Financial Conglomeration: Efficiency, Productivity and Strategic Drive

Transcription

1 Financial Conglomeration: Efficiency, Productivity and Strategic Drive Barbara Casu Claudia Girardone University of Wales, Bangor Middlesex University Business School Abstract Consolidation in the global banking industry has resulted in the emergence of financial conglomerates that conduct an extensive range of businesses with a group structure. To date, however, few studies have investigated the performance features of such groups. This study aims to extend the literature by evaluating the cost characteristics (scale, scope and x-efficiency), profit efficiency and productivity change of Italian financial conglomerates during the 1990s using both parametric and non-parametric statistical approaches. By using multiple frontier techniques the robustness of the results can be tested (methodological cross-checking). The impact of diversification and growth strategies on cost and profit efficiency is also investigated. The results seem to indicate that Italian banking groups have benefited from a consistent improvement in profit efficiency, while they have not experienced a clear increase in cost efficiency. Indeed, profit efficient banking groups display a high risk-high return profile. The restructuring process and the trend towards conglomeration do not appear to have translated into scale efficiency gains. In contrast, scope economies results give a positive indication of the benefits of diversification. JEL classification: G21; D2. Keywords: Financial Conglomerates; Cost Efficiency; Profit Efficiency; Productivity; Diversification Strategies. Corresponding author: Middlesex University Business School, Middlesex University, The Burroughs, Hendon, London NW4 4BT (United Kingdom); Tel:+44 (0) ; Fax:+44 (0) ; c.girardone@mdx.ac.uk

2 1. Introduction In recent years conglomeration has become a major trend in financial markets, emerging as a leading strategy of banks faced with technological progress, international consolidation of markets and deregulation of geographical or product restrictions. In the EU, financial conglomeration was encouraged by the Second Banking Directive (1989) which enabled banks to operate as universal banks: that is to engage, directly or through subsidiaries, in other financial activities, such as financial instruments, factoring, leasing and merchant banking. In the US, the repeal of many provisions of the Glass-Steagall Act will imply that many banks will either consolidate with other banks or converge with non-bank financial firms (Soper, 2001). In this context, financial conglomerates are defined as a group of enterprises, formed by different types of financial institutions (Van den Berghe, 1995). Group organisational structure is believed to bring about, on the one hand the possibility of exploiting greater cost economies and, on the other hand the capacity of the group to isolate risk from its different activities. On the revenue side, the ability of financial conglomerates to distribute a full range of banking, securities and insurance services may increase their earning potential and lead to a more stable profit stream. Customers may value a bundled supply of financial services more than separate offers for reasons of transactions and information costs (Vander Vennet, 2001). On the other hand, it is argued that such structure has drawbacks, such as conflict of interest and concentration of power (Saunders, 1994)

3 Despite the theoretical debate, there seem to be little empirical evidence on the efficiency of conglomerate banking structure. Verweire (1999) puts forward three main reasons for the scarcity of empirical research on the performance consequences of financial conglomerates. First, financial conglomeration is still prohibited in a number of countries (most noticeably, until recently, in the US). Secondly, the trend towards financial conglomerates is relatively new. Finally, researchers in the financial industry services have adopted a fragmentary approach focusing either on banking, securities or the insurance industry. Vander Vennet (2001) analyses the cost and profit efficiency of European financial conglomerates and universal banks and suggests that conglomerates are more revenue efficient than specialised banks and that the degree of both cost and profit efficiency is higher in universal banks than in non-universal banks. These results seem to indicate that the current trend towards financial conglomeration may lead to a more efficient banking system. This study aims to extend the existing literature by analysing a sample of Italian financial conglomerates in the second part of the 1990s. The Italian case can provide a benchmark for a group of continental European countries, where the process of deregulation, progressive liberalisation and despecialisation, which was brought about by the implementation of Second Banking Directive, resulted in considerable changes and rationalisation in the structure of the banking sectors. This paper focuses on the assessment of the cost characteristics (scale, scope and x-efficiency), profit efficiency and productivity change of Italian financial conglomerates. The study evaluates cost characteristics by employing the - 3 -

4 parametric Stochastic Frontier Approach (SFA) and Distribution Free Approach (DFA), and the non-parametric Data Envelopment Analysis (DEA) methodologies. By using multiple frontier techniques the robustness of the results can be tested (methodological cross-checking). Alternative profit efficiency is estimated using SFA; whereas the Malmquist Total Factor Productivity (TFP) Index is used to measure productivity change over time. Furthermore, the informativeness of the efficiency measures is assessed against other measures of bank performance and management quality. The results seem to indicate that Italian banking groups have benefited from a consistent improvement in profit efficiency, while they have not experienced a clear increase in cost efficiency. Indeed, profit efficient banking groups display a high risk-high return profile. The restructuring process and the trend towards conglomeration do not appear to have translated into scale efficiency gains. In contrast, scope economies results give a positive indication of the benefits of diversification. The paper is set out as follows: Section 2 illustrates the methodological approaches and data used for the empirical analysis. Section 3 reports the results and the final section is the conclusions

5 2. Methodology and Data This section briefly describes the parametric and non-parametric methodological approaches followed, it illustrates the sample and discusses the measurement of the inputs and the outputs used in our efficiency analysis Parametric Approach This study employs a Fourier-flexible which is a global approximation that includes a standard translog plus Fourier trigonometric terms. 1 The resulting mixed cost and alternative profit functions can be written as: lntc [ or ln PT ] = i= 1 j= 1 [ λi cos zi + θ i sin zi ] 2 2 [ λij cos ( zi + z j ) + θ ij sin( zi + z j )] i= 1 j= 1 ij i j i= 1 j= 1 i= 1 j= ij [ λijk cos ( zi + z j + zk ) + θ ijk sin( zi + z j + zk )] + ε i i= 1 j i k j k i α i i= 1 α ln Q + δ ln Q ln Q ρ ln Q ln P + 2 j i 4 i i= j= 1 β ln P + 3 j j γ ij ln Pi ln Pj (1) where TC is a measure of the costs of production, comprising operating costs and interest paid on deposits (PT is profit before tax); the Q ( i =1, 2) are output i quantities, the P j ( j =1, 2, 3) are input prices; the z i are adjusted values of the natural log of output Qi ln so that they span the interval [. 1 2π,.9 2π ] and is the two-component error term as defined in (1). The formula for z i is (. 2 µ a + µ lnq ( 9 2π.1 π ) ( b a) π ) where [ b] i a, is the range of ln Qi and µ As in Berger and Mester (1997a,b), the alternative - 5 -

6 profit function uses essentially the same specification with a few changes. First, the dependent variable for the profit function replaces the normalised lntc with ln [ ] min ( PT P ) + ( PT P ) 1, where ( PT ) min indicates the absolute value of the P 3 min minimum value of ( PT P 3 ) over all banks. Thus, the constant θ = ( PT P 3 ) + 1 is added to every firm s dependent variable in the profit function so that the natural log is taken of a positive number, since minimum profits are typically negative. Thus for the firm with the lowest value of ( PT P 3 ) for that year, the dependent variable will be ln(1)=0. For the alternative profit function this is the only change in specification, since the exogenous variables are identical to those for the cost function. Moreover, standard symmetry and linear restrictions on (1) have to be imposed on the translog portion of the function. 3 In accordance with the assumed constraint of linear homogeneity in prices, TC, PT, P 1 and P 2 are normalised by the price of capital, P 3. 4 It is also important to mention that consideration of input share equations embodying Shephard s Lemma restrictions is excluded in order to allow for the possibility of allocative inefficiency (see, for example, Berger and Mester, 1997a). The Fourier terms are included only for the outputs, leaving the input price effects to be described solely by the translog term (see, for example, Berger et al., 1997 and Altunbas et al., 2000). The Fourier terms for the input prices are excluded in order to conserve the limited number of Fourier terms for the output quantities used to measure economies of scale. We employ the standard Stochastic Frontier Approach (SFA) to generate - 6 -

7 estimates of X-efficiencies for each banking group over the years along the lines first suggested by Aigner et al. (1977). Specifically, we employ the Battese and Coelli (1992) model of a stochastic frontier function for panel data with firm effects which are assumed to be distributed as truncated normal random variables (µ 0) 5 and are also permitted to vary systematically with time (see for more details on the SFA methodology Battese and Corra, 1977; Battese and Coelli, 1993; Coelli et al., 1998; Girardone, 2000). We also estimate bank efficiency for the period using the parametric Distribution Free Approach (DFA) (Berger, 1993). The average residual of each bank i is used in the computation of efficiency; it is measured by the ratio of the average residual of each bank i to the minimum average residual across the banks of the sample, assumed to be the average residual of the most efficient bank. Since the extreme values of these inefficiency estimates may reflect substantial random components, we use truncation to reassign less extreme values to banks with the most extreme values for each subperiod (see Berger and Mester, 1997a; Maudos et al., 1998). Following Dietsch et al. (1998) in order to overcome the problem of assuming that the inefficiency term is equal to the residual, we use DFA to study the evolution of efficiency for our sample of banks by making the observation period for the study slide by computing efficiency over the periods then and The cost and alternative profit equations are estimated separately for each subperiod, allowing the coefficients to vary to reflect changes in technology, regulation and market environment. Finally, economies of scale and scope have also been estimated for our sample of Italian banking groups

8 2.2. Non-parametric Frontier This study employs the standard DEA approach 7 ; specifically the efficiency measures are the result of the implementation of a Variable Returns to Scale (VRS), input-oriented cost minimisation model. Since price information is available, both technical (TE) and allocative efficiency (AE) were measured. Allocative efficiency in input selection involves selecting that mix of inputs which produce a given quantity of output at a minimum cost, given the prevalent input prices. Allocative and technical efficiency combine to provide an overall measure of economic efficiency. The total cost efficiency, or economic efficiency, of the i- th DMU is calculated as the ratio of minimum cost to observed cost. Cost efficiency (CE) can be seen also as the product of technical and allocative efficiencies (CE = TE * AE); in other words, firms having higher costs than the frontier may be so either because they are not using the most efficient technology (technical inefficiency) or/and because they are not using the cost minimising input mix (allocative inefficiency). 8 The efficiency estimates obtained from the aforementioned non-parametric approach express the relative efficiency at a given point in time. In order to estimate efficiency and productivity changes over time, the Malmquist Productivity Index (MPI) is calculated. The Malmquist TFP index 9 measures the TFP change between two data points by calculating the ratio of the distances of each data point relative to a common technology. Following Färe et al. (1994) the Malmquist (output oriented) TFP change index between period s (the base period) and period t is given by: - 8 -

9 ( y, x ) ( yt, xt ) ( y, x ) s t d 0 t t d 0 m 0 ( ys, xs, yt, xt ) = s t (2) d 0 ( ys, xs ) d 0 s s s where the notation d ( x, y ) 0 t t represents the distance from the period t observation to the period s technology. A value of M 0 greater than one will indicate positive TFP growth from the period s to period t while a value less than one indicates TFP decline. Note that equation (2) is, in fact, the geometric mean of two TFP indices, the first evaluated with respect to period s technology and the second with respect to period t technology Data Sample and Input -Output Variables This study focuses on the cost and profit efficiency characteristics and productivity change of Italian banking conglomerates and it attempts to assess the effects of diversification and growth strategies on their efficiency levels. The regulation of banking groups is one of the most important innovations introduced by the 1994 Banking Law 10, which embodied the EU Second Banking Directive. In particular, it included specific rules concerning banking groups supervision and consolidated balance sheet data. 11 Therefore, the sample comprises only Italian banking groups, as identified by the Bank of Italy and registered in Albo dei Gruppi Bancari (Banking Groups Register), to form an unbalanced panel of 168 observations distributed in the following way: 36 groups in 1996, 40 groups in 1997, 44 in 1998 and 48 groups in The choice of using an unbalanced panel is to allow the investigation of the impact on cost and profit efficiency of the restructuring process that has taken place in Italy during the years under - 9 -

10 study. 13 Indeed, over the second half of the 1990s, the reorganization of production and distribution processes carried out by the main banking and insurance conglomerates, together with the use of new information technologies brought the cost structure of the Italian banking system in line with those of main continental European countries (Bank of Italy, 2000). The data used to construct the estimates for the empirical analysis are consolidated financial data drawn from the BankScope database. Other relevant information was obtained from Bilbank v. 2.11, the Association of Italian Bankers (ABI) dataset (ABI, 2000) and Mediobanca (2000). Choosing the appropriate definition of bank output is a relevant issue for research into banks cost efficiency. While the multiproduct nature of the banking firm is widely recognised, there is still no agreement as to the explicit definition and measurement of banks inputs and outputs. Generally, each definition of input and output carries with it a particular set of banking concepts, which influence and limit the analysis of the production characteristics of the industry. The approach to output definition used in this study is a variation of the intermediation approach, which was originally developed by Sealey and Lindley (1977) and posits that total loans and securities are outputs, whereas deposits along with labour and capital are inputs to the production process of banking firms. Specifically, the input variables used in this study are: the average cost of labour (personnel expenses/average number of personnel); deposits (interest expenses/customer and short-term funding) and capital (total capital expenses/total fixed assets). The output variables are total loans and other earning

11 assets. (Table A1 in the Appendix provides the descriptive statistics of the variables used in the parametric and non-parametric models). 3. Results and Discussion 3.1. Efficiency Estimates Table 1 reports descriptive statistics of the cost X-efficiency measures derived both from SFA and DEA. The stochastic cost function is specified both as a translog and a Fourier-flexible; DEA technical, allocative and cost efficiency are calculated. 14 Overall, Italian banking groups seem to display relatively high inefficiency scores ranging between 22% and 35%. The Fourier-flexible estimation yielded on average slightly lower efficiency results than the translog; however the level of dispersion of average efficiency scores is quite similar. The two specifications translog and Fourier-flexible have been subjected to the likelihood ratio test statistic in order to identify which of the two was giving the best fitting to our sample. As concerns the cost function the likelihood ratio statistics is equal to Since the critical value of χ =29.14 and 69.86>29.14, the translog form 14,0. 01 is rejected in favour of the Fourier. This gives greater support to the choice of the Fourier-flexible as the more appropriate functional form to evaluate the cost efficiency of Italian financial conglomerates. In addition, both models ranked the individual banking groups in almost the same order. The Pearson correlation coefficients between the Fourier-flexible and translog are always positive and statistically significant at the 0.01 level (ranging from to 0.788)

12 The SFA findings reported in Table 1 suggest a steady improvement in cost efficiency over the period under study. According to the results, Italian banking conglomerates seem to have managed to reduce overall cost inefficiency from 29.5% in 1996 to 22.2% in 1999 (translog specification) and from 34.8% in 1996 to 28.3% (Fourier specification). 15 To test the robustness of these results we applied the non-parametric DEA methodology on the same data set. Table 1 reports the efficiency scores, where technical, allocative and cost efficiencies were measured. Consistent with the previous estimation and in line with recent studies (Berger et al., 1997) the inefficiency ranges between 15% and 25%. The overall cost inefficiency seems to be attributable more to technical inefficiency, that is banking groups are not using the most efficient technology, rather than allocative inefficiency (i.e. not using the cost minimising input mix). The stochastic and DEA frontiers are reasonably similar in magnitude and also show similar variation in efficiency levels (standard deviations averaging around 10%). Despite these similarities in range and variance of the efficiency score, the trend in the DEA technical, allocative and cost efficiency is increasing between 1996 and 1998 and shows a rather sharp decrease in 1999 (for example TE decreased from in 1998 to in 1999, that is an increase in inefficiency of 18.7%). Table 1 here

13 This difference in trend prompted us to further test the robustness of the results by calculating X-efficiency using DFA. This method of testing is referred to as methodological cross-checking (Charnes et al., 1981; Ferrier and Lovell, 1990 and Eisenbeis et al., 1999). In case the results derived from the DFA estimation confirmed the DEA results this could imply that differences between the efficiency trends highlighted beforehand are not due to differences in the methodological assumptions of the estimation procedures (parametric v. nonparametric) but they may be due to other factors. One of such factors could be related to the use of time-varying panel data where situations in which some firms may be relatively efficient initially but become relatively less efficient in subsequent periods cannot be taken into account (Coelli et al., 1998). To test this assumption, we also present the results derived from the re-estimation of the SFA for the subperiods , and Table 2 here The calculation of the parametric DFA (Table 2) shows that the results seem to be consistent with the non-parametric DEA efficiency estimates illustrated above. 16 Note that the usual truncation at 5% was carried out (see Berger and Mester, 1997a, Dietsch et al., 1998 and Maudos et al., 1998). Cost efficiency levels with 10% truncation were also calculated (see Table A2 in the Appendix) and results seem to be in line with those reported in Table 2. The fact that the change from 5% to 10% truncation does no substantially alter the levels of

14 efficiency leads us to consider 5% a reasonable level of truncation for valuing the results. 17 The correlation coefficients between the results for each year are always positive and significant at the.01 level, thus supporting the condition of consistency over time of the efficiency scores 18. As shown in Table 2, mean (and median) efficiency scores for Italian banking groups seem to decrease between 1998 and 1999 (from to for the translog and from to for the Fourier). Since the DFA efficiency estimates display a decreasing trend, in line with the non-parametric DEA scores, we also re-estimated the SFA by computing efficiency over the periods then and , balancing the panels every two years. Results are reported in Table 2. Overall, they seem to confirm that in the subperiod there has been a decline in average efficiency levels that could not be picked up by the estimation employing the 4- year panel data. This outcome challenges the validity of results of estimations carried out using panel data over long time periods. A common criticism of bank cost studies relates to the issue of ignoring the profit side of the banks operations. Recently, studies employing profit functions or investigating both banks cost and profit efficiency have gradually acquired greater importance. The rationale for these studies is that banks that show the highest inefficiency and incur the highest costs might be able to generate more profits than the more cost-efficient banks (see, for instance, Berger and Mester, 1997a,b; De Young and Hasan, 1998, Maudos et al., 1998). Table 3 reports the results of profit efficiency derived from the estimation of the SFA for translog and

15 Fourier models. The Pearson correlation coefficients between the two models are highly positive and statistically significant at the 0.01 level (ranging between and 0.992). However, the likelihood ratio test for the profit functions seems to suggest that the translog model should give a better fitting compared to the Fourier flexible (the likelihood ratio statistics is equal to 4.49 which is < than the critical value of 29.14). 19. Overall, profit efficiency seems to increase over all the years under study. This seems to indicate that, while costs increased over the period, revenues increased even more, so that the Italian banking groups profitability increased. Table 3 also shows the profit efficiency results using the DFA approach. (Profit efficiency levels with 10% truncation are reported in Table A2 in the Appendix). DFA profit efficiency estimates seem to be smaller than those derived from the estimation of the SFA for translog and Fourier models and the displayed trend is ambiguous. Indeed, hypothesis H 0 of equality of mean X-efficiencies over the period under study showed that the null hypothesis has not been rejected for the translog. Table 3 here Overall, the results proved consistent across methodologies and seem to suggest that between 1996 and 1999 whereas Italian banking groups have not experienced a clear increase in cost efficiency, on the profit side the strategic choices carried out by Italian banking groups seem to have brought about a consistent improvement in profit efficiency

16 3.2. Scale and Scope Economies Table 4 reports the results on economies of scale 20 and scope 21 derived from the estimation of the cost function using a stochastic Fourier specification. Results show that significant scale diseconomies were found for the mean banking group over the years under study. Similar results were achieved for the median banking groups 22. Table 4 here The presence of scale diseconomies is confirmed by the results of the TFP Index (see Section 3.3), where the average annual mean displays decreasing returns to scale for the years 1998 and Scale diseconomies seem to indicate that the restructuring process and the trend towards conglomeration has not translated into scale efficiency gains. The inability of bank groups to exploit relevant scale economies and significant cost reductions may be related to high operating and/or fixed costs associated with the restructuring process (including for example typical problems of organisational rigidity and overstaffing). Similarly, the acquisition of banks experiencing distress that occurred in the mid- 1990s may have impacted on the cost characteristics of the institutions included in the sample. In addition, large investments in new banking technologies have represented considerable costs, the benefits of which will not feed through until a later date. These new investments in IT are expected to bring about significant

17 reductions in the costs of processing various banking transactions and lower overall operating costs. The potential achievement of scope economies and diversification is often considered one of the main justifications for conglomerate structure. Despite the persistent trend of all classes of intermediaries towards a more diversified output, in the literature there is no clear empirical evidence of economies of scope. The presence of economies of scope was also tested on our sample. Results are reported in Table 4 and seem to indicate that Italian banking groups have been able to exploit significant economies of scope over the period The changes in the market structure and in the economic conditions which have taken place over the last few years, might have had a great impact on the cost/output of the market. In fact, our results are consistent with recent studies that show strong evidence of scope economies for the largest banks across virtually all European banking markets (see, for instance, European Commission, 1997). The presence of significant cost synergies seems to suggest that Italian banking groups, while not operating at the optimum size, have been able to exploit the advantages of diversification. 3.3 Productivity Change Following Färe et al. (1994) the Malmquist (output-orientated) TFP change index (M 0 ) has been calculated 23. A value of M 0 greater than one indicates positive TFP growth while a value less than one indicates TFP decline over the period. The TFP Index has been decomposed into two components: Technological

18 Change, which reflects improvement or deterioration in the performance of best practice DMUs and Technical Efficiency Change, which reflects the convergence towards or divergence from the best practice on part of the remaining DMUs. The approach has been further extended by decomposing the technical efficiency change into scale efficiency and pure technical efficiency 24. The productivity change results are summarised in Table 5. The annual entries in each column are geometric means of results for individual banks and the period results reported in the last row are geometric means of the annual geometric means. Table 5 here Overall, it is possible to detect TFP growth during the period. However, this growth shows an inflection in 1999 (-4%). The overall productivity growth seems to have been brought about by a deterioration of the performance of the best practice institutions (-10% on average) and the convergence towards best practice on the part of the remaining banks (+33%) on average. This improvement in productive efficiency results mostly from a sharp increase in pure technical efficiency (+33% over the period) rather than in scale efficiency (1% over the period). Such results need to be confronted by the change of direction witnessed in the 1998/99 period, where best practice banks seems to have consistently improved their performance (+11%) whereas the catching up effect seems to have come to a halt (-15%). These results seem to suggest that best practice banks have

19 overcome difficulties derived from the restructuring process and are able to start reaping the benefits from strategic changes and investment in technology, therefore showing a marked productivity increase. 3.4 Efficiency Estimates and Bank Strategies In order to investigate the impact of diversification and growth strategies on cost and profit efficiency, firm-specific measures of efficiency derived from SFA, DFA and DEA approaches, are regressed on a set of independent variables relevant to the banking business for The set of potential correlates with bank efficiency is chosen in such a way that several strategic aspects of banking activities are considered: for instance, change in bank size, asset quality, equity and diversification strategies (see Eisenbeis et al., 1999; Cyree et al., 2000). A logistic functional form rather than a linear regression model is used because the value of the inefficiency estimates, E( ε ) ˆ ranges between 0 and u i Variables included in the estimation are proxies for managerial competence and strategies implemented. For example, banking groups might have chosen to purchase and restructure banks in troubled conditions (i.e. with high costs and high non-performing loans); or enter new markets in order to increase fee income. The first variable included in the logistic regression is the ratio of nonperforming loans to gross loans (NONPERF) as a proxy for the quality of the management of credit risk. A high ratio may reflect a deliberate high risk high return strategy or simply mismanagement. It is often hypothesised (Berger and DeYoung, 1997) that higher ratios of problem loans would reflect, ceteris paribus, i

20 lax internal controls and hence may be associated with inefficient operations. However, it can also reflect a highly aggressive acquisition policy of less profitable institutions. Therefore, we expect this coefficient to be negative in relation to cost efficiency whereas it might be positive in relation to profit efficiency. The second proxy is the ratio of book value of equity to total assets (EQUITY). The hypothesis is that the higher the capital ratio the more efficient the institution is likely to be. This is because the ratio also reflects the degree to which shareholders have their own capital at risk in the institution and therefore it may reflect their incentives to monitor management and assure that the institution operates efficiently (see Eisenbais et al., 1999). The variable GROWTH is a proxy for asset growth, as measured by the growth rate of the institution between 1996 and 1999, and it is thought of as reflecting the ability to manage growth and to be positively related to efficiency. The return on average asset (ROAA) is a standard measure of profitability and should be positively correlated to bank efficiency. However, a high ROAA may motivate institutions to exploit superior managerial skills and engage in M&As (Focarelli et al., 1999), therefore experiencing higher costs during the restructuring period. Another standard indicator of efficiency is LABB (the ratio of labour cost to gross income) and it is expected to be negatively related with efficiency. However, bank managers may choose to diversify into other areas or product lines, thus requiring more costly highly skilled personnel. The variable LOANAS (the ratio of loans to total assets) is a proxy reflecting how loaned-up a bank is. A high value for this variable could either reflect a comparative advantage in managing credit risk, since the

21 bank is focused on traditional lending activities; or the inability/unwillingness of the management to create new growth opportunities. On the other hand, the variable FEEINCO (the ratio of fee income to total gross income) reflects the proportional importance of fee-based products offered by an institution. As pointed out by Focarelli et al. (1999) in Italy financial services are very profitable and have expanded rapidly over the last few years, but commission income still accounts for a small proportion of total revenues. We expect the variable to be positively related to profit efficiency. In the case of cost efficiency, results can be mixed because while banks with expertise in the field may be able to strengthen their positions, other institutions may incur into higher costs when choosing to diversify their strategies into new markets/products. Finally, in order to test the impact of different diversification strategies we categorise banks into a growth category if the proportion of the activity is 70% or more of all growth activity. 26 In this way, we use two different dummies: the first (BANSUBS) reflecting the diversification of the group compared to the traditional banking business (the ratio of bank subsidiaries over total subsidiaries of the group) and the second (ITASUBS) reflecting the degree of internationalisation of the group (the ratio of Italian subsidiaries over total subsidiaries of the group). The models estimated to examine the relationship between cost (or profit) efficiency and bank strategies are thus: θ = β + β NONPERF+ β EQUITY+ β GROWTH+ β ROAA+ i + β LABB+ β LOANAS+ β FEEINCO+ β BANSUBS+ β ITASUBS+ ε i (3)

22 where θ i is the cost (profit) efficiency measure estimated from the different models implemented. Table 6 reports the results of the logistic regression analysis for cost and profit efficiency estimates respectively. Table 6 here On the cost efficiency side, consistently with our hypotheses, efficient banking groups seem to have higher levels of equity over assets, higher growth rates, lower non-performing loans and lower staff costs. The coefficients of the EQUITY variable are significantly greater than zero in most estimations, validating the hypothesis that the higher the capital ratio, the more efficient the institution. Furthermore, as pointed out by Mester (1996), such results could also be an indication that higher capital ratios may prevent moral hazard. Another statistically significant relationship is between efficient institutions and asset growth (GROWTH) thus indicating that efficient banking groups appear to have followed a growth strategy. The results also reflect the ability of these best practice institutions to manage growth over the period Interestingly, the most cost efficient banking groups seem to be also the least profitable, that is the coefficient for Return on Assets (ROAA) is negative. As expected, efficient banks tend to have a lower percentage of labour costs/gross income (the coefficient for LABB is negative) and a lower percentage

23 of non-performing loans over total loans (the coefficient on NONPERF is negative in all estimations and statistically significant in some). Furthermore, efficient banking groups seem to display a higher level of loans to total assets, as the variable LOANAS presents a relatively high value. This may reflect a comparative advantage in managing credit risk. As well, the ratio of fee income to total gross income (FEEINCO), when significant, is positively related to cost efficiency. Finally, as in Eisenbeis et al. (1999) it should be noted that the overall explanatory power of regressions employing parametric frontiers estimates of efficiency seem to be higher than for DEA estimates. On the profit side, the variable NONPERF, where significant, is positive thus implying that profit efficient banking groups may have a high risk-high return profile. They may have followed an aggressive acquisition strategy (the GROWTH variable is negative and statistically significant for SFA estimations) becoming involved with less profitable institutions. As concerns the variable EQUITY results are mixed and show a negative sign in one case. The sign and significance of LABB may suggest that more profitable banking groups have diversified in areas that have required large investments in high skilled staff. Finally, there seems to be no evidence that efficient banking group are more diversified, since the coefficient on BANSUB and ITASUB are always insignificantly different from zero both for the cost and profit side. 4. Conclusions This paper focuses on the cost characteristics (scale, scope and x-efficiency),

24 profit efficiency and productivity change of Italian financial conglomerates. Furthermore, the informativeness of the efficiency measures is assessed against other measures of bank performance and management quality and the impact of diversification and growth strategies on cost and productive efficiency are investigated. Overall, results seem to suggest that over the period Italian banking groups have not experienced a clear improvement in cost efficiency and productivity. The methodological cross checking allowed us to test the robustness of the results. As concerns the profit side, the strategic choices carried out by Italian banking groups seem to have brought about a consistent improvement in their profit efficiency, which is also confirmed by the increase in their ROA over the period under study. Scale diseconomies seem to indicate that the restructuring process and the trend towards conglomeration has not translated into scale efficiency gains. In contrast, scope economies results seem to give a positive indication of the benefits of diversification. These findings indicate that joint production of key banking services can reduce costs considerably in comparison to separate production. Logistic regression estimates carried out to investigate the determinants of cost and profit efficiency seem to indicate that, on the cost efficiency side, efficient banking groups seem to have higher levels of equity over assets, higher growth rates, lower non-performing loans and lower staff costs. On the profit side, results seem to imply that profit efficient banking groups have a high risk-high return profile

25 In sum, over the period the competitive advantage of Italian banking groups seems to be mainly due to the benefits derived from diversification that has increased their profitability

26 Acknowledgments Comments by P. Molyneux, E. Thanassoulis, and L. Weill are gratefully acknowledged

27 Appendix Table A1 Descriptive Statistics: Total Cost, Profit, Outputs and Input Prices a,b YEARS OBS. MEAN MEDIAN MIN MAX STD DEV. Total costs (TC) Pre-tax Profits (PT) Output 1 (Q1) Output 2 (Q2) Price of Input 1 (P1) Price of Input 2 (P2) Price of Input 3 (P3) a Values in millions. b TC =Total Cost (Personnel Expenses + Interest expenses + Total Capital Expenses) ; PT = Pre- Tax Profits; Q 1 =Total Loans; Q 2 = Other Earning Assets; P 1 = Personnel Expenses/Average Number of Personnel; P 2 = Interest Expenses/Customer and Short Term Funding; P 3 = Total Capital Expenses/Total Fixed Assets

28 Table A2 DFA Cost and Profit Efficiency (10% truncation) a COST PROFIT Translog Fourier Translog Fourier Mean Med. Mean Med. Mean Med. Mean Med. 1996/97 (70) /98 (70) /99 (70) a The numbers in brackets refer to the number of observations for each subperiods

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