5. Empirical Results
5.1 Coefficient Estimates of Group Frontiers
To validate the use of MDDF, it is crucial to test the null hypothesis that the
6 The entry for the number of employees is missing for many sample banks. Although the item of personnel expense is available, it is missing entirely in Bosnia and Bulgaria for 1995-1997, in Serbia for 1995-1999, and in Poland for 1995-1996. In addition, some countries have merely a few (less than three) observations on this variable for several years. The item of total assets net of fixed assets is instead used as a proxy for the number of employees. Altunbas et al. (2000, 2001), Weill (2004), and Fries and Taci (2005), to mention a few, utilize the same definition for labor.
7 The variable of density of demand for deposits is used by several previous works. Unfortunately, this variable is not available in Serbia and hence is overlooked here.
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banking systems among countries undertake the same technology. If the hypothesis is not rejected, then researchers can simply pool the data from different countries and estimate a common frontier. There is no need to establish a MDDF.
Following Battese et al. (2004), the likelihood-ratio (LR) test statistic
1 2
2{ln( ) ln( )}
L
= −L
−L
is computed, where ln( )L = -30120 is the value of the
1 likelihood function for the stochastic DDF estimated by pooling the data for all groups, and ln( )L = -22664 is the sum of the values of the likelihood functions for
2 the 17 country frontiers. The value of the LR statistic is equal to 14912 and the hypothesis is decisively rejected at the 1% level of significance with 706 degrees of freedom. Banks of different countries are indeed operating under heterogenous technologies.5.2 Group-specific Technical Inefficiency
Table 1 presents average inefficiency scores for each country over time, and mean inefficiency scores are in the last column. These measures tell us how many units of outputs and inputs (undesirables), on average, should be increased and reduced, respectively, in order to be able to produce on the efficient group frontier. A higher inefficiency score of a bank implies that the observed input-output mix of the bank deviates farther away from the group-specific frontier and that the bank is less technically efficient. It is worth mentioning that these average inefficiency scores across countries are not comparable since they are gauged against heterogeneous frontiers.
Average technical inefficiency scores in Macedonia and Estonia are the lowest and equal to 0.38 and 0.97, respectively. Macedonian banks should simultaneously decrease 0.38 units (millions of US dollars) of both inputs and the undesirable and increase 0.38 units of outputs in order to attain the efficient frontier. The measure of
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0.97 for Estonian banks can be analogously explained. On the contrary, average technical inefficiency scores for Polish and Romanian banks are the highest and equal to 4.11 and 5.94, respectively.
[Insert Table 1 Here]
5.3 Empirical Results of the MDDF
Table 2 presents the parameter estimates of the MDDF, in which more than one half of the parameters are significantly estimated at least at the 10% level, based on uncorrected standard errors.8 Only one of the three macro-environmental variables, i.e., HHI, is significantly estimated. Its negative coefficient estimate reflects that banks in more concentrated markets are more technically efficient.
Koutsomanoli-Filippaki et al. (2009) yield similar evidence. This result can be justified using the contestable theory, proposed by Baumol (1982). When those markets under consideration become more concentrated, incumbent banks behave competitively to discourage entry; otherwise higher prices and profits will induce potential competitors to enter and to share the market. A number of works
investigating transition countries, such as Mamatzakis et al. (2008), Fries and Taci (2005), and Yildirim and Philippatos (2007) reach similar findings.
[Insert Table 2 Here]
We next use the parameter estimates to compute the conditional mean of Um in (10), which is exactly the TGD estimate. Table 3 presents the average TGD measures over time and various mean inefficiency measures for each sample country. Moreover, Figure 2 draws the trend of these average TGD measures. The measure falls from 1996 to 1998 and then rises until 2003, or the year before some of the sample countries joined the EU. The measure goes down in the first two years after entering
8 There are merely 15 estimates attain statistical significance on the ground of corrected standard errors. This confirms that the composed error is indeed heteroskedastic, which causes the original standard errors to be underestimated and then the t-statistics tend to lie in the critical region.
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the EU, followed by three years of slight increases. Generally speaking, a
representative bank’s TGD measure in the CEE countries slightly worsens during the sample period, and enrollment in the EU prompts an increase in an average bank’s technology somewhat.
[Insert Table 3 Here]
[Insert Figure 2 Here]
The average TGD measure is equal to 5.32, which gauges the difference between the metafrontier and the group-specific frontier. An average bank is able to produce 5.32 million US dollars of more desirables and less undesirable, respectively, and to employ 5.32 million US dollars of fewer inputs, if it adopts the potential technology to provide financial products. Latvia has the lowest mean TGD (2.68), followed by Slovenia (2.79) and Moldova (3.00), while Czech Republic (17.77), Serbia (13.91), and Romania (10.47) are at the other end of the spectrum. It is noticeable that most of the countries have higher average TGDs than their average technical inefficiency measures, with the exception of Slovenia. This implies that the main source of inefficiency comes from the failure of our sample banks to undertake the potential technology, instead of managerial inabilities. Bank managers are suggested to adopt new innovations swiftly to enhance their production technology in such a way as to be able to produce on the metafrontier. By doing so, their outputs can be largely
increased, accompanied by a decrease in both inputs and the undesirable output.
Figure 3 depicts the scatter diagram for each country with the horizontal and vertical axes being the mean values of the group-specific technical inefficiency score and TGD, respectively. Countries located at the lower-left quadrant reflect that their banks outperform those of the remaining countries in the other three quadrants on average, because the former have a smaller average inefficiency score and a narrower average technology gap. In this regard, banks in Latvia, Bosnia, Bulgaria, Estonia,
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Croatia, Hungary, Lithuania, Moldova, Macedonia, Slovakia, and Ukraine perform relatively well.
The average technical inefficiency of Romania’s banks stands the highest, along with the third highest average TGD, indicating that their managerial abilities and production technology have large room for improvement. Banks in Poland, Russia, and Slovenia have worse technical efficiency measures, but adopt advanced
production technology, while the reverse is true for banks in Czech Republic and Serbia. The two countries’ banks have better managerial abilities, but undertake less sophisticated technology.
[Insert Figure 3 Here]