BANKING AND ECONOMIC ACTIVITY PERFORMANCE: AN EMPIRICAL STUDY AT THE COUNTRY LEVEL*
by
ANA LOZANO-VIVAS University of Málaga
and
JESÚS T. PASTOR Universidad Miguel Hernández
This paper examines the contribution of the banking sector to overall economic activity for a sample of 15 OECD countries during an 18-year period. Resorting to a recently defined global Malmquist index, we detect productivity growth in both cases and study the influence of its two com-ponents, efficiency change and technical change. We further analyze the relationship between each of the two banking productivity components and economic productivity.
1 I
Financial development has been seen as fundamental to the explanation of economic growth due to the fact that financial systems contribute to savings allocation and preserve the efficiency of the payments systems. This issue has opened an important empirical and theoretical debate on the growth–finance relationship during the last decade, with the analysis of different growth–finance models, both at the theoretical and at the empirical level (see Carettoni et al., 2001). The most relevant result obtained from this debate is that financial development boosts overall economic growth (see the reviews in Beck et al. (2000) and in Thiel (2001), as well as the recent paper by Guiso
et al. (2004)).
The aim of this paper is to contribute to the above debate from a specific perspective. More precisely, we would like to examine the influence of banking productivity—as a specific component of the financial system—on economic productivity, and its implications for the economic growth mechanism. Hence banking productivity will be considered as a proxy for financial development, while economic productivity will be considered as a proxy of economic
© 2006 The Authors
Journal compilation © 2006 Blackwell Publishing Ltd and The University of Manchester.
Published by Blackwell Publishing Ltd, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA 469
* The authors would like to thank two anonymous referees, and Philip Arestis, Georgios Chortareas and the rest of the participants at the Money, Macro and Finance conference at the University of Essex (June 2005), as well as the conference participants at the European Workshop on Efficiency and Productivity Analysis (EWEPA VIII, Oviedo, Spain), for helpful comments. Financial support from MCT and FEDER grant no. BEC2002-02852 is gratefully acknowledged. Ana Lozano-Vivas also acknowledges finan-cial support from ‘Ayudas a la investigación en el área de Estudios Europeos’ of the Fun-dación BBVA in the Project entitled ‘Integración, diferenciación y estabilidad de las instituciones bancarias europeas’.
growth. The latter relationship is straightforward, since economic productiv-ity has been identified as one of the main sources of economic growth. The former relationship requires us to explain the next chain of proxy variables, in a reverse order: banking productivity, banking efficiency, financial ciency and, finally, financial development. As a matter of fact, financial effi-ciency has been frequently used as a proxy of financial development in several economic growth–finance models (see Pagano, 1993; Mariani and Padoan, 2003). Moreover, the last mentioned papers together with Shan and Morris (2003) relate financial efficiency to economic productivity. On the other hand, banking efficiency can be considered as a proxy for financial efficiency in order to explain economic productivity for at least three documented reasons. First, Greenwood and Jovanovic (1990) argue that increased productivity of invest-ment leads to faster economic growth, while Bencivenga and Smith (1991) reveal that banking activity also increases this type of productivity. Second, Thiel (2001) points out the relevant role that banks play in the financial systems of the majority of the industrial countries; and third, Berger et al. (2004) establish that relatively healthy banking systems are associated with better overall economic performance. Finally, we have considered banking productivity instead of banking efficiency because of the nature of our empir-ical analysis, which is based on panel data. More concretely, we would like to capture the influence of banking activity on the overall level of economic activity over a period of time by performing a year-by-year analysis.
Economic and banking productivity are analyzed in the context of the frontier production approach following Färe et al. (1994) but resorting to a newly defined Malmquist index, the so-called global Malmquist index (see Lovell and Pastor, 2005). We prefer the global Malmquist index to the usual adjacent Malmquist index for the simple reason that the former measure and its components satisfy circularity, which increases its reliability and coherence.
In what follows we provide an analysis of the synergy between banking and economic productivity. In particular, we investigate which of the two components of banking productivity growth, efficiency change and/or tech-nical change, is more beneficial for economic productivity. The only prece-dent is provided by Lozano-Vivas and Pastor (2006) where the relationship in question was studied without considering the Malmquist indices nor the decomposition attempted in this paper.
The paper is organized as follows. Section 2 revises the methodological issues. Section 3 describes the data and Section 4 presents the empirical results. Finally, Section 5 concludes.
2 M
We start by analyzing the similarity or divergence between banking and eco-nomic productivity (see also Lozano-Vivas and Pastor, 2006). The main goal
of our paper is to test whether this similarity or divergence is due to efficiency and/or technological change.
To address this goal, we evaluate first the economic and the banking pro-ductivity for a number of countries over a number of years using the global Malmquist productivity index (Lovell and Pastor, 2005). To build this global Malmquist productivity index for economic and banking levels requires first performing two separate data envelopment analyses (DEAs).
The first DEA captures the relative overall economic efficiency of a set of 15 Organization of Economic Cooperation and Development (OECD) countries over an 18-year period by evaluating the overall economic efficiency of each country and each year with respect to a common global DEA fron-tier. The resort to a common global frontier is justified by the fact that it allows us to define a new productivity index.
Our second DEA considers the corresponding banking variables in a new DEA model, which offers a yearly efficiency banking score for each country-year. This second DEA considers the same set of countries on a yearly basis and evaluates banking efficiency across countries and across years.
This approach requires the identification of the most efficient country-years in order to construct a behavioral reference or a benchmark for overall economic activity and for the banking activity of the rest of the country-years. The distance that separates a unit from its benchmark in each of the two models measures the relative efficiency performance of this unit.
Economic efficiency is measured using constant returns to scale (CRS) output-based DEA models, following the pioneering work of Färe et al. (1994). In fact, the nature of the two inputs and one output of our model suggests the selection of an output-oriented model. The decision on the returns to scale is given beforehand. As Grifell-Tatjé and Lovell (1995) have shown, for the evaluation of productivities CRS models are a must. Nonethe-less, the difference between the CRS model and the variable returns to scale model was tested in Lozano-Vivas and Pastor (2006), ending up with very little divergence. Consequently, we consider the CCR model in our analysis (see Charnes and Cooper, 1978, for full details on the CCR model1
), which is both radial and CRS. We have also resorted to the CCR output-oriented model in our second DEA for the same reasons mentioned above. The math-ematical formulation of the models is
(1)
subject to
maxj
© 2006 The Authors
1The CCR model is probably the most widely used and best-known DEA model. It is the DEA model used in frontier analysis when a CRS relationship is assumed between inputs and outputs. This model calculates the overall efficiency for each unit, where both pure techni-cal efficiency and stechni-cale efficiency are aggregated into one value. It was the first DEA model to be developed, labeled CCR after Charnes, Cooper and Rhodes (1978).
where Yis the matrix of outputs of all the units in the sample,Xthe corre-sponding matrix of inputs, (X0,Y0) the unit to be rated and jits efficiency
score.
Once the economic or banking efficiency of each country and each year with respect to a common global DEA frontier is obtained, we are able to calculate productivity measures using the global Malmquist productivity index (Lovell and Pastor, 2005) as follows:
(2)
where each distance function is the inverse of the corresponding first-level effi-ciency score. The upper index indicates that we are dealing with the global fron-tier while the lower index indicates that we are resorting to a CRS model. This new index is always circular and feasible, even if we are dealing with its vari-able returns to scale version (see Lovell and Pastor, 2005). Moreover,McGcan be decomposed into managerial efficiency and technological components as
(3)
where ECcis the usual efficiency change indicator and BPGcG,s≤1 is a best-practice gap between TcGand Tcsmeasured along rays (xs,ys),s=t,t+1. BPCc is the change in BPGc, and provides a new measure of technical change. What matters for technical change, and hence for productivity change, is whether projections onto period t +1 technology ofactually utilized period t+1 data are closer to or farther away from the global technology than are projections onto period ttechnology ofactually utilizedperiod tdata. Summarizing, ECc characterizes management’s contribution to productivity change in the form of efficiency change, and BPCccaptures the contribution of changes in the utilized regions of the benchmark technologies.
=ECc×BPCc
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At this stage, we are able to analyze two main questions. First, we would like to disentangle which of the two components of the global Malmquist productivity index explains better the productivity change over time for overall economic and banking activity. Second, we would like to analyze the similarity or divergences between economic and banking activity with the aim of testing whether this similarity or divergence is due to efficiency and/or technological change.
The analysis of the first question will allow us to determine whether eco-nomic and banking productivity are improving over time and whether this improvement is due to diffusion of best-practice techniques (improvement in efficiency) or to innovation (technical progress). Once the evolution of the economic and banking productivity (and its components) has been analyzed, the question of convergence can be studied. The second question should also allow us to obtain additional insights on the nature of the link between finan-cial and real variables by exploring whether banking productivity, and its components, has any influence on economic productivity and its convergence.
3 D
The data sets used in this paper correspond to the data sets described and used in Lozano-Vivas and Pastor (2006). Consequently, we consider two samples (one for the overall economy and the other for the banking sector) covering 15 OECD countries and the period 1980–97. These data sets com-prise the following countries: Austria, Belgium, Canada, Denmark, Finland, France, Germany, Italy, Japan, the Netherlands, Norway, Spain, Sweden, the UK and the USA. The variables used for the purposes of this paper are dis-cussed in the rest of this section.
Gross domestic product (GDP) is used as a measure of aggregate output, expressed in international prices with a base year of 1995. Aggregate input proxies comprise aggregated labor, measured by the number of employees, and aggregated gross capital stock, expressed in international prices (base year 1995), for each country. These data are obtained from the International Financial Statistics of the International Monetary Fund.2
The banking sector variables used as outputs measure the services pro-vided by financial intermediaries. In particular, our aggregate measures of banking outputs are total loans, other earning assets and total deposits. Labor, other operating expenses except personal expenses and interest expenses are our aggregate inputs. All the banking variables are expressed in international prices (base year 1995). These data were gathered from the OECD publications of bank profitability data.
© 2006 The Authors
2GDP and gross capital stock are expressed in international prices using purchasing power parity dollars. Capital is measured as aggregated investment, which is a measure of capital stock based on a perpetual inventory method (Easterly and Levine, 2005).
The data used for measuring the economy as a whole (coming from the International Monetary Fund) and for measuring the banking performance (coming from the OECD bank profitability) consist of integrated, consistent and coherent macroeconomic and banking accounts. These are based on a set of internationally agreed concepts, definitions, classifications and accounting rules that allow us to make an international comparison based on the same standard. The choice of the period 1980–97, and of the vari-ables used, has been dictated in part by the availability of consistent data series across countries and over time.
4 E R
4.1 Economic Performance
The application of the methodology mentioned in Section 2 provides for each country-year its corresponding global Malmquist productivity index and its decomposition into managerial and technological components. We now present a summary description of the average economic performance of each country over the entire 1980–97 time period instead of presenting the dis-aggregated result for each country and year. Values exceeding 1 for the geometric means of the global Malmquist productivity index or any of its components imply country performance improvements or progress over time. Table 1 shows the (geometric) average economic productivity, efficiency and technical change for each country and for the total time period and their respective dispersion. Looking first at the bottom of Table 1, we observe that, on average, there exists an improvement of productivity over the 1980–97
© 2006 The Authors
T1
AEP, EC TC
Productivity Standard Efficiency Standard Technical Standard
Countries change deviation change deviation change deviation
Austria 1.010 0.027 0.996 0.029 1.014 0.034
Belgium 1.017 0.048 1.004 0.031 1.014 0.038
Canada 1.011 0.033 1.002 0.044 1.009 0.031
Denmark 1.006 0.069 0.991 0.053 1.014 0.053
Finland 1.023 0.068 1.012 0.076 1.012 0.033
France 1.013 0.021 1.005 0.019 1.008 0.018
Germany 1.012 0.039 0.997 0.043 1.014 0.023
Italy 1.018 0.018 1.009 0.017 1.009 0.024
Japan 1.021 0.023 1.012 0.019 1.009 0.019
Netherlands 1.004 0.030 0.992 0.026 1.013 0.026
Norway 1.020 0.036 1.009 0.025 1.011 0.021
Spain 1.011 0.041 0.996 0.053 1.016 0.044
Sweden 1.018 0.061 1.004 0.069 1.015 0.052
UK 1.007 0.059 0.995 0.062 1.012 0.051
USA 1.009 0.021 1.000 0.000 1.009 0.021
period for the sample countries. The average change in the global Malmquist productivity index was around 1.3 per cent per year for the sample as a whole. Most of the 1.3 per cent annual growth of total factor productivity stems from technological change.
To distinguish possible different patterns of productivity growth and its components across countries, we can observe the (geometric) average of pro-ductivity, efficiency and technical changes for each country in our sample. In all countries, the average productivity level shows improvements in produc-tivity, which indicates productivity growth of the economies over time. Lozano-Vivas and Pastor (2006) obtain the same results but evaluate the change—or ratio—of any two consecutive efficiency scores for each country over the considered time period as a productivity index. All countries expe-rience technical progress over time. However, six out of the 15 countries exhibit a slow decline of economic efficiency over time.
A few observations about individual economies are worth making. Note first that the results show differences among countries. Finland and Japan are the countries with higher average productivity growth over time where, in the case of Japan, this improvement in productivity over the period is largely due to positive efficiency change. However, in the case of Finland, efficiency and technical change are contributing the same amount to productivity growth. The USA is the most efficient over the period. Moreover, for the case of the USA, we can observe that all the productivity change, 0.90 percentage points, was due to technical change, a result that is in agreement with those of Färe
et al. (1994).
It can be noticed that the improvement found in terms of the average productivity level for every country could constitute the starting point for convergence analysis. The concept of convergence in the macroeconomic lit-erature requires that economies with low initial efficiency will catch up at a faster rate than economies with high initial efficiency.3
The results reveal that the countries that have obtained higher productivity over time are the ones that started with higher initial global frontier inefficiency levels (see Table 2, where columns 3, 5 and 7 show the initial efficiency from the global and year frontier as well as the initial technical level, i.e. BPG vector corresponding to the first year4
). In fact, the three countries with the worst initial global effi-ciency scores (Japan 0.531, Finland 0.552 and Norway 0.606) are included in the group of countries with the higher productivity improvement.5
© 2006 The Authors
3Färe et al. (1994) analyzed convergence using Malmquist indices to measure productivity growth. This approach allows the decomposition of productivity growth into changes in technical efficiency (which allows diffusion to be identified) and shifts in technology (which allows innovation to be identified) over time.
4BPG stands for the best-practice gap between the technology of the global frontier and the tech-nology of the year frontier at each point of time.
5More results on convergence are available in Lozano-Vivas and Pastor (2006). The authors tested convergence by using the notions ofb-convergence and s-convergence of Barro and Sala-i-Martin (1992).
As a consequence of the Malmquist decomposition, we can study here both the efficiency change and the technical change trend over time and its possible convergence. In fact, the same countries mentioned above also reg-ister the lower initial year-efficiency scores (Japan 0.619, Finland 0.722 and Norway 0.725), and the higher improvements in terms of efficiency changes (Japan and Finland: 1.012, and Norway 1.009). Hence, the new results suggest that the least efficient countries are approaching the global economic frontier at a faster rate. However, if we observe technical change in relation to the initial technical level, it can be seen that the countries with higher inno-vation at the beginning of the period are those that achieve a higher average technical change. The latter result suggests that while there exists convergence in terms of productivity growth this convergence is due exclusively to effi-ciency change (diffusion) of best-practice technologies.
4.2 Banking Performance
We report banking productivity, efficiency and technical change for the coun-tries in our sample in (geometric) average levels as before. Table 3 shows the average banking performance level for each country for the total time period. We find that no gains in technical efficiency were recorded over the time in the banking sector. The positive average (across the countries over the period) rate of total factor productivity level estimated was exclusively due to tech-nical progress.
© 2006 The Authors
T2
A IEP, EC T C
Initial Initial
efficiency efficiency Initial
Productivity (global Efficiency (year Technical technical
Countries change frontier) change frontier) change level
Austria 1.010 0.676 0.996 0.855 1.014 1.038
Belgium 1.017 0.744 1.004 0.941 1.014 1.061
Canada 1.011 0.689 1.002 0.832 1.009 1.039
Denmark 1.006 0.728 0.991 0.941 1.014 1.128
Finland 1.023 0.552 1.012 0.722 1.012 1.075
France 1.013 0.723 1.005 0.846 1.008 1.005
Germany 1.012 0.680 0.997 0.860 1.014 1.031
Italy 1.018 0.695 1.009 0.811 1.009 1.005
Japan 1.021 0.531 1.012 0.619 1.009 1.004
Netherlands 1.004 0.747 0.992 0.926 1.013 1.012
Norway 1.020 0.606 1.009 0.725 1.011 1.022
Spain 1.011 0.664 0.996 0.863 1.016 1.093
Sweden 1.018 0.731 1.004 0.942 1.015 1.116
UK 1.007 0.782 0.995 1 1.012 1.114
USA 1.009 0.857 1.000 1 1.009 1.006
More precisely, the results at the country level indicate that almost all countries (except for Austria, Canada and the Netherlands) portray an improvement in productivity for the whole time period. In terms of the tech-nical efficiency change, nearly half of the countries experience deterioration over the period. Turning to the technical change record, although it is true that improvement is evident for all countries, the differences between coun-tries are only relatively small.
Table 4 contains information about average productivity, efficiency and technical change as well as the initial efficiency from the global and year fron-tier and the initial technical level. The results of Table 4 suggest again that the less efficient banking industries of a country are catching up to the world and year banking frontier (columns 2 to 5). In terms of technical change, we observe a similar pattern. This last result is the only one that does not match with the results obtained for the overall economic convergence.
The similarity between the economic and banking performance trends over time suggests the possibility of investigating deeply the synergy between banking and economic performance as noted above.
4.3 Estimating the Economic and Banking Relationship
We now look at the economic and banking performance relationship. We replicate a relatively well-established standard framework for empirical esti-mation of the growth–finance nexus (King and Levine, 1993). Our main goal is to investigate to what extent banking productivity and its components
© 2006 The Authors
T3
ABP, EC TC
Productivity Standard Efficiency Standard Technical Standard change deviation change deviation change deviation
Austria 0.998 0.084 0.982 0.064 1.017 0.096
Belgium 1.028 0.041 1.000 0.046 1.028 0.056
Canada 0.981 0.054 0.958 0.113 1.023 0.117
Denmark 1.033 0.155 1.011 0.132 1.021 0.093
Finland 1.059 0.092 0.993 0.154 1.067 0.121
France 1.042 0.137 1.021 0.162 1.020 0.093
Germany 1.006 0.054 0.994 0.060 1.012 0.083
Italy 1.014 0.080 0.991 0.097 1.023 0.099
Japan 1.023 0.035 1.000 0.000 1.023 0.035
Netherlands 0.992 0.104 0.971 0.233 1.022 0.135
Norway 1.037 0.088 0.986 0.202 1.052 0.201
Spain 1.038 0.109 0.995 0.085 1.043 0.102
Sweden 1.029 0.235 1.002 0.296 1.028 0.376
UK 1.032 0.100 0.995 0.095 1.038 0.118
USA 1.025 0.060 1.003 0.050 1.021 0.054
(efficiency and technical change) affect economic productivity and its convergence.
The standard form of the regression is the following:
(4)
where Xiis the average value over time of total factor productivity growth in the ith country,Fi1is a conditioning variable defined as the log of the initial
efficiency (a convergence effect) and Zi is a measure of average banking performance (in our case defined by banking productivity growth and its components).
By estimating the above regression, we attempt to analyze the economic productivity benefits associated with a good performance in terms of pro-ductivity, efficiency and technical change of the banking sector at the country level. Our first estimation starts by analyzing the relationship between average banking productivity and economic productivity, by regressing overall eco-nomic productivity growth with the 1980 ecoeco-nomic efficiency (convergence effect) and the average banking productivity growth over time. These results are already established in Lozano-Vivas and Pastor (2006). They are reported here in order to facilitate the subsequent comparisons.
The breakdown of banking productivity growth into efficiency and tech-nical change allows us to analyze in detail which component of banking pro-ductivity growth may have more relevance for economic propro-ductivity convergence (see Table 5, rows 2 and 3). On the one hand, the results show
Xi=aFi1+bZi+ui
© 2006 The Authors
T4
A IBP, EC T C
Initial Initial
efficiency efficiency Initial
Productivity (global Efficiency (year Technical technical
Countries change frontier) change frontier) change level
Austria 0.998 0.727 0.982 1.000 1.017 1.000
Belgium 1.028 0.626 1.000 1.000 1.028 1.028
Canada 0.981 0.675 0.958 1.000 1.023 1.036
Denmark 1.033 0.496 1.011 0.726 1.021 1.013
Finland 1.059 0.325 0.993 1.000 1.067 0.963
France 1.042 0.438 1.021 0.692 1.020 1.096
Germany 1.006 0.733 0.994 1.000 1.012 0.968
Italy 1.014 0.635 0.991 1.000 1.023 1.101
Japan 1.023 0.680 1.000 1.000 1.023 1.059
Netherlands 0.992 0.590 0.971 0.917 1.022 1.087
Norway 1.037 0.368 0.986 0.870 1.052 0.978
Spain 1.038 0.399 0.995 0.857 1.043 1.009
Sweden 1.029 0.456 1.002 0.730 1.028 1.004
UK 1.032 0.408 0.995 0.778 1.038 0.947
USA 1.025 0.660 1.003 0.941 1.021 0.985
that banking efficiency change seems not to affect economic productivity convergence. On the other hand, the banking technical change enters signif-icantly and positively, suggesting that the innovation in banking has a sig-nificant impact on economic productivity growth and its convergence. These results emphasize the role that the banking sector plays in explaining eco-nomic productivity improvement over time. It seems that banking develop-ment spurs growth through banking innovation by contributing to economic productivity progress and its convergence.
In Section 4.1, we found that the diffusion (identified by changes in tech-nical efficiency) provides support for economic productivity convergence, in accordance with Färe et al. (1994). In order to reinforce the relevance of the diffusion effect, we now analyze the subsample of countries that expresses diffusion of the technology over time (which requires that ECc > 1 and
McG≥1).
First we regress economic productivity growth on the 1980 economic efficiency (convergence effect) and the average banking productivity, effi-ciency and technological change over time, as we did previously. The results are as follows. (i) When banking productivity growth enters as an independ-ent variable (first row of Table 6), we find that initially less efficiindepend-ent countries tend to show a higher catching-up than initially more efficient countries (− 0.024 at the 1 per cent significance level using alternatively conventional and bootstrap inference). As could be expected, banking productivity has a greater impact in economic productivity and its convergence than with the
© 2006 The Authors
T5
C- AC EPR AC BP(P, E
TC P1980–97)
Dependent variable Initial economic efficiency Average banking R2 (global frontier) productivity
Average economic −0.029* 0.085* 0.567
productivity [−3.16] [2.41]
Initial economic efficiency Average efficiency (global frontier) change in banking
Average economic −0.034* 0.076 0.524
productivity [−3.59] [1.01]
Initial economic efficiency Average technical (global frontier) change in banking
Average economic −0.027* 0.099* 0.528
productivity [−2.60] [3.15]
Notes: Student’s tin square brackets.
*Significant at the 0.05, or better, level. We have performed significance tests with the ‘conventional’ variance–covariance matrix (conventional inference) and with the ‘corrected’ matrix obtained by the boot-strap procedure (bootboot-strap inference) as proposed by Simar and Wilson (2003). The bias correction of the variance–covariance matrix has not generated any modification in decisions in the parameter significance test. In this and all the subsequent economic and banking relationship analyses performed, we have obtained similar results. In all cases the corresponding bias correction gives rise to the same significant level of the tests as in the conventional case but with tighter corrected confidence intervals.
whole sample (compare Tables 6 and 5). (ii) As before, the diffusion in banking over time has not maintained a significant role in supporting eco-nomic productivity convergence (second row of Table 6). (iii) The rise of innovation in banking over time seems to have increased economic produc-tivity and contributes significantly to its convergence process (0.192, signifi-cant at the 1 per cent level of significance using conventional and bootstrap inference). These results suggest that, while there is evidence of an economic and banking relationship across countries, the economic and banking nexus is far from being homogeneous. Actually, banking productivity has a larger effect on economic productivity in countries with higher economic diffusion. In general, banking performance and, in particular, intensification of banking innovation seem to have played an important role in boosting growth through productivity enhancement helping to get economic productivity convergence.6
5 C
This paper attempts to validate empirically the idea that economies with more efficient financial systems perform better. A fairly new methodological tool has been considered in our productivity analysis, namely the global Malmquist indices.
© 2006 The Authors
T6
C-AC EPR AC BP(P, E TC) CTED E
P, 1980–97
Dependent variable Initial economic efficiency Average banking R2 (global frontier) productivity
Average economic −0.024* 0.152* 0.743
productivity [−3.33] [3.48]
Initial economic efficiency Average efficiency (global frontier) change in banking
Average economic −0.033* 0.011 0.484
productivity [−2.51] [0.907]
Initial economic efficiency Average technical (global frontier) change in banking
Average economic −0.017* 0.192* 0.801
productivity [−2.51] [4.37]
Notes:Student’s tin square brackets. *Significant at the 0.05, or better, level.
6In order to check the robustness of our results, we have re-estimated the regressions corre-sponding to Tables 5 and 6 using panel data, and we have reached the same findings. In addition, we tested the causal relationship between banking and overall economic per-formance following Graff (2002). The results obtained are that the causation runs from banking to overall economic performance in our sample.
Our procedure is based on the initial estimation of efficiencies by means of two worldwide—CRS—production frontiers: the overall economic fron-tier and the overall banking fronfron-tier. The estimation of the efficiencies allows us to study productivity and its two-factor decomposition resorting to the aforementioned global Malmquist index. This productivity index and its two components—efficiency change and technical change—satisfy circularity, increasing the reliability and coherence of the usual Malmquist indices.
First, we analyze whether the productivity change of the banking activ-ity and its two factors (efficiency change and technical change) show simi-larity or divergence with the evolution of the overall economic productivity, as well as with their convergence trend. Based on these results, we then turn our attention to investigate the relationship between banking and economic performance using the empirical models already presented in the finan-cial–economic nexus literature. If banking and economic performance show a similar trend over a given time period then the question of association between overall economic performance and banking performance is set forth. Our analysis allows us to investigate the synergy between banking and eco-nomic performance not only detecting the relationship between them but also investigating which of the two components of banking productivity is playing a fundamental role in explaining economic growth through the channel of economic productivity.
Our results reveal that (i) banking and economic performance paint a picture of considerable similarity; (ii) there is convergence within the banking sector in terms of productivity, efficiency change and technical change and with regard to the economy in terms of productivity and efficiency change only; (iii) in terms of the banking sector the results suggest that technical progress seems to play in favor of productivity convergence but not in terms of the economy as a whole; (iv) banking performance affects positively eco-nomic productivity and its convergence; and (v) in particular, it is the tech-nical change, the component of banking productivity change, that has a significant impact on economic productivity and its convergence.
In general, the results are consistent with the idea that banking per-formance and, in particular, intensification of banking innovation seem to have played an important role in boosting growth through productivity enhancement, helping to generate economic productivity convergence. The results could suggest that if there are no distortions in the banking sector (i.e. the banking sector performs productively efficiently) then economic activity will increase due to the presence of a higher rate of banking innovation. These results seem to be stronger for the case of countries that show a higher economic diffusion over time.
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