ea_2016_1-2

UDC: 336.717

339.13
JEL: G21, G17

COBISS.SR-ID: 224804620
ORIGINAL SCIENTIFIC PAPER

Comparative Analysis of Levels of Banking Sector Markets
Concentration in CEE Region

Kaličanin Tijana,1 Belgrade Banking Academy, Belgrade, Serbia
Hanić Azra,2 Belgrade Banking Academy, Belgrade, Serbia

ABSTRACT – Banking sector is an important development factor of the total financial and
economic system. Number of banks and dispersion of their individual shares defines competitive

structure of banking sector, manifested by its concentration. Market concentration may be presented

as a function of a number of enterprises competing in a market and their individual comparative

market shares. Concentration analysis is significant with regard to the fact that the level of

concentration demonstrates relations among competitors in a market, as well as influence each

competitor has on consumers, branch and economy as a whole. Starting from 2008 crisis, domino

effect of which hit banking sector and has had a huge impact on it, authors of this paper aimed at

establishing a level of market concentration of the banking sector in Serbia and countries in the region

in 2015 by application of a large number of market concentration indicators.

KEY WORDS: market concentration, banking sector, CEE region, competition

Introduction

Market of a country is as developed as its competition is able to function on it.
Competition has to be constantly stimulated and protected by mechanisms in line with the
European integration processes and policy focused on market economy development.
Competition as such has been a particularly sensitive issue in transitional countries such as
Serbia and countries in the region. Initial structural changes brought about raising an issue
and need for a higher competiveness level in Serbia and elsewhere. Every country aspiring to
join the EU and integration processes ought to develop legal norms and apply EU
regulations.

Theory of competition has been developing together with economic sciences. Complexity
and dynamics are two main characteristics of every economic system. However, every
economic system and its specifics differ from state to state and depend on the period in time
in question; hence competition occurs in different forms in practice. Competition
concentration and intensity ratio has been grounded on Harvard and Chicago School

1 Zmaj Jovina 12, Belgrade, Serbia, e-mail: tijana.kalicanin@bba.edu.rs
2 Zmaj Jovina 12, Belgrade, Serbia, e-mail: azra.hanic@bba.edu.rs

Economic Analysis (2016, Vol. 49, No. 1-2, 59-72)

findings. Naturally, other schools have had an influence on understanding this relation,
however economists from the above mentioned schools have had the most powerful impact
on application of antimonopoly policy in America.

Establishing whether a commercial entity has a market power or not depends not only on
the market structure but on a number of other factors as well, such as: number and market
power of a competitor, substitution in consumption capacity, price character, barriers in
getting in and out of the market, technological development, innovations, etc. (Labus, 2008,
pp. 177).

Central and East European financial system has been rapidly developing last couple of
years, contemporary regulations have been introduced and new financial institutions have
been established contributing to maintenance of macroeconomic stability in the region.
Nonetheless, macroeconomic sector in the region, underdeveloped even before the recession
took place, has been a highly risky place for investments and unstable in comparison with
the Western Europe.

Certain reforms were carried out in 90s in the CEE region. They included privatization of
state run banks and opening of local financial sectors towards foreign investors. Banks thus
lost their primary role, free assets mobilisation function, public trust in baking system was
weakened due to inability of deponents to have their assets at their disposal, as well as
because of bad experiences with several pyramidal banks during that decade (Erić, Jović,
2012, pp. 6).

World economic crisis has spilt over to Europe through financial institutions (banks in
the first place) present in American financial market. The first world crisis signs in the region
reflected in both drop in liquidity in financial sector and hindered reform of financial
institutions as well. Slowed down economic activities came as a result of withdrawal of
deposits from banks by citizens, hindered credit activities of banks towards economy and
population, increased foreign investment costs, reinforced pressure on foreign exchange rate,
as well as credit risk uplift (Bošnjak, 2008, pp. 14). Lack of capital increased the value of
financial assets, already limited, which has thus slowed production and economic growth
down.

Shortage of innovativeness in financial sector has been present in Serbia and neighboring
countries (Western Balkans ones in particular) due to shallow and underdeveloped market,
political, economic and credit risk, high poverty rate, underdeveloped stock exchange
business element, currency oscillations, a high inflation level and other microeconomic
indicators, such as: foreign trade deficit, public debt, foreign direct investment flow, etc. In
addition to this, transitional countries share common banking sector issues: instability of
industrial structure, nonexistence of firm rules, narrowed assortment to key services only,
excessive capital price, absence of clear strategic objectives (Tipurić, Kolaković, Dumičić,
2002, pp. 470).

Levels of concentration

Considering the fact that market power and market share concepts are often used as
synonyms in economic literature, market power measurement procedures are most
frequently reduced to measuring market concentration, while a firm’s market share scope is

Kaličanin T., et al., Comparative Analysis, EA (2016, Vol. 49, No. 1-2, 59-72) 61

a main indicator of market power. This paper continues analyzing concentration level in the
market and indicators measuring it, which have most often been applied to the banking
sector.

Branch offer concentration is a very important feature of market structures and holds a
prominent position in studying them. The offer concentration implies a level total range of
offers or sale in one market is concentrated on a smaller number of firms (Jakšić, et al., 2006,
pp. 327).

In order to establish offer concentration level market shares of individual participants
have been most often used. Individual firms are constantly trying to increase their market
shares due to a positive correlation between market share and profitability. A higher market
share leads to a higher level of offer concentration (Amato and Wilder, 2004, pp. 412). A huge
number of factors have an impact on offer concentration levels in a market. They can
however be divided into main ones, crucial for determining a concentration level, and
auxiliary, bearing less importance in definition of offer concentration. Main factors
determining offer concentration level are the following:

1. number of competitors doing business in the market,

2. dispersion of market shares among competitors, and

3. entry and exit barriers from a branch.
This research will focus on impact the number of participants in the market have and

dispersion of the market share among them. Indicators used to measure concentration level
need to take these elements into consideration pertaining to markets with more than one
participant on the offer side. There is no need to measure offer concentration with classic
monopolies since there is only one firm with 100% market share. Each offer concentration
indicator is at the top level in this type of market (Šaj, 2005, pp. 171).

Methodology

This research has analyzed concentration levels in banking sectors of the Republic of
Serbia, Croatia, Bulgaria, Montenegro and Bosnia and Herzegovina through a comparative
analysis. It is important to underline that previous empirical research has proved that
application of one market concentration indicator may lead to relatively contradictory
conclusions on banking market concentration level since each indicator applied in this paper
has both its advantages and disadvantages. Aiming at obtaining the clearest possible picture
of banking sector concentration, one needs to apply a whole range of indicators. Applied
indicators have been used against granted loans and collected deposits sums of each
individual bank, as two main items in a bank’s balance sheets.

Herfindahl – Hirschman index is a sum of the squares of market share of firms within an
industry (Martin, 2002, pp. 336). Generally speaking, a higher numeric value of HHI
indicates lower competitiveness intensity, i.e. a greater economic power of market
participants. HHI, due to its set-up, has analytical advantage over concentration ratio, which
has been reflected in the fact that this index gives higher ponder to larger firms. It can be
presented by the following equation (Šaj, 2005, pp. 172):

Economic Analysis (2016, Vol. 49, No. 1-2, 59-72)

��� = � ���
�

�

Where ‘si’ stands for market share of ‘i’ firms, and ‘N’ stands for a total number of firms
in the market.

Importance of this index is reflected in the fact that even though it looks at individual
market shares of all firms in a branch, it recognizes separately presence of firms with large
market shares, significantly increasing its value (Lipczynski and Wilson, 2001, pp. 110).

Theoretically speaking, this index can have values between 0 and 10000. In case of
atomized offer when there is a huge number of producers and when each offer is around 0
and index value is around 0. Index value with monopolies is 10000, since a monopoly firm’s
offer equals whole branch’s offer (Begović, et al., 2002, pp. 33).

Figure 1. Market types by HHI values

Source: Begović, B., Bukvić, R., Mijatović, B., Paunović, M., Sepi, R. and Hiber, D.,

2002, pp. 35

Low index value therefore suits competitive industry without dominant players. Should
all firms have equal shares, then reciprocal index value reflects a number of firms in a given
industry. When shares differ, reciprocal index value indicates ‘equivalent’ number of firms
in the industry.

Applicability of the statistics (statistical measure) aiming at disclosure and further
prevention of harmful monopolies setting up, is however directly linked to a respective
market definition (which primarily relies on substitution concept) (Hanić, 2011, pp. 5).

Share index of an ‘n’ firm or a concentration ratio is an indicator, i.e. a sum of market
shares of ‘n’ largest firms on the market and is a very simple to understand as such
(Waldman and Jensen, 2001, pp. 95). It can be presented as a following formula (Savić, 2000,
pp. 4):

CRn = ∑ �
��

Where ‘Xi’ stands for individual market share of an ‘I’ firm:

Xi = (qi/Q)100

HHI index value Offer concentration value

HHI < 1000 Low concentration value
1000 ≤ HHI ≤ 1800 Medium concentration value
1800 ≤ HHI ≤ 2600 High concentration value

2600 ≤ HHI ≤ 10000 Very high concentration value
HHI = 10000 Monopoly concentration value

Kaličanin T., et al., Comparative Analysis, EA (2016, Vol. 49, No. 1-2, 59-72) 63

Where ‘qi’ is an offer of ‘i’ firm and ‘Q’ is an offer of whole branch. ‘N’ in practice is a
number between 4 and 10. Government’s agencies in charge of following offer concentration
levels in a country stipulate a number of firms that would be calculated against this
indicator, using this coefficient as an official indicator (Martin, 2002, pp. 337).

In case of an atomized offer when there is a limitless number of producers, its share is
around zero hence index values is a round zero, too. Contrary situation is with a monopoly,
when there is one producer only, the production of which equals the total branch production
and index value is 100 (Waldman and Jensen, 2001, pp. 95). A borderline between low and
high levels of branch offer concentration, measured by this index, cannot be unified and
largely depends on the market itself. EU considers a high level of offer concentration when
CR4 index exceeds 25. Unlike the EU, in the US borderline CR4 index value is 50, hence
markets with index above 50 are considered to be high concentration ones; those between 25
and 50, moderately concentrated, and the ones below 25, non-concentrated markets (Savić,
2000, pp. 6-8).

Level of concentration can be established by Lorenz curve as well, which is an important
instrument used in statistical analysis. It was named after American economist Lorenz who
designed this curve first. It was initially applied only when studying equality of distribution
of financial profits among members of a society (a state) (Šolak, 1996, pp. 38).

Application of Lorenz curve in reviewing levels of offers’ concentration in a market, aims
at illustrating equality in distribution of market shares among firms.

Figure 2. Lorenz curve

Source: Lipczynski, J., Wilson J., 2001, pp. 109 (% offer; % business entities

from the smallest to the largest)

X axis shows firms from the smallest to the biggest (in percentages), and Y axis shows
percentage of offer at firm’s disposal in interval from 0 to 100% offer. Line 0A at 45 degrees is
seen as a total equality line, i.e. balanced distribution of offers among producers.

If firms are along this line, then all firms are of same size. Therefore, for example point C
shows that 50% of firms looks after 50% production and offer on the given market. It then
implies that remaining 50% firms looks after 50% production and offer on the same market

Economic Analysis (2016, Vol. 49, No. 1-2, 59-72)

(Lipczynski and Wilson, 2001, pp. 110). To conclude, competition on this market is made of
firms of identical size. Distribution of market share is balanced among individual firms and
this is somewhere halfway between a total equality and a total inequality of market shares.

Main disadvantage of Lorenz curve is the fact that it is primarily focused on unbalanced
distribution of market shares among individual firms. Number of firms in establishing
concentration level does not play an important role hence one firms with 100% market share
and ten firms with 10% market share each will be at the same 45 degrees curve.

Gini coefficient as a concentration measurement tool has been based on Lorenz curve’s
underpinning logic. Starting from the given Lorenz curve chart, Gini coefficient can be
defined by the following equation (Bajec, et. al., 2005, pp. 17):

G =
�

��� ∑ (�� −
�
�

��

� )��

Where ‘n’ stands for a number of firms, ‘μ’ is an average firm’s sale on the given market,
‘ri’ is a rank an ‘i’ firm is holding (firms are ranked by sale scale or marker share from the
smallest to the biggest) and ‘qi’ is sale scale of an ‘i’ firm.

Main disadvantage of this indicator is the fact that it neglects number of firms and is
exclusively focused on inequality in offer among them. Industrial branch with two firms of
identical size has the same Gini coefficient value as industry with 100 firms of same size,
even though competition structure of these two industries is completely different
(Lipczynski and Wilson, 2001, pp. 110). It is particularly difficult to establish coefficient for a
market with one firm only. Result will be 0 for this market if the above written equation is
strictly applied, which implies that offer is equally distributed among firms (one in this case).

Entropy coefficient is a sum of market shares of individual firms and natural logarithms
of their reciprocal values as in the following formula:

∑
=

=
n

i Xi
XiEI

1
ln

Where ‘Xi’ stands for market share of an ‘i’ firm expressed in relative numbers.

Coefficient value is defined between two extreme cases: value EI =0 where one firm sets
up the whole offer and value EI = log (n) when in on branch there are n firms of the same
size.

Entropy coefficient reflects a level of stability in a branch. Market stability is assessed
from a seller’s viewpoint. Market is stable when buyers have difficulty in or cannot change a
seller at all. This is a monopoly situation when one firms sets up the overall branch offer.

Market is unstable when buyers can relatively easy change a seller which is a case when
there is a large number of offer making participants on the market. Entropy coefficient value,
unlike other analyzed concentration indicators, is inverse to the concentration level (smaller
entropy coefficient implies a higher level of concentration on the market and vice versa).

Kaličanin T., et al., Comparative Analysis, EA (2016, Vol. 49, No. 1-2, 59-72) 65

This indicator has been taken from theory of information where it was used to assess
certainty level of a decision. In addition to this, it can be used to measure uncertainty in
different market structures. When there is one firm only on the market, uncertainty for a
monopolist to keep a buyer is a minimal, since the buyer has neither options to choose a
different sellers, nor can he/she find a substitute to meet his/her needs.

Opposite scenario is with a full competition and a maximal uncertainty, due to a huge
number of firms with equal market shares, so that consumers can choose who they will buy a
product from, as well as which product they will be using to meet their needs (Ilić, et. al.,
2003, pp. 213).

Main advantage of entropy coefficient is reflected in the fact that it can be divided into
components unlike other concentration indicators. This is most often done when there are
groups of firms belonging to different clusters by size, different industries or different
countries. Thus entropy coefficient can be established within different groups as well as
among different groups.

Results and discussion

Next table shows maker shares of leading banks, five leading banks and ten leading
banks from selected CEE region countries. Serbia has the lowest value CR1 concentration
coefficient with Banca Intesa has the largest market share in granted loans and collected
deposits of 15.7% out of 30 banks. Bulgaria and Montenegro follow. In Bulgaria leading
Unicredit bank has the largest market share of 17,8% in loans, i.e. 18,66% in deposits.
Montenegrin Commercial Bank AD Podgorica has a similar share in the marker when it
comes to loans and deposits, i.e. 18,12% and 17,00% respectively. Leading banks in Croatia
and Bosnia and Herzegovina have a far larger share on banking markets in these countries.
Unicredit bank Mostar participates with 24,85% in the total amount of granted loans in B&H
and 25,88% in collected deposits. Zagrebačka bank Zagreb has the largest market share of all,
26,82% in loans and 24,87% in deposits. Application of CR1 coefficients underlines that there
are two groups of leading banks, firstly those taking up almost one fifth of the overall
banking market and secondly those that take up one fourth. Unicredit bank, present in three
out of five analyzed markets, is a leading in Bosnia, but second largest in Serbia by market
share size.

Table 1. Banking sector concentration ratio in 2015

Country
CR1 CR5 CR10

Loans Deposits Loans Deposits Loans Deposits
Serbia 15,75 15,73 55,17 55,19 77,53 77,58
B&H 24,85 25,88 70,23 71,44 92,31 91,28
MNE 18,12 17,00 66,92 69,12 96,12 96,79
Croatia 26,82 24,87 74,43 73,15 93,58 92,65
Bulgaria 17,80 18,66 59,05 55,11 82,92 79,57

Source: Authors

Economic Analysis (2016, Vol. 49, No. 1-2, 59-72)

Out of 30 banks in Serbia and 28 in Bulgaria, the first five banks have a sum market share
of 55,17% in loans and 55,19% in deposits, i.e. 59,05% in loans and 55,11% in deposits
respectively. There is an equal number of banks in B&H and Montenegro, 17, and five largest
ones in B&H have a sum market share of 70,23% and 71,44%, while in Montenegro that
figure is slightly smaller, 66,92% in loans and 69,12% in deposits. Out of 27 banks in Croatia,
market share of five leading ones has the highest value in comparison with previously
analyzed countries, 70,23% in loans and 71,44% in deposits. Application of CR5 coefficient
underlines that out of two groups of banks, one with 17 banks in banking market and the
other with 27-30 banks, five leading banks on Croatian banking market has the highest
market share.

Application of CR10 coefficient leads to approximately same results. Market shares of ten
leading banks in Croatia, B&H and Montenegro take up 91-96% of total banking market in
loans and deposits; in Bulgaria ten leading banks participate with 82,92% in the total amount
of granted loans and slightly less, 79,57% in total amount of collected deposits. Based on the
results, it can be concluded that ten leading banks have the smallest sum market share in the
Serbian banking market in comparison with leading banks from analyzed countries, i.e.
77,53% total loan market and 77,58% total deposit potential market.

Table 2. Herfindahl-Hirschman index of CEE region banking sector in 2015

Country
HHI 1/HHI

Loan Deposits Loans Deposits
B&H 1342 1476 0,0007 0,0007
Serbia 817 814 0,0012 0,0012
MNE 1137 1158 0,0009 0,0009
Croatia 1472 1386 0,0007 0,0007
Bulgaria 921 865 0,0011 0,0012

Source: Authors

Based on the results obtained by application of Herfindahl-Hirschman index, it may be

concluded that selected countries can be divided into two groups of markets, non-
concentration and medium concentration markets. Serbia with HHI value of 817 in loans and
814 in deposits belongs to the first group, i.e. low concentration level market. Similar market
structure is to be found in Bulgaria as well, with HHI values for loans and deposits are 921
and 865 respectively. Three remaining countries, B&H, Montenegro and Croatia have much
higher values of applied HHI on loans and deposits, which puts them into the second group
of countries with a medium concentration.

B&H has the highest HHI values for loans – 1342 and 1476 for deposits and Croatia with
almost inverse values - 1472 for loans and 1386 for deposits. Montenegro has somewhat
lower HHI values than previous two countries, i.e. 1137 for loans and 1158 for deposits,
nonetheless belongs to the same group as B&H and Croatia – medium concentration
markets. Having in mind a negative correlation of this index with the level of competition, it
may be concluded that there is a lower level of competitiveness in Bosnian, Montenegrin and
Croatian banking markets than in the Serbian and Bulgarian one. Sub-columns within 1/HHI

Kaličanin T., et al., Comparative Analysis, EA (2016, Vol. 49, No. 1-2, 59-72) 67

column show how many banks would be able to reach given HHI values, provided there are
equal market shares.

Table 3 shows entropy coefficient and relative entropy values against loans and deposits
of the CEE regional banking sector in 2015. Column log (n) illustrates maximal entropy
values, namely values providing that all banks have equal market shares. Obtained entropy
values for loans and deposits are contrasted to maximal entropy in order to establish
dispersion of market shares, i.e. levels of instability and uncertainty. Total entropy has
certain shortcomings when entropy in a number of countries in banking sector is reviewed.
When there is a different number of banks in analyzed countries, entropy coefficient should
be adjusted and made comparable among countries. Relative entropy coefficient is to be
calculated in this context. Calculated entropy E and maximal entropy for a given number of
banks ratio provides for relative distance concentration indicator, ranging from 0 (the highest
concentration) to 1 (the lowest concentration). This indicator takes differences in number of
banks in analyzed countries into consideration and is a significant corrector of results
obtained on the basis of the total entropy.

Table 3. Entropy coefficient and relative entropy of the CEE region banking sector in 2015

Country
Entropy Relative entropy

Loans log (n) Deposits log (n) Loans Deposits
B&H 1,00 1,23 0,99 1,23 0,80 0,80
Serbia 1,21 1,47 1,20 1,48 0,81 0,82
MNE 1,00 1,14 0,99 1,14 0,87 0,86
Croatia 0,98 1,43 1,01 1,43 0,69 0,70
Bulgaria 1,16 1,44 1,19 1,44 0,81 0,82

Source: Authors

Relative distance concentration results underscore that there is a high level of instability
and uncertainty present in almost all analyzed countries, i.e. bank services beneficiaries have
significant substitution possibilities. Relative distance concentration is the only indicator in
this group of applied indicator in a positive correlation with competition, i.e. a higher value
of this indicator implies stronger competition in the market. Advantage of this coefficient is
reflected in the fact that enables comparison among countries having taken differences in
number of banks into consideration. The results show that banking market structure is a low
concentration one, pointing at a high level of competition. The strongest intensity of
competition is to be found on Montenegrin banking market with relative entropy amounts at
values of 0,87and 0,86 for loans and deposits respectively. Bulgaria and Serbia follow with
same relative entropy values, namely 0,81for loans and 0,82 for deposits for Serbia and 0,80
for both in Bulgaria. The lowest level of competition in banking markets in comparison with
all analyzed countries is present in Croatia based on relative entropy values results.

Economic Analysis (2016, Vol. 49, No. 1-2, 59-72)

Table 4. Gini coefficient of the CEE region banking sector in 2015

Country
Gini coefficient

Loans Deposits
B&H 54,59 55,57
Serbia 59,24 59,28
MNE 43,28 44,73
Croatia 72,63 71,35
Bulgaria 60,87 57,18

Source: Authors

It may be concluded by application of Gini coefficient and analysis of the results
presented in the table above that Croatia with values of 72,63 for loans and 71,35 for deposits
is in a group of high concentration markets, i.e. that there is a group of banks with large
market shares and a group of banks (a larger group than the first one) with small market
shares. Main issue with this coefficient is that it fails to take a number of banks into
consideration. However, value of this coefficient closer to 100 indicates presence of several
leaders in the market, i.e. those with large market share, and that rest of the market is
divided into a large number of individual small market shares of other business entities. In
line with this, it can be concluded that these two groups of market shares are quite equal in
Serbia, Bosnia and Herzegovina and Montenegro. Gini coefficient for Bulgaria is 60,87 for
loans and 57,18 for deposits, which points at a slightly larger dispersion of market shares
than in other countries. This is however smaller than on the Croatian banking market.

Bearing on mind that Gini coefficient does not take differences in numbers of business
entities, i.e. banks into consideration, it is important to have in mind number of banks on the
market when analyzing this coefficient so that values are correctly interpreted. Values can
otherwise lead to misconception and misinterpretation of results. In order to avoid making
this mistake, it is recommended that Lorenz curve is created, the logic of which is a
foundation for this coefficient, and which offers a clear graphic illustration of market shares
dispersions.

Charts 1-10 show Lorenz curves against market shares of loans and deposits of the CEE
region banks in 2015, clearly illustration marker shares dispersion. X axis shows market
shares of banks from the smallest to the highest (in percentages), and Y axis shows
percentage of offer (granted loans and collected deposits)at banks’ disposal in their balance
sheets, in intervals from 0 to 100%. Charts illustrate that market distribution between loans
and deposit is a very similar one, therefore it will be further on analyzed together. Should we
take 20% of the best ranked banks into consideration (i.e. 20% of banks with the highest
market shares), 20% of largest banks in Montenegro has 20% of the market, 60% of markets
in Serbia, Bulgaria and Bosnia and Herzegovina, while 20% of the best ranked banks in
Croatia has a share of up to 80%, which has been reiterated by the previous Gini coefficient
analysis. If the curve at the beginning is closer to x axis, from left to right, it implies presence
of banks with a very small market share (1% each and smaller), and a number of these banks
depends on the length of the curve near x axis.

Kaličanin T., et al., Comparative Analysis, EA (2016, Vol. 49, No. 1-2, 59-72) 69

40% of the best ranked banks in B&H and Serbia have 80% market shares; the same
group of banks in Montenegro has around 75%. Two fifth of the best ranked banks in
Bulgaria have 85% share of the total Bulgarian banking market, while in Croatia, the same
percentage of the best ranked banks totals to even 95% of the market share, implying that
remaining 60% banks ‘share’ no more than 5%. It can be noted in all charts that a point on
the curve above number 40 (representing 60% of the best ranked banks) is close to x axis.
This, further on, points at the fact that 40% of the worst ranked banks ‘share’ rest of the
market. 40% of banks in Montenegro with the lowest market shares share around 10% of the
total market share. The curve is at approximately same distance from x axis in case of Serbia,
Bulgaria and Bosnia and Herzegovina. Accordingly, 40% of the worst ranked banks in above
countries total to only 5% of the overall banking market. Croatia’s 40% of the worst ranked
banks share only a couple of percentages of the total banking market considering loans and
deposits. What is significant with Lorenz curve and visible at first glance is that the larger the
surface on the right, the more market leaders present, i.e. groups of banks (proportionally
smaller) with a significant market shares in the overall banking market. Contrary to this,
larger surface on the left implies presence of proportionally larger number of banks sharing a
very small percentage of the total marker share.

Chart 1. Lorenz curves – loans and deposits, CEE region, 2015

100

0 20 40 60 80 100

MNE loan

equals

lorenz

100

0 20 40 60 80 100

MNE DEP

equals

lorenz

100

0 20 40 60 80 100

SRB loan

equals

lorenz

100

0 20 40 60 80 100

SRB DEP

equals

lorenz

100

0 20 40 60 80 100

B&H loan

equals

lorenz

100

0 20 40 60 80 100

B&H DEP

equals

lorenz

Economic Analysis (2016, Vol. 49, No. 1-2, 59-72)

Source: Authors

Conclusion

Herfindahl-Hirschman index values describe two levels of market concentration, i.e. low
concentration and medium ones. Even though HHI values obtained in Montenegro, Bosnia
and Herzegovina and Croatia are above 1000, which groups them among medium
concentration market structures, these values fail to reflect a genuine competitiveness
bearing on mind presence of market leaders contributing to a higher index value with their
marker shares.

Application of CR1 concentration ratio demonstrates a huge difference among market
shares of leading banks, ranging from 17-27%. Unicredit bank runs business in three out of
five analyzed banking markets and is a leading one in Bosnia and Herzegovina and Bulgaria,
while in Serbia it comes second best after Banca Intesa.

Gini coefficient shortcomings, providing an incomplete picture of market concentration,
have been overcome by creation of Lorenz curves, graphically presenting differences among
banking sectors market structures in the CEE region. Serbia, Bulgaria and Bosnia and
Herzegovina have the most similar distribution of market shares. Montenegro results show
the smallest proportional number of banks in relation to the total number of banks with
market share up to 1% and at the same time banks with a significant market share. The most
striking dispersion and deviation, in comparison with other analyzed countries, has been
found on Croatian banking market, where 11 banks share 3% of the total market, while 6 out
of 27 share up to 80% of the market.

It could be concluded that banking sector market structures of these selected countries
are similar with a high competitiveness level by the CEE regional market concentration level
analysis applying a cluster of selected indicators. Even though some coefficient values are
contradictory and once their shortcomings are removed – taking a number of banks into

100

0 20 40 60 80 100

Bulgaria loan

equals

lorenz

100

0 20 40 60 80 100

Bulgaria DEP

equals

lorenz

100

0 20 40 60 80 100

Croatia loan

equals

lorenz

100

0 20 40 60 80 100

Croatia DEP

equals

lorenz

Kaličanin T., et al., Comparative Analysis, EA (2016, Vol. 49, No. 1-2, 59-72) 71

consideration, presence of market leaders and putting obtained and maximal values into
ratio – comparative analysis among countries proves feasible, providing a realistic picture of
banking sector market structures individually.

Aiming at drawing good quality conclusions about market structure (nature of the
market and relations among respective competitors) in banking sector, qualitative
assessment of common and specific elements typical for this sector is necessary along with a
level of concentration quantitative analysis.

In dealing with this topic, various reports by organizations and institutions have been
studied, based mainly on analysis of one or maximum two most frequently applied
concentration indicators (CR and HHI). Aiming at obtaining the clearest possible picture of
market competition in financial sector, it is recommended that decision makers in respective
institutions and regulatory bodies incorporate analyses of a maximal number of
concentration indicators in their reports on level of concentration in financial sector.

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www.nbs.rs
www.cb-mn.org
www.bnb.bg
www.hnb.hr
www.cbbh.ba

Komparativna analiza nivoa tržišne koncentracije
bankarskih sektora CEE regiona

REZIME – Bankarski sektor je značajan faktor razvoja ukupnog finansijskog i ekonomskog
sistema. Brojnost banaka i disperzija njihovih pojedinačnih udela određuju konkurentsku strukturu

bankarskog sektora, koja se iskazuje koncentracijom. Koncentracija tržišta se može predstaviti kao

funkcija broja preduzeća koja međusobno konkurišu na tržištu i njihovih pojedinačnih relativnih

tržišnih učešća. Analiza koncentracije je bitna s obzirom na to da stepen koncentracije ukazuje na

odnos među konkurentima na određenom tržištu kao i na uticaj koji svako od konkurenata ima na

potrošače, granu i privredu u celini. Počevši od krize 2008. godine koja je domino efektom pogodila

bankarski sektor i imala veliki uticaj na isti u ovom radu autori su želeli da utvrde nivo tržišne

koncentracije bankarskog sektora Srbije i zemalja u regionu u 2015. godini upotrebom većeg broja

pokazatelja tržišne koncentracije.

KLJUČNE REČI: tržišna koncentracija, bankarski sektor, CEE region, konkurencija

Article history: Received: 8 May, 2016
Accepted: 12 May, 2016