DON’T STAND SO CLOSE TO ME: BANKING UNION AND
SUPERVISORY STYLES. DOES FITNESS MATTER?
Alessandro Carrettaa,b, Vincenzo Farinaa, Franco Fiordelisib,c ,
Paola Schwizerd, Francesco Saverio Stentella Lopesc,e, *
a
University of Rome Tor Vergata, Italy
b
SDA Bocconi, Italy
c
University of Rome III, Italy
d
University of Parma, Italy
e
Tilburg University, The Netherlands
Abstract
Supervisory style is diverse across countries (Barth, Caprio, Levine, 2013) and our
paper investigates the impact of various styles on bank stability. A distinctive trait of
our work is that we measure supervisory styles in terms of cultural determinants,
through text data processing of the official discourses made by the head of European
supervisory authorities. Results indicate that supervisory styles have heterogeneous
impact on European banking systems, however in crisis periods a cooperative and less
formal approach has a positive impact on the speed at which banks absorb external
shock to their distance to default.
Very Preliminarily Draft
Please do not quote without the authors’ permission
Keywords: Supervisory style; Banking Stability; Text analysis.
JEL Classification:
____________________________
*
Corresponding author: University of Rome III, Department of Business Studies, Via S. D’Amico, 77,
tel: +39 06 5733 5672, e-mail: francescosaverio.stentellalopes@uniroma3.it and Tilburg University
and CenTER, Warandelaan 2, P.O.box 9015, The Netherlands, e-mail: F.S.Stentella@uvt.nl
1
1. Introduction
The financial crisis stimulated a debate on the appropriateness of the regulatory and
supervisory approaches pursued in the run-up to the crisis and prompted regulators to
consider important changes in regulation and supervision (R&S).
Financial crisis was the result not only of incomplete regulation, but also of
ineffective supervision (Barth, Caprio and Levine, 2013; Blanchard, 2008; US Financial
Crisis Inquiry Commission, 2011). Consequently, supervisors are requested to
formulate and implement new, more intrusive supervisory strategies (Adams 2013). In
order to improve the global financial architecture, one of the suggested corrective
actions regards the definition of mechanisms for more effective, coordinated actions,
both to reduce the risk of crises, and to address them when they occur.
In this perspective, in the autumn 2012 the European Commission proposed a single
supervisory mechanism (SSM) for banks led by the European Central Bank (ECB) as a
fundamental step in strengthening the Economic and Monetary Union. The proposal
aims at the establishment of an integrated Banking Union that will include a common
prudential regulation, a homogeneous deposit protection scheme and a single bank
resolution mechanism. This is supposed to lead to an enhanced supervision and control
over international banks and to ensure a level playing field for financial institutions,
thereby reducing the risk of contagion (Allen, Babus and Carletti, 2012).
The Banking Union involves a transfer to the European level of the regulatory and
institutional framework responsible for safeguarding the robustness and stability of the
banking industry. In this context of relative (but not absolute) harmonization of
regulatory requirements, the supervisory style – regulation being equal – plays a very
central role, because the European Central Bank will be in charge of R&S in
2
cooperation with national supervisors. Whereas only the most significant banks will fall
under the direct supervision of the ECB, less significant banks, while remaining under
national supervision, will not be excluded from the ECB’s supervisory reach. National
supervisory authorities will have to conform to ECB regulations, guidelines and general
instructions and be subject to the ECB’s broad oversight mandate over the functioning
of the SSM. Changing the structure of regulation cannot itself guarantee effective
supervision (Abrams and Taylor, 2000). It could just be an answer to the desire of “do
something” especially in the aftermath of a financial crisis. In our opinion, the
unification of the supervision for financial sector may improve effectiveness and
efficiency of regulation only if supervisory styles fit the characteristics of the new R&S
framework.
Since the lack of a uniform supervisory style, the central question is now: what
supervisory styles are more effective to ensure the stability of EU financial and banking
sectors?
Our paper aims to answer this question by examining empirically the relationship
between supervisory styles and the soundness of EU financial institutions. For this
reason, our work is related to the literature that focuses on the relationship between
banking regulation and banking fragility.
In detail, we first assess EU supervisory styles of national supervisors, in order to
evaluate to what extent they could affect the functioning of a Single Supervisory
Mechanism. Following, we examine empirically the existence of a relationship between
supervisory styles and the soundness of EU financial institutions using a panel dataset
that contains information on supervisory styles and the soundness of financial
institutions for all EU27 countries over the period 1999-2012.
3
A distinctive trait of our work is that we measure supervisory styles both in terms of
cultural determinants, through text data processing of the official discourses made by
the head of European supervisory authorities, and in terms of intensity of supervision
intervention, by examining to what extent laws are actually implemented.
In order to assess strengths and vulnerabilities of financial systems that could arise
from certain supervisory cultures and law enforcement levels, we consider a set of
financial soundness indicators referring to bank development, bank stability, bank
fragility, bank efficiency, bank performance and integrity in bank lending.
Our results indicate that the supervisory styles have heterogeneous impact on the
stability of European banking system, the style of national supervisors has also an
impact on the speed at which banks recover their stability after external shocks. We find
that in crisis periods a more collaborative approach can increase this speed of
adjustment. Inversely an academic or business language in a crisis period lead the
external shock to have a more lasting impact on banks’ stability.
The contribution of this paper is manifold. First, this study provides new insight into
the question of whether financial supervision affect financial soundness, by examining
the role exercised by supervisory styles. Second, we apply a distinctive to measure
supervisory styles through text data processing of official discourses of supervisory
authorities. Finally, we compare supervisory styles adopted by EU national supervisors
in order to evaluate to what extent heterogeneity could affect the functioning of the
SSM.
Our paper has some important policy implications. Understanding the nature of
differences in supervisory styles among European countries, likely to determine
difficulties in the implementation of a “unique European approach”, is useful for the
4
choice of the appropriate tools for managing change and transition. In fact, if it is true
that supervisory styles are quite different among EU countries, the establishment of a
Banking Union could require a severe “fitness effort” from ECB, in order to properly
manage the relationship with the different styles of EU national supervisors.
The rest of the paper is structured as follows. In section 2, we refer to main literature
and define the research hypotheses; section 3 describes the methods; section 4 presents
the results and conclusions are drawn in section 5.
2. Literature & Research Hypotheses
Supervisory style can be defined as the behaviour that supervisors adopt in
supervision, according to different mixes of tools, resources and available information.
When analysing supervisory styles we can move between two extremes, thus
determining an ample set of possible supervisory interventions’ configurations
(Carretta, Farina, Schwizer, 2010). On the one side, there is a style characterized by
formal,
vertical
relationships
between
supervisors
and
supervised
financial
intermediaries; the emphasis is on general, prescriptive rules of conduct; ongoing
supervision is prevailing and it is based on frequent and detailed reporting; sanctions are
imposed mostly because of formal and technical failures; the focus on the enhancement
of internal governance of supervised banks is weak. On the other side there is a
supervisory style characterized by horizontal relationships between supervisors and
supervised financial intermediaries and is oriented towards cooperation and advisory;
the emphasis is on comprehension and sharing of best practices; inspections are
prevailing; supervisor are geographically close to supervised entities; sanctions are
5
mostly imposed because of failures in internal organization and control; the focus on the
enhancement of corporate and internal governance of supervised banks is strong.
What factors affect supervisory styles? Despite the national regulations move
towards convergence, we still have significant divergences in the supervisory styles of
various countries. For instance, differences relate to timing of the assessments
(periodical vs. continuous), intensity and frequency of on-site examinations and of
meetings with directors and senior management, types of reports or information
required from supervised institutions, set of supervisory tools to incentivise firms to
remediate deficiencies (Financial Stability Board, 2013).
According to many authors (Blinder 2010; Masciandaro, Nieto and Quintyn, 2009;
Seelig and Novoa, 2009; Arnone and Gambini, 2007; Čihàk and Podpiera, 2007;
Masciandaro, Quintyn 2007; Barth, Caprio, Levine 2006; Di Giorgio and Di Noia 2005;
Fleming, Lewellyn and Carmichael, 2004; Quintyn and Taylor 2004; Barth, Nolle,
Phumiwasana, Yago 2003; Abrams and Taylor, 2002; Goodhart 2002), these differences
mainly depend on the architecture of the financial supervision (e.g. if the supervisor is
the central bank, if there is a unique supervisor or many, etc.).
Might differences in supervisory styles affect supervisory performance? In principle,
given the two goals of stability and efficiency of the financial system, some supervisory
styles could have positive effects on one goal but not, to the same extent, on the other
one; they could reach their objectives either in the short or in the long run. A style that
shows to be very demanding in terms of formal compliance could increase compliance
costs, thus reducing the efficiency of the supervised banks. Alternatively, a style that
appears more oriented towards cooperation and proximity, by stimulating a learning
6
process on safe and sound management practices, could result in an efficiency increase
in the medium term, but might create some instability problem in the short term.
However, answering to this question is not a simple matter. First, supervisory
performance could be measured in different ways, considering the effectiveness (e.g. the
reduction of the probability of crisis events) and/or the efficiency (e.g. the reduction of
compliance costs both for the single intermediaries and for the economic and financial
system as a whole).
Second, there is the difficulty of proving architecture-style-performance causality.
In fact, a change in banks’ risk profile might have little or nothing to do with a
particular supervisory architecture or style, but could simply be the result of a change in
economic conditions or some other exogenous factor (Cavelaars, de Haan, Hilbers, and
Stellinga, 2013).
At this regard, some studies (Barth, Dopico, Nolle and Wilcox, 2002; Barth, Caprio
and Levine 2004 and 2008; Masciandaro, Vega Pansini, Quintyn 2011; Cihàk,
Demirgüç-Kunt, Martínez Pería and Mohseni-Cheraghlou 2012) examine the
relationship between the architecture of financial supervision and supervisory
performance.
Key issues for the architecture of the financial supervision are: i) supervision
structure (i.e., whether there should be one or multiple supervisory authorities, and
whether the central bank should be involved in bank supervision); ii) supervision scope
(i.e., whether bank supervisory authorities should supervise other financial service
industries); iii) supervisory independence (i.e., the degree to which bank supervisors
should be subject to political and economic policy pressure and influence).
7
Supervisory performance variables include: i) definition of regulatory capital, ii)
level of discretion with which banks calculate capital requirements, iii) restrictions to
engage in non-bank activities such as insurance, investment banking, real estate or nonfinancial activities; iv) non-performing loans and provisioning requirements; v) the
treatment of bad loans and loan losses; vi) the level of exposition of the country to a
systemic financial crisis.
Nevertheless, these studies reach un-conclusive results. In synthesis, due to the pros
and cons of each supervisory model, there are no strong theoretical arguments in favour
of any particular architecture of financial supervision.
Other studies assess whether and to what extent laws are actually implemented and
focus on the relationship between supervisory intervention and supervisory performance
(Berger, Davies and Flannery, 2000; DeYoung, R., Flannery, M., Lang, W. and
Sorescu, S., 2001; Bhattacharya, Plank, Strobl and Zechner, 2002; Gunther and Moore,
2003; Delis and Staikouras, 2011).
In principle, the level of supervision applied by national authorities must be
commensurate with the potential destabilization risk that banks pose to their own
domestic financial systems, as well as the broader international financial system. Intense
supervision can help prevent banks from engaging in excessive risk-taking behaviour
and thus improve bank development, performance and stability.
All in all, the above research suggests that supervisory style is the link between
financial supervision architecture and supervisory performance. In fact, it reflects
financial supervision architecture and affects supervisory performance by influencing
the intensity of supervisory intervention.
8
According to Financial Stability Board (2012), supervisory performance should be
also a consequence of supervisory culture and different types of skills and resource
levels. Therefore supervisory culture is an aspect considered particularly important by
ECB representatives: “In the supervisory field cooperation has historically been looser,
and as a result there are several different supervisory traditions and philosophies that
we need to unite into a single system. In other words, we are working together to create
a single supervisory culture and find our leitmotif, but it will take time” (Speech by Jörg
Asmussen, Member of the Executive Board of the ECB,
at the Atlantic
Council,
London, 9 July 2013).
As an example, the dominant culture of some supervisors is centred around
economists, while other supervisors tend to be dominated by accountants and lawyers.
This could lead to different perspectives when considering macro-prudential and microprudential objectives and therefore to distinctive approaches when supervising
individual institutions. The risk is to have micro policies that can destructive at the
macro level (Schoenmaker, 2012).
However, to our knowledge no research exists on the relationship between
supervisory culture and supervisory performance. Therefore, we find it particularly
interesting and innovative to examine the effect of the cultural traits underlying
supervisory style on financial soundness.
On this basis, our research question is: Given the new European supervisory
framework, what supervisory styles - measured in terms of cultural determinants and in
terms of intensity of supervision intervention - are more effective to ensure the stability
of financial and banking sectors?
9
3. Data and Variables
We test our hypothesis using a sample of European banks with a consolidated balance
sheet, the result is a sample of 1313 European banks spanning from 1999 to 2011 for a
total of 4220 observations. The accounting data come from Bankscope, the
macroeconomic data from the database of the World Bank. All variables used in the
paper are reviewed in table 1.
[Insert here Table 1]
3.1. Text analysis
Text analysis refers to any technique used to examine, in a systematic and objective
manner, the specific characteristics of a text (Weber, 1990; Stone, Dunphy, Smith, and
Ogilvie, 1966). The use of text analysis for cultural studies is based on the assumption
that distinctive values of organizations are reflected in the documentation that they
produce (e.g. reports on financial statements, presentations, and speeches), and that the
language used provides the key to their interpretation (Schein, 1985).
We develop a measure of supervisory style through the analysis of the official
discourses made, during the period 1999 - 2012, by the head of each R&S authority of
European Union countries (overall, we analysed 4.240.360 words) on the basis of the
Harvard IV Psycho Social Dictionary (Kelly and Stone, 1975; Dunphy, Bullard and
Crossing, 1974) and of the Lasswell Value Dictionary (Namenwirth and Weber, 1987;
Lasswell and Namenwirth,1969)1.
Similarly to other applications of text analysis (see for example: Carretta, Farina,
1
The spreadsheet format can be downloaded at: www.wjh.harvard.edu/~inquirer/spreadsheet_guide.htm
10
Fiordelisi, Martelli and Schwizer, 2011; Tetlock, Saar-Tsechansky and Macskassy,
2008; Tetlock 2007), we define some language dimensions counting the number of
words in the corpus of discourses for each supervisory authority that falls within
selected Harvard IV and Lasswell Value categories2 (table 2):
[Insert here table 2]
Power (Figure 1 panel A) refers to a more or less authoritative supervisory style
and is defined as the degree (expressed as the number of hits per 1000) to which
discourses include terms from the Harvard IV category Power, indicating a concern
with power, control or authority.
Cooperative (Figure 1 panel B) refers to a more or less cooperative supervisory
style and is defined as the degree (expressed as the number of hits per 1000) to which
discourses include terms from the Lasswell Value Dictionary category PowerCop,
indicating ways for cooperating. Formal (Figure 1 panel C) refers to a more or less
formal language and is defined as the degree (expressed as the number of hits per 1000)
to which discourses include terms from the Harvard IV categories Academic + Legal,
indicating intellectual, educational and legal matters. Business (Figure 1 panel D) refers
to a more or less business oriented language and is defined as the degree (expressed as
the number of hits per 1000) to which discourses include terms from the Harvard IV
category Econ@, indicating economic and business oriented matters. Evaluation (Figure
1 panel E) refers to a more or less judgement oriented language and is defined as the
degree (expressed as the number of hits per 1000) to which discourses include terms
2
Each category contains a list of words and word senses.
11
from the Harvard IV category Econ@, indicating judgement and evaluation, including
means-ends judgement.
3.2 Banks stability
Bank stability is measured using the Z-score (among the more recent studies: Houston,
Lin, Lin, and Ma (2010),Demirguc-Kunt and Huizinga (2010), Laeven and Levine
(2009)). The Z-score is a proxy for the banks’ distance to default and it is calculated as:
𝐸
+𝑅𝑂𝐴𝑖𝑡
𝑖𝑡
𝑍𝑖𝑡 = 𝑇𝐴𝜎(𝑅𝑂𝐴)
(2)
𝑖
𝐸
Where 𝑇𝐴 denotes the leverage ratio: the share of total equity in total asset (𝑇𝐴𝑖𝑡 ),
𝑖𝑡
𝑅𝑂𝐴𝑖𝑡 is the return on asset and 𝜎(𝑅𝑂𝐴) is the standard deviation of the return on asset.
The Z-score can be interpreted as the number of standard deviations by which the
banks’ profitability has to fall to devour the entire capital buffer. An high Z-score
implies, then, a lower default probability. Table 1 provides the evolution over time of
the average Z-score in our sample by country.
[Insert here Table 3]
𝐿
We also control for the share of loans in total asset (𝑇𝐴 ), for the share of non𝑖𝑡
interest revenue on total revenue (𝑁𝐼𝑅𝑖𝑡 ), for the dimension of each bank measured as
the logarithm of the Total Asset (ln⁡(𝑇𝐴𝑖𝑡 )). We also control for some macroeconomic
determinants of banks stability, which are the inflation and for the market capitalization.
[Insert here Table 4]
12
4. Econometric design (FF)
We analyze the impact that supervisory style has an on banks’ distance to default. Our
assumption is that each bank choses the level of stability which fits best its features.
Therefore we start testing if the supervisory style has a direct impact on the level of
stability chosen by each bank. In a frictionless world banks will maintain their stability
at chosen level. However banks’ stability isn’t fully under control of banks management
and when it is driven away from the chosen level by external shocks banks will try to
bring their soundness back to the chosen level. In this framework the banks soundness
can be modelled as a weighted average of the chosen level of stability and the past
distance to default. We can then write it as:
𝑍𝑖𝑡 = 𝛾𝑍𝑖𝑡∗ + (1 − 𝛾)𝑍𝑖𝑡−1 + 𝜀𝑖𝑡 ⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡(2)
Where 𝑍𝑖𝑡∗ is the desired level of stability⁡𝑍𝑖𝑡 is the distance to default at time t and 𝛾 is
the average speed at which banks will bring back the stability on the desired level.
Therefore the smaller the 𝛾, the more rigid the banks level of stability is and the longer
it takes for a bank to achieve its target level after an external shock to bank stability.
In this paper we also test if different style of supervision can affect the speed of
adjustment 𝛾. Following Berger et al. (2008) and Oztekin and Flannery (2012) our tests
for the effect of supervisory style on the banks stability is developed in two steps.
13
First, we define the optimal level of stability as a function of banks and country
futures among which we also have the supervisory style. Particularly we write the
desired level of stability as:
∗
𝑍𝑖𝑗𝑡
= ⁡ 𝛽 𝑋 𝑋𝑖𝑡−1 + 𝛽 𝑐 𝐶𝑗𝑡−1 + 𝜏𝑡 ⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡(3)
Where 𝑋𝑖𝑡−1 is a vector of bank characteristics and 𝐶𝑗𝑡−1 are country variables.
Plugging (2) into (1) and rearranging we obtain:
𝑍𝑖𝑗𝑡 − 𝑍𝑖𝑗𝑡−1 = 𝛾(𝛽 𝑋 𝑋𝑖𝑡−1 + 𝛽 𝑐 𝐶𝑗𝑡−1 + 𝜏𝑡 − 𝑍𝑖𝑗𝑡−1 ) + 𝜀𝑖𝑗𝑡 ⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡(4)
We estimate (4) using Arellano Bond (1991), the direct effect will be given by
the coefficient on the supervisory style variables 𝛽 𝑐 , however as outlined in Oztekin and
Flannery (2012) it is plausible that banks characteristics partially reflect the supervisory
style which in turn determine the speed of adjustment to the target level of stability.
To test then if the supervisory style has an impact on the speed of adjustment we
use the coefficient estimated in the first step to calculate 𝑍𝑖𝑡∗ . Then, we subtract the
actual level of stability from the calculated desired level of soundness to have a proxy
for the deviation of each bank from its desired level of stability which we name 𝐺𝐴𝑃𝑖𝑡 ,
finally letting the adjustment speed be a function of banks and country characteristics
we can rewrite (2) as. Follows:
𝑍𝑖𝑡 − 𝑍𝑖𝑡−1 = 𝜌𝑖𝑗𝑡 (𝐺𝐴𝑃𝑖𝑡 ) + 𝜀𝑖𝑗𝑡 ⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡(5)
Where 𝜌𝑖𝑗𝑡 is the following:
14
𝜌𝑖𝑗𝑡 = Λ𝑄𝑖𝑗𝑡−1 = γ0 + Λ𝑋 𝑋𝑖𝑡−1 + Λ𝐶 𝐶𝑗𝑡−1 ⁡⁡⁡⁡⁡⁡⁡⁡(6)
In final, we estimate Λ using OLS form (6):
𝑍𝑖𝑗𝑡 − 𝑍𝑖𝑗𝑡−1 = Λ𝑄𝑖𝑗𝑡−1 (𝐺𝐴𝑃𝑖𝑡 ) + 𝜀𝑖𝑗𝑡 ⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡(7)
The coefficients’ vector Λ𝐶 will give us an estimation of the impact of the
supervisory style on the speed of adjustment.
5. Results
Table 5 outlines how both the speed of adjustment and the direct impact of supervisory
styles on banks stability change widely across countries. Particularly a standard
deviation increase in the usage of words relate to power in the head R&S authority
speeches has a negative and significant impact on the banks stability of France,
Germany and Portugal, however it has a positive impact on the stability of Belgian
banks. A increase in formal approach proxy by our academic and legal style has a
positive impact on the stability of Dutch and Sweden banks but it generates a negative
impact on Belgian and Portuguese banks.
[insert here table 5]
15
Finally one standard deviation increase in words related to judgment ( 𝑒𝑣𝑎𝑙𝑗𝑖𝑡 )
increases the stability of German and Sweden banks by respectively 0.0427 and 1.9625,
however it decreases the stability of German banks by 0.0427.
Moreover the speed of adjustment spans form a minimum of 0.28 in Germany to a
maximum of 0.83 in Ireland. All those evidence outlines how there are still very relevnt
different standing between the stability of European banking systems and the
effectiveness of different supervisory style in different state members. Therefore in the
second stage of our analysis we run a unique regression for the whole sample to
estimate if despite of the very relevant differences still standing among European
banking systems the different supervisory styles have a significant impact on the speed
at which banks absorb external shocks to their distance to default.
Table 6 reports the coefficient from the second step of our analysis estimated using
equation (6). The regression model outlines how despite of the high heterogeneity of
European banking system a standard deviation in the usage of words relate to power
leads to more rigid level of stability since a standard deviation increase in 𝑝𝑜𝑤𝑒𝑟𝑗𝑡
decreases the speed of adjustment by 0.0086. An opposite effect is provided by evaluation
words, an increase in words related to judgments in the supervisory authority speeches,
in fact leads to an higher speed of adjustment of the banks stability level.
Finally in model (2) we test if there are any differences in the effect that the
supervisory styles have on the speed of adjustments in normal periods and in crisis
periods. Model (2) of table (6) provides estimated coefficient for this test. We provide
evidences of how the significance of the power approach becomes statistically
indistinguishable than 0 cancelling out its negative effect if we spilt the sample in crisis
and no crisis periods. In crisis periods the usage of academic and legal words has a
16
negative impact on the speed at which banks absorb external shock to their stability,
conversely an increase in the cooperative style result in an increase in the adjustment
speed, this effect however is economically very small representing only an increase of
one basis point.
Finally in both our model we can observe how a crisis periods seems to increase the
speed of adjustment and how the banks with a distance to default above the chosen level
have on average a slower speed of adjustment.
7. Conclusions
The recent financial crisis has stimulated the debate on bank regulation and
supervision’s effectiveness. Bank regulatory and supervisory styles are remarkably
diverse across countries, even in the European Union. The Banking Union will however
require that national authorities conform to ECB regulations, guidelines and
instructions, thereby adapting their policies and tools to a homogeneous model.
In this paper, we assess and compare R&S styles adopted until now by the
European national supervisors, considering the language of different supervisors as a
proxy of different styles in R&S. Using a sample of European banks, we outline how
important difference still stand among European systems. Those difference lead to an
highly heterogeneous impact of the supervisory style on banks stability across different
states. This issue makes difficult to assess univocally which is the most effective
supervision style among European country. However if we move the focus to the speed
at which the banks systems absorb external shock to their stability, we outline how on
average an authoritative approach tend to decrease the speed of adjustment, while a
17
judgmental approach tend to increase the capacity of European banking system to
absorb external shocks to their distance to default. However if we distinguish between
crisis and no crisis periods the negative impact of authoritative approach becomes
statistically indistinguishable than zero and a cooperative approach have a small
positive effect.
Our contribution is manifold. First, this study provides new insight into the
question of whether financial supervision affect financial soundness, by examining the
role exercised by supervisory styles. Second, we apply a distinctive to measure
supervisory styles through text data processing of official discourses of European
supervisory authorities. Finally, we compare supervisory styles adopted by European
national supervisors in order to evaluate to what extent heterogeneity could affect the
functioning of the SSM.
Our paper has some important policy implications when we posit that the
establishment of a Banking Union requires a severe “fitness effort” from ECB, in order
to properly manage the relationship with the different styles of EU national supervisors.
18
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22
Table 1
This table reviews all the variables used in the paper and the database from which they have been
collected
Variable
Description
Database
A proxy for banks’ distance to default
Bankscope
𝑍𝑖𝑗𝑡
Percentage of words relates to power, control, authority in the
𝑝𝑜𝑤𝑒𝑟𝑗𝑡
analysed speech
Text analysis
Percentage of words indicating ways for cooperating in the
𝑐𝑜𝑜𝑝𝑗𝑡
analysed speech
Text analysis
Percentage of words relates to intellectual, educational and legal
𝑎𝑐𝑎𝑑⁡𝑙𝑒𝑔𝑎𝑙𝑗𝑡
matters in the analysed speech
Text analysis
Percentage of words relates to economic and business oriented
𝑏𝑢𝑠𝑖𝑛𝑒𝑠𝑠𝑗𝑖𝑡
issues in the analysed speech
Text analysis
Percentage of words implying judgement and evaluation,
𝑒𝑣𝑎𝑙𝑗𝑖𝑡
including means-ends judgement in the analysed speech
Text analysis
𝑖𝑛𝑓𝑙𝑎𝑡𝑖𝑜𝑛𝑗𝑡
Inflation rate
World bank
𝑚𝑘𝑡⁡𝑐𝑎𝑝𝑗𝑡
Market capitalization
World bank
𝐿⁄𝑇𝐴𝑖𝑡
Share of loans in total asset
Bankscope
Share of non-interest revenue on total revenue
Bankscope
𝑁𝐼𝑅⁄𝑇𝑅𝑖𝑡
Natural logarithm of total asset
Bankscope
ln⁡(𝑇𝐴)𝑖𝑡
Share of money market funding in total funding
Bankscope
𝑤ℎ. 𝑓𝑢𝑛𝑑𝑖𝑡
23
Table 2
Language categories drawn from Harvard IV Dictionary and Lasswell Value Dictionary
Category
Source
Description
Examples
Power
Harvard IV
Words in this category indicate a concern
with power, control, authority
accomplish,
admonish, affirm,
…
Words in this category indicate ways for
cooperating
accordance, agree,
coordination, …
Lasswell
PowCoop
Value
Dictionary
Academic
Harvard IV Words in this category relate to intellectual,
+ Legal
educational and legal matters
Econ@
Harvard IV
Words in this category relate to economic
and business oriented matters
Eval@
Harvard IV
Words in this category imply judgements
and evaluation, including means-ends
judgement
certify,
examination,
knowledge, …
auction, board,
business, …
austere,
competence,
discordant, …
24
Table 3
This table reports the average Z-index in Europe* between 1999-2008. The Z-index is the number of
ROA’s standard deviation needed to consume the entire capital buffer of each bank, therefore it a
proxy for banks distance to default.
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
AUSTRIA
3.41
3.18
3.26
3.21
3.15
3.10
2.90
2.85
2.74
2.92
2.94
2.93
2.92
BELGIUM
2.96
2.94
2.83
2.81
2.89
2.86
2.61
2.62
2.55
3.09
2.93
2.88
2.84
BULGARIA
3.02
3.15
3.03
3.04
3.13
3.00
2.94
2.78
2.74
2.85
2.90
2.72
3.14
FINLAND
2.79
2.84
2.81
2.68
2.62
2.52
2.55
2.86
2.83
2.81
2.82
2.95
2.94
FRANCE
3.45
3.48
3.56
3.64
3.66
3.65
3.60
3.56
3.48
3.58
3.60
3.76
3.76
GERMANY
3.09
3.00
3.07
3.19
2.96
3.03
2.92
2.82
2.78
2.95
2.88
3.03
2.99
GREECE
3.02
2.91
2.46
2.38
2.39
2.28
1.99
1.57
1.40
1.14
1.38
0.77
IRELAND
3.03
3.13
3.01
3.22
3.14
3.08
2.39
2.04
1.87
1.81
2.55
3.25
2.38
ITALY
3.77
3.77
3.71
3.73
3.87
3.68
3.16
3.08
3.07
3.14
3.14
3.07
3.19
LUXEMBOURG
3.08
3.28
3.05
3.05
2.97
2.74
2.42
2.30
2.71
2.49
3.14
2.96
3.09
NETHERLANDS
3.09
3.19
3.14
3.09
2.89
2.83
2.73
2.71
2.76
3.09
2.98
3.14
3.10
PORTUGAL
3.36
3.51
3.50
3.58
3.43
3.34
3.18
3.15
3.13
2.94
2.95
2.88
2.89
SPAIN
4.06
3.91
4.04
4.12
4.12
3.89
3.47
3.40
3.44
3.38
3.43
3.29
3.50
SWEDEN
3.38
3.36
3.40
3.17
3.06
2.57
2.55
2.52
2.31
2.85
2.76
2.90
2.88
UK
3.42
3.41
3.52
3.49
3.42
3.25
2.99
2.87
2.81
2.90
2.92
2.96
3.03
FULL SAMPLE
3.41
3.40
3.45
3.46
3.41
3.32
3.07
2.99
2.95
3.09
3.13
3.19
3.24
*Cyprus, Estonia, Lithuania, Iceland, Hungary, Latvia, Slovenia are excluded from the sample because of
the lack of data
25
Variable
𝑝𝑜𝑤𝑒𝑟𝑗𝑡
𝑐𝑜𝑜𝑝𝑗𝑡
𝑎𝑐𝑎𝑑⁡𝑙𝑒𝑔𝑎𝑙𝑗𝑡
𝑏𝑢𝑠𝑖𝑛𝑒𝑠𝑠𝑗𝑖𝑡
𝑒𝑣𝑎𝑙𝑗𝑖𝑡
𝑖𝑛𝑓𝑙𝑎𝑡𝑖𝑜𝑛𝑗𝑖𝑡
𝑚𝑘𝑡⁡𝑐𝑎𝑝𝑗𝑖𝑡
𝐿⁄𝑇𝐴𝑖𝑡
𝑁𝐼𝑅⁄𝑇𝑅𝑖𝑡
ln⁡(𝑇𝐴)𝑖𝑡
𝑤ℎ. 𝑓𝑢𝑛𝑑𝑖𝑡
Table 4
Descriptive statistics of all variables used in the paper
Observations
Mean
Std.Dev
Min
5876
0.0228
0.0038
0.0071
5876
0.0036
0.0019
0.0000
5876
0.0120
0.0032
0.0000
5876
0.0508
0.0077
0.0000
5876
0.0011
0.0005
0.0000
5876
2.0873
1.0925
-4.4799
5876
90.0933
65.1739
0.2073
5866
0.5600
0.2542
0.0000
5869
0.2625
0.2219
0.0000
5876
9.0670
2.0893
2.4849
5852
0.3679
0.2972
0.0000
Max
0.0344
0.0117
0.0495
0.0715
0.0027
12.3488
365.4001
0.9989
1.0000
15.5787
1.0000
26
Table 5
Results from equation (3) estimated using Arellano Bond 1991, using 𝑍𝑖𝑗𝑡−1 ⁡dependent variable and running
one equation per country
.
𝑍𝑖𝑗𝑡−1
𝑝𝑜𝑤𝑒𝑟𝑗𝑖𝑡−1
𝑐𝑜𝑜𝑝𝑗𝑖𝑡−1
𝑙𝑒𝑔𝑎𝑙𝑗𝑖𝑡−1
𝑏𝑢𝑠𝑖𝑛𝑒𝑠𝑠𝑗𝑖𝑡−1
𝑒𝑣𝑎𝑙𝑗𝑖𝑡−1
𝑂𝑏𝑠
𝐵𝑎𝑛𝑘𝑠
AUSTRIA
0.7354***
-0.0235
0.1273
-0.1830
-0.3104
0.0043
339
66
BELGIUM
0.2790
0.2163*
0.0096
-0.0907***
0.0618
0.0322
114
32
BULGARIA
5.0636
-2.0304
1.5817
-0.7868
0.0463
1.3515
38
10
FINLAND
0.4158
-0.6145
-2.0631
-0.7478
1.7059
0.2892
45
15
FRANCE
0.2947***
-0.1280***
-0.0190
-0.0952
0.0370
-0.0750
1,176
281
GERMANY
0.2872**
-0.3452***
-0.1918***
-0.0333
0.0718***
0.0427***
422
101
GREECE
-0.0257
0.0270
-0.4214
0.1732
0.2293
-0.1750
88
27
IRELAND
0.8301*
1.2860
0.4520
-2.3698
0.5868
-1.8332
53
30
ITALY
-0.0692
-0.1413
0.0701
0.2081
-0.0489
-0.3134
428
130
LUXEMBOURG
0.0163
0.1031
0.0302
-0.0435
0.0782***
-0.1175*
49
18
NETHERLANDS
0.4865***
0.0397
0.0602**
0.1749**
-0.0670*
-0.0093
72
29
PORTUGAL
0.6556***
-0.0369***
0.1022
-0.1127***
-0.1354***
-0.1880***
96
30
SPAIN
0.3366
0.0249
-0.2309
-0.0230
0.1257
0.0064
421
148
SWEDEN
0.1036
-0.0467
0.1672***
0.6832***
0.6303***
1.9625***
157
39
UK
0.0670
0.0346
-0.0809
837
190
-0.0041
0.0414
-0.0768**
*** p<0.01, ** p<0.05, * p<0.1
27
Table 6
Results from the estimation of the coefficient of eq (6) using OLS, all variable are the result of
the interaction between the deviation from the estimated chosen level of stability and the bank
specific and country variable standardized for an easier results’ interpretation.
Δln𝑍𝑖𝑗𝑡
Δln𝑍𝑖𝑗𝑡
(1)
(2)
𝑝𝑜𝑤𝑒𝑟𝑗𝑖𝑡−1
-0.0086***
-0.0096
0.0031
0.0058
𝑐𝑜𝑜𝑝𝑗𝑖𝑡−1
-0.0007
-0.0067
0.0021
0.0042
𝑎𝑐𝑎𝑑⁡𝑙𝑒𝑔𝑎𝑙𝑗𝑖𝑡−1
0.0039
0.0193***
0.0056
0.0066
𝑏𝑢𝑠𝑖𝑛𝑒𝑠𝑠𝑗𝑖𝑡−1
-0.0020
0.0018
0.0033
0.0051
𝑒𝑣𝑎𝑙𝑗𝑖𝑡−1
0.0074**
0.0044
0.0034
0.0071
𝑖𝑛𝑓𝑙𝑎𝑡𝑖𝑜𝑛𝑗𝑖𝑡−1
0.0023
0.0017
0.0032
0.0035
𝑚𝑘𝑡⁡𝑐𝑎𝑝𝑗𝑖𝑡−1
0.0002***
0.0002***
0.0000
0.0000
𝐿⁄𝑇𝐴𝑖𝑡−1
-0.0047
-0.0044
0.0054
0.0055
0.0032
0.0007
𝑁𝐼𝑅⁄𝑇𝑅𝑖𝑡−1
0.0219
0.0220
-0.0018
-0.0018
ln⁡(𝑇𝐴)𝑖𝑡−1
0.0034
0.0034
-0.0005
-0.0002
𝑤ℎ. 𝑓𝑢𝑛𝑑𝑖𝑡−1
0.0038
0.0038
𝑐𝑟𝑖𝑠𝑖𝑠 ∗ 𝑝𝑜𝑤𝑒𝑟𝑗𝑖𝑡−1
0.0023
0.0067
𝑐𝑟𝑖𝑠𝑖𝑠 ∗ 𝑐𝑜𝑜𝑝𝑗𝑖𝑡−1
0.0077*
0.0047
𝑐𝑟𝑖𝑠𝑖𝑠 ∗ 𝑎𝑐𝑎𝑑⁡𝑙𝑒𝑔𝑎𝑙𝑗𝑖𝑡−1
-0.0226**
0.0088
𝑐𝑟𝑖𝑠𝑖𝑠 ∗ 𝑏𝑢𝑠𝑖𝑛𝑒𝑠𝑠𝑗𝑖𝑡−1
-0.0079
0.0070
𝑐𝑟𝑖𝑠𝑖𝑠 ∗ 𝑒𝑣𝑎𝑙𝑗𝑖𝑡−1
0.0021
0.0084
0.0229***
0.0262***
𝐶𝑟𝑖𝑠𝑖𝑠
0.0070
0.0074
-0.0198**
-0.0185**
𝐼(𝐺𝐴𝑃 > 0)
0.0079
0.0079
28
𝐶𝑜𝑛𝑠𝑡𝑎𝑛𝑡
0.0003
0.0071
-0.0034
0.0075
𝑂𝑏𝑠𝑒𝑟𝑣𝑎𝑡𝑖𝑜𝑛𝑠
6,003
6,003
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
29
Figure 1
Text analysis of the official discourses made, during the period 1999 - 2012, by the head
of each R&S authority of European Union countries (hits per 1000 words)
Panel A Power
Panel B Cooperative
Panel C Academic and Legal
Panel D Business
Panel E evaluation
30
31