THE EFFECT OF BANK M&As ON EFFICIENCY: THE PORTUGUESE EXPERIENCE

Victor Mendes * Universidade do Porto - Faculdade de Economia - CEMPRE R. Dr. Roberto Frias 4200 Porto - PORTUGAL Ph. 351-22-5571100 email: [email protected]

João Rebelo Universidade de Trás-os-Montes e Alto Douro - Dep. de Economia e Sociologia email: [email protected]

November 1999

Keywords: Banking; Mergers; Acquisitions; Efficiency; Portugal

JEL classification:

* Corresponding author.

1 THE EFFECT OF BANK M&As ON EFFICIENCY: THE PORTUGUESE EXPERIENCE

1. Introduction

Bank mergers and acquisitions may enable banking firms to benefit from new business opportunities that have been created by changes in the regulatory and technological environment. Amongst these changes, the following have been listed: technological progress, improvements in financial condition, excess capacity or financial distress in the industry, international consolidation of markets, and deregulation of geographical or product restrictions. Improvements in communication technology allow greater dissemination of information; lower costs of communication strengthen competition insofar as physically distant institutions become active competitors. In the words of Moore and Siems (1998) “The two forces of technology and deregulation, working together, have fueled change that increasingly blurs accepted boundaries of time, geography, language, industries, enterprises, economies and regulations” (p.3). Berger and Mester (1997) suggest that economies of scale in the banking industry may have increased in the nineties relative to the eighties; this could possible be explained by technological progress. Improvements in banks’ financial conditions may help increase the volume of M&As. In the USA, bank profitability was very high in the nineties. In Europe that was not necessarily true. However, in some European countries (Belgium, the Netherlands, Norway, Sweden and UK) bank return on equity figures reach very high levels in the mid-1990s (Molyneux, 1999). Excess capacity might be reduced or eliminated through consolidation. In Europe there appears to be indicators of excess capacity (Berger et al 1999, Molyneux 1999). As for the international consolidation of markets, Berger et al (1999) argue that “the transfers of securities, goods, and services in international markets creates demands for currency deposit, loan, and other services by international financial institutions” (p.150). Europe has undergone substantial deregulation. The European Banking directives have created new opportunities for banks to operate across national boundaries. The advent of EMU may also foster bank consolidation in Europe. However, the political dimension of M&As in Europe cannot be forgotten. “A fundamental belief that financial institutions should not be controlled by foreigners has (so far) almost prevented any cross-border merger” (Boot 1999, p.610). All the above mentioned changes in the regulatory and technological environment seem to exist in Portugal in the nineties1. As regards consolidation, the nineties witnessed the reprivatization process and the first merger operation in Portuguese banking in the last 15 years. This paper aims at studying the effect of such acquisitions on cost efficiency. We also study the effect of merger operations on efficiency, simulating the 1998-BPI merger operation as well as some other possible merger operations. The paper is structured as follows. Section 2 reviews the literature 1 See, for example, Rebelo and Mendes (1999), Mendes and Rebelo (1999).

2 on the behavior of European and Portuguese banks. Section 3 describes the models we use to estimate efficiency and section 4 contains the results. Section 5 concludes.

2. Review of the literature

Bank consolidation may be justified in terms of the ‘getting bigger’ argument, allowing increased market power in setting prices on retail services2. Some studies have found that banks in more concentrated markets pay lower rates on deposits and charge higher interest rates on small business loans (Berger and Hannan, 1997; Hannan, 1991). However, other studies have found that this relationship lacked intensity in the nineties (Hannan, 1997; Radecki, 1998). On the other hand, recent research for the USA (Radecki, 1998) shows that large banks sometimes set uniform rates for a state or region, not for the local market. As for the effect of concentration on performance, Maudos (1998) in his study on the Spanish banking industry during 1990-93 concludes that “the results obtained allow us to accept the so-called ‘modified efficient structure hypothesis’ since efficiency positively affects profitability, although market power, reflected in market share, does so as well” (p. 199). Goldberg and Rai (1996) with a sample that includes the bigger banks in 11 European countries during 1988-91 concludes: “We do not find a positive and significant relationship between concentration and profitability” (p.745). In Molyneux and Forbes (1995) a pool of banks from 18 European countries for the years 1986 to 1989 is used. The conclusion is that “the results presented here generally support the traditional SCP approach. That is, results suggest that concentration in the European banking market lowers the cost of collusion between firms and results in higher than normal profits for all market participants” (p.158). In a study on Portuguese banking for the period 1990-97, Mendes and Rebelo (1999) concludes: “we cannot reject the hybrid collusion/efficiency hypothesis in Portuguese banking during the nineties. In line with these results, we could argue that the known difficulty that Portuguese banks experienced in the period under scrutiny to rapidly decrease nominal interest rates could be the result of a collusion process, either tacitly or explicitly negotiated amongst the banks of the system. The spatial competition and branch location variables are not significant, suggesting that local market power and rural branch networks do not lead to superior performances.” Some other studies have focused their attention on the effects of size on efficiency. Although studies on US banking using data for the 80s show little or no cost- efficiency improvement following M&As, evidence for the nineties is mixed. As for profit-efficiency, the evidence is even more inconclusive. However, the few existing European studies point towards the same general direction: increased efficiency following M&As (Vander Vennet 1996, Altunbas et all 1997, Resti 1998, Haynes and Thompson 1999).

2 Along with the getting big argument, reputation-building incentives could be thought of as possible explanations for mergers. If greater ability is necessary to (successfully) run a bigger bank, then engaging in merger activities should benefit manager reputation as well as executive compensation. On the other hand, if market agents perceive large banks to be too big to fail, then incentives to increase size may exist.

3 3. The Models

In this paper we want to study the effect of size on efficiency. The measurement of productive (in)efficiency and the estimation of production frontiers have been jointly done since Farrel’s (1957) seminal paper. The so-called X-inefficiency, or deviations from the efficient cost frontier, are the result of technical inefficiency (the firm could produce the same output with lower levels of inputs) and allocative inefficiency (given their relative prices, the used combination of inputs is different from the cost- minimizing combination). The DEA (data envelopment analysis) method is a non-parametric method widely used. The method uses linear programming techniques in the estimation of frontier (cost, in our case) functions; firms on the frontier are considered efficient. Other firms are compared with the ‘best practice’ units and inefficiency levels are computed using the estimated frontier. For a firm s facing input price vector ws and producing the output vector ys, x*s is the cost minimizing input vector. The operating productive efficiency index (EPG) is computed as EPGs = wsx*s/wsxs, that is, EPGs=minimum cost/actual cost. For the case of a bank producing three outputs with three inputs we need to solve the following linear programming problem

Min wisxis subject to

yjs <=  zsyjs , j = 1, 2, 3. s (1) xis >=  zsxis , i = 1, 2, 3. s zs >= 0 , s = 1, ..., n  zs = 1 s where wi = price of input i; xi = input i; yj = output j; z = intensity vector which allows convex combinations of observed input and output quantities; s is the firm index and n is the sample number of observations.

The SFA-T (Stochastic Frontier Approach - Traditional) method assumes that banks are cost minimizing firms; their production process can be represented by the stochastic frontier cost function

(2) lnC = lnC(Y,w,t,b) + u + v where C represents variable cost, Y is the output vector, w is the input price vector, t represents time and captures possible technological changes,  represents the vector of parameters to be estimated, u is the one-sided, non negative, stochastic element that

4 represents cost inefficiency3, and v is a classical random error term, independent from u. It immediately follows that the stochastic and deterministic models are equivalent when v=0.

We assume the following translog variable cost function,

3 3 1 3 3 ln Cst = a 0 + е ai ln Yist + е bi ln wist + е е sij ln Yist ln Yjst i=1 i=1 2 i=1 j=1 1 3 3 3 3 (3) + е е dkl ln wkst ln wlst + е е mki ln wkst ln Yist + vst + ust 2 k =1l=1 k =1i=1 where Cst = variable cost for observation s in year t; yist = output i (i=1, 2, 3) for observation s in year t; wkst = price of input k (k=1, 2, 3) for observation s in year t; Greek symbols = parameters to be estimated; vst = cost inefficiency for observation s in year t; ust = random error term. Symmetry restrictions on the second order parameters and linear homogeneity in input prices were imposed prior to the estimation of the model. Variable costs, the cost of deposits and labor costs are expressed in terms of w3.

The SFA-E (Stochastic Frontier Approach – Endogenous) method assumes that inefficiency is endogenously explained. The model, suggested by Battese el al (1999), includes equation (3) along with the equation (4) below, describing the behavior of inefficiency. It is assumed that v follows the truncated (at zero) normal distribution

2 vit ~ N(mit, s ) with unknown variance, and mean which is a function of exogenous factors, that is

5 (4) mit = q0 + е q0 jDjit + q1 Zit i=1

where Dj represents dummy variables equal to one if the bank is private (D1), privatized 2 2 2 g ә s /(s u + s )

(D2), new (in the market after 1990 – D3), foreign (D4) and if it is a member of a bank group (D5). Z is a time trend. The parameters of equations (3) and (4) are simultaneously estimated by maximum likelihood using the software frontier 4.1 (Coelli 1996). The variance parameters are reparametrized using

2 2 2 g ә s /(s u + s )

Upon estimation of the model parameters, bank indices of technical inefficiency are computed using the Jondrow et al (1982) method.

3 We assume that u follows the half-normal distribution.

5 4. Data and results

Research in banking has followed two alternative approaches, intermediation and production. From an empirical standpoint one does not seem to have a clear edge over the other. On the other hand, there does not exist consensus on the variables that best define bank output. In this paper we use the intermediation approach, summarized in the following definition of the variables used:

Outputs:

y1= loans to clients, net of provisions;

y2 = loans to credit institutions + bonds (net of provisions);

y3 = off-balance products (proxied by commissions received + net profits from financial operations).

Inputs:

x1 = deposits (from clients + credit institutions + bonds);

x2 = number of employees;

x3 = fixed assets.

Input prices:

w1 = price of x1, defined as (interest paid and similar costs + commissions paid)/x1;

w2 = price of x2, defined as labor costs/number of employees;

w3 = price of x3, defined as (depreciation + other administrative costs + other operating costs)/net fixed assets.

C = variable cost, defined as the sum of financial costs, labor costs and operating costs.

We use data from banks’ annual balance sheets and income statements, for the years 1990 to 1997. The information was collected from the Boletim Informativo da Associação Portuguesa de Bancos and banks’ annual reports, and we use non- consolidated data. The sample includes almost all banks operating in Portugal in that period4. The sample does not include banks in their first (incomplete) year of activity.

Table 1: Summary information on the input/output variables. Variable Description Unit Average Min Max C. V. 3 y1 loans to clients 10 contos * 178 406 188 1 781 190 150 3 y2 loans to fin inst. 10 contos * 219 492 5 2 114 911 163 3 y2 off-balance 10 contos * 3 717 1 31 799 153 3 x1 deposits 10 contos * 417 060 353 3 978 557 151 x2 employees number 1 601 8 10 227 141 3 x3 fixed assets 10 contos * 11 926 15 116 964 171 w1 price of deposits 0.093 0.015 0.452 50 3 w2 price of labor 10 contos * 4.543 2.174 12.188 36 w3 price of materials 0.963 0.145 8.753 133 C variable cost 103 contos * 44 777 202 340 129 139 * Deflated values, at 1990 prices.

4 Banco Hispano is not included.

6 Summary information on the input/output variables is in table 1. There are no ‘strange’ surprises; the sample seems to combine well different-sized institutions (as shown by the Coefficient of Variation – CV). On the other hand, loans to financial institutions represent the largest output share and price variability is lower than input variability, suggesting strong competition in the input markets. Inefficiency indices estimates are presented in annexes 2, 3 and 4, respectively for DEA, the SFA-T and the SFA-E methods. Summary information of our results is in table 2.

Table 2 – Inefficiency estimates: summary results Average Min Max C. Variation INEFI: DEA 0.373 0.000 1.801 58 INEFI: SFA-T 0.182 0.048 0.698 93 INEFI: SFA-E 0.098 0.004 0.321 94

The Pearson correlation coefficients between these indices are:

- INEFI: DEA/INEFI: SFA-T: 0.669 - INEFI: DEA/INEFI: SFA-E: 0.243 - INEFI: SFA-E/INEFI: SFA-T: 0.208

The three correlation coefficients are significant at the 5% level, hence suggesting consistent results from these methods. As expected, inefficiency indices computed from the DEA method are the highest and INEFI: SFA-E the lowest, allowing us to conclude that, on average, the efficiency lower bound is 63%. That is, if all banks were able to use the best practice technology, there could be (maximum) savings of around 37% of observed costs. Table 3 contains inefficiency breakdown by type of organization. Results show that if a bank is member of a group then it is more cost efficient. They also show that foreign banks are more cost efficient5. On the contrary, results for the age of the institution are not consistent across methods. Old banks (in the market before 1990) show higher inefficiency levels for the DEA and SFA-E methods, but lower for the SFA-T method. As for the structure of property, government-owned institutions are not very efficient: on average, inefficiency represents something in the vicinity of 21-44% of costs. But privatized banks are more efficient than public institutions thus suggesting improved efficiency scores after reprivatization.

Table 3: Cost inefficiency, by type of organization DEA SFA-T SFA-E Private banks 0.388 0.178 0.045 Privatized banks 0.295 0.172 0.126 Government-owned banks 0.440 0.209 0.217 New bank 0.367 0.206 0.062

5 But not necessarily more profit-efficient.

7 Old bank 0.375 0.176 0.109 Foreign bank 0.336 0.161 0.005 Domestic bank 0.392 0.193 0.145 Member of a bank group 0.234 0.167 0.098 Non-member of a bank group 0.453 0.192 0.098

Off all the existing government-owned banks6, only 2 still remain in public hands: the Caixa Geral de Depósitos (CGD) and the Banco Nacional Ultramarino (BNU). The reprivatization process took off in 1989 with the Banco Totta & Açores (see annex 1). The last two banks to be privatized were the BFE (3 rd phase in 7February1997) and BCA (2nd phase in 9December1996). These reprivatized banks constitute the bulk of the acquisitions that occurred in the nineties and are the ones analyzed in this paper. Table 4 shows inefficiency scores on an annual basis for privatized banks. Results for BANIF, BCA, BESCL and BFB are consistent for the three methods, suggesting increasing efficiency after privatization. As regards the other privatized banks (BFE, BPA, BPSM, CPP and UBP), the evidence is not convincing: when we use the SFA method with endogenous inefficiency (SFA-E) there seems to be a clear efficiency increase, but that is not the case for the other two methods. We may therefore conclude that in general cost efficiency levels improved after privatization, hence suggesting better cost controls and input rationalization in the aftermath of the control change.

6 All but three small private foreign banks were nationalized following the April 25, 1974, revolution. At that time these three small banks had a combined market share of 2% approximately.

8 Table 4: Inefficiency scores for privatized banks BANIF 1990 1991 1992 1993 1994 1995 1996 1997 23.11.92 DEA 0.795 0.513 0.608 0.767 0.493 0.456 0.460 0.497 SFA-T 0.257 0.178 0.179 0.234 0.207 0.172 0.152 0.157 SFA-E 0.302 0.302 0.295 0.268 0.008 0.007 0.259 0.115

BCA 1990 1991 1992 1993 1994 1995 1996 1997 2.7,9.12 DEA 1.611 1.538 1.208 1.674 1.155 0.988 1.062 1.247 SFA-T 0.663 0.547 0.416 0.571 0.362 0.345 0.288 0.318 SFA-E 0.286 0.284 0.285 0.273 0.278 0.273 0.271 0.107

BESCL 1990 1991 1992 1993 1994 1995 1996 1997 9.7.91 25.2.92 DEA 0.297 0.471 0.190 0.185 0.166 0.145 0.089 0.000 SFA-T 0.164 0.200 0.121 0.188 0.108 0.102 0.110 0.110 SFA-E 0.304 0.296 0.137 0.128 0.131 0.127 0.122 0.118

BFB 1990 1991 1992 1993 1994 1995 1996 1997 27.8.91 20.7.92 DEA 0.736 0.490 0.429 0.565 0.377 0.305 0.044 0.111 SFA-T 0.274 0.202 0.225 0.285 0.176 0.149 0.120 0.159 SFA-E 0.299 0.299 0.131 0.124 0.127 0.124 0.122 0.115

BFE 1990 1991 1992 1993 1994 1995 1996 1997 27.12.94 28.8.96 7.2.97 DEA 0.054 0.178 0.028 0.157 0.105 0.117 0.072 0.145 SFA-T 0.065 0.083 0.063 0.109 0.087 0.119 0.106 0.104 SFA-E 0.321 0.166 0.166 0.154 0.152 0.127 0.113 0.121

BPA 1990 1991 1992 1993 1994 1995 1996 1997 11.12.90 25.5.92 7.7.93 24.3.95 DEA 0.235 0.314 0.052 0.000 0.000 0.033 0.000 0.016 SFA-T 0.134 0.169 0.085 0.130 0.089 0.114 0.120 0.133 SFA-E 0.307 0.155 0.159 0.150 0.133 0.127 0.122 0.117

BPSM 1990 1991 1992 1993 1994 1995 1996 1997 16.11.94 28.3.95 DEA 0.000 0.091 0.376 0.570 0.307 0.091 0.145 0.032 SFA-T 0.175 0.154 0.159 0.261 0.204 0.236 0.241 0.277 SFA-E 0.302 0.300 0.296 0.283 0.181 0.118 0.114 0.109

CPP 1990 1991 1992 1993 1994 1995 1996 1997 2.12.92 DEA 0.647 0.565 0.420 0.639 0.355 0.353 0.294 0.300 SFA-T 0.215 0.230 0.176 0.256 0.122 0.126 0.125 0.162 SFA-E 0.301 0.295 0.293 0.124 0.129 0.126 0.121 0.114

UBP 1990 1991 1992 1993 1994 1995 1996 1997 3.2.93 11.7.95

9 DEA 0.721 0.497 0.416 0.972 0.479 0.513 0.506 0.404 SFA-T 0.283 0.215 0.202 0.444 0.205 0.191 0.207 0.268 SFA-E 0.298 0.297 0.148 0.118 0.125 0.121 0.115 0.108 Note: For each bank, the first line contains the dates of the different privatization phases.

As for merger experiences, only one took place in Portugal in the last decade: the Banco BPI is the bank resulting from the merger of Banco Borges & Irmão (BBI), Banco Fonsecas & Burnay (BFB) and Banco de Fomento e Exterior (BFE) which took place in the first half of 1998. Our data set is for the period 1990-97; hence we do not have any information for Banco BPI. Therefore, we used 1997 data for the 3 above- mentioned banks to simulate this merger operation. This operation will be referred to as case 1. We also use 1997 information to simulate 3 other possible merger operations that one way or another the media has been talking about in the last two months. Hereafter, they will be referred to as cases 2, 3 and 4, and involve the following simulations:

Case 2: CGD+BNU Case 3: BCP+BPSM Case 4: BTA+CPP+BSN.

We will assume two different scenarios, respectively A and B, in order to test the sensibility of our results. Under scenario A, the resulting bank produces output levels equal to the arithmetic sums of the individual banks involved in the simulated merger. Under scenario B, the resulting bank uses 3 inputs in order to produce outputs which are lower than the sum of individual banks’ input and output levels7. However, we also assume that there will not be any laid-off employees (labor legislation prevents that) and that there will not be any branch closures8. We also assume that there are no further cost synergies from the (restructured) product mix. The two scenarios are summarized in table 5.

Table 5: Merger scenarios. Variable Scenario A Scenario B

Y1 Sum of individual banks Sum of individual banks

Y2 Sum of individual banks 50% of the sum

Y3 Sum of individual banks 50% of the sum

X1 Sum of individual banks 50% of the sum

X2 Sum of individual banks Sum of individual banks

X3 Sum of individual banks 75% of the sum

W1 Average of individual banks Average of individual banks

W2 Average of individual banks Average of individual banks

W3 Average of individual banks Average of individual banks

7 The reason is that whenever we consolidate interbank operations the result is smaller than the sum of parts. 8 The rationalization of the branch network, with the closure of overlapping branches is another possible source of increased efficiency. Hence, we can interpret our results as a lowerbound to efficiency effects of these mergers.

10 We have used all three models to simulate these merger operations. Results are summarized in table 6. In general, our simulations show that any of the mergers can generate substantial cost savings, which are lower for the BCP+BPSM case. The most successful cases seem to be the first two, that is, the BFB+BBI+BFB and CGD+BNU.

Table 6: Merger simulation – Inefficiency indices

BFB+BBI+BFE CGD+BNU BCP+BPSM BTA+CPP+BSN DEA* 0.256 0.119 0.059 0.116 Scenario A 0.055 0.000 0.000 0.000 Scenario B 0.075 0.000 0.040 0.000 SFA-T* 0.164 0.126 0.225 0.172 Scenario A 0.000 0.000 0.000 0.000 Scenario B 0.000 0.000 0.000 0.000 SFA-E* 0.116 0.134 0.077 0.079 Scenario A 0.003 0.003 0.003 0.003 Scenario B 0.000 0.000 0.000 0.000 * Effective 1997 unweighted average of the banks involved.

5. Conclusion

From a cost standpoint, bank privatization seems to have improved cost- efficiency levels of the banks involved. As for the simulated mergers, they seem to help increase efficiency in input utilization. All in all, our results suggest that there is an economic reasoning for future merger and acquisitions in Portuguese banking. Acquisitions will allow stronger organizations to gain control of weaker banks, thus helping to increase input efficiency. Mergers will allow the banking industry to take advantage of the opportunities created by improved technology and deregulation. In the future, banks, big and small, will increasingly use “state-of-the-art” technology, so that they can meet customer needs whenever they want, wherever they need. However, small banks will not disappear. Small businesses rely, and will continue to rely, on small banks to raise capital. Families will continue to do business, the old fashioned way, with small local banks, as well as with larger organizations9. The recent merger wave in world banking may raise fears of lower competition in the banking industry. Bank studies in European banking have supported the traditional structure-performance-paradigm. However, it looks like concentration has not increased substantially at the local level. At the same time, technological improvements are bringing new sources of competition to local banking markets, including nonbank alternatives. This is not to say that bank supervision should be relaxed. The increasing consumer credit problems bring about new concerns regarding supervision matters: the safety and soundness of the payments system must never be jeopardized.

9 According to Kwast (1999), in the USA 75% of households’ checking, savings, credit line, and other services are obtained at financial institutions located within 25 km of their workplace or home. Survey data also shows that 98% of households and 92% of small businesses use local banks.

11 References

Altunbas, Y., P. Molyneux and J. Thornton (1997). “Big Bank Mergers in Europe: An Analysis of the Cost Implications”. Economica 64, pp.317-329. Battese (1999). Berger, A. N., R. S. Demsetz and P. E. Strahan (1999). “The Consolidation of the Financial Services Industry: Causes, Consequences, and Implications for the Future”. Journal of Banking & Finance 23, nº 2-4, pp.135-194. Berger, A. N., and T. H. Hannan (1997). “Using Measures of Firm Efficiency to Distinguish Among Alternative Explanations of the Structure-Performance Relationship”. Managerial Finance 23, pp.6-31. Berger, A. N., and L. J. Mester (1997). “Inside the Black Box: What Explains Differences in the Efficiencies of Financial Institutions?”. Journal of Banking and Finance 21, pp.895-947. Boot, A. W. A. (1999). “European Lessons on Consolidation in Banking”. Journal of Banking & Finance 23, nº 2-4, pp.609-613. Coelli, T., (1996). A “Guide to DEAP: Version 2.1: A Data Envelopment Analysis (Computer) Program”. CEPA Working Paper 96/08, University of New England, Armidale. Farrel, M. J. (1957). “The Measurement of Productive Efficiency”, Journal of the Royal Statistical Society - Series A (General) - Part III, Vol.12, p.253-281. Goldberg, L. G. and A. Rai (1996). “The Structure-Performance Relationship for European Banking”, Journal of Banking and Finance, 20, p.745-771. Hannan, T. H. (1997). “Market Share Inequality, the Number of Competitors, and the HHI: An Examination of Bank Pricing”. Review of Industrial Organization 12, pp.23-35. Hannan, T. H. (1991). “Bank Commercial Loan Markets and the Role of Market Structure: Evidence From Surveys of Commercial Lending”. Journal of Banking and Finance 15, pp. 133-149. Haynes, M. and S. Thompson (1999). “The Productivity Effects of Bank Mergers: Evidence From the UK Building Societies”. Journal of Banking and Finance 23, pp.825-846. Jondrow (1992). Kwast, M. L. (1999). “Bank Mergers: What Should Policymakers Do?”. Journal of Banking & Finance 23, pp.629-636. Maudos, Joaquín (1998). “Market Structure and Performance in Spanish Banking Using a Direct Measure of Efficiency”, Applied Financial Economics, 8, p.191-200. Mendes, V. and J. Rebelo (1999). “Productive Efficiency, Technological Change and Productivity in the Portuguese Banking Industry: The Years 1990-95”. Applied Financial Economics 9, 513-521. Molyneux, P. (1999). “Does Size Matter? Financial Restructuring Under EMU”. Manuscript, University of Wales. Molyneux, P. and W. Forbes (1995). “Market Structure and Performance in European Banking”, Applied Economics, 27, p.155-159. Moore, R. R. and T. F. Siems (1998). “Bank Mergers: Creating Value or Destroying Competition?”.Financial Industry Studies, Federal Reserve Bank of Dallas, Third Quarter 1998, pp.1-6. Radecki, L. J. (1998). “Small Expanding Geographic Reach of Retail Banking Markets”. Economic Policy Review, Federal Reserve Bank of New York, nº 4, pp.15-34. Rebelo, J. and V. Mendes (1999). “Malmquist Indices of Productivity Change in Portuguese Banking: The Deregulation Period”. Mimeo, UTAD/FEP.

12 Resti, A. (1998). “Regulation Can Foster Mergers; Can Mergers Foster Efficiency? The Italian Case”. Journal of Economics and Business 50, pp.157-169. Vander Vennet, R. (1996). “The Effect of Mergers and Acquisitions on the Efficiency and Profitability of EC Credit Institutions”. Journal of Banking anf Finance 20, pp.1531-1558.

13 Annex 1: (Re)Privatizations in Portuguese Banking

Date Banco Totta & Açores (BTA) 1st phase 22-03-1989 10-07-1989 2nd phase 31-07-1990 last phase 19-11-1996 Banco Português do Atlântico (BPA) 1st phase 11-12-1990 2nd phase 25-05-1992 3rd phase 07-07-1993 4th phase 24-03-1995 Sociedade Financeira Portuguesa (SFP)a 06-05-1991 Banco Espírito Santo & Comercial de Lisboa (BESCL) 1st phase 09-07-1991 2nd phase 25-02-1992 Banco Fonsecas & Burnay (BFB) 1st phase 27-08-1991 2nd phase 20-07-1992 Banco Internacional do Funchal (BANIF) 23-11-1992 Crédito Predial Português (CPP) 02-12-1992 União de Bancos Portugueses (UBP)b 1st phase 03-02-1993 2nd phase 11-07-1995 Banco de Fomento e Exterior (BFE) 1st phase 27-12-1994 2nd phase 28-08-1996 3rd phase 07-02-1997 Banco Pinto & Sotto Mayor (BPSM) 1st phase 16-11-1994 2nd phase 28-03-1995 Banco Comercial dos Açores (BCA) 1st phase 02-07-96 2nd phase 09-12-96 a Changed its name from “Sociedade Financeira Portuguesa – Banco de Investimento, S.A.” to “Banco Mello S.A.”. On 28 June 1996, changed the name again to “Banco Mello Investimentos, S.A.”. b On 28 June 1996 changed its name to “Banco Mello Comercial, S.A.”

Sources:

Sousa, Fernando Freire de, Ricardo Cruz. 1995. O Processo de Privatizações em Portugal. Porto: Associação Industrial Portuguesa. Associação da Bolsa de Derivados do Porto. 1996. Processos de (Re)privatização - Sociedades Cotadas (1989/1996). Porto: Associação da Bolsa de Derivados do Porto. Ministério das Finanças. 1999. Privatizações e Regulação: A Experiência Portuguesa. Lisboa: Direcção Geral de Estudos e Previsão, Ministério das Finanças.

14 15 Annex 2: Cost-inefficiency indexes – DEA method

Bank 1990 1991 1992 1993 1994 1995 1996 1997 Average 1 0.279 0.304 0.242 0.125 0.065 0.079 0.114 0.000 0.151 2 0.795 0.513 0.608 0.767 0.493 0.456 0.460 0.497 0.573 3 0.653 1.146 0.912 0.650 0.374 0.391 0.647 0.718 0.686 4 0.355 0.135 0.080 1.513 0.773 0.634 0.821 0.447 0.595 5 0.608 0.464 0.389 0.603 0.311 0.353 0.404 0.511 0.455 6 0.456 0.616 0.000 0.946 0.534 0.462 0.639 0.701 0.544 7 1.611 1.538 1.208 1.674 1.155 0.988 1.062 1.247 1.310 8 0.650 0.883 0.623 0.739 0.751 0.650 0.715 0.418 0.679 9 0.486 0.391 0.534 0.239 0.279 0.386 10 0.276 0.321 0.047 0.157 0.145 0.094 0.000 0.085 0.141 11 0.297 0.471 0.190 0.185 0.166 0.145 0.089 0.000 0.193 12 0.368 0.040 0.357 0.000 0.191 13 0.563 0.855 0.890 0.835 0.555 0.477 0.546 0.585 0.663 14 0.736 0.490 0.429 0.565 0.377 0.305 0.044 0.111 0.382 15 0.054 0.178 0.028 0.157 0.105 0.117 0.072 0.145 0.107 16 0.449 0.508 0.377 0.608 0.326 0.220 0.302 0.263 0.382 17 0.096 0.074 0.000 0.000 0.043 18 1.375 1.083 0.692 0.715 0.770 0.558 0.866 19 0.000 0.000 0.000 20 0.399 0.495 0.443 0.645 0.357 0.439 0.441 0.418 0.455 21 0.481 0.471 0.235 0.335 0.337 0.342 0.256 0.238 0.337 22 0.235 0.314 0.052 0.000 0.000 0.033 0.000 0.016 0.081 23 0.000 0.111 0.062 0.106 0.076 0.087 0.000 0.000 0.055 24 0.000 0.287 0.739 1.262 0.661 0.590 25 0.000 0.091 0.376 0.570 0.307 0.091 0.145 0.032 0.201 26 0.131 0.035 0.105 0.000 0.068 27 0.422 0.321 0.172 0.136 0.070 0.095 0.057 0.048 0.165 28 0.093 0.000 0.055 0.000 0.012 0.155 0.393 0.160 0.108 29 0.110 0.010 0.000 0.082 0.033 0.000 0.079 0.045 30 0.012 0.000 0.000 0.085 0.058 0.050 0.060 0.000 0.033 31 0.140 0.350 0.042 0.177 32 0.121 0.000 0.000 0.000 0.030 33 0.038 0.043 0.183 0.086 0.000 0.101 0.200 0.016 0.084 34 0.745 0.684 0.456 0.546 0.408 0.391 0.410 0.443 0.510 35 0.647 0.565 0.420 0.639 0.355 0.353 0.294 0.300 0.447 36 0.319 0.337 0.821 0.493 37 0.227 0.116 0.078 0.068 0.041 0.049 0.000 0.083 38 1.801 1.732 1.433 1.655 39 0.167 0.000 0.000 0.087 0.063 40 0.555 0.686 0.678 0.815 0.684 41 0.279 0.239 0.206 0.274 0.304 0.355 0.361 0.350 0.296 42 0.168 0.152 0.172 0.164 43 0.511 0.366 0.639 0.603 0.497 0.497 0.105 0.460 44 0.658 0.667 0.751 0.859 0.553 0.502 0.511 0.000 0.562 45 0.721 0.497 0.416 0.972 0.479 0.513 0.506 0.404 0.564

16 46 0.256 0.209 0.093 0.186 Average 0.436 0.442 0.357 0.503 0.320 0.324 0.365 0.302 0.373

Annex 3: Cost-inefficiency indexes – Traditional SFA method

Bank 1990 1991 1992 1993 1994 1995 1996 1997 Average 1 0.073 0.110 0.086 0.122 0.082 0.083 0.099 0.117 0.097 2 0.257 0.178 0.179 0.234 0.207 0.172 0.152 0.157 0.192 3 0.222 0.351 0.245 0.204 0.170 0.205 0.219 0.322 0.242 4 0.082 0.075 0.111 0.488 0.142 0.174 0.110 0.120 0.163 5 0.261 0.205 0.181 0.443 0.161 0.167 0.183 0.229 0.229 6 0.200 0.169 0.058 0.282 0.185 0.144 0.176 0.189 0.175 7 0.663 0.547 0.416 0.571 0.362 0.345 0.288 0.318 0.439 8 0.362 0.289 0.215 0.253 0.232 0.189 0.224 0.202 0.246 9 0.191 0.115 0.159 0.098 0.097 0.132 10 0.181 0.182 0.154 0.166 0.151 0.142 0.170 0.172 0.165 11 0.164 0.200 0.121 0.188 0.108 0.102 0.110 0.110 0.138 12 0.134 0.083 0.102 0.084 0.101 13 0.201 0.354 0.352 0.215 0.148 0.149 0.129 0.150 0.212 14 0.274 0.202 0.225 0.285 0.176 0.149 0.120 0.159 0.199 15 0.065 0.083 0.063 0.109 0.087 0.119 0.106 0.104 0.092 16 0.154 0.254 0.191 0.196 0.103 0.090 0.110 0.128 0.153 17 0.129 0.147 0.086 0.257 0.155 18 0.365 0.327 0.257 0.202 0.186 0.147 0.247 19 0.133 0.175 0.154 20 0.219 0.160 0.199 0.214 0.118 0.105 0.098 0.092 0.151 21 0.380 0.253 0.154 0.224 0.143 0.146 0.133 0.131 0.196 22 0.134 0.169 0.085 0.130 0.089 0.114 0.120 0.133 0.122 23 0.054 0.093 0.176 0.106 0.188 0.372 0.337 0.225 0.194 24 0.249 0.166 0.198 0.188 0.203 0.201 25 0.175 0.154 0.159 0.261 0.204 0.236 0.241 0.277 0.213 26 0.326 0.223 0.111 0.135 0.199 27 0.181 0.146 0.100 0.135 0.092 0.121 0.118 0.219 0.139 28 0.081 0.050 0.048 0.073 0.082 0.077 0.103 0.086 0.075 29 0.078 0.083 0.085 0.174 0.109 0.081 0.103 0.102 30 0.103 0.108 0.104 0.155 0.134 0.103 0.120 0.120 0.118 31 0.073 0.099 0.083 0.085 32 0.062 0.105 0.148 0.202 0.129 33 0.062 0.051 0.074 0.148 0.067 0.070 0.086 0.123 0.085 34 0.201 0.202 0.168 0.207 0.153 0.192 0.131 0.134 0.174 35 0.215 0.230 0.176 0.256 0.122 0.126 0.125 0.162 0.176 36 0.358 0.324 0.329 0.337 37 0.096 0.176 0.170 0.284 0.290 0.246 0.395 0.237 38 0.592 0.565 0.643 0.600 39 0.173 0.066 0.129 0.068 0.109 40 0.108 0.186 0.165 0.342 0.200

17 41 0.111 0.124 0.081 0.080 0.083 0.123 0.124 0.199 0.116 42 0.105 0.077 0.074 0.085 43 0.111 0.109 0.167 0.141 0.135 0.175 0.210 0.150 44 0.234 0.205 0.253 0.345 0.175 0.169 0.243 0.373 0.250 45 0.283 0.215 0.202 0.444 0.205 0.191 0.207 0.268 0.252 46 0.329 0.292 0.698 0.440 Average 0.195 0.180 0.162 0.226 0.157 0.173 0.169 0.207 0.182

Annex 4: Cost-inefficiency indexes – SFA method, with endogenous inefficiency effects

Bank 1990 1991 1992 1993 1994 1995 1996 1997 Average 1 0.005 0.006 0.006 0.005 0.005 0.005 0.005 0.005 0.005 2 0.302 0.302 0.295 0.268 0.008 0.007 0.259 0.115 0.195 3 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.005 4 0.006 0.006 0.005 0.005 0.005 0.005 0.005 0.005 0.005 5 0.155 0.298 0.151 0.134 0.145 0.122 0.117 0.111 0.154 6 0.199 0.005 0.207 0.005 0.006 0.181 0.174 0.005 0.098 7 0.286 0.284 0.285 0.273 0.278 0.273 0.271 0.107 0.257 8 0.189 0.188 0.187 0.181 0.179 0.177 0.170 0.167 0.180 9 0.033 0.033 0.029 0.030 0.029 0.031 10 0.198 0.194 0.190 0.185 0.055 0.052 0.048 0.045 0.121 11 0.304 0.296 0.137 0.128 0.131 0.127 0.122 0.118 0.170 12 0.030 0.031 0.029 0.027 0.029 13 0.005 0.005 0.005 0.186 0.187 0.005 0.005 0.005 0.050 14 0.299 0.299 0.131 0.124 0.127 0.124 0.122 0.115 0.168 15 0.321 0.166 0.166 0.154 0.152 0.127 0.113 0.121 0.165 16 0.203 0.190 0.060 0.057 0.060 0.058 0.052 0.047 0.091 17 0.028 0.026 0.029 0.022 0.026 18 0.133 0.130 0.127 0.127 0.123 0.122 0.127 19 0.134 0.126 0.130 20 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.005 21 0.147 0.149 0.152 0.144 0.145 0.141 0.139 0.134 0.144 22 0.307 0.155 0.159 0.150 0.133 0.127 0.122 0.117 0.159 23 0.219 0.207 0.063 0.063 0.055 0.047 0.042 0.044 0.093 24 0.137 0.135 0.178 0.126 0.120 0.139 25 0.302 0.300 0.296 0.283 0.181 0.118 0.114 0.109 0.213 26 0.005 0.005 0.005 0.004 0.005 27 0.304 0.009 0.282 0.132 0.133 0.126 0.123 0.118 0.153 28 0.006 0.006 0.006 0.005 0.005 0.194 0.005 0.005 0.029 29 0.006 0.005 0.005 0.005 0.005 0.057 0.051 0.019 30 0.165 0.160 0.157 0.147 0.149 0.147 0.140 0.135 0.150 31 0.006 0.006 0.005 0.006 32 0.034 0.029 0.026 0.023 0.028

18 33 0.006 0.006 0.006 0.005 0.006 0.005 0.005 0.005 0.005 34 0.005 0.006 0.005 0.005 0.005 0.004 0.005 0.005 0.005 35 0.301 0.295 0.293 0.124 0.129 0.126 0.121 0.114 0.188 36 0.023 0.022 0.021 0.022 37 0.004 0.004 0.004 0.005 0.004 0.004 0.004 0.004 38 0.114 0.113 0.107 0.111 39 0.132 0.143 0.113 0.134 0.130 40 0.141 0.129 0.126 0.111 0.127 41 0.006 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.005 42 0.004 0.004 0.004 0.004 43 0.152 0.147 0.138 0.137 0.027 0.025 0.023 0.093 44 0.197 0.005 0.186 0.177 0.182 0.178 0.167 0.155 0.156 45 0.298 0.297 0.148 0.118 0.125 0.121 0.115 0.108 0.166 46 0.004 0.004 0.004 0.004 Average 0.164 0.123 0.115 0.103 0.088 0.080 0.079 0.065 0.098

19