Printer Friendly

Does bank performance contribute to economic growth in the European Union?

INTRODUCTION

The recent protracted financial crisis has raised attention to the importance of bank performance for economic growth all over the world and very particularly in the European Union (EU). In the context of the EU, the relevance of banking institutions in the process of financing economic growth is generally accepted, although their specific roles are controversial, as has been clearly recognised among others by Goddard et al. (2007) and Molyneux (2009).

Banks and other financial institutions are usually supposed to guarantee the financing of productive investments and activities as they mobilise and allocate financial resources, and also by means of their specific money-creation processes through bank credit. Moreover, there is a general consensus that well-functioning markets and financial institutions may decrease transaction costs and asymmetric information problems.

The analysis of the importance of bank performance for economic growth has been the object of discussion for decades and intensified after the renowned contribution of King and Levine (1993). During the last two decades there has been an increase in empirical studies at the aggregate level, which explain output variables with financial ratios and variables such as liquid liabilities, bank loans to the private sector, and stock market capitalisation, which may be representative of the development of financial systems and institutions.

Khan and Senhadji (2000), analysing the literature concerning the empirical evidence of the relationship between financial development and economic growth, concluded that the results of these studies indicate that while the general effects of financial development on the outputs are positive, the size of these effects varies with the different variables considered, with the indicators of financial development and with the estimation method, data frequency, or the defined functional form of the relationship.

At the same time, authors such as Rajan and Zingales (1998) have argued that there is no clear causality between financial development and economic growth. Rather than adhering to the traditional explanation of economic growth by the proxy of financial development, these authors test the hypothesis that financial markets and banking institutions not only reduce the cost of financing, but also help to combat the problems provoked by asymmetrical information, concluding with their test that the sectors most dependent on external financing will be the ones that grow faster and in line with the development of the financial markets and institutions to which these sectors have access.

More recently, Hassan et al. (2011) provided evidence from panel data estimations and concluded that there is a general positive relationship between financial development and economic growth in low- and middle-income countries classified by economic region, although it seems that well-functioning financial systems may be necessary for mostly developing countries, but not a sufficient condition to reach steady and sustainable economic growth.

Other authors analyse the importance of financial markets' performance for real economic growth, but put particular emphasis on the relations between business and financial cycles. For example, Borio (2012) considers that it is not possible to understand macroeconomics, business fluctuations, and policy challenges in recent decades without understanding financial cycles. At the same time, Claessens et al. (2012) empirically test the interactions between business and financial cycles with monthly data for 44 countries, covering the time period 1960-2010, and mostly conclude that recessions associated with financial disruptions tend to be longer and deeper, emphasising the importance of financial market developments for the real economy.

Another strand of literature takes into account the development of the global trend of bank consolidation, and increases the theoretical debates and empirical tests analysing the particular relationship between bank market concentration and bank performance. Until the 1990s, there was a general belief that mergers did not clearly contribute to bank performance improvements and several empirical findings were consistent with the traditional structure-conduct-performance statements, in particular with the 'quiet life hypothesis' (among others, Hannan and Berger, 1991; Houston and Ryngaert, 1994; Pilloff, 1996). But from the year 2000, this general consensus was broken and particular attention was paid to specific characteristics of the banking markets such as the presence of asymmetric information, contagion phenomena, and imperfect competition, or the specific impacts of bank concentration, competition, and regulation on bank performance (eg De Bandt and Davis, 2000; Bikker and Haaf, 2002; Berger et al, 2004; Hasan et al, 2009; Schaeck et al, 2009).

However, it is generally recognised that not many works have addressed the possible relationship between economic growth and banking market structure, and also between economic growth and bank performance, namely measuring this bank performance through bank efficiency.

One of the few examples of these works can be found in Maudos and Fernandez de Guevara (2009), who used different measures of bank market competition for a sample of 21 countries and 53 economic sectors during the time period 1993-2003, concluding not only that there is a positive effect of financial development on economic growth, but also that the exercise of bank market power promotes economic growth.

Different conclusions were obtained by Claessens and Laeven (2005), who used industry-specific and country-specific data for 16 countries for the time periods 1980-1990 and 1980-1997 to estimate a measure of banking competition based on industrial organisation theory and then related this competition measure to the growth of industries. Their findings point to the evidence that greater competition in countries' banking systems will contribute to the faster growth of financially dependent industries, and so there is no support for the hypothesis that market power is good for access to financing and promoting economic growth.

Carbo Valverde et al. (2003) analyse the relationship between financial market competition and economic growth in five large regions in Spain and conclude that the differences in competition are not associated with improved regional growth.

Regarding the measurement of the quality of the financial development and its possible influence on economic growth, Hasan et al. (2009) use a sample of 147 regions in 11 European countries between 1996 and 2004, and conclude that regional economic growth benefits significantly from more efficient banks.

This paper contributes to the literature with the analysis of the possible contribution of bank market structure and the performance of the banking institutions to economic growth, here represented by per capita gross national income. To our knowledge, not many authors have addressed these issues in the particular context of all EU member states during the last decade, taking into account the influence of the international financial crisis, and considering the specific influence of bank market structure and of bank performance on economic growth. To represent bank market structure here, we use a bank market concentration measure and bank performance is proxied both by the capital ratio of bank equity to bank total assets and also a Data Envelopment Analysis (DEA) bank cost-efficiency measure.

The main empirical results confirm the controversial influence of bank market structure on economic growth. However, they clearly reveal that the equity to total assets ratio had a significant negative influence on economic growth before and after the beginning of the recent financial crisis. Simultaneously, and in line with the findings of Hasan et al. (2009), we conclude that DEA bank efficiency positively contributes to economic growth, although not as statistically significantly for the years after the beginning of the crisis.

The paper is organised as follows: The section 'Data and methodology' presents the data used and the methodological framework; The section 'Results obtained with the generalised method of moments (GMM) estimates' reports the results of the dynamic panel estimations; and the section 'Concluding remarks' concludes.

DATA AND METHODOLOGY

This paper uses dynamic Generalised Method of Moments (GMM) panel estimations in order to analyse mostly the effects on economic growth of bank market concentration and bank performance, here represented both by the ratio of bank equity to bank total assets and by a DEA bank efficiency measure. The economic growth of each EU member state is represented by the natural logarithm of the country's gross national income at current prices per capita.

As control variables we include nominal short-term interest rates (a proxy for monetary policy interest rates) and general government net lending/ borrowing (taking into account the recognised importance of public finances for economic growth and also the fact that some EU countries recently faced a sovereign debt crisis).

These macroeconomic data, that is, the two control variables and also the dependent variable (the natural logarithm of the gross national income at current prices per capita), are all sourced from the European Commission AMECO database.

The data used to obtain the bank market concentration measure and to represent bank performance (more precisely, the equity to total assets ratio and the inputs and outputs used to obtain the DEA bank efficiency measure) are sourced from the privately owned financial database maintained by the Bureau van Dijk: Bankscope.

Bank market concentration

Bank market concentration is measured through one of the most popular indicators: the percentage share of the total assets held by the three largest banking institutions (C3) of each EU member state.

The C3 results are presented in Table 1 and clearly show that, with some exceptions, there is a general increase in bank market concentration. The exceptions are to be found in the Netherlands and Greece and most particularly in certain new EU member states, namely Bulgaria, Ireland, Latvia, Lithuania, Malta, Poland, Romania, and the Slovak Republic.

On the other hand, and in spite of the general increase in EU bank market concentration during the considered time period, the levels of concentration continue to be relatively low in the relevantly developed EU countries, namely France, Germany, Spain, and the United Kingdom.

Ratio of bank equity to bank total assets

The ratio of equity to total assets is one of the most important capital ratios, representing the book value of equity divided by total assets. Taking into account that equity represents a cushion against asset malfunction, this ratio measures the amount of protection afforded to the bank by the equity invested in the bank: the higher this ratio is, the more protected the bank is. The equity to total assets ratio also measures bank leverage levels and reflects the differences in risk preferences across banks.

Table 2 reports the values of the ratio of bank equity to bank total assets. The values reveal that in spite of some clear country differences and important oscillations, there is a general tendency of this ratio increasing during the considered time period, particularly as a response to the international financial crisis that began in 2007. Nevertheless, exceptions showing a decrease of this ratio and thus the decrease of bank protection are to be found in some of the new EU member states (such as Bulgaria and Romania) and, very particularly, in some of the developed EU countries such as Denmark, France, the Netherlands, and the United Kingdom.

Data envelopment analysis (DEA) bank efficiency

The analysis of efficiency is usually based on the estimation of efficiency frontiers with the best combinations of the different inputs and outputs of the production process and then on the analysis of the deviations from the frontier that correspond to the losses of efficiency.

Most of the empirical studies on the measurement of bank efficiency adopt either parametric methods, such as the Stochastic Frontier Analysis, or non-parametric methods, in particular Data Envelopment Analysis (DEA).

Here, we will adopt the DEA methodology, which was originally presented in Charnes et al. (1978), assuming constant returns to scale, which can be accepted as optimal but only in the long run. Later, Banker et al. (1984) introduced an additional convexity constraint ([lambda]) and allowed for variable returns to scale. Following also Coelli et al. (1998), Thanassoulis (2001), and Thanassoulis et al. (2007), we can assume that at any time t, there are N decision-making units that use a set of X inputs (X = [x.sub.1], [x.sub.2], ..., [x.sub.k]) to produce a set of Y outputs (Y = [y.sub.1], [y.sub.2], thus obtaining the DEA input-oriented efficiency measure of every i DMU, solving the following optimisation problem: (1)

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]

The DEA approach provides, for every i DMU (here every country's banking sector), a scalar efficiency score ([[theta].sub.i] [less than or equal to] 1). If [[theta].sub.i] = 1; the DMU lies on the efficient frontier and will be considered an efficient unit. On the contrary, if [[theta].sub.i] < 1 the DMU lies below the efficient frontier and will be considered an inefficient unit; moreover, (1 - [[theta].sub.i]) will always be the measure of its inefficiency.

Here we follow the intermediation approach, considering that the banks' total costs will depend on three bank outputs (total loans, total securities, and other earning assets), and also on three bank inputs (borrowed funds, physical capital, and labour).

More specifically, using the Bankscope database we define the outputs and the inputs of the cost function with the following variables:

Outputs:

1. Total loans = the natural logarithm of the loans

2. Total securities = the natural logarithm of the total securities

3. Other earning assets = the natural logarithm of the difference between the total earning assets and the total loans.

Inputs:

1. Price of borrowed funds = the natural logarithm of the ratio of interest expenses to the sum of deposits

2. Price of physical capital = the natural logarithm of the ratio of non-interest expenses to fixed asset

3. Price of labour = the natural logarithm of the ratio of personnel expenses to total assets (2)

The results obtained are provided in Table 3 and reveal not only some year-on-year oscillations, but also a clear general tendency towards the increase of DEA bank efficiency between 1999 and 2013.

The good bank performance of the economically more developed EU member states is proven by the frequency of the achievement and maintenance before and after the beginning of the international financial crisis in the DEA efficiency frontier (represented by the value 1) of countries such as Belgium, France, Germany, Luxembourg, the Netherlands, and the United Kingdom.

Using the presented information, and also two control variables (the interest rate and the government net lending/borrowing) the basic model to be estimated in this paper is:

[GNP.sub.i,t] = [a.sub.0] + [[alpha].sub.1] interest [rate.sub.i,t] + [[alpha].sub.2] government net lending/[borrowing.sub.i,t] + [[alpha].sub.3] bank market [concentration.sub.i,t] + [[alpha].sub.4] ratio of equity to total [assets.sub.i,t] + [[alpha].sub.5] bank [efficiency.sub.i,t] + [[epsilon].sub.i,t] (1)

where

GNP=the natural logarithm of gross national income, at current prices, per capita;

1 = EU country (i = 1, ..., 28 in Panel 1; i=1, ..., 22 in Panel 2);

t = year (t = 1999, 2013 in interval A; t = 1999, ..., 2007 in interval B; t = 2008, ..., 2013 in interval C);

interest rate = the nominal short-term interest rate;

government net lending/borrowing = general government annual net lending or net borrowing;

bank market concentration = the percentage share of total assets held in each country by the three largest banking institutions (C3);

ratio of equity to total assets = the book value of equity divided by total assets;

bank efficiency = Data Envelopment Analysis (DEAJ bank cost-efficiency measure; and

[epsilon] = error term.

Before proceeding with the estimations of the presented equation some panel properties of the variables are investigated. First, we test the stationarity of the series using the Levin-Lin-Chu panel unit root test (Levin et al., 2002), and then we test the cointegration between the series by applying the Westerlund (2007) cointegration tests.

Stationarity of the series

We test the stationarity of the series with the Levin-Lin-Chu panel unit root test (Levin et al, 2002), which may be viewed as a pooled Dickey-Fuller test or as an augmented Dickey-Fuller test, when lags are included to account for serial correlation in the errors and the null hypothesis is the existence of non-stationarity. The basic augmented Dickey-Fuller equation is [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII].

The null hypothesis is that the series is non-stationary or integrated of order 1. The Levin-Lin-Chu panel unit root test derives a statistic (t-star), which is distributed as a standard normal under the null hypothesis of non-stationarity. The test accounts for individual effects, time effects and a possible time trend, but it assumes that each cross-section in the panel shares the same auto-regressive coefficient, meaning that all series in the panel have the same degree of mean-reversion.

This test is adequate for heterogeneous panels of moderate size such as the panels used in this paper. The main results obtained are reported in Table 4 and all Levin-Lin-Chu t-star statistics are clearly significant at all the usual testing levels, allowing us to reject the existence of the null hypothesis and to conclude that the considered series are stationary.

Cointegration between the series

Cointegration is tested with the implementation of the four panel tests developed by Westerlund (2007), which test for the absence of cointegration by determining whether individual panel members are error correcting. These tests are flexible and work well in unbalanced, heterogeneous, and/or relatively small panels, allowing for dependence both between and within cross-panel units.

The application of these panel cointegration tests to the i series included in one panel considers for each moment t (during the time interval t = 0, the following error-correction model:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII].

The Westerlund cointegration tests provide four test statistics: Gt, Ga, Pt, and Pa. The Gt and Ga statistics test HO: [a.sub.i] = 0 for all i versus HI: [a.sub.i] < 0 for at least one of the series, i, starting from a weighted average of the individually estimated coefficients [a.sub.i] and their respective t-ratios.

The Pt and Pa test statistics consider the pooled information of all panel cross-section units to test HO: [a.sub.i] = 0 for all i versus H1: [a.sub.i] < 0 for all cross-section units. Thus, the rejection of HO must always be taken as the rejection of cointegration for the entire panel. Any single cross-section unit can cause the rejection of HO and it is not possible to identify which cross-section unit is responsible for this rejection.

Table 5 presents the p-values obtained with the Westerlund cointegration tests (Westerlund, 2007) for both of the considered panels of EU countries (the values of the statistics and the Z-values are also available and will be provided on request).

These results reveal that, in both panels, cointegration cannot be rejected between the series of the natural logarithm of the per capita national income and bank market concentration, and also to some extent (according to the presented [G.sub.t] and [P.sub.t] results) between the series of the natural logarithm of the per capita national income and the nominal short-term interest rate, indicating that these series tend to move together in the long run.

On the other hand, there is clear evidence that the series of the natural logarithm of the per capita national income do not move together with the series of the government net lending/borrowing, and also, although not as clearly (because of the [G.sub.t] p-values), with the series of the ratio of bank equity to bank total assets.

In what concerns the cointegration between the series of the natural logarithm of the per capita national income and the DEA bank efficiency, the results are not completely unanimous. Nevertheless, the [G.sub.t] and [P.sub.t] p-values clearly point to the non-rejection of their cointegration in both considered panels of EU countries.

Summarising, we may say that these results do not provide clear evidence of the existence or non-existence of cointegration between the dependent variable (the natural logarithm of the per capita GDP) and each of the considered explanatory variables (in particular those representing bank market structure and bank performance). Under these conditions, in this paper we opt not to develop a cointegration approach, namely testing the relevant cointegrating vectors and analysing the long-run relationships between the variables.

RESULTS OBTAINED WITH THE GENERALISED METHOD OF MOMENTS (GMM) ESTIMATES

In our estimation we chose to apply the GMM system. GMM uses cross-country information and jointly estimates the equations in first differences and in levels, with first differences instrumented by lagged levels of the dependent and independent variables and levels instrumented by the first differences of the regressors.

In order to test the consistency of the GMM estimates, namely the validity of the additional instruments, we follow the tests proposed by Arellano and Bond (1991). They are used to test autocorrelation, that is, the assumption that the error term is not serially correlated using the differenced error term; so, by construction, the autocorrelation of the first order, AR(1), is supposed to be validated, but not the autocorrelation of the second order, AR(2), or autocorrelation of a higher order. In addition, the validity of the instruments is tested through the Sargan statistic, which is robust to heteroscedasticity and autocorrelation.

In the estimations we take into account the particular challenges that the EU countries had to face during the last decade, namely adapting to the implementation of the European Monetary Union and to the enormous enlargement of the EU with new member states, facing the consequences of the international financial crisis, and also facing the possible interactions between business and financial cycles. (3) We also take into account the availability of data and we consider the following panels;

* Panel 1: EU 28, including all current EU member states, and

* Panel 2: EU 22, excluding countries that were under the 'Troika's' financial assistance (Greece, Ireland, and Portugal) and also countries that recently faced growth problems and/or troubles in their financial systems (Cyprus, Italy, and Spain).

For both panels we also consider three time periods:

* Period A: 1999-2013, using all available data in the Bankscope database and taking into account that the European Monetary Union was established in 1999;

* Period B: 1999-2007, including the years before the international financial crisis that deeply affected Europe in 2008; and

* Period C: 2008-2013, covering the period after the beginning of the global financial crisis.

Table 6 presents the results obtained for all panels using GMM one-step estimates. In all situations the Wald tests clearly confirm the overall fit of the considered model.

The quality of these estimates in both panels is corroborated by the results obtained with the Arellano-Bond tests (Arellano and Bond, 1991) as they always reject the null hypothesis of no autocorrelation of the first order and (with the exception of only Panel 2 - B) do not reject the hypothesis of no autocorrelation of the second order.

Moreover, the Sargan test results for the overidentifying restrictions allow us to consider that the included instruments in our estimations are valid.

In general the results are very similar for both panels, revealing that concerning the issues addressed in this paper, the differences across EU member states are not very relevant, namely the differences between the six countries that more clearly suffered the consequences of the international crisis and the other countries.

In what relates to the control variables, we confirm the same kind of results for both panels: the nominal short-term interest rate exerts in all situations a statistically very significant negative influence on the per capita national income growth, underlining the importance of monetary policy for economic growth. More precisely, and as expected, our results suggest that an increase of the nominal short-term interest rate, which is often taken as a proxy of the monetary policy interest rate, will contribute to the decrease of the growth of the per capita GDP (more precisely the natural logarithm of gross national income, at current prices, per capita taken from the AMECO database).

On the other hand, the other control variable, government net lending/ borrowing, always contributes positively to per capita national income growth and the results are statistically significant at 1 % for the entire time period (1999-2013) and for the years after the beginning of the global financial crisis (2008-2013).

As for the influence on economic growth of the bank market conditions (here represented by the bank market concentration C3 measure), the results confirm the recognised ambiguity of these market (competition) conditions. In general, the results obtained are not statistically significant. Nevertheless, considering Panel 1 with all EU 28 countries, we obtain a 10% statistically positive influence, but only for the sub-interval before the international crisis (1999-2007), revealing that in those years bank concentration, which can also be identified with less bank market competition, was in line with per capita income growth.

However, if we exclude the six EU countries that had clear and strong problems with the financial crisis and look at the results of Panel 2 - EU 22, it looks as though that bank market concentration had a negative influence (statistically significant at 10%) during the entire time period (1999-2013), revealing that for those 22 EU countries bank competition can promote economic growth. And this result is not surprising, taking into account what we said before: in general the bank market concentration is lower (meaning that bank market competition is higher) in the more developed EU countries.

Regarding the chosen variables representing bank performance, on the one hand the results obtained for the capital ratio (bank equity to bank total assets), which is a recognised good measure of bank protection, reveal that in both panels, and during all considered time periods, more protected banks, that is, those with lower leverage levels, had a negative influence on per capita income growth. This was true before and after the beginning of the international financial crisis, meaning that bank risk preferences were important in explaining economic growth and that very cautious banks did not provide the necessary financial support to guarantee the increase of the per capita gross national income of EU countries.

On the other hand, if bank performance is proxied by the DEA bank efficiency measure, our results confirm the assumption that well-functioning bank institutions will contribute positively to economic growth, although they did not do so as strongly for the years after the beginning of the international financial crisis (here the time period 2008-2013). The explanations for this statistically less significant relevance of bank efficiency for economic growth after the crisis is surely connected with the consequences of the crisis in the EU financial system as well as with strict requirements, namely in terms of capital ratios and legal regulations that EU banks were obliged to respect as a response to the financial crisis.

CONCLUDING REMARKS

This paper uses dynamic GMM panel estimations and includes two control variables to explain economic growth in all current EU member states for the time period 1999-2013 and the sub-intervals before and after the beginning of the recent international financial crisis.

Regarding the control variables, one represents the influence of the monetary policy rate [proxied by the nominal short-term interest rate) and the second is the government net lending/borrowing (having in mind the recognised relevance of public finances for economic growth and the recent sovereign debt crisis faced by many EU countries).

As expected, in all considered situations, the increase of the nominal short-term interest rates clearly contributes to the decrease of per capita national income growth. The results obtained for the other control variable (government net lending/borrowing) are also not surprising, as they always exert a positive influence on economic growth. Moreover, this influence is statistically very significant when we consider the entire time period (1999-2013) and for the years after the beginning of the global financial crisis (2008-2013), when many EU countries had to pay particular attention to their public finances and to their possible consequences for national income growth.

In what concerns the importance for economic growth of the bank market concentration C3 measure, the increase of which may be accepted as synonymous with less market competition, the obtained results are neither statistically very significant nor unanimous, confirming that this issue deserves to be the object of further empirical analysis and discussion. Nevertheless, as the results that we obtained during the entire interval (1999-2013) are statistically relevant at 10%, but only for Panel 2 (including only the 22 EU countries that were less clearly affected by the international crisis), we confirm the 'quiet life hypothesis', meaning that the increase in bank market concentration (meaning less market competition) negatively contributes to per capita national income growth. On the other hand, and also with results that are statistically relevant at 10%, but now for Panel 1 (including all 28 current EU member states) and only for the subinterval including the years before the international crisis (1999-2007), the increase of bank market concentration can be considered as promoting economic growth. These findings are mostly in line with the concerns of those authors that, particularly after the year 2000, contradict the 'quiet life hypothesis' and emphasise that some specific characteristics of the banking markets contain the presence of asymmetric information, contagion phenomena, and imperfect competition that surely affected many EU banking markets before the beginning of the international financial crisis.

Bank performance is represented in this paper by two specific variables: a financial capital ratio (bank assets to bank total assets) and a DEA bank cost-efficiency measure.

According to the results obtained for the bank equity to bank total assets ratio, in all considered situations the increase of this ratio had a statistically very strong negative influence on per capita income growth. These findings confirm that this ratio is a good measure of bank protection, as when this protection collapses with the increase of the bank leverage levels it will be associated with the increase of financial resources promoting economic growth, and so more bank protection will contribute to less per capita income growth. The recognition of these facts is one of the justifications for the theoretical and political discussions associated with the relevance of bank market regulations and the importance of bank behaviour not only for promoting economic growth but also for avoiding financial crisis. Clearly, on the one hand high bank leverage levels will provide more financial resources to promote economic growth, but on the other hand these high leverage levels (here identified with low bank equity to bank total assets ratios) will be synonymous with high risks, and less protected banks may become problematic and contribute to financial and economic crises.

Finally, in what concerns the influence of DEA bank efficiency on economic growth, our findings always point to a clear positive contribution of the increase of bank efficiency to economic growth. Nevertheless, the results are statistically not as significant for the sub-interval 2008-2013, which includes the years after the beginning of the international financial crisis. So, in spite of the specific challenges, namely in terms of capital ratios, that the EU banking institutions had to face with the crisis, our findings clearly confirm the general assumption that well-functioning bank institutions will contribute positively to economic growth.

Acknowledgements

The author would like to thank the participants at the 15th Annual Conference of the International Network for Economic Research (INFER), Orleans, 29 May-1 June 2013, and especially to Professor Camelia Turcu and to three anonymous referees for their most helpful comments, criticisms, and suggestions. The usual disclaimer remains.

REFERENCES

Arellano, M and Bond, S. 1991: Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies 58(2): 277-297.

Banker, RD, Charnes, A and Cooper, WW. 1984: Some models for the estimating technical and scale inefficiencies in data envelopment analysis. Management Science 30(9): 1078-1092.

Berger, AN, Demirguc-Kunt, A, Levine, R and Haubrich, JG. 2004: Bank concentration and competition: An evolution in the making. Journal of Money, Credit, and Banking 36(3): 433-451.

Bikker, JA and Haaf, K. 2002: Competition, concentration and their relationship: An empirical analysis of the banking industry. Journal of Banking & Finance 26(11): 2191-2214.

Borio, C. 2012: The financial cycle and macroeconomics: What have we learnt?, Bank for International Settlements Working Paper No. 395, www.bis.org.

Carbo Valverde, S, Humphrey, DB and Rodriguez Fernandez, F. 2003: Deregulation, bank competition and regional growth. Regional Studies 37(3): 227-237.

Charnes, A, Cooper, WW and Rhodes, E. 1978: Measuring the efficiency of decision making units. European Journal of Operational Research 2(6): 429-444.

Claessens, S and Laeven, L. 2005: Financial dependence, banking sector competition, and economic growth. Journal of the European Economic Association 3 (1): 179-207.

Claessens, S, Rose, MA and Terrones, ME. 2012: How do business and financial cycles interact? Journal of International Economics 87(1): 178-190.

Coelli, TJ, Prasada Rao, DS and Battese, GE. 1998: An introduction to efficiency and productivity analysis. Kluwer Academic Publishers: Norwell, MA.

De Bandt, O and Davis, EP. 2000: Competition, contestability and market structure in European banking sectors on the eve of EMU. Journal of Banking & Finance 24(6): 1045-1066.

Goddard, J, Molyneux, P, Wilson, JOS and Tavakoli, M. 2007: European banking: An overview. Journal of Banking & Finance 31 (7): 1911-1935.

Hannan, TH and Berger, AN. 1991: The rigidity of prices: Evidence from the banking industry. The American Economic Review 81 (4): 938-945.

Hasan, I, Koetter, M and Wedow, M. 2009: Regional growth and finance in Europe: Is there a quality effect of bank efficiency? Journal of Banking & Finance 33(8): 1446-1453.

Hassan, MR, Sanchez, B and Yu, J-S. 2011: Financial development and economic growth: New evidence from panel data. The Quarterly Review of Economics and Finance 51(1): 88-104.

Houston, JF and Ryngaert, MD. 1994: The overall gains from large bank mergers. Journal of Banking & Finance 18(6): 1155-1176.

Rhan, MS and Senhadji, AS. 2000: Financial development and economic growth: An overview, IMF Working Paper No. 209. Available at SSRN: http://ssrn.com/abstract=880870.

King, RG and Levine, R. 1993: Finance and growth: Schumpeter might be right. The Quarterly Journal of Economics 108(3): 717-737.

Levin, A, Lin, C-F and Chu, C-SJ. 2002: Unit root tests in panel data: Asymptotic and finite-sample properties. Journal of Econometrics 108(1): 1-24.

Maudos, J and Fernandez de Guevara, J. 2009: Banking Competition, Financial Dependence and Economic Growth, Munich Personal RePEc Archive Paper No. 15254, https://mpra.ub .uni-muenchen.de/15254/.

Molyneux, P. 2009: Do mergers improve bank productivity and performance? In: Balling, M, Gnan, E, Lierman, F and Schoder, JP (eds). Productivity in the Financial Services Sector. SUERF: Vienna, pp. 23-43.

Pilloff, SJ. 1996: Performance changes and shareholder wealth creation associated with mergers of publicly traded banking institutions. Journal of Money, Credit and Banking 28(3): 294-310.

Rajan, RG and Zingales, L. 1998: Financial dependence and growth. The American Economic Review 88(3): 559-586.

Schaeck, K, Cihak, M and Wolfe, S. 2009: Are competitive banking systems more stable? Journal of Money, Credit and Banking 41(4): 711-734.

Thanassoulis, E. 2001: Introduction to the Theory and Application of Data Envelopment Analysis. A Foundation Text with Integrated Software. Kluwer Academic Publishers: Norwell, MA.

Thanassoulis, E, Portela, MCS and Despic, O. 2007: Data envelopment analysis: The mathematical programming approach to efficiency analysis. In: Fried, HO, Knox Lovell, CA and Schmidt, SS (eds). The Measurement of Productive Efficiency and Productivity Growth. Oxford University Press: Oxford, pp. 251-420.

Weill, L. 2004: Measuring cost efficiency in European banking: A comparison of frontier techniques. Journal of Productivity Analysis 21(2): 133-152.

Westerlund, J. 2007: Testing for error correction in panel data. Oxford Bulletin of Economics and Statistics 69(6): 709-748.

(1) For more details on this problem, see, among others, Coelli et al. (1998) and Thanassoulis et al. (2007).

(2) The Bankscope database does not provide the number of employees for the bank sector of all EU countries. So, following the approach adopted, among others, by Weill [2004), we use the ratio of personnel expenses to total assets as a proxy for the price of labour.

(3) Concerning these issues, we are grateful to three anonymous referees for their suggestions, namely to the referee who gave the Borio (2012) and Claessens et al. (2012) references.

CANDIDA FERREIRA

Economics, ISEG, Universidade de Lisboa and UECE, R Miguel Lupi, 20, Lisboa 1249-078, Portugal.

E-mail: candidaf@iseg.ulisboa.pt
Table 1: Concentration measure (C3)

                      1999     2000     2001     2002     2003    2004

Austria               85.09    80.4     83.71    82.14    80.57   70.89
Belgium               43.19    41.51    38.51    37.86    38.7    38.59
Bulgaria              73.79    68.57    65.1     60.31    68.69   59.82
Croatia               69.6     68.7     67.49    66.92    71.79   74.87
Cyprus                75.7     76.26    70.04    69.66    72.24   68.94
Czech Republic        65.97    76.65    81.09    79.2     80.36   72.29
Denmark               73.17    71.23    33.21    31.94    31.82   88.57
Estonia               98.9     98.74    99.94    99.93    99.92   99.82
Finland               99.12   100      100      100      100      98.07
France                33.84    33.75    45.86    65.57    66.16   72.15
Germany               48.98    56.88    57.88    50.29    48.63   48.44
Greece               100      100      100      100      100      59.51
Hungary               58       52.29    58.43    55.93    55.98   53.84
Ireland               77.13    76.78    82.08    75.99   100      95.28
Italy                 58.42    67.89    51.42    57.17    58.56   87.34
Latvia                79.87    80.45    80.76    75.97    68.09   61.92
Lithuania             92.64    86.43    86.74    85.13    81.33   78.97
Luxembourg            27.34    29.51    37.23    34.35    34.95   33.88
Malta                100       95.21   100      100       92.41   98.07
The Netherlands       55.24    53.33    56.41    58.87    55.34   83.52
Poland                70.69    77.42    53.18    60.07    84.28   48.14
Portugal              66.65    78.68    93.05    99.38    99.58   98.18
Romania               76.94    70.69    69.16    63.34    61.08   58.34
Slovak Republic       79.62    78.68    76.86    76.81    76.38   74.72
Slovenia              64.7     62.96    66.89    66.69    64.33   62.96
Spain                 47.67    63.42    66.78    73.77    88.45   70.75
Sweden                96.58    97.6     88.16    86.92    87.07   46.88
The United Kingdom    34.6     41.44    43.32    44.36    40.04   64.81

                     2005    2006    2007    2008    2009    2010

Austria              73.09   65.65   67.65   60.44   56.48   63.23
Belgium              81.47   80.99   81.86   79.14   74.7    71.2
Bulgaria             53.78   50.04   57.81   52.57   52.25   42.93
Croatia              67.73   55.41   55.71   56.09   56.87   57.25
Cyprus               66.93   75.66   82.47   80.64   81.19   89.67
Czech Republic       73.4    73.56   71.41   70.53   66.55   67.41
Denmark              86.44   84.25   84.46   84.17   82.71   83.58
Estonia              99.8    98.68   98.54   99.24   99.18   98.51
Finland              98.09   98.44   97.22   93.15   92.51   93.47
France               44.66   45.03   45.01   47.45   54.33   54.42
Germany              46.96   54.89   56.66   59.05   61.15   63.33
Greece               60.64   60.9    58.89   58.89   59      59.83
Hungary              53.99   54.47   55.9    54.41   57.16   58.2
Ireland              68.34   65.44   65.94   70.96   69.95   67.17
Italy                55.64   55.39   60.54   61.94   59.89   64.73
Latvia               63.98   63.14   60.75   62.77   59.74   51.98
Lithuania            72.06   72.56   72.67   73.59   71.26   66.21
Luxembourg           41.48   39.51   30.14   29.25   29.82   30.5
Malta                98.02   92.3    97.76   91.76   90.22   87.2
The Netherlands      89.25   91.28   91.67   89.5    81.31   79.91
Poland               46.22   44.62   47      41.06   42.53   41.46
Portugal             82.83   80.65   79.67   82.83   82.11   81.45
Romania              59.44   59.21   52.38   49.68   45.94   45.76
Slovak Republic      68.02   73.54   68.34   68.23   63.37   62.06
Slovenia             59.83   60.14   61.24   58.85   57.7    57.12
Spain                57.63   53.63   53.11   48.98   48.88   43.42
Sweden               95.58   95.12   95.46   95.6    94.23   91.93
The United Kingdom   49.09   49.04   56.21   62.92   53.69   55.07

                     2011    2012    2013

Austria              63.74   62.8    64.99
Belgium              68.47   64.84   65.56
Bulgaria             41.51   41.3    50.01
Croatia              58.72   60.14   61.36
Cyprus               89      93.14   93.62
Czech Republic       66.79   64.63   66.77
Denmark              83.35   83.81   84.58
Estonia              95.84   94.5    93.99
Finland              94.4    93.21   94.57
France               53.93   52.18   52.88
Germany              64.17   63.55   68.53
Greece               72.78   72.02   78.06
Hungary              58.44   58.38   61.38
Ireland              66.83   74.17   77.81
Italy                64.88   63.39   70.17
Latvia               49.98   49.97   49.99
Lithuania            79.04   83.93   81.47
Luxembourg           31.5    32.57   35.94
Malta                87.07   86.25   83.91
The Netherlands      82.64   83.9    86.2
Poland               40.91   39.93   41.36
Portugal             78.5    79.79   79.8
Romania              46.53   47.04   47.42
Slovak Republic      62.85   59.69   63.6
Slovenia             56.24   54.24   57.85
Spain                48.41   52.19   53.36
Sweden               91.75   90.96   90.67
The United Kingdom   55.16   53.5    50.55

Source: Author's calculations using data sourced
from the Bureau van Dijk Bankscope database.

Table 2: Ratio of equity to total assets

                     1999    2000    2001    2002    2003    2004

Austria               3.97    3.75    4.3     4.39    5.52    5.51
Belgium               4.76    5.3     5.05    5.23    5.8     4.99
Bulgaria             17.99   16.12   13.5    13.31   14.24   11.84
Croatia              12.6    11.06    8.94    8.82    8.36    8.54
Cyprus                8.07    9.93    9.89    9.99    7.26    6.25
Czech Republic        6.58    6.87    5.91    6.49    7.21    7.79
Denmark               7.11    8.28   12.62   12.2    13.62    4.7
Estonia              13.79   12.06   10.5    10.98   10.64   10.26
Finland               5.07    4.88    7.86   85.37   14.37    4.72
France                4.71    5.03    4.45    4.33    4.67    4.55
Germany               3.65    3.67    4.02    3.54    3.85    3.77
Greece               16.66   21.37   12.77   15.85    7.76    5.89
Hungary               7.33    7.96    8.29    9.06    8.79    9.11
Ireland               5.33    4.47    3.6     9.01    7.9     5.28
Italy                 5.12    6       6.67    6.33    8.22    6.4
Latvia               10.52    9.02    9.04    8.8     9.14    8.6
Lithuania            11.19   12.19   11.39   11.85    9.77    8.63
Luxembourg            3.26    3.42    3.56    4.13    4.12    4.54
Malta                 6.66    6.99    7.46    7.32   23.71   20.84
The Netherlands       5.23    5.09    5.34    5.02    5.58    2.95
Poland                9.28    9.72   10.87   14.64    8.21   11.11
Portugal              4.86    4.87    4.09    3.37    3.66    4.94
Romania              16.96   16.31   17.17   15.6    13.97   12.15
Slovak Republic       5.19    6.7     7.52    8.33    8.69    8.3
Slovenia              9.78    9.87    9.35    9.41    9.39    9.31
Spain                 7.27    8.22    8.62    9.45   10.8     6.81
Sweden                4.07    4.07    5.36    5.73    5.94   13.04
The United Kingdom    7.44    8.1    10.48   11.23   12.47    4.66

                     2005    2006    2007    2008    2009    2010

Austria               5.71    6.82    8.06    6.68    7.65    7.62
Belgium               3.13    3.3     4.13    3.17    4.28    4.75
Bulgaria             11.24   10.8    10.99   11.64   13.21   13.73
Croatia               9.19   10      12.23   13.22   13.66   13.77
Cyprus                5.64    8.63    8.32    5.87    7.23    8.55
Czech Republic        7.78    7.35    6.84    8.26    8.98    9.08
Denmark               4.24    4.65    4.22    3.75    4.24    4.34
Estonia               8.66    7.6     7.68    9.27    8.55    9.77
Finland               9.03    9.42    7.2     5.25    5.44    4.57
France                3.68    3.87    3.51    2.98    4       4.3
Germany               4.31    3.55    3.88    3.39    4.23    4.43
Greece                7.07    7.9     7.62    5.76    5.76    7.02
Hungary               8.9     9.12    8.99    8.57    9.5     9.81
Ireland               4.52    4.55    4.47    4.34    5.24    4.79
Italy                 6.71    6.77    6.98    6.44    7.1     7.31
Latvia                8.13    7.88    8.29    7.84    8.86    8.77
Lithuania             7.6     7.05    7.19    7.56    6.63    7.75
Luxembourg            4.63    4.64    4.43    4.51    5.47    5.66
Malta                19.41   20.78   16.58   12.61   16.35   15.46
The Netherlands       2.83    2.9     3.83    2.56    3.15    3.58
Poland               10.66   10.49   10.27    9.18   10.41   10.75
Portugal              5.74    6.44    6.09    5.66    6.61    5.97
Romania              10.85    9.77    8.84    9.13    9.62   10.62
Slovak Republic       8.4     7.92    8.24    8.32    8.76    9.18
Slovenia              8.47    8.04    7.79    7.9     7.62    7.56
Spain                 6.3     6.17    6.41    5.72    6.2     5.46
Sweden                4.21    4.32    4.31    4.2     4.88    5.16
The United Kingdom    3.59    3.59    3.83    2.66    3.98    4.54

                     2011    2012    2013

Austria               7.32    7.68    7.59
Belgium               4.43    5.51    6.21
Bulgaria             13.84   13.84   13.25
Croatia              13.52   14.33   13.98
Cyprus                5.83    8.55    9.71
Czech Republic        9.25   10.35   10.2
Denmark               4.5     4.67    5.06
Estonia              16.69   18.97   19.48
Finland               3.54    3.55    4.1
France                4.17    4.33    4.68
Germany               4.73    5.34    5.78
Greece                0.93   -0.43    8.31
Hungary               9.63   10.88   11.35
Ireland               8.59    9.66    8.83
Italy                 6.45    6.62    6.39
Latvia               10.02   10.11    9.93
Lithuania             9.96   11.46   11.84
Luxembourg            5.68    6.79    7.11
Malta                15.3    16.46   16.23
The Netherlands       3.32    4.08    4.2
Poland               10.47   11.65   11.56
Portugal              5.52    6.74    6.58
Romania              11      10.21   10.6
Slovak Republic       9.96   10.77   10.75
Slovenia              7.58    7.76    9.21
Spain                 5.92    5       6.59
Sweden                4.92    5.04    5.65
The United Kingdom    4.58    4.76    4.88

Source: Bureau van Dijk Bankscope database.

Table 3: Yearly Data Envelopment Analysis (DEA)
cost-efficiency measures obtained for all EU
member states

                     1999    2000    2001    2002    2003    2004

Austria              0.579   0.753   0.515   3.754   0.820   0.639
Belgium              0.930   0.921   0.804   1.000   1.000   0.937
Bulgaria             0.892   1.000   0.855   1.000   1.000   1.000
Croatia              0.853   0.886   0.728   3.793   0.620   0.596
Cyprus               0.308   0.415   0.377   3.431   0.401   0.425
Czech Republic       0.706   0.663   0.571   3.790   0.833   0.733
Denmark              1.000   1.000   0.443   3.735   1.000   0.706
Estonia              0.416   0.581   0.489   3.477   0.480   0.543
Finland              0.276   0.337   0.148   1.000   1.000   0.672
France               1.000   1.000   1.000   1.000   0.935   1.000
Germany              1.000   1.000   1.000   1.000   1.000   1.000
Greece               0.508   0.119   0.362   0.261   0.423   0.998
Hungary              0.380   0.522   0.431   0.493   0.512   0.413
Ireland              1.000   1.000   0.959   0.491   0.458   0.515
Italy                1.000   1.000   0.704   0.750   0.501   0.705
Latvia               0.601   0.636   0.466   0.53    0.563   0.807
Lithuania            0.781   0.749   0.644   0.887   0.875   0.947
Luxembourg           1.000   1.000   1.000   1.000   1.000   1.000
Malta                0.292   0.545   0.475   0.640   0.683   0.712
The Netherlands      0.541   1.000   1.000   1.000   1.000   1.000
Poland               1.000   0.752   0.622   1.000   1.000   0.541
Portugal             0.244   1.000   0.624   1.000   0.965   0.410

Romania              0.786   0.676   0.940   0.986   0.756   0.731
Slovak Republic      0.606   1.000   0.840   0.866   0.82    0.819
Slovenia             0.597   0.688   0.701   0.626   0.537   0.486
Spain                1.000   1.000   0.845   0.750   0.493   1.000
Sweden               1.000   1.000   1.000   1.000   1.000   0.610
The United Kingdom   1.000   1.000   1.000   1.000   1.000   1.000

                     2005    2006    2007    2008    2009    2010

Austria              0.655   0.666   0.669   0.735   0.682   0.622
Belgium              0.702   0.851   0.628   0.755   0.859   0.891
Bulgaria             1.000   1.000   1.000   0.880   0.818   0.734
Croatia              0.615   0.662   0.740   0.759   0.719   0.762
Cyprus               0.442   0.638   0.622   0.689   0.612   0.503
Czech Republic       0.751   1.000   0.957   0.942   0.893   0.784
Denmark              0.731   0.922   0.687   0.790   0.667   0.794
Estonia              0.772   0.858   0.729   1.000   0.655   1.000
Finland              0.637   0.730   0.611   0.934   0.855   1.000
France               1.000   1.000   1.000   1.000   1.000   1.000
Germany              0.853   0.886   1.000   0.842   1.000   1.000
Greece               0.991   0.969   0.885   0.892   0.951   1.000
Hungary              0.451   0.551   0.575   0.509   0.441   0.494
Ireland              0.804   0.883   0.623   0.801   1.000   1.000
Italy                1.000   1.000   0.925   0.834   0.864   0.948
Latvia               0.788   0.673   0.680   0.639   0.645   0.660
Lithuania            1.000   0.957   0.921   0.772   0.688   0.678
Luxembourg           1.000   1.000   0.964   1.000   1.000   1.000
Malta                0.768   0.932   0.869   0.903   1.000   0.921
The Netherlands      1.000   1.000   1.000   1.000   1.000   1.000
Poland               0.498   0.964   0.942   0.856   0.630   0.583
Portugal             0.511   0.598   0.568   0.580   0.607   0.623
Romania              0.668   0.646   0.726   0.592   0.581   0.611
Slovak Republic      1.000   0.976   1.000   1.000   1.000   1.000
Slovenia             0.585   0.669   0.707   0.688   0.705   0.692
Spain                1.000   1.000   1.000   1.000   1.000   1.000
Sweden               0.641   0.723   0.489   0.624   0.568   0.748
The United Kingdom   1.000   1.000   1.000   1.000   1.000   1.000

                     2011    2012    2013

Austria              0.788   0.610   0.975
Belgium              1.000   0.755   1.000
Bulgaria             0.873   0.708   0.952
Croatia              0.882   0.795   0.985
Cyprus               0.577   0.553   0.619
Czech Republic       0.994   0.924   1.000
Denmark              0.824   0.716   0.916
Estonia              0.918   1.000   1.000
Finland              1.000   1.000   1.000
France               1.000   1.000   1.000
Germany              1.000   1.000   0.935
Greece               1.000   1.000   1.000
Hungary              0.598   0.515   0.577
Ireland              0.911   0.578   0.886
Italy                0.927   0.994   0.997
Latvia               1.000   0.912   1.000
Lithuania            0.928   0.702   0.873
Luxembourg           1.000   0.676   1.000
Malta                1.000   1.000   1.000
The Netherlands      1.000   1.000   1.000
Poland               0.739   0.482   0.660
Portugal             0.706   0.575   0.862
Romania              0.678   0.560   0.677
Slovak Republic      1.000   0.937   0.840
Slovenia             0.813   0.711   0.837
Spain                1.000   1.000   1.000
Sweden               0.758   0.688   0.748
The United Kingdom   1.000   1.000   1.000

Source: Author's calculations using data sourced
from the Bureau van Dijk Bankscope database.

Table 4: Levin-Lin-Chu panel unit root test

Variables                                   Panel 1 - EU 28
                                            (P1 A: 1999-2013)

                                             t-star      P>t

Natural log of the per capita                -5.26517   0.0000
  gross national income
Nominal short-term interest rate            -19.38331   0.0000
Government net lending/borrowing             -3.74542   0.0001
Bank market concentration                    -4.55126   0.0000
Ratio of bank equity to bank total assets    -6.95812   0.0000
DEA bank efficiency                          -8.40442   0.0000

Variables                                   Panel 2 - EU 22
                                            (P2 A: 1999-2013)

                                             t-star      P>t

Natural log of the per capita                -5.28534   0.0000
  gross national income
Nominal short-term interest rate            -15.82860   0.0000
Government net lending/borrowing             -3.56866   0.0002
Bank market concentration                    -4.02627   0.0000
Ratio of bank equity to bank total assets    -6.39360   0.0000
DEA bank efficiency                          -7.92980   0.0000

Table 5: Westerlund panel cointegration test (p-values)

Cointegration between the                    Panel 1 - EU 28 (Pl-A:
series of variables                                1999-2013)

                                        Gt      Ga      Pt      Pa

Natural log of the per capita gross    0.000   0.996   0.007   0.461
  national income and nominal short-
  term interest rate
Natural log of the per capita gross    0.207   1.000   0.739   0.976
  national income and government net
  lending/borrowing
Natural log of the per capita gross    0.006   0.169   0.000   0.000
  national income and bank market
  concentration
Natural log of the per capita gross    0.072   0.941   0.609   0.782
  national income and ratio of bank
  equity to bank total assets
Natural log of the per capita gross    0.008   0.770   0.016   0.158
  national income and DEA bank
  efficiency

Cointegration between the                    Panel 2 - EU 22 (P2-A:
series of variables                                1999-2013)

                                        Gt      Ga      Pt      Pa

Natural log of the per capita gross    0.000   0.991   0.020   0.479
  national income and nominal short-
  term interest rate
Natural log of the per capita gross    0.709   0.999   0.816   0.971
  national income and government net
  lending/borrowing
Natural log of the per capita gross    0.050   0.324   0.002   0.000
  national income and bank market
  concentration
Natural log of the per capita gross    0.076   0.942   0.716   0.836
  national income and ratio of bank
  equity to bank total assets
Natural log of the per capita gross    0.064   0.957   0.039   0.284
  national income and DEA bank
  efficiency

Table 6: Results obtained with dynamic GMM one-step system estimations

Panel 1 - EU 28

                                   PANEL 1-A (1999-2013)

                                                            P>
                                                         [absolute
                                     coef.        Z     value of z]

Nominal short-term interest rate   -0.102199    -8.67      0.000
Government net lending/borrowing    0.0361498    3.21      0.001
Bank market concentration          -0.0069661   -1.09      0.278
Ratio of bank equity to bank       -0.093506    -7.32      0.000
  total assets
DEA bank efficiency                 1.394033     3.58      0.000
Constant                            3.404441     5.53      0.000

                                   Wald [chi square] (5) = 264.49
                                   Prob. > [chi square] = 0.000
Arellano-Bond test for AR(1)       z = -3.65
  in first differences             Pr > z = 0.000
Arellano-Bond test for AR(2)       z = -1.04
  in first differences             Pr > z = 0.298
Sargan test of overid.             [chi square] (23) = 171.71
  restrictions                     Prob. > [chi square] = 0.000
Number of observations             420

                                   PANEL 1-B    (1999-2007)

                                                            P>
                                                         [absolute
                                     coef.        Z     value of z]

Nominal short-term interest rate   -0.1170908   -6.23      0.000
Government net lending/borrowing    0.0100156    0.25      0.806
Bank market concentration           0.01993      1.81      0.070
Ratio of bank equity to bank       -0.0562786   -2.42      0.016
  total assets
DEA bank efficiency                 3.064111     4.72      0.000
Constant                           -0.0279268   -0.03      0.980

                                   Wald [chi square] (5) = 117.39
                                   Prob. > [chi square] = 0.000
Arellano-Bond test for AR(1)       z = -1.74
  in first differences             Pr > z = 0.083
Arellano-Bond test for AR(2)       z = -1.39
  in first differences             Pr > z = 0.165
Sargan test of overid.             [chi square] (11) = 86.51
  restrictions                     Prob. > [chi square] = 0.000
Number of observations             252

                                   PANEL 1-Cl   (2008-2013)

                                                            P>
                                                         [absolute
                                     coef.        Z     value of z]

Nominal short-term interest rate   -0.0757305   -4.23      0.000
Government net lending/borrowing    0.0533712    3.43      0.001
Bank market concentration           -.0073907   -0.53      0.596
Ratio of bank equity to bank       -0.1264874   -7.24      0.000
  total assets
DEA bank efficiency                 0.0328352    0.09      0.926
Constant                            4.83344      4.52      0.000

                                   Wald [chi square] (5) = 86.2
                                   Prob. > [chi square] = 0.000
Arellano-Bond test for AR(1)       z = -1.78
  in first differences             Pr > z = 0.075
Arellano-Bond test for AR(2)       z = -1.40
  in first differences             Pr > z = 0.160
Sargan test of overid.             [chi square] (5) = 5.94
  restrictions                     Prob. > [chi square] = 0.312
Number of observations             168

Panel 2 - EU 22

                                      PANEL2-A (1999-2013)

                                                           P >
                                                           [absolute
                                      coef.        Z       value of z]

Nominal short-term interest rate      -0.0742289   -5.00   0.000
Government net lending/borrowing       0.0258307    1.43   0.153
Bank market concentration              -.0121716   -1.83   0.067
Ratio of bank equity to bank          -0.1006469   -6.85   0.000
  total assets
DEA bank efficiency                    2.576677     5.03   0.000
Constant                               2.65505      4.05   0.000
                                      Wald [chi square] (5) = 217.50
                                      Prob. > [chi square] = 0.000
Arellano-Bond test for AR(1)          z = -4.04
  in first differences                Pr > z = 0.000
Arellano-Bond test for AR(2)          z = -1.36
  in first differences                Pr > z = 0.173
Sargan test of overid. Restrictions   [chi square] (23) = 124.72
                                      Prob. > [chi square] = 0.000
Number of observations                330

                                      PANEL2-B (1999-2007)

                                                           P >
                                                           [absolute
                                      coef.        Z       value of z]

Nominal short-term interest rate      -0.0654121   -2.61   0.009
Government net lending/borrowing       0.0487936    1.05   0.292
Bank market concentration              0.0129485    1.16   0.245
Ratio of bank equity to bank          -0.0731273   -2.75   0.006
  total assets
DEA bank efficiency                    4.298334     5.36   0.000
Constant                              -0.681047    -0.60   0.546
                                      Wald [chi square] (5) = 102.48
                                      Prob. > [chi square] = 0.000
Arellano-Bond test for AR(1)          z = -2.37
  in first differences                Pr > z = 0.018
Arellano-Bond test for AR(2)          z = -2.89
  in first differences                Pr > z = 0.004
Sargan test of overid. Restrictions   [chi square] (11) = 62.33
                                      Prob. > [chi square] = 0.000
Number of observations                198

                                      PANEL2-C (2008-2013)

                                                           P >
                                                           [absolute
                                      coef.        Z       value of z]

Nominal short-term interest rate      -0.1093013   -5.55   0.000
Government net lending/borrowing       0.0803335    3.99   0.000
Bank market concentration              0.0017324    0.21   0.834
Ratio of bank equity to bank          -0.1620307   -9.14   0.000
  total assets
DEA bank efficiency                   -0.7542794   -1.52   0.129
Constant                               5.341475     7.58   0.000
                                      Wald [chi square] (5) = 99.41
                                      Prob. > [chi square] = 0.000
Arellano-Bond test for AR(1)          z = -2.30
  in first differences                Pr > z = 0.021
Arellano-Bond test for AR(2)          z = 0.72.
  in first differences                Pr > z = 0.472
Sargan test of overid. Restrictions   [chi square] (5) = 11.12
                                      Prob. > [chi square] 2 = 0.049
Number of observations                132

Dependent variable: natural logarithm
of the per capita national income.
COPYRIGHT 2016 Association for Comparative Economic Studies
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2016 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Title Annotation:Symposium Article
Author:Ferreira, Candida
Publication:Comparative Economic Studies
Article Type:Report
Geographic Code:4E
Date:Jun 1, 2016
Words:10090
Previous Article:Current accounts in the European Union and the sectoral influence: an empirical assessment.
Next Article:Structural and cyclical determinants of bank interest-rate pass-through in the Eurozone.
Topics:

Terms of use | Privacy policy | Copyright © 2019 Farlex, Inc. | Feedback | For webmasters