of banking crises to differentiate between crisis and non-crisis periods (which is roughly the average frequency of banking crises in our

AC

sample), we notice that our model has a fairly high predictive ability, appreciatively 80% of correctly predicted banking crises. Regarding the control variables, GDP/capita, GDP growth, and Financial openness do not significantly affect the occurrence of banking crises.6 In contrast, variables capturing contagion effects and other types of crises are significantly related to the occurrence of banking crises. Finally, consistent with Demirguc-Kunt & 5

The PCA results for the computation of the composite financial development variables (FD index, FD size, and FD activity) are available upon request. 6 The lack of significance of the level of economic development might be supported by the conclusions of Reinhart & Rogoff (2013), showing that, from a historical perspective, developed and developing countries have been equally exposed to banking crises.; we investigate this issue in more detail in section V. The lack of a significant effect of financial openness is consistent with the findings of Bekaert et al. (2011).

9

ACCEPTED MANUSCRIPT Detragiache (1998, 2005), higher Inflation is associated with a significant increase in the probability of banking crises. Let us now look at the effect of the aggregated indexes of financial development on the occurrence of banking crises, as reported in Table 1. According to columns (1a)-(1d), the level, growth, and volatility of the FD index have no significant effect either separately or jointly. Moreover, considering the size and activity of the banking sector, columns (2a)-(3d) show that only the growth of FD size and the level of FD activity influence the occurrence of

CR IP T

banking crises; in particular, the effect of these two variables remains significant when considering the horse-race specifications in columns (2d) and (3d). Finally, we consider estimations of the joint effect of FD size and FD activity composite indexes in columns (4a)(4c) by including their level, growth, and volatility, as well as a model with only significant variables in column (5). Although the estimations confirm the significance of the level of FD

AN US

activity, the loss of significance for the growth of FD size in column (5) of Table 1 suggests

AC

CE

PT

ED

M

that a more disaggregated analysis might be worthwhile.

10

CR IP T

Level (1a) 0.0191 [0.0285]

0.00229 [0.00363] 0.098 [0.116]

3D (1d) 0.014 [0.0281] 0.00201 [0.00346] 0.0802 [0.115]

Level (2a)

FD size Growth Vol. (2b) (2c)

3D (2d)

0.00578* [0.00338] -0.0361 [0.115]

Level (3a)

0.00245 [0.0295] 0.00597* [0.00343] -0.0698 [0.122]

FD activity Growth Vol. (3b) (3c)

0.0405** [0.0159]

0.000194 [0.00302]

0.00382** [0.00158] 0.0250*** [0.00421] 0.0366* [0.0189] 0.130*** [0.0317] 0.00609 [0.00975] 0.00606 [0.00595] 0.0189*** [0.00675] 0.0188 [0.0132] Yes 866 110 -201.50 0.45 0.00 0.79

0.00375** [0.00158] 0.0257*** [0.00416] 0.0317* [0.0190] 0.130*** [0.0322] 0.00872 [0.0105] 0.00631 [0.00597] 0.0186*** [0.00715] 0.0203 [0.0132] Yes 866 111 -201.70 0.45 0.00 0.80

0.00366** [0.00161] 0.0253*** [0.00421] 0.0368* [0.0191] 0.136*** [0.0311] 0.00935 [0.00723] 0.00846 [0.00606] 0.0182*** [0.00599] 0.018 [0.0127] Yes 864 111 -199.50 0.45 0.00 0.79

0.00376** [0.00159] 0.0257*** [0.00417] 0.0313 [0.0191] 0.130*** [0.0321] 0.0104 [0.00716] 0.00641 [0.00599] 0.0182*** [0.00596] 0.0209 [0.0129] Yes 866 111 -201.70 0.45 0.00 0.79

0.00367** [0.00161] 0.0254*** [0.00421] 0.0378** [0.0193] 0.135*** [0.0313] 0.00893 [0.0103] 0.00851 [0.00611] 0.0193*** [0.00693] 0.0181 [0.0132] Yes 864 111 -199.30 0.45 0.00 0.80

M

0.00384** [0.00158] 0.0250*** [0.00410] 0.0321* [0.0191] 0.129*** [0.0320] 0.00951 [0.00729] 0.00506 [0.00587] 0.0168*** [0.00568] 0.0204 [0.0130] Yes 866 110 -202.60 0.44 0.00 0.79

ED

0.00386** [0.00159] 0.0249*** [0.00419] 0.0364* [0.0187] 0.131*** [0.0317] 0.00961 [0.00722] 0.00614 [0.00615] 0.0181*** [0.00597] 0.0203 [0.0128] Yes 866 110 -201.80 0.45 0.00 0.80

PT

0.00382** [0.00157] 0.0252*** [0.00413] 0.0349* [0.0189] 0.130*** [0.0319] 0.00608 [0.00995] 0.00533 [0.00592] 0.0199*** [0.00705] 0.02 [0.0133] Yes 868 110 -202.60 0.44 0.00 0.79

FD size vs FD activity Level Growth Vol. (4a) (4b) (4c)

3D (3d)

0.00385** [0.00154] 0.0237*** [0.00391] 0.0377** [0.0187] 0.123*** [0.0319] 0.003 [0.00762] 0.00507 [0.00558] 0.0215*** [0.00598] 0.0164 [0.0126] Yes 879 111 -201.60 0.45 0.00 0.80

0.00383** [0.00158] 0.0247*** [0.00396] 0.0351* [0.0188] 0.128*** [0.0319] 0.00939 [0.00704] 0.00498 [0.00607] 0.0178*** [0.00590] 0.0186 [0.0129] Yes 877 111 -203.30 0.44 0.00 0.80

0.0275 [0.0642] 0.00385** [0.00158] 0.0244*** [0.00390] 0.0337* [0.0186] 0.130*** [0.0312] 0.00974 [0.00701] 0.00444 [0.00588] 0.0172*** [0.00563] 0.0184 [0.0129] Yes 879 111 -204.00 0.44 0.00 0.79

0.00576* [0.00340]

0.00516 [0.00341] -0.0708 [0.130]

0.0486*** [0.0167] -0.00016 [0.00309] -0.0542 [0.0709] 0.00382** [0.00153] 0.0241*** [0.00399] 0.0403** [0.0185] 0.120*** [0.0321] 0.00113 [0.00764] 0.00592 [0.00568] 0.0233*** [0.00603] 0.0168 [0.0126] Yes 877 111 -200.30 0.45 0.00 0.79

0.0459*** [0.0169]

0.0388** [0.0160] 0.000388 [0.00300]



0.00386** [0.00157] 0.0246*** [0.00421] 0.0352* [0.0193] 0.125*** [0.0327] 0.00789 [0.0103] 0.00709 [0.00584] 0.0196*** [0.00681] 0.0198 [0.0129] Yes 859 110 -199.10 0.45 0.00 0.80



0.00368** [0.00162] 0.0254*** [0.00435] 0.0377* [0.0197] 0.136*** [0.0318] 0.00961 [0.00735] 0.00834 [0.00624] 0.0182*** [0.00602] 0.0181 [0.0129] Yes 856 110 -199.00 0.45 0.00 0.79

0.0419 [0.0720] 0.00384** 0.00378** [0.00162] [0.00159] 0.0255*** 0.0247*** [0.00424] [0.00430] 0.0334* 0.0425** [0.0191] [0.0189] 0.131*** 0.130*** [0.0320] [0.0313] 0.0106 0.0032 [0.00728] [0.00785] 0.00641 0.00885 [0.00599] [0.00571] 0.0183*** 0.0223*** [0.00593] [0.00627] 0.0211 0.0167 [0.0131] [0.0126] Yes Yes 859 857 110 110 -201.40 -197.10 0.45 0.46 0.00 0.00 0.79 0.80

Note for Tables 1-3: displayed coefficients are marginal effects. Standard errors reported in brackets are robust to within-country correlations. Pseudo-R2 is proxied by the ratio LL  LL / LL , where LL0 and LL1 0 1 0 denote the absolute value of the log likelihood in a model with only a constant term and the full model, respectively. The Wald p-value refers to a Wald test of the joint significance of our covariates. Correct predictions are computed by applying a threshold of 10% to the predicted probabilities of banking crises in order to differentiate between crisis and non-crisis periods. ***p<0.01, **p<0.05, *p<0.1.

11

Sign. var. (5)

-0.0223 [0.0289]

AN US

0.00716 [0.0300]

CE

Financial openness Time trend Obs. Countries Log likelih. Pseudo-R2 Wald p-val Corr. pred.

Table 1. Financial development and the occurrence of banking crises: aggregated analysis FD index Growth Vol. (1b) (1c)

AC

FD index level FD index growth FD index volatility FD size level FD size growth FD size volatility FD activity level FD activity growth FD activity volatility Global crises Regional crises Currency crises Debt crises GDP/ capita GDP Growth Inflation

ACCEPTED MANUSCRIPT

CR IP T

ACCEPTED MANUSCRIPT

Table 2. Financial development and the occurrence of banking crises: disaggregated analysis (FD size variables)

M3/GDP growth

M3/GDP Growth Vol. (1b) (1c)

0.0136** [0.00564]

M3/GDP vol.

-0.0028 [0.0160]

3D (1d) -0.0217 [0.0229] 0.0145** [0.00580] -0.00432 [0.0181]

Banks assets/GDP level

0.0114 [0.0181]

Banks assets/GDP growth

0.00648 [0.00443]

Banks assets/GDP vol.

0.0145 [0.0136]

Bank ratio level Bank ratio growth Bank ratio vol.

Debt crises GDP/capita GDP growth Inflation Financial openness

AC

Time trend Obs./Countries/Periods Log likelihood/Pseudo-R2 Wald stat./p-value Correct predictions

0.00402** [0.00159] 0.0248*** [0.00401] 0.029 [0.0180] 0.114*** [0.0290] 0.0143 [0.00886] 0.00444 [0.00572] 0.0152** [0.00736] 0.0168 [0.0126] Yes 884/111/8.0 -198.8/0.44 112.7/0.00 0.80

0.00372** [0.00154] 0.0247*** [0.00381] 0.0341* [0.0181] 0.127*** [0.0315] 0.00538 [0.0105] 0.00465 [0.00579] 0.0198*** [0.00755] 0.0179 [0.0128] Yes 889/112/7.9 -204.2/0.44 107.2/0.00 0.80

M

0.00376** [0.00156] 0.0247*** [0.00389] 0.0326* [0.0189] 0.129*** [0.0313] 0.00968 [0.00703] 0.00495 [0.00593] 0.0178*** [0.00573] 0.0192 [0.0129] Yes 885/111/8.0 -203.8/0.44 105.8/0.00 0.80

ED

Currency crises

0.00392** [0.00157] 0.0250*** [0.00409] 0.0307* [0.0184] 0.115*** [0.0289] 0.00963 [0.00701] 0.00445 [0.00564] 0.0185*** [0.00584] 0.0159 [0.0125] Yes 884/111/8.0 -199.7/0.44 110.6/0.00 0.79

PT

Regional crises

0.00384** [0.00156] 0.0245*** [0.00383] 0.0305* [0.0181] 0.129*** [0.0312] 0.0127 [0.00868] 0.00503 [0.00593] 0.0151** [0.00722] 0.0195 [0.0127] Yes 885/111/8.0 -203.5/0.44 107.1/0.00 0.80

CE

Global crises

Bank assets/GDP Growth Vol. (2b) (2c)

Level (2a)

3D (2d)

AN US

M3/GDP level

Level (1a) -0.0151 [0.0199]

0.00388** [0.00156] 0.0246*** [0.00386] 0.0334* [0.0181] 0.119*** [0.0293] 0.00865 [0.00685] 0.00474 [0.00579] 0.0180*** [0.00588] 0.0171 [0.0128] Yes 888/112/7.9 -202.8/0.44 104.8/0.00 0.79

12

0.00372** [0.00155] 0.0244*** [0.00377] 0.0330* [0.0184] 0.126*** [0.0316] 0.00734 [0.00744] 0.00481 [0.00579] 0.0169*** [0.00564] 0.0168 [0.0127] Yes 889/112/7.9 -203.9/0.44 108.4/0.00 0.79

Level (3a)

Bank ratio Growth Vol. (3b) (3c)

3D (3d)

0.00311 [0.0201] 0.00597 [0.00444] 0.0102 [0.0156]

0.00384** [0.00155] 0.0246*** [0.00380] 0.0342* [0.0177] 0.117*** [0.0299] 0.0061 [0.0106] 0.00486 [0.00571] 0.0183** [0.00749] 0.0162 [0.0127] Yes 888/112/7.9 -202.5/0.44 106.7/0.00 0.80

0.0131 [0.0257]

0.0201 [0.0262] -0.0123** -0.0136** [0.00527] [0.00580] -0.0212 -0.0219 [0.0135] [0.0135] 0.00392** 0.00397** 0.00409*** 0.00417*** [0.00160] [0.00160] [0.00158] [0.00160] 0.0253*** 0.0247*** 0.0250*** 0.0245*** [0.00391] [0.00401] [0.00397] [0.00395] 0.0369* 0.0409** 0.0374* 0.0444** [0.0203] [0.0203] [0.0205] [0.0200] 0.127*** 0.128*** 0.130*** 0.125*** [0.0323] [0.0323] [0.0320] [0.0329] 0.00809 0.00967 0.00682 0.00397 [0.00840] [0.00709] [0.00717] [0.00842] 0.00557 0.0105 0.00542 0.0097 [0.00604] [0.00647] [0.00598] [0.00648] 0.0197*** 0.0192*** 0.0212*** 0.0249*** [0.00681] [0.00594] [0.00622] [0.00686] 0.0208 0.0199 0.0187 0.0191 [0.0131] [0.0131] [0.0131] [0.0131] Yes Yes Yes Yes 872/112/7.8 870/112/7.8 872/112/7.8 870/112/7.8 -204.3/0.44 -201.4/0.44 -203.4/0.44 -200.1/0.44 105.1/0.00 103.2/0.00 109.2/0.00 108.7/0.00 0.79 0.80 0.80 0.79

CR IP T

ACCEPTED MANUSCRIPT

Table 3. Financial development and the occurrence of banking crises: disaggregated analysis (FD activity variables) Level (1a) 0.019 [0.0171]

Credits/GDP level Credits/GDP growth

Credits/GDP Growth Vol. (1b) (1c)

0.00713* [0.00426] 0.0116 [0.0142]

Credits/Deposits level

0.00106 [0.00512]

Credits/Deposits vol. M3/GDP growth Bank ratio growth Credits/Deposits level

Regional crises

0.00386** [0.00154] 0.0245*** [0.00385] 0.0326* [0.0179] 0.117*** [0.0295] 0.00905 [0.00687] 0.00315 [0.00563] 0.0177*** [0.00584] 0.0166 [0.0128] Yes 888/112/7.9 -202.5/0.44 108.2/0.00 0.79

0.00372** [0.00155] 0.0243*** [0.00378] 0.0329* [0.0185] 0.128*** [0.0311] 0.00758 [0.00746] 0.00465 [0.00577] 0.0172*** [0.00576] 0.0172 [0.0129] Yes 889/112/7.9 -204.1/0.44 108.7/0.00 0.80

ED

Currency crises

0.00364** [0.00152] 0.0247*** [0.00380] 0.0356** [0.0180] 0.126*** [0.0316] 0.0022 [0.0101] 0.00465 [0.00575] 0.0219*** [0.00781] 0.0174 [0.0129] Yes 889/112/7.9 -203.6/0.44 108.2/0.00 0.80

Debt crises GDP/capita

PT

GDP growth

CE

Time trend Obs./Countries/Periods Log likelihood/Pseudo-R2 Wald stat./p-value Correct predictions

0.00375** [0.00152] 0.0246*** [0.00381] 0.0350** [0.0176] 0.115*** [0.0294] 0.00279 [0.0101] 0.00326 [0.00557] 0.0216*** [0.00792] 0.0162 [0.0129] Yes 888/112/7.9 -201.9/0.45 109.7/0.00 0.80

M

Global crises

AC

Credits/Deposits Growth Vol. (2b) (2c)

0.0459*** [0.0153]

Credits/Deposits growth

Financial openness

Level (2a)

AN US

Credits/GDP vol.

Inflation

3D (1d) 0.0162 [0.0198] 0.00689 [0.00441] 0.000337 [0.0164]

13

0.0143 [0.0110]

3D (2d)

Sign. var. (3)

0.0481*** [0.0181] 0.00111 [0.00510] 0.000748 [0.0124]

0.0137** [0.00589] -0.0153*** [0.00575] 0.0480*** [0.0153] 0.00408*** 0.00397** 0.00410*** 0.00410*** 0.00415** [0.00158] [0.00159] [0.00158] [0.00158] [0.00162] 0.0233*** 0.0246*** 0.0238*** 0.0235*** 0.0242*** [0.00370] [0.00385] [0.00367] [0.00373] [0.00433] 0.0419** 0.0397** 0.0400** 0.0452** 0.0389** [0.0203] [0.0200] [0.0202] [0.0197] [0.0191] 0.120*** 0.127*** 0.130*** 0.118*** 0.109*** [0.0323] [0.0319] [0.0313] [0.0325] [0.0304] 0.00547 0.00903 0.0105 0.00479 0.00672 [0.00733] [0.00700] [0.00684] [0.00754] [0.00778] 0.00374 0.00483 0.0045 0.00452 0.00923 [0.00569] [0.00599] [0.00586] [0.00569] [0.00601] 0.0221*** 0.0182*** 0.0168*** 0.0224*** 0.0233*** [0.00590] [0.00589] [0.00552] [0.00617] [0.00603] 0.0202 0.0193 0.0184 0.0203 0.0188 [0.0130] [0.0132] [0.0130] [0.0131] [0.0128] Yes Yes Yes Yes Yes 881/111/7.9 879/111/7.9 881/111/7.9 879/111/7.9 857/110/7.9 -203.1/0.44 -205.2/0.44 -205.2/0.44 -202.0/0.44 -191.6/0.44 114.0/0.00 105.3/0.00 111.5/0.00 114.5/0.00 116.2/0.00 0.79 0.79 0.79 0.80 0.80

ACCEPTED MANUSCRIPT Tables 2 and 3 report estimations for the disaggregated variables of the size and activity of the banking sector, respectively. Overall, the results are consistent with those based on aggregated measures, since all significant variables are related to the growth of the size and the level of the activity of the banking sector. Regarding the size of the banking sector, Table 2 shows the following. First, the growth of M3/GDP has a significant and positive effect on the occurrence of banking crises (see columns (1b) and (1d)). Second, according to columns (3b) and (3d), an increase in the

CR IP T

growth of Bank ratio is associated with a significant decrease in the probability of banking crises. Taken together, these two opposite-sign effects may explain the statistical weakness of the variable growth of FD size (its coefficient is not significant in column (5) of Table 1). Regarding the activity of the banking sector, Table 3 shows that the level of Credits/Deposits is associated with a significant increase in the occurrence of banking crises (see column (2d)).

AN US

To summarize, our baseline analysis reveals that only some features of financial development may be relevant to our understanding of the occurrence of banking crises: (1) the growth of M3/GDP and the level of Credits/Deposits are associated with an increase in the occurrence of banking crises. This result suggests that abundant liquidity and higher banks’ indebtedness may be related to banking crises.

M

(2) the growth of the Bank ratio is associated with a decrease in the occurrence of banking crises. This suggests that a large amount of commercial banks’ assets

ED

relative to the Central Bank’s assets may be related to fewer banking crises, for example, because commercial banks have more efficient resource allocation and

PT

better risk management.

Importantly, the effect of these three variables remains robust when considered in the horse-

CE

race regression reported in column (3) of Table 3. Moreover: (3) there are two variables whose effects are only slightly non-significant: the

AC

volatility of the Bank ratio (p-value 10.5%, column (3d) of Table 2), and the growth of Credits/GDP (p-value 11.9%, column (1d) of Table 3). Given the importance of credit variables in the existing literature (as noted in the introduction), we re-estimated the horse-race regression (3) in Table 3 augmented with the level of Credits/GDP, the growth of Credits/GDP, and both measures jointly. While the baseline findings are confirmed, we report that these two credit measures are never significantly associated with the occurrence of banking crises (results are available upon request). Nevertheless, these variables will be carefully scrutinized in the robustness analysis. 14

ACCEPTED MANUSCRIPT

IV. Robustness We investigate the robustness of our baseline results by accounting for potential unobserved heterogeneity, possible autocorrelation of the error term, additional controls, and an alternative measure of the occurrence of banking crises. To save space, we present only the coefficients of interest from horse-race regressions in Table 4 (full results are available upon

CR IP T

request).

4.1. Unobserved heterogeneity

We estimate a random-effects logit model to account for potential country-unobserved heterogeneity. The results indicate that the likelihood ratio test systematically accepts the null hypothesis of the absence of country-unobserved heterogeneity; this is confirmed by an

AN US

estimated share of the variance of the dependent variable (Crisis) captured by country random effects close to zero. Thus, the random-effects logit estimations are comparable to those of the pooled logit model, confirming that our baseline findings are not affected by countryunobserved heterogeneity (see line [1] in Table 4).7

M

4.2. Autocorrelation

We account for potential within-country autocorrelation of the error term by estimating a

ED

population-averaged logit model. Compared to the pooled logit model used as a benchmark, this model places no restriction on the structure of the correlation of errors in the within-

PT

country dimension of the data (see Cameron & Trivedi, 2005). Line [2] in Table 4 reveals a significant effect of the growth of M3/GDP and of the Bank ratio, and of the level of

CE

Credits/Deposits, consistent with the baseline findings. In addition, the importance of the variable Bank ratio is strengthened, since a change in its volatility now significantly affects

AC

the occurrence of banking crises (its effect was only weakly non-significant in our baseline estimations).8

7

We also investigate the issue of unobserved heterogeneity specific to each country during the 3-year period before banking crises, which may affect both financial development and the occurrence of banking crises (e.g., the quality of banking sector’s regulation, the extent of bank competition, or the presence of a deposit insurance scheme, see Demirguc-Kunt & Detragiache, 2005; Davis & Karim, 2008a). Following Giavazzi & Tabellini (2005), we re-estimated our baseline model with the additional control variable Pre-crisis, equal to 1 during the period preceding the outbreak of a banking crisis and to 0 otherwise. The estimations, which are available upon request, confirm our baseline findings. 8 Comparable results (available upon request) are obtained in a pooled-probit model that assumes a normal distribution of errors.

15

ACCEPTED MANUSCRIPT 4.3. Additional controls Drawing upon Demirguc-Kunt & Detragiache (2005) and Davis & Karim (2008a), we sequentially introduce ten additional determinants of banking crises in our baseline specification: inflation volatility, the real interest rate, terms of trade volatility, exchange rate volatility, money supply M2/reserves, foreign direct investment (FDI)/GDP, current account balance/GDP, gross fixed capital formation (GFCF)/GDP, trade openness/GDP, and the quality of institutions measured by the Polity2 index. These variables are based on data from

CR IP T

the World Bank’s World Development Indicators (2016), except for the terms of trade volatility, exchange rate volatility, and the Polity2 index, which come from the UN Conference on Trade and Development Statistics (2013), the Penn World Table 7.1 (2013), and the Polity IV database from Marshall & Jaggers (2010), respectively (see the Online Appendix for details). To avoid a potential simultaneity bias, all variables are lagged one

AN US

period.

Except for trade openness and, to some extent, FDI, these additional control variables are not significant, suggesting that our baseline model does not suffer from an important omitted variable bias. Keeping this in mind, the main results from the horse-race regressions reported on line [3] of Table 4 show the following. First, the results for the growth of

M

M3/GDP, the growth of the Bank ratio, and the level of Credits/Deposits are consistent with our baseline estimations. Second, the significance of the volatility of the Bank ratio depends

ED

on the specification considered. Third, the level and growth of Credits/GDP do not exert a significant effect on the occurrence of banking crises in the second-stage horse-race

PT

regressions, thus confirming our baseline results.

CE

4.4. Alternative measures for the occurrence of banking crises Following the existing literature, our baseline estimation draws upon a dummy measure for

AC

the occurrence of banking crises.9 However, as suggested by Bussière & Fratzscher (2006) for currency crises and by Hardy & Pazarbasioglu (1999) and Caggiano et al. (2014, 2016) for banking crises, this can be a source of potential bias because the value Crisis=0 includes periods characterized by different macroeconomic and financial conditions. To account for this potential heterogeneity, we build the polytomous-ordered variable Crisis_poly set to 1 for the period preceding the outbreak of a banking crisis, 2 for the period when the banking crisis occurs, and 0 otherwise (data on banking crises are again obtained from Laeven & Valencia, 9

The baseline results are robust when redefining the dependent variable to be equal to 1 only when a banking crisis is systemic (the results are available upon request).

16

ACCEPTED MANUSCRIPT 2013). This approach allows us to differentiate between periods preceding banking crises that may be associated with strong financial and real expansion (Crisis_poly=1), periods associated with the occurrence of banking crises characterized by important financial and real contraction (Crisis_poly=2), and periods with relatively greater financial and macroeconomic stability (Crisis_poly=0). Using Crisis_poly as the new dependent variable, we estimate a pooled ordered logit model. 10 The horse-race estimations on line [4] in Table 4, in which we report the marginal

CR IP T

effect for the value Crisis_poly=2 (i.e., for the probability of banking crisis outbreak), show a positive effect of the growth of M3/GDP and the level of Credits/Deposits that is consistent with our baseline estimations.11 However, the volatility, but not the growth, of the Bank ratio is associated with a significant decrease in the probability of banking crises. In addition, although the level and growth of Credits/GDP were found to be significantly and positively

AN US

correlated with banking crises when jointly accounting for the level, growth, and volatility of the Credits/GDP variable, this result is not robust when considering the horse-race regression that includes all significant financial development variables, and, therefore, it is not reported in Table 4. In particular, the statistical weakness of the effect of the growth of Credit/GDP is

M

consistent with the results from the multinomial logit model of Caggiano et al. (2014).12

Table 4. Financial development and the occurrence of banking crises: Robustness

ED

Aggregated FD measures G.FDsize L.FDactivity 0.00516 0.0388** [0.00341] [0.0160] 0.00516 0.0388** [0.00337] [0.0184] 0.00483 0.0409*** [0.00335] [0.0153]

[1] RE Logit [2] Autocorrelation

PT

[0] Baseline

CE

[3] Additional controls a (10 controls) [4] Alternative bk. crises (Pooled ordered logit)

G.M3/GDP 0.0137** [0.00589] 0.0137*** [0.00527] 0.0138** [0.00586]

1 out of 10

Always

8 out of 10

0.00192 [0.00228]

0.0587*** [0.0124]

0.00918** [0.00465]

Disaggregated FD measures G.BankRatio V.BankRatio L.Credits/Deposits -0.0153*** 0.0480*** [0.00575] [0.0153] -0.0153** 0.0480*** [0.00625] [0.0194] -0.0156** -0.0254** 0.0472*** [0.00615] [0.0129] [0.0141] Always

6 out of 10

Always

-0.0199* [0.0116]

0.0587*** [0.0158]

AC

Note: displayed coefficients are marginal effects from horse-race regressions. Standard errors reported in brackets are robust to within-country correlations. L., G., and V. in front of a variable stand for its level, growth, and volatility, respectively. a on this line we report the number of significant coefficients. ***p<0.01, **p<0.05, *p<0.1. 10

Comparable results arise from the estimation of a random-effects ordered logit model that accounts for country-unobserved heterogeneity (the results are available upon request). 11 Caggiano et al. (2016) similarly report a significant and positive effect of the level of Credits/Deposits in binomial and multinomial logit models. 12 Finally, as previously indicated, the use of transformed variables in our baseline analysis may not be neutral; for example, Davis & Karim (2008a) find that the non-significant effect of the private-credit-to-GDP ratio becomes significant when they use its standardized transformation. Consequently, we performed three sets of robustness regressions with untransformed: (i) financial development variables; (ii) control variables; and (iii) financial development and control variables. We report that the effects of the growth of M3/GDP, the growth of the Bank ratio, and the level of Credits/Deposits remained significant in the horse-race regressions in all three specifications, consistent with our baseline analysis (the results are available upon request).

17

ACCEPTED MANUSCRIPT

Consequently, the findings of the robustness analysis can be summarized as follows: (1) the level of activity of the banking sector is robustly related to the occurrence of banking crises. At a disaggregated level, this result is supported by the level of Credits/Deposits, whose coefficient is significant and positive in all horse-race regressions; (2) the effect of the growth of the size of the banking sector is only rarely significant

CR IP T

in the horse-race regressions, which may be related to opposite-sign effects at the disaggregated level: the growth of M3/GDP positively affects the occurrence of banking crises, while the negative effect of the Bank ratio is driven by its growth and its volatility (in most specifications), its growth (in a few specifications with

AN US

additional controls), or its volatility (when using a polytomous measure of crises).

Largely corroborating the baseline findings, these results are used as a benchmark in the following section devoted to the analysis of potential heterogeneity.

V. Heterogeneity

M

We investigate three potential sources of heterogeneity related to nonlinearities in the effect of financial development variables, the time span of the pre-crisis dynamics of financial

PT

to Online Appendix A3.

ED

development, and the level of economic development. To save space, all results are relegated

5.1. Nonlinearities

CE

Following recent empirical studies that highlight a nonlinear relationship between financial development and economic growth (e.g. Law & Singh, 2014), we explore the presence of

AC

such nonlinearities in our setup. A simple way to determine whether the effect of financial development on the occurrence of banking crises is nonlinear involves considering its squared term in the pooled logit model; however, as it is well known for binary choice models, such an approach would complicate the interpretation of the marginal effects (see Ai & Norton, 2003). Alternatively, Bekaert et al. (2011) compute predicted probabilities of banking crises at the 25% and 75% percentiles of the overall distribution of their different explanatory variables, including the credit-to-GDP ratio. Capitalizing on this idea, we divide each financial development variable into quintiles and jointly introduce them into our pooled logit model. As an illustration, if we consider the FD index variable, we replace it with the five 18

ACCEPTED MANUSCRIPT variables FDQi, i=1, 2, 3, 4, and 5, where, e.g., FDQ1 equals the FD index for observations in the first quintile of the distribution of the FD index and 0 otherwise. This approach allows us to evaluate the marginal effect of financial development variables on the occurrence of banking crises for each quintile. However, since this strategy leads to an important increase in the number of variables, we only separately account for each set of quintiles associated with each financial development variable. The results in the Online Appendix A3 can be summarized as follows. First, the

CR IP T

financial development pattern associated with the occurrence of banking crises is consistent with the benchmark findings, namely, (i) no effect of the aggregated FD index; (ii) a significant effect of the level of FD activity irrespective of the quintile considered; and (iii) a significant effect of the growth of M3/GDP, the growth of the Bank ratio, and the level of Credits/Deposits for all quintiles. Second, the marginal effect of these variables increases

AN US

across quintiles, except for the growth of the Bank ratio, whose effect decreases in absolute value. 13 Third, for some variables, the significance of their effect varies across quintiles. An increase in the volatility of the Bank ratio is associated with a significant decrease in the occurrence of banking crises only for countries in the top two quintiles; such a significant effect similarly arises for the bottom quintiles of the growth of Bank assets/GDP and of

M

Credits/GDP. Consequently, our analysis suggests that the effect of financial development variables may vary in magnitude, and sometimes even in significance, across quintiles.

ED

However, due to the difficulty of running horse-race regressions on quintiles, these results (and particularly those for the variables not found to be significant in the benchmark analysis,

PT

namely, Credits/GDP and Bank assets/GDP) should be interpreted with much caution.

CE

5.2. The pre-crisis dynamics of financial development The literature suggests that accounting for different lags of financial development variables

AC

may be important when estimating the determinants of banking crises. For example, when jointly accounting for its first five lags, Schularick & Taylor (2012) show that the effect of the growth of the credit-to-GDP ratio is significant only for some specific lags, a result confirmed and extended by Aikman et al. (2015) to the joint presence of the first five lags of the growth of broad money-to-GDP. However, considering lags would be a fairly complicated task in our analysis involving a large number of financial development variables. Instead, we extend our

13

With respect to the bottom quintile, the marginal effect in the top quintile is more than 1.5 times higher for the level of M3/GDP and approximately 30% lower in absolute value for the growth of the Bank ratio.

19

ACCEPTED MANUSCRIPT benchmark analysis that considers a 3-year period panel by performing estimations on a 5year period panel and on a yearly panel.14 The use of a 5-year period panel provides a medium-term perspective on the pre-crisis dynamics of the banking sector. Horse-race estimations reveal that the growth of the FD size index, of M3/GDP, and of the Bank ratio lose their significance compared with the benchmark findings. In contrast, an increase in the level of FD activity, particularly in the

banking crises from a medium-term perspective.

CR IP T

level of Credits/Deposits, is associated with a significant increase in the occurrence of

The use of yearly data provides a macroeconomic short-term perspective on the precrisis dynamics of the banking sector.15 Compared to the results for the 5-year period panel, the estimations based on yearly data are closer to our previous findings. First, at the aggregated level, only the effect of the level of the FD activity index is significant. Second,

AN US

horse-race regressions support a significant influence of the growth of the Bank ratio and the level of Credits/Deposits. Consequently, except for the loss of significance of the growth of M3/GDP, the results are comparable to those in our benchmark analysis.

5.3. The level of economic development

M

Given that previous studies showed that the effect of financial development on economic growth changes with the level of economic development (e.g., Deidda & Fattouh, 2002; Rioja

ED

& Valev, 2004), we investigate the presence of such effects for the occurrence of banking crises. Several arguments could support potential differences in terms of banking crisis

PT

exposure between developed and developing countries: (i) developing countries rely more heavily on the banking sector than on financial markets for external financing (Mishkin,

CE

2015); (ii) banking regulation and supervision are more advanced in developed countries (Barth et al., 2013); and (iii) developing countries experienced a delayed and faster

AC

liberalization of their financial systems (Reinhart & Rogoff, 2009). Based on the World Bank’s classification, we divide our sample into three groups corresponding to low- (40 countries), intermediate- (46 countries), and high-income countries (26 countries). The results based on the estimations reported in Online Appendix A3 can be

summarized as follows. At the aggregated level, the effect of the level of FD activity (growth 14

We would like to thank an anonymous Referee for suggesting this analysis. To avoid a potential simultaneity bias, the financial development and control variables are three-year lagged (except for contagion, currency crises, and debt crises variables, which are entered contemporaneously, as in the baseline analysis). Regarding the yearly volatility of financial development variables, we use a five-year rolling window for the computation of standard deviations. 15

20

ACCEPTED MANUSCRIPT of FD size) index is always (sometimes) significant. However, more important differences across the three groups of countries emerge at the disaggregated level. The growth of M3/GDP and of the Bank ratio significantly affect the occurrence of banking crises in intermediate-income countries, and the level of Credits/Deposits loses its significance compared to the benchmark results. Moreover, the latter variable and the volatility of the Bank ratio significantly affect the occurrence of banking crises in low-income countries. Finally, none of these four financial development variables, which were previously found to

CR IP T

affect the occurrence of banking crises, are significant for high-income countries; instead, the effects of the level of M3/GDP and of Credits/GDP are significant. Keeping in mind that these results, particularly those for high-income countries, should be interpreted with caution given the small number of observations in each group, our analysis suggests the presence of heterogeneity related to the level of economic development in the relationship between

AN US

financial development and the occurrence of banking crises.

VI. Conclusion

This paper provides an in-depth empirical analysis of the relationship between financial development and the occurrence of banking crises. Adding to the existing literature, we

M

adopted a “horse-race” strategy by accounting for several variables that capture the size and the activity of the banking sector, and by considering the joint effect of their level, growth,

ED

and volatility.

Using a large dataset of 113 banking crises observed in 112 countries during the 1980-

PT

2009 period, we found that the level of activity of the banking sector and, to some extent, the growth of its size significantly affect the occurrence of banking crises. At a more

CE

disaggregated level, the growth of M3/GDP and the level of Credits/Deposits exert a positive effect, while the growth and sometimes the volatility of the Bank ratio negatively affect the

AC

occurrence of banking crises. In addition, our horse-race regressions, which account for the joint impact of the level, growth, and volatility of financial development variables, failed to reveal a significant effect of the different measures of Credits/GDP. Moreover, these results remained robust when accounting for unobserved

heterogeneity, possible autocorrelation of the error term, a large set of additional controls, and an alternative measure of the occurrence of banking crises. Finally, we emphasized heterogeneity in the relationship between financial development and the occurrence of banking crises related to nonlinearities in the effect of financial development, the time span for the pre-crisis dynamics of financial development, and the level of economic development. 21

ACCEPTED MANUSCRIPT Overall, our results provide new insights into the determinants of banking crises and suggest that only some dimensions of financial development are significantly associated with the occurrence of banking crises. In the current international environment characterized by a surge in financial crises, our findings could motivate further research. First, compared to our analysis devoted to banking crises, a future study could investigate the specific features of financial development that might affect other types of financial crises, including currency or sovereign debt crises. Second, it would be interesting to explore the precise dimensions of

AC

CE

PT

ED

M

AN US

CR IP T

financial development that might influence the duration and costs of banking crises.

22

ACCEPTED MANUSCRIPT References

AC

CE

PT

ED

M

AN US

CR IP T

C. Ai & E.C. Norton (2003). Interaction terms in logit and probit models, Economics Letters, vol. 80, pp. 123-129. D. Aikman, A.G. Haldane & B.D. Nelson (2013). Curbing the credit cycle, Economic Journal, vol. 125, 1072-1109. L. Alessi, A. Antunes, J. Babecky, S. Baltussen, M. Behn, D. Bonfim, O. Bush, C. Detken, J. Frost, R. Guimaraes, T. Havranek, M. Joy, K. Kauko, J. Mateju, N. Monteiro, B. Neudorfer, T. Peltonen, P. M. M. Rodrigues, M. Rusnak, W. Schudel, M. Sigmund, H. Stremmel, K. Smidkova, R. van Tilburg, B. Vasicek, and D. Zigraiova (2015). Comparing different early warning systems: Results from a horse race competition among members of the Macro-prudential Research Network, mimeo. L. Alessi & C. Detken (2018). Identifying excessive credit growth and leverage, Journal of Financial Stability, vol. 35, 215-225. R. Barrell, E.P. Davis, D. Karim & I. Liadze (2010). Bank regulation, property prices and early warning systems for banking crises in OECD countries, Journal of Banking and Finance, vol. 34, pp. 2255-2264. J.R. Barth, G. Caprio & R. Levine (2013). Bank Regulation and Supervision in 180 Countries from 1999 to 2011, Journal of Financial Economic Policy, vol. 5, pp. 111-219. T. Beck (2012). The Role of Finance in Economic Development: Benefits, Risks, and Politics, in D. Mueller (Ed.), The Oxford Handbook of Capitalism, Chapter 6, pp. 161-203, Oxford: Oxford University Press. T. Beck, T. Chen, C. Lin & F.M. Song (2016). Financial innovation: The bright and the dark sides, Journal of Banking and Finance, vol. 72, pp. 28-51. T. Beck, H. Degryse & C. Kneer (2014). Is more finance better? Disentangling intermediation and size effects of financial systems, Journal of Financial Stability, vol. 10, pp. 50-64. T. Beck, A. Demirguc-Kunt & R. Levine (2006). Bank concentration, competition, and crises: First results, Journal of Banking and Finance, vol. 30, 1581-1603. G. Bekaert, C.R. Harvey & C. Lundblad (2005). Does financial liberalization spur growth?, Journal of Financial Economics, vol. 77, pp. 3-55. G. Bekaert, C.R. Harvey & C. Lundblad (2006). Growth volatility and financial liberalization, Journal of International Money and Finance, vol. 25, pp. 370-403. G. Bekaert, C.R. Harvey & C. Lundblad (2011). Financial Openness and Productivity, World Development, vol. 39, pp.1-19. G. Bekaert, C.R. Harvey, A. Kiguel & X. Wang (2016). Globalization and Asset Returns, Annual Review of Financial Economics, vol. 8, pp. 221-288. F. Betz, S. Oprica, T.A. Peltonen & P. Sarlin (2014). Predicting distress in European banks, Journal of Banking and Finance, vol. 45, pp. 225-241. M. Bussiere & M. Fratzscher (2006). Toward a new early warning system of financial crises, Journal of International Money and Finance, vol. 25, pp. 953-973. G. Caggiano, P. Calice & L. Leonida (2014). Early warning systems and systemic banking crises in low income countries: A multinomial logit approach, Journal of Banking and Finance, vol. 47, 258-269. G. Caggiano, P. Calice, L. Leonida & G. Kapetanios (2016). Comparing logit-based early warning systems: Does the duration of systemic banking crises matter?, Journal of Empirical Finance, vol. 37, 104-116. A.C. Cameron & P.K. Trivedi (2005). Microeconometrics. Methods and Applications, First Edition, Cambridge University Press. G. Caprio & D. Klingebiel (2003). Episodes of Systemic and Borderline Financial Crises, World Bank Dataset. 23

ACCEPTED MANUSCRIPT

AC

CE

PT

ED

M

AN US

CR IP T

M.D. Chinn & H. Ito (2011). The Chinn-Ito Index: A de jure measure of financial openness, available at: http://web.pdx.edu/~ito/Chinn-Ito_website.htm. S. Claessens, G. Dell’Ariccia, D. Igan & L. Laeven (2013). Lessons and Policy Implications from the Global Financial Crisis, in G. Caprio (Ed.), The Evidence and Impact of Financial Globalization, Chapter 49, pp. 753-761, Amsterdam: Elsevier. P. Congdon (2003). Applied Bayesian Modelling, Wiley Series in Probability and Statistics, John Wiley and Sons Ltd, West Sussex, England. E.P. Davis & D. Karim (2008a). Comparing early warning systems for banking crisis, Journal of Financial Stability, vol. 4, pp. 89-120. E.P. Davis & D. Karim (2008b). Could early warning systems have helped to predict the sub-prime crisis?, National Institute Economic Review, vol. 206, pp. 35-47. L. Deidda & B. Fattouh (2002). Non-linearity between finance and growth. Economics Letters, vol. 74, pp.339-345. A. Demirguc-Kunt & E. Detragiache (1998). The Determinants of Banking Crises in Developing and Developed Countries, IMF Staff Papers, vol. 45, pp.81-109. A. Demirguc-Kunt & E. Detragiache (2005). Cross-Country Empirical Studies of Systemic Banking Distress: A Survey, IMF Working Paper no. 05/96. M. Drehmann, C. Borio & K. Tsatsaronis (2011). Anchoring Countercyclical Capital Buffers: The Role of Credit Aggregates, International Journal of Central Banking, vol. 7, pp. 189-240. R. Duttagupta & P. Cashin (2011). Anatomy of banking crises in developing and emerging market countries, Journal of International Money and Finance, vol. 30, pp. 354-376. B. Eichengreen & C. Arteta (2000). Banking Crises in Emerging Markets: Presumptions and Evidence, Center for International and Development Economics Research (CIDER) Working Paper no. C00-115, University of California Berkeley. F. Giavazzi & G. Tabellini (2005). Economic and Political Liberalizations, Journal of Monetary Economics, vol. 52, pp. 1297-1330. D.C. Hardy & C. Pazarbasioglu (1999). Determinants and Leading Indicators of Banking Crises: Further Evidence, IMF Staff Papers, vol. 46, pp. 247-258. O. Jorda, M. Schularick & A.M. Taylor (2016). Sovereigns versus Banks: Credit, Crises, And Consequences, Journal of the European Economic Association, vol. 14, pp. 45-79. K. Kauko (2014). How to foresee banking crises? A survey of the empirical literature, Economic Systems, vol. 38, pp. 289-308. G.L. Kaminsky & C.M. Reinhart (1999). The Twin Crises: The Causes of Banking and Balance-of-Payments Problems, American Economic Review, vol. 89, pp. 473-500. M. Kumar, U. Moorthy & W. Perraudin (2003). Predicting emerging market currency crashes, Journal of Empirical Finance, vol. 10, pp. 427-454. L. Laeven & F. Valencia (2013). Systemic Banking Crises Database, IMF Economic Review, vol. 61, pp. 225-270. S.H. Law & N. Singh (2014). Does too much finance harm economic growth?, Journal of Banking and Finance, vol. 41, pp. 36-44. M. Lo Duca & T. Peltonen (2013). Assessing systemic risks and predicting systemic events, Journal of Banking and Finance, vol. 37, pp. 2183-2195. M.G. Marshall & K. Jaggers (2010). Polity IV Project: political regime characteristics and transitions, 1800-2007, available at: http://www.systemicpeace.org/polity. F.S. Mishkin (2015). The Economics of Money, Banking, and Financial Markets, The Pearson Series in Economics. D. Quinn, M. Schindler & A.M. Toyoda (2011). Assessing Measures of Financial Openness and Integration, IMF Economic Review, vol. 59, pp. 488-522. R. Rajan (2005). Has Financial Development Made The World Riskier?, NBER wp 11728.

24

ACCEPTED MANUSCRIPT

AC

CE

PT

ED

M

AN US

CR IP T

C.M. Reinhart & K.S. Rogoff (2009). This time is different. Eight centuries of financial folly, Princeton, NJ: Princeton University Press. C.M. Reinhart & K.S. Rogoff (2011). From Financial Crash to Debt Crisis, American Economic Review, vol. 101, pp. 1676-1706. C.M. Reinhart & K.S. Rogoff (2013). Banking crises: An equal opportunity menace, Journal of Banking and Finance, vol. 37, pp. 4557-4573. F. Rioja & N. Valev (2004). Finance and the sources of growth at various stages of economic development, Economic Inquiry, vol. 42, pp. 127-140. N. Samargandi, J. Firdmuc & S. Ghosh (2015). Is the Relationship Between Financial Development and Economic Growth Monotonic? Evidence from a Sample of Middle-Income Countries, World Development vol. 68, pp.66-81. M. Schularick & A.M. Taylor (2012). Credit Booms Gone Bust: Monetary Policy, Leverage Cycles and Financial Crises, 1870-2008, American Economic Review, vol. 102, pp. 10291061.

25