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Empirical Economics

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01-12-2014

Bank competition in transition countries: Are those markets really in equilibrium?

Authors: Tai-Hsin Huang, Nan-Hung Liu

Published in: Empirical Economics | Issue 4/2014

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Abstract

This paper investigates the degree of market competition in the banking industries of 17 Central and Eastern European (CEE) countries over the period 1994–2008, using the H statistic proposed by Panzar and Rosse (J Ind Econ 35:443–456, 1987). Differing from previous works, this paper applies the newly developed censored stochastic frontier model (CSFM) to test whether these markets have achieved long-run equilibrium, which is an issue overlooked by previous literature. The CSFM appears to be preferable to the conventional one that requires adding a unity to the dependent variable return on assets for all observations, making the so-derived dependent variable non-negative. One can then take the natural logarithm of this distorted dependent variable for a subsequent equilibrium test. Inconsistent parameter estimates may occur and lead to doubtful testing results. Empirical outcomes show that a majority of the banking markets under study experience rising H statistics during the sample period and are operating under monopolistic competition. Moreover, our results indicate that some CEE banking sectors are characterized as contestable markets that may have contributed to the recent deregulation and liberalization progress. More importantly, the CSFM confirms that most of the banking sectors are in long-run equilibrium, justifying the use of the Panzar–Rosse model, while the conventional, transformed dependent variable approach tends to reject the equilibrium hypothesis in more sample countries.

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The conventional structure-conduct-performance hypothesis claims that firms with greater market power are able to set prices that are less favorable to consumers in more concentrated markets, implying a positive relationship between profitability and measures of market structures, such as the CR and HHI.

Berger et al. (2004), Maudos and Fernández de Guevara (2004), Fernández de Guevara et al. (2005), Beck et al. (2006), and Alegria and Schaeck (2008) showed the restrictions of using concentration measures as indicators for the degree of competitiveness in the banking industry.

The Lerner index (Lerner 1934) is another popular indicator of a non-structural method, which is a well-established measure of market power at the bank level. See for example, Maudos and Fernández de Guevara (2004, 2007), Fernández de Guevara et al. (2005, 2007), Berger et al. (2009), Turk-Ariss (2010), Agoraki et al. (2011), and Maudos and Solís (2011).

Please see the Appendix.

In the area of efficiency and productivity analysis, the distance between a firm’s actual profit and its potential profit reflects the firm’s technical inefficiency, arising from managerial inability.

They are Bosnia, Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Macedonia, Moldova, Poland, Romania, Russia, Serbia, Slovakia, Slovenia, and Ukraine.

Nathan and Neave (1989) argued that it is hardly acknowledged that Canadian banks, despite their meagerness in amount, would exert market power in large corporate loans, because many foreign banks compete for them at the same time. Any traditionally assumed characteristic of a perfectly competitive market is not fit for the Canadian banking industry.

Baumol et al. (1982) argued that market structure is not the only determinant of competition and claimed that, even with the existence of so few firms, competition within the market will be maintained due to the free entry or exit condition.

They include the structure method indicator, HHI, the non-structural one, the H statistic, the Lerner index, net interest margin, and return on assets. Among these indicators, they are only weakly positively related to each other.

Three assumptions are often imposed on the H statistic in the literature: (1) there is only a single output; (2) the production function is homothetic; and (3) all the input prices for the sample firms change proportionately. Note that some of them seem to be inconsistent with reality.

Nathan and Neave argued that many Canadian banks rent or lease their operational premises from those state-run corporations at inauthentic prices. They believed that Shaffer’s measure for unit capital price is inappropriate.

We follow Turk-Ariss (2009) in calculating this ratio by other administration expenses and other operating expenses over total fixed assets.

The funding sources from Bankscope Database consist of deposits, money market funding, and other funding. Therefore, our WF is calculated as the ratio of interest expenses to the sum of total deposits, total money market funding, and total other funding.

CS is included to assess the influence of the capital structure on revenue.

Many studies in the literature calculate NPL as either the ratio of non-performing loans to total loans (Bikker and Haaf 2002) or the ratio of risk capital to total assets (Molyneux et al. 1994; Coccorese 2009). However, the serious issue of missing data presses us into seeking an alternative—regarding loan loss reserves as a substitute for the bad loans variable instead.

Nathan and Neave (1989) calculated the ratio of a bank’s total branches to all branches in the entire banking system as a substitute for MS.

This statement is valid under the assumption that what banks can solicit in the deposit market is constant.

Readers are suggested to refer to the appendix of Tsay et al. (2013) for a detailed derivation of \(I_{app} (Q_\textit{it} )\).

We would have considered banks in Albania and Belarus, but found it infeasible, because of the serious problem of missing data. We decide to give up the two markets due to this problem.

They are Bulgaria, Croatia, Latvia, Russia, Serbia, and Ukraine.

The macroeconomic environment is a little bit unstable in 2008 due to the impact from the global financial crisis.

The estimation results for the PR model over the whole sample period are not shown to save space, but are available upon request to the authors.

Poland’s market is found to have H statistic greater than unity and significantly different from unity.

Table 3

Parameter estimates of the PR Model by Countries

	Bosnia		Bulgaria		Croatia		Czech		Estonia		Hungary
	1994–2001	2002–2008	1994–2001	2002–2008	1994–2001	2002–2008	1994–2001	2002–2008	1994–2001	2002–2008	1994–2001	2002–2008
Constant	2.276***	0.482	\(-\)3.857	NA	NA	NA	0.181	NA	1.459	\(-\)1.202	NA	NA
	(0.406)	(0.963)	(2.386)				(1.862)		(0.988)	(1.189)
ln WL	0.237***	0.221*	0.039	0.309***	\(-\)0.118	0.042	0.213	0.586***	0.218***	0.565***	\(-\)0.015	\(-\)0.005
	(0.033)	(0.119)	(0.110)	(0.066)	(0.088)	(0.071)	(0.135)	(0.066)	(0.066)	(0.118)	(0.073)	(0.091)
ln WK	0.109***	\(-\)0.026	0.244***	\(-\)0.078***	0.085***	0.045	0.108	0.116***	\(-\)0.025	\(-\)0.042	0.027	0.318***
	(0.020)	(0.035)	(0.089)	(0.029)	(0.031)	(0.029)	(0.078)	(0.044)	(0.034)	(0.047)	(0.041)	(0.053)
ln WF	0.321***	0.183**	0.618***	0.163***	0.087	0.284***	0.389***	0.299***	0.219**	0.288***	0.313***	0.168***
	(0.022)	(0.072)	(0.117)	(0.046)	(0.069)	(0.075)	(0.085)	(0.047)	(0.096)	(0.089)	(0.041)	(0.042)
ln NPL	\(-\)0.006	0.240***	0.170*	0.059*	\(-\)0.082*	0.082**	\(-\)0.014	0.048**	\(-\)0.058*	\(-\)0.072*	0.008	\(-\)0.067
	(0.018)	(0.060)	(0.096)	(0.032)	(0.045)	(0.036)	(0.052)	(0.024)	(0.033)	(0.040)	(0.024)	(0.079)
ln TA	\(-\)0.008	0.119**	0.159*	0.040	0.256***	\(-\)0.207***	\(-\)0.040	0.021	\(-\)0.062***	0.044	\(-\)0.484***	\(-\)0.010
	(0.040)	(0.053)	(0.094)	(0.033)	(0.070)	(0.051)	(0.065)	0.047	(0.023)	(0.034)	(0.139)	(0.103)
ln MS	\(-\)0.107***	\(-\)0.130	0.385	\(-\)0.008	0.186*	0.154	0.064	\(-\)0.098*	\(-\)0.086	0.149	0.265	0.021
	(0.024)	(0.084)	(0.305)	(0.089)	(0.101)	(0.151)	(0.228)	(0.052)	(0.091)	(0.127)	(0.553)	(0.860)
ln MIX	\(-\)0.035***	\(-\)0.017	\(-\)0.065	\(-\)0.012	0.006	\(-\)0.011	\(-\)0.017	\(-\)0.020	0.019	0.004	\(-\)0.055	0.073
	(0.006)	(0.019)	(0.075)	(0.014)	(0.021)	(0.008)	(0.050)	(0.019)	(0.022)	(0.010)	(0.089)	(0.109)
ln CS	0.055**	0.139**	0.222***	0.036	\(-\)0.040	0.129**	\(-\)0.023	0.210	\(-\)0.024	0.070*	\(-\)0.116	0.127
	(0.027)	(0.068)	(0.032)	(0.045)	(0.075)	(0.049)	(0.116)	(0.059)	(0.056)	(0 .040)	(0.145)	(0.132)
ln OI	0.504***	0.209***	\(-\)0.024	0.094	0.086**	0.041**	0.103**	0.101**	0.220***	0.023	0.034	0.039
	(0.056)	(0.068)	(0.102)	(0.046)	(0.039)	(0.020)	(0.043)	(0.041)	(0.051)	(0.036)	(0.040)	(0.027)
Adj. \(R^{2}\)	0.985	0.985	0.768	0.819	0.414	0.740	0.171	0.921	0.688	0.805	0.697	0.786
No. of obs	22	79	66	112	238	174	127	110	49	32	90	114
H statistic	0.667	0.378	0.901	0.395	0.055	0.370	0.710	1.000	0.412	0.811	0.325	0.481
Wald-test (H = 0)\(^\mathrm{a}\)	136.12***	5.36**	24.20***	30.36***	0.23	16.67***	16.41***	141.93***	13.64***	42.19***	9.94***	16.36***
Wald-test (H = 1)\(^\mathrm{b}\)	34.06***	14.53***	0.29	71.17***	68.40***	48.18***	2.74*	0.17E-06	27.79***	2.28	42.83***	19.01***

	Latvia		Lithuania		Macedonia		Moldova		Poland		Romania
	1994–2001	2002–2008	1994–2001	2002–2008	1994–2001	2002–2008	1994–2001	2002–2008	1994–2001	2002–2008	1994–2001	2002–2008
Constant	\(-\)3.155	NA	\(-\)0.853	0.418	\(-\)10.77***	\(-\)0.043	1.704*	0.513	NA	NA	\(-\)0.122	0.909
	(4.363)		(2.613)	(1.292)	(1.733)	(0.582)	(0.957)	(0.692)			(0.585)	(0.721)
ln WL	0.002	0.067	0.189	0.041	0.046	0.082	0.054	0.359***	0.035	0.514***	0.130***	0.109**
	(0.108)	(0.059)	(0.135)	(0.068)	(0.131)	(0.089)	(0.096)	(0.067)	(0.034)	(0.051)	(0.043)	(0.044)
ln WK	\(-\)0.136	0.017	0.356***	0.023	0.061	0.016	0.005	0.087***	\(-\)0.008	0.063*	0.116***	0.026
	(0.100)	(0.024)	(0.102)	(0.026)	(0.067)	(0.026)	(0.045)	(0.033)	(0.036)	(0.033)	(0.034)	(0.021)
ln WF	0.272***	0.321***	0.254***	0.365***	\(-\)0.028	0.208***	0.234***	0.160***	0.220***	0.548***	0.598***	0.441***
	(0.104)	(0.038)	(0.083)	(0.043)	(0.060)	(0.062)	(0.048)	(0.054)	(0.029)	(0.045)	(0.043)	(0.032)
ln NPL	0.153**	0.038**	0.137***	0.039	0.170***	\(-\)0.025	0.019	\(-\)0.004	0.030	\(-\)0.065*	0.008	0.030**
	(0.062)	(0.018)	(0.044)	(0.032)	(0.042)	(0.022)	(0.040)	(0.049)	(0.035)	(0.038)	(0.022)	(0.013)
ln TA	\(-\)0.031	\(-\)0.007	\(-\)0.004	\(-\)0.033	\(-\)0.008	\(-\)0.005	0.024	0.017	\(-\)0.087***	0.046	0.055***	\(-\)0.010
	(0.074)	(0.029)	(0.052)	(0.021)	(0.086)	(0.047)	(0.060)	(0.036)	(0.033)	(0.031)	(0.018)	(0.015)
ln MS	0.539	0.422	0.267	0.043	1.257***	0.005	\(-\)0.116	\(-\)0.026	0.095	\(-\)0.061	0.039	\(-\)0.075
	(0.603)	(0.364)	(0.337)	(0.150)	(0.171)	(0.077)	(0.095)	(0.067)	(0.090)	(0.063)	(0.075)	(0.102)
ln MIX	\(-\)0.015	\(-\)0.013	\(-\)0.075*	\(-\)0.023	\(-\)0.008	0.009	0.003	\(-\)0.001	\(-\)0.134***	0.032	0.008	\(-\)0.040***
	(0.049)	(0.013)	(0.044)	(0.024)	(0.046)	(0.013)	(0.022)	(0.016)	(0.027)	(0.021)	(0.017)	(0.012)
ln CS	0.117*	0.225***	0.066*	0.081	0.268**	\(-\)0.022	0.129	0.084	\(-\)0.024	0.071	0.0003	\(-\)0.098**
	(0.066)	(0.050)	(0.039)	(0.053)	(0.118)	(0.080)	(0.112)	(0.057)	(0.034)	(0.056)	(0.003)	(0.040)
ln OI	0.241***	0.094***	0.267***	0.252***	0.040	0.321***	0.508***	0.137***	\(-\)0.011	0.011	0.169***	0.187***
	(0.074)	(0.030)	(0.074)	(0.039)	(0.059)	(0.054)	(0.133)	(0.047)	(0.014)	(0.008)	(0.042)	(0.027)
Adj. \(R^{2}\)	0.204	0.826	0.593	0.853	0.604	0.696	0.874	0.590	0.606	0.911	0.912	0.905
No. of obs	151	141	66	59	65	68	21	70	174	155	86	114
H statistic	0.138	0.407	0.798	0.429	0.079	0.306	0.294	0.606	0.247	1.125	0.844	0.576
Wald-test (H = 0)\(^\mathrm{a}\)	0.48	33.51***	24.58***	22.67***	0.26	9.90***	6.44**	36.19***	20.13***	292.04***	162.89***	81.64***
Wald-test (H = 1)\(^\mathrm{b}\)	18.59***	71.34***	1.57	40.09***	35.72***	50.70***	37.08***	15.31***	186.48***	3.62*	5.54**	44.23***

	Russia		Serbia		Slovakia		Slovenia		Ukraine
	1994–2001	2002–2008	1994–2001	2002–2008	1994–2001	2002–2008	1994–2001	2002–2008	1994–2001	2002–2008
Constant	NA	NA	\(-\)2.950	NA	NA	NA	\(-\)2.520***	0.766	NA	NA
			(2.976)				(0.928)	(0.700)
ln WL	0.113**	0.417***	0.328**	\(-\)0.104	\(-\)0.257	0.048	0.161**	\(-\)0.005	\(-\)0.069	0.272***
	(0.051)	(0.013)	(0.148)	(0.097)	(0.154)	(0.084)	(0.077)	(0.077)	(0.068)	(0.033)
ln WK	0.051	0.237***	0.424***	0.169***	0.513***	0.009	\(-\)0.015	0.025***	0.045	0.043***
	(0.047)	(0.006)	(0.145)	(0.044)	(0.105)	(0.051)	(0.027)	(0.009)	(0.043)	(0.014)
ln WF	0.387***	0.194***	0.320**	0.234***	0.421***	0.565***	0.384***	0.488***	0.361***	0.363***
	(0.052)	(0.009)	(0.138)	(0.083)	(0.105)	(0.047)	(0.070)	(0.043)	(0.058)	(0.032)
ln NPL	\(-\)0.060	0.102***	\(-\)0.219*	0.121***	0.58E-04	\(-\)0.002	\(-\)0.016	0.111***	0.029	0.050**
	(0.043)	(0.008)	(0.116)	(0.044)	(0.052)	(0.017)	(0.028)	(0.027)	(0.039)	(0.023)
ln TA	\(-\)0.035	0.064***	0.179	\(-\)0.355***	\(-\)0.001	\(-\)0.087**	0.038	\(-\)0.037*	\(-\)0.173***	0.013
	(0.053)	(0.010)	(0.125)	(0.074)	(0.100)	(0.037)	(0.034)	(0.019)	(0.065)	(0.015)
ln MS	0.009	0.151***	0.290	\(-\)0.699***	\(-\)0.534***	0.031	0.111	0.029	0.286***	0.094***
	(0.125)	(0.017)	(0.296)	(0.151)	(0.190)	(0.044)	(0.096)	(0.067)	(0.086)	(0.034)
ln MIX	\(-\)0.036	\(-\)0.003	0.063	0.026	\(-\)0.238	\(-\)0.031	\(-\)0.010	\(-\)0.012	\(-\)0.072***	0.006
	(0.028)	(0.003)	(0.056)	(0.025)	(0.084)	(0.023)	(0.025)	(0.009)	(0.026)	(0.012)
ln CS	0.033	0.143***	0.345	0.135	0.035	0.064**	0.072	0.157***	\(-\)0.012	0.041
	(0.066)	(0.016)	(0.312)	(0.090)	(0.052)	(0.031)	(0.074)	(0.043)	(0.025)	(0.028)
ln OI	\(-\)0.000	\(-\)0.028***	0.310*	0.148**	0.171***	0.098**	\(-\)0.163**	0.150***	0.123**	0.044***
	(0.032)	(0.005)	(0.175)	(0.065)	(0.055)	(0.042)	(0.081)	(0.037)	(0.053)	(0.014)
Adj. \(R^{2}\)	0.684	0.794	0.740	0.645	0.779	0.943	0.545	0.876	0.828	0.899
No. of obs	339	3908	40	164	75	58	58	65	100	207
H statistic	0.551	0.848	1.072	0.298	0.677	0.621	0.530	0.508	0.337	0.678
Wald-test (H = 0)\(^\mathrm{a}\)	45.32***	3487.2***	15.66***	6.30**	10.39***	28.95***	36.27***	32.50***	11.65***	305.04***
Wald-test (H = 1)\(^\mathrm{b}\)	30.18***	112.9***	0.07	34.85***	2.37	10.75***	28.47***	30.55***	45.09***	68.71***

The estimated standard errors are shown in parentheses below the coefficients

NA the equation is estimated by the fixed-effects model

*, **, and *** Significant at the 10, 5, and 1 % levels, respectively

\(^\mathrm{a}\) \(\chi ^{2}\) Statistics of the Wald test for the hypothesis of H = 0

\(^\mathrm{a}\) \(\chi ^{2}\) Statistics of the Wald test for the hypothesis of H = 1

The information is provided by the EBRD’s Transition Reports, IMF, and World Bank, and the same applies for the rest of this section.

Estonia’s economy has the characteristics of a balanced budget, almost non-existent public debt, a free trade regime, a competitive commercial banking sector, a hospitable environment for foreign investment, innovative e-services, and even mobile-based services. Such success in its economic progress gave it the nickname “Baltic Tiger.”

Poland is the 6th largest economy in the EU and has been the most rapid economic growing country in Eastern Europe, with an average annual rate of 5 % GDP growth. One of the advantages that Poland enjoys is its excellent location at the heart of Europe. The other one is its law-favoring foreign investors and specialized government institutions in charge of foreign investment. Many foreign banks set up their regional headquarters in Poland.

There is only one case, i.e., WK for Bulgaria in 2002–2008, having a significantly negative association with total revenues under the 1 % level of significance.

We use loan loss reserves as a proxy for non-performing loans. However, reserves do not mean actual occurrences of bad loans, but are more like a provision for future possibilities. Therefore, our estimation here might reveal a message that more reserves represent more loans being made by banks, which rake in great revenues, but makes NPL have a positive relationship with a bank’s income.

Utilizing the interest revenue as the dependent variable, Gelos and Roldós (2004) also yielded a similar ordering of the H statistics in Hungary and Poland, with the exception of Czech, for the period 1994–1999.

It is crucial to note that a market achieving equilibrium does not hinder its competitive conditions from changing. However, such changes are usually in a tardy way (Mamatzakis et al. 2005; Drakos and Konstantinou 2005). Furthermore, this equilibrium test belongs to an analysis of partial equilibrium, which requires that the variations in the equilibrium price and quantity are neither significantly affected by nor have significant impacts on the price levels in other markets. However, this requirement seems to be too difficult to hold, because the banking industry is in fact interrelated with other markets particularly through the employment of various resources. Therefore, in a perfect case it would be better to take the exchange values between the involved industries into consideration (Coccorese 1998).

The estimation results of Eq. (9) by the CSFM for the two sub-periods and the entire period are not reported, but available upon request to the authors. Likewise, the regression results of the other two models are not reported to save space.

We do not show the testing results for the entire sample period 1994–2008, since the structural change is detected by the Chow test in all countries (except for Moldova), when the sample period is split into 1994–2001 and 2002–2008. Thus, the test statistics for the entire period are invalid.

A non-concentrated market is usually defined by an HHI below 1,000, a moderately concentrated market has a HHI ranging between 1,000 and 1,800, and a highly concentrated market has a HHI exceeding 1800. On the other hand, a market is effectively competitive if its \(\hbox {CR}_{4}\) falls below 40 %.

In the period 1994–2001, the banking markets of Croatia, Latvia, and Macedonia can be characterized as a monopoly.

Baumol (1982) emphasized that with barriers for new entrants to the market being non-existent or low, incumbent firms are always vulnerable to hit-and-run entry when they try to exercise their potential market power. Therefore, incumbent firms are forced to behave competitively to discourage potential entrants, even in a concentrated industry.

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Title: Bank competition in transition countries: Are those markets really in equilibrium?
Authors: Tai-Hsin Huang
Nan-Hung Liu
Publication date: 01-12-2014
Publisher: Springer Berlin Heidelberg
Published in: Empirical Economics / Issue 4/2014
Print ISSN: 0377-7332
Electronic ISSN: 1435-8921
DOI: https://doi.org/10.1007/s00181-013-0778-2

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