How to Identify the Best Target in the M&A Banking

How to Identify the Best Target in the M&A Banking Operations?

Case of Cross-Border Strategies in Europe by Line of Activity 1

Mehrez BEN SLAMA *, Faculty of Economics and Management of Mahdia, Tunisia and Lille School of Management Research Center, France Hassouna FEDHILA , University of Manouba, Tunisia Dhafer SAIDANE , University of Lille 3 and Lille School of Management Research Center, France

Abstract During the last decades, the European banking system has known some deep changes. They have led to mergers and acquisitions (M&As). The available studies show that the failure rate of theses M&As is relatively high. Cross-border operations are more exposed to this risk. The high failure rate is due to the cultural and contextual differences between the M&A participants, differences which make the process of integration particularly difficult. Thus, the success of M&As depends on the choice of adequate M&A targets. This choice constitutes the main challenge for company leadership. The aim of this paper is therefore to determine the factors which permit to identify the best M&A targets. Our contribution compared to that of previous research is that we study M&As and the identification of targets by line of bank activities. On the basis of a sample made up of 1071 European banks, between 2000 and 2006, we use a Logit Multinomial Model. Our main results show that the target banks tend to be specialized in investment and market activities while the acquiring banks tend to approach themselves to the universal bank model. JEL classifications: C35, G21, G24, G34 Keywords: cross-border M&A, bank, identification of targets, activity, Multinomial Logit

1 We would like to thank Mokhtar KOUKI, Pascal GRANDIN, Frederic ROMON, and the team of the Lille School of Management Research Center for helping us obtain the data and for their helpful comments and discussions.

* Corresponding author Email Addresses: [email protected] (Ben Slama M.) ; [email protected] (Fedhila H.) ; [email protected] (Saidane D.)

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1. Introduction In the current context of globalisation, market deregulation, and technological innovations, the world banking activity has experienced some deep changes. They result in the operations of Merger and Acquisitions (M&A). The volume of M&As for the financial sector has increased substantially. At the end of 2007, the number of M&As in the financial sector amounted to about $730,892 billion, an increase of 29.1% compared to the one in 2006 and of 48.6% compared to that of 2005.2 Banking is classified as the most active sector in term of volume of M&As. This sector represents 16% of the world’s M&A activity3. As a consequence of this evolution, the number of financial companies in the world has declined in a linear way. At the end of 2005, the Euro area (12 countries) counted 6,308 credit institutions, 2.8% less than in 2004 and 12.5% less than in 20014. The decrease of the number of U.S. banks from 1994 to 2006 amounts to 29%5. We observe that this banking restructuring resulted in two forms of integration: integration by diversification and integration by specialization. The former led certain banks to widen their competencies and use a complete range of products and services at lower costs. As far as concentration by diversification is concerned, we can note the merger between BNP and Paribas (1999), the buy-out of Dresdner Bank by the insurer Allianz (2001), the repurchase of International Household by HSBC (2002) and the repurchase of Banque Directe by Axa (2002). In the latter case, the banks are concentrating on their core activity (detail or whole) and thus hope to generate economies of scale. As far as detail activity is concerned, we note the following: the merger of Uncredito and Credito Italiano (1998), the merger between Santander and the Central Hispano (1999), the takeover of the Credit Commercial de France by the group HSBC (2000), the merger of Abbey in HSBC (2004), the acquisition of BNL by BNP-Paribas (2006), the merger between UniCredit and Capitalia (2007) and the acquisition of ABN Amro by the consortium formed by RBS, Fortis and Santander (2007). M&As generally result in two phases: domestic consolidation and international expansion. The European banking restructuring is currently at the beginning of its second phase: international expansion. This phase is characterized primarily by European operations supported by the actions and the policies taken by the European Central Bank and the European commission in order to integrate the European financial systems for a better allocation of capital through Europe and to support the banking competition. Then, the regulatory harmonization (Basle II, IAS/IFRS), which meant to improve transparency and reduce bank risks, was a catalyst of this process of consolidation. The question of the benefits of this bank restructuring on the performance of the European banks is of great interest to management. The available studies show that the rate of failure of M&As is relatively high. Approximately, one operation out of two fails (Demeure (2000) and Habeck et al. (2001)). Cross border operations are more exposed to this risk because of the cultural and contextual differences between the participants. These facts can render the process of integration particularly difficult (Vander Vennet (2002)).

2 Thomson ONE Banker, “Mergers & Acquisitions Review - Fourth Quarter 2007”, Global M&A Financial Advisory. 3 Thomson ONE Banker, “Mergers & Acquisitions Review - Fourth Quarter 2007”, Global M&A Financial Advisory. 4 Association Française des Investisseurs en Capital, « La fiche technique du capital investissement », Mars 2007. 5 Federal Reserve Bank of Chicago, 2006 annual report.

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The success of M&As is in fact conditioned by an adequacy between the target bank and the initiator institution on cultural, organizational and financial aspects. The process of M&A rests on several stages, one of which is the identification of the best targets. So, the success of cross-border M&As depends on the choice of targets. Companies’ leadership must face this challenge. Studies within this framework are, however, rare. Indeed, Pasiouras et al. (2007a) noted only about thirty studies exploring the problem of the identification of M&A targets. However, a part of this work has tried to integrate the cross border dimension of M&As. No study so far has integrated the strategies of restructuring per line of activity. The objective of this paper is to determine the elements that permit to identify the best targets in cross-border M&As. Our contribution compared to previous research is that we examine M&As and identify M&A targets by line of activity. These M&As are considered today within the framework of the universal bank concept based on the strategy of “one stop shop.” Moreover, according to the methodological plan, we use the Multinomial Logit approach as a procedure to identify the targets and initiators. To our knowledge only Pasiouras et al. (2007b) applied this technique in this context. Powell (2004) argued the superiority of the multinomial model over the binomial model. This procedure allows integrating various modalities for the dependent variable (there are only two modalities for the binomial models). In the next section, we discuss the prior research related to the identification of the targets in cross-border M&As banking operations. We then specify our methodology, followed by results and interpretations. Finally, we present our conclusions from this research and identify future research opportunity. 2. Review of previous studies The former studies on the identification of targets concerned non-banking firms. Then, recent ones were done on banks. Palepu (1986)’s works constitute the principal contribution in regards to the identification of merger and acquisition targets. He found that the target generally has an inefficient management and a certain inadequacy between its resources and growth. Company size is negatively correlated to its likelihood to become a target. Besides, Trimbath and al. (2001) concluded that the probability of “being acquired” increases for relatively inefficient firms. In another context, other works have tried to distinguish between M&As as hostile or friendly6. Powell (2001) found that the probability that a merger is carried out by amicable agreement increases with the increase of the target’s leverage (debt/stockholders' equity) and decreases with its size. While in the case of a hostile offer, this probability increases when the size and the market-to-book ratio (market cap of the firm/accounting value) of the target increase. It decreases when the liquidity and the operating profit over capital invested ratio of the target decrease. Nuttall (1999) analyzed the probability of success or failure of a friendly or hostile offer. Its principal conclusions concentrated on the fact that the probability of a merger is high if the target is a small company. However, this probability decreases in case the target company with a high level of financial leverage.

6 Aktas et al.. (2008) “provide a theoretical analysis where we model takeovers as a two-stage process. The initial stage corresponds to a one-to-one negotiation with the target. If the negotiation fails, there is a second stage in which either a takeover battle among rivals occurs, or the target firm organizes a competitive auction”.

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This study is interested in banks. Indeed, in the banking domain, empirical works are rare. Furthermore, the nature of the banking activity and the regulatory environment in which banks operate require a distinction at the level of the measure of variables. Pasiouras et al. (2007a) develop a multi-criteria model of classification to identify the targets in European banking M&As. They find that the target banks are usually small in size and the transaction cost of the M&As operation is relatively low. So, the two fundamental criteria for the selection of the banking target are the target size and the transaction cost. However, one of the limits of former works is that they consider generally only the fundamental characteristics of the banking target (financial aspects) without considering the environment in which they operate. Indeed, in the case of cross-border operations, it is essential to study the role of the institutional and legal variables in the process of identification of banking targets. These two factors are determined within the framework of a cross-border merger, Buch and DeLong (2004) and Pasiouras et al. (2007b). Besides the financial, regulatory and institutional aspects, it is advisable to take into account the activity’s factor. This functional component of the banks will be able to bring a new element of essential precision regarding the identification of the targets. Ayadi (2007) suggested a new conceptual approach of M&As through the introduction of the activity’s concept. Indeed, banking reorganizations are done more and more today according to the activity logic, in particular within the framework of the universal bank or the “one stop shop.” Moreover, according to certain estimates, more than half of the domestic or European financial conglomerates consist on M&As (ECB (2000)7). The multi-specialization of banks around a core activity thus became the key element in order to generate economies of scope or scale. But a good number of banking M&As aim at the reinforcement of specialization in a core activity in the name of economies of scale. Several assumptions and factors were proposed in previous studies. Three dimensions will be introduced into this study. The first one is based on the financial characteristics of the banks. The second one, deals with the characteristics of the institutional and legal environment in which the target and the initiator operate. The third one, introduces the lines of activity of banks into the process of identification of the banking targets. Later in this section, we will integrate these various elements of analysis to present the various factors of the targets’ identification in cross-border banking M&As and we will formulate the hypotheses of this study. The financial characteristics of the banks, as predictive factors, were introduced into several works. Their relevance with regard to the identification of targets has been unanimously approved. The work of Palepu (1986), whose object are the non-banking firms, constitutes the principal contribution to our subject. So the first hypothesis of this research is: H1: the banks’ financial specificities have an effect on the identification of targets in cross-border bank M&As.

7 European Central Bank, annual report 2000.

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In this study, we are going to investigate two main characteristics related to the financial specificities of banks, namely; the inefficient management and the size of the banks. Therefore, this hypothesis lies on two assumptions. The assumption of inefficient management supposes that management does not succeed in maximizing the value of the firm. The firm is thus likely to be acquired by another firm and management is likely to be replaced (Manne (1965)). This assumption rests on the idea that the acquisition of a badly managed bank is justified by the potential profit of replacing inefficient management. Hawawini and Swary (1990) show that a firm whose management is weak tends to be a gravitational target in carrying out a M&A. It will generate important future profits once inefficient management has been eliminated. In the same line, Moore (1996), Focarelli et al. (2001), Wheelock and Wilson (2004) and Pasiouras and Gaganis (2006) find that the least efficient banks tend to be potential M&A targets. Amidst the assumption of an inefficient management, we also find the concept of inadequacy between the financial resources of a firm and its growth. We have to consider a situation of low growth concomitant with important resources and vice versa. These situations present potential profits for the bidder banks. Banks having this inadequacy tend to be targets in carrying out a M&A (Cosh et al. (1980); Levine and Aaronovitch (1981)). So, the assumption is: H1-1: the hypothesis of inefficient bank management has an effect on the identification of targets in cross-border bank M&As. Also, the probability of takeover decreases when the size of the target increases. This implies that small firms tend to be relatively profitable and less expensive in order to become targets of a M&A (Palepu (1986), Wheelock and Wilson (2004), Pasiouras and Gaganis (2006)). So, the assumption is: H1-1: the hypothesis of bank size has an effect on the identification of targets in cross-border bank M&As. The identification of targets in cross-border M&As also calls for the consideration of environmental variables related to banks’ activities. As we know, only Pasiouras et al. (2007b) introduced the role of variables related to the regulation and the banking supervision into the process of identification of targets. Other works, like those of Buch and DeLong (2004), Focarelli et al. (2001) and Rossi and Volpin (2004), were interested in the determinants of the operations of cross-border M&As. Buch and DeLong (2004) found that the banks on the most regulated markets tend to be targets of cross-border M&As. They measure the regulation through the level of supervision and the degree of transparency. The banks in a financial system characterized by transparency thus tend to be gravitational targets. They permit to well evaluate their financial solidity for foreign banks. In the same way, a rigid system of supervision supports the externalization of the banks. It reassures the bidder banks as to the good performance of the banking system in the target countries. Pasiouras et al. (2007b) found that the acquiring banks operate in the least supervised legal systems that do not impose restrictions on the activities of the banks. Their systems are

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characterized by transparency. However, their results are not significant for the target banks with regard to transparency. Besides, the empirical validations made by Hannan and Pillof (2004) and by Pasiouras et al. (2007b) show that capital requirements, through regulatory capital (RC), constitute an important factor for the development of a merger. They justify their results by the fact that regulators authorize banks, equipped with an excess RC, to take part in a takeover. This is true as long as the level of their RC remains satisfactory after the acquisition. Lastly, Pasiouras et al. (2007b) introduced another legal aspect which intervenes in the development of M&As. It is about the level of geographic diversification and the liquidity authorized by the regulators. Indeed, Liang and Rhoades (1988) mention that the international expansion of the banks enables them to reduce their risk of insolvency through a reduction of their credit risk and liquidity. The second hypothesis of our research is: H2 – The regulatory banking specificities have an effect on the identification of targets in cross-border bank M&As. We will use the information in the database established by Barth et al. (2001) to capture the legal aspects of the financial systems where the banks operate. This database contains a list of the legal indicators of the banking systems of several countries. Nowadays, the strategies for banks’ globalization are a function of lines of activity. We can schematically distinguish three poles for the banking activity8:

� Activities of detail (which integrate financial services such as: (consumer credit, factoring) and non-financial services (real estate, car rentals, computer material, etc),

� Investment and financing bank activities (IFB). � Management of assets activities, bank insurance, and private banking.

The consideration of banks’ lines of activities in the process of identification of the targets constitutes one of the contributions of our study. Indeed, the universality of the banks and the diversification which results from it suppose that the choice of a banking target cannot be considered without the consideration of its line of activity. This dimension seems one of the factors key to the success or failure of M&A banking. Ayadi (2007) thus introduced “a new conceptual approach” by linking two criteria characterizing M&As into a grid:

� initial activity of the establishments implied in a merger and acquisition; � geographical location of the operation.

Ayadi (2007) identified six industrial strategies, according to whether the initial activities of the banks implied in a M&A are homogeneous or heterogeneous and according to whether the operation is regional, national or international. She then evaluated the performance of the M&As, performed in the EU 15 with Norway between 1996 and 2000. These results testify to the importance of two criteria of the grid with regard to the evaluation of M&As.

8 Pastré (2006)

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Our study relates to M&As resulting in cross-border operations by a diversification or specialization strategy. To considerate the diversity of the banking business, we will proceed like Laeven et al. (2007) by constructing indicators of measurement of activities and diversification. These indicators will allow us to distinguish the banks having a specialized portfolio from those which have a portfolio diversified on one activity or another. Our last research hypothesis is: H3 - the lines of activity of the bank have an effect on the identification of targets in cross-border bank M&As. 3. Methodology 3.1. Sample To build our sample, European banks were chosen (EU 15). The data available is over the period 1999-2005. It is supposed that a bank can be a target or bidder in a cross-border M&A or that a bank is not to be implicated in such a M&A (non-involved) (Pasiouras et al. (2007b)). A cross-border M&A is an operation where the head office of the target is not located in the same country as that of the bidder. In other words, when a subsidiary company of a French bank located in Italy acquires an Italian bank, this operation is regarded as cross-border. Let us note that we do not fix a condition on the degree of control of the target. As to sampling, we proceeded like Hannan and Rhoades (1987), Pasiouras and Zopounidis (2008), and Pasiouras et al. (2007b) by using an unmatched sample. This consists of the use of the totality of banks for which data is available and permits to consider all information available in our analysis. However, other studies privileged the “matched sample” method ((Powell (1997); Pasiouras and Gaganis (2007)). This method permits to avoid the high costs of data-gathering generated by the first method as well as the inaccuracy of its estimates. Our sample contains detail banks, cooperative banks, mortgage banks, investment banks, savings institutions and public credit institutions. The full number of banks satisfying these criteria is 1071. M&As, performed between 2000 and 2006 in the fifteen European countries, are sumarized in table 19. The observations vary from one year to other because of unavailability of data for certain banks.

[Insert table 1 here] 3.2. Definition and measurement of the variables There are four types of variables introduced in the model. First, there are financial variables related to the financial characteristics of the banks. Next, there are contextual variables reflecting the regulatory and institutional specificities of the banks10. Then, there are variables related to the lines of activity. Lastly, there are control variables. These variables are summarized in Table 2.

[Insert table 2 here]

9 Dates and descriptions of the operations of M&A are provided by the Zephyr database. 10 The financial variables with those related to the lines of activity are extracted from the Bankscope database. The contextual variables (legal) are described by Barth et al. (2001). The variables of control are taken from the World Bank database.

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A- Financial variables: The financial variables are five. Poor management is measured by the variable “return on equity” (ROE), Cudd and Duggal (2000) ; Pasiouras et al. (2007b). The inadequacy between resources and growth is clarified through a dummy variable (IndGRR), Cosh et al. (1980) ; Levine and Aaronovitch (1981). It takes value 1 if the bank represents one of the following cases: considerable low growth/ strong resources or strong growth/ poor resources. It takes zero value in the opposite case. The two components (growth and resources) are measured by “the growth rate of ratio total operating income/net loans” and “net loans/total assets,” respectively. The level of these two components is known as low if it is lower than the average level of the banks in our sample. The level is raised in the opposite case. To measure the variable Size, we adjust it by country to account for differences in average characteristics of banks across countries11, Palepu (1986) ; Wheelock and Wilson (2004) ; Pasiouras and Gaganis (2006), Pasiouras et al. (2007b). We measured to some extent the market share of the banks in their countries. B- Contextual variables: Theses variables reflect the regulatory and institutional specificities of the banks. Five variables will be measured in our study to evaluate the impact of the legal system on the probability of the banks to become targets or bidders: the index of supervision (Superv), the transparency index (Transp), the requirement on capital (ECap), the level of restriction of activities (Restric), the level of diversification, and the liquidity authorized (DivLiq), Buch and DeLong (2004) ; Pasiouras et al. (2007b). The construction of these indices is presented in Table 2. C- Variable related to lines of bank activity: To build variables related to the lines of activities of the banks, we will proceed like Laeven et al. (2007) did. Five measurements constituting the contributions of this study will be introduced. Through these measurements, we will try to identify the principal activities of the banks, as well as the degree of diversification of their portfolios. Indeed, in a context of universality, the banks have the opportunity to develop several activities. In this study, we will distinguish between activities of “pure” intermediation and activities of the BFI (Banque de Financement et d’Investissement) and management of assets - known as “wholesale banking”. In other words, we will distinguish between the detail activity (traditional) which consists of transforming the deposits into credits and the activity of wholesale (non-traditional). To characterize the principal activity of the bank, three measurements of activities were built. Laeven et al. (2007) proposed two measures. The first is based on assets (ActivSheet). It is equal to the ratio “Net loans/ total operating assets.” The operating assets include the totals credits and the other operating assets. The second measurement is based on the income statement (ActivInc). It is equal to the ratio “net interest revenue/total operating income.” Let us note that Laeven et al. (2007) consider that the ActivSheet variable is adapted to measure the level of activity of the bank. However we estimated that the credits can also generate other operating revenue i.e. commissions. In the same way the investment securities provide interest. For this reason, we proposed a new measure (ActivSheetInc). It is presented as follows: 11 Standardizing by country averages deflates raw values and expresses size in relation to the average in the country.

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income) operating totalassets earning total

loansnet (ActivSheet ×= Ln

This measurement supposes that the detail activity participate in the income of the bank, i.e. in the formation of the total operating income proportionally to its weight in the balance sheet, i.e. proportionally to the value of earning assets. We introduced the three measures to study their level of significance. A high level of these measures signals that the bank specializes in a detail activity, while a low level signals a specialization in whole activities, i.e. IFB. Laeven et al. (2007) introduced the measures of diversification of the banks’ portfolio. These measurements indicate whether the bank specializes in detail activities or whole activities or whether it has several activities at the same time12. The construction of these measurements is established on the basis of the balance sheet (DivSheet) and on the basis of the income statement (DivInc). They are presented as follows:

assets earning Total

assets earningother - loansNet 1DivSheet −=

income operating total

revenues operatingother - revenueinterest Net 1Divrt −=

A low level of these two measurements means that the bank specializes in detail activities or activities of IFB, while a high level means a diversified bank portfolio. It is clear that the measurements of activity and diversification are interconnected but at the same time complementary. A bank that specializes in an unspecified activity has a level of diversification equal to zero. Nevertheless, this measurement of diversification says nothing about the nature of its activity. It is the measurement of the activity that indicates this nature. D- Control variables Like Rossi and Volpin (2004), we will introduce (log) Gross domestic product per capita, (GDPP) to measure the health of the economy and (CRGDP) growth rate of the GDP to measure the change in the macroeconomic conditions. The target banks generally operate in countries with weak Gross Domestic Products per capita and are characterized by a high growth rate of GDP compared to the one of the bidder’s country. We forecast a negative sign for log GDP per capita and a positive sign for the growth rate of the GDP. We will introduce other control variables like the growth rate of the population (CRPOP), the level of inflation deflated by the GDP (INFL), and the log of Direct Foreign Investments (FDI). We evaluate their effects on a bank participating in a merger and cross-border acquisition.

12 According to Laeven et al. (2007), these measurements take values between 0 and 1. It should be noted that the remark of Laeven et al. (2007) concerning the values that ActivInc and Divrt can take is not completely right. For a bank whose activity of intermediation is overdrawn (net interest revenue is negative), the measurement of activity and of diversification of its portfolio could be negative. (Example: let us suppose net interest revenue = (9,000); other operating income = 10,000. All made calculations, ActivInc= -9; Divrt=-18)

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3.3. Model In this study, we will use the Multinomial Logit model. The Logit model13 is one of the models most used to predict the probability that an event happens14. The model is written in the following form:

∑=

====2

0

exp

exp)()(

k

X

X

iii

ki

ji

XFjyPpβ

β

β =∀j {1, 2} ; =∀i {1…N} 1

Where,

ip : Probability of realization of an event j for a bank i;

iy : Dependent variable which takes as values 0, 1 or 2. It informs us about the various

modalities (status) that bank i may have. It indicates that a bank may: - be non-involved (status quo) in a M&A (modality of reference) (status 0), or - be a target in a M&A (status 1), or - be a bidder in a M&A (status 2)

iX : Vector of the explanatory variables representing financial, contextual, and industrial

specificities of bank i for year t-115. β : Parameters to be estimated, associated with the various explanatory variables. For a multinomial Logit model with m+1 modalities, Hurlin (2003) accentuates that:

� The parameters associated with the reference modality, generally 0, are standardized to zero: only the parameters associated with m modalities can be estimated.

� The parameters of the model are interpreted as deviations from the reference modality

(i.e. from the parameters0β of modality 0) ( a positive coefficient (negative) for the target method increases (decrease) the probability “of being acquired” compared to the “status quo” modality, i.e. non involved)

On the other hand, the quantity i

iij p

pc

−=

1 corresponds to the probability of realization of the

event ( jyi = ) compared to the reference modality ( 0=iy ) for bank i. This quantity is called

“odds.” In the Logit model, it corresponds simply to the quantity “ βiXexp ”. It rather expresses

the probability of realization of the event jyi = than 0=iy for bank i. When a variable 1x

increases by one unit, this probability increases by a value equal to “ )exp( 1B ”. 13 The choice of the Logit model rather than Probit is explained by the inequality of the frequencies in the sample between the “status quo” banks, targets, and purchasers. 14 The results of Jagtiani et al. (2003) affirm that the use of simple linear models, like Logit, in the early identification of the banking failures give more satisfactory results than those with more complex methods, like the nonparametric models. 15 We proceeded like Pasiouras et al. (2007b). We supposed that the operations carried out during the year T reflect contextual, and industrial specificities of the banks during year T-1. So, in our study, we chose the mergers carried out between 2000 and 2006 of which various variables were noted between 1999 and 2005.

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4. Results and interpretations In this section, we will continue with a descriptive analysis of the variables and a discussion of the results of the Logit multinomial estimates. 4. 1. Descriptive analysis The descriptive statistics related to the variables are summarized in Table 3. They are presented according to bank categories (status quo, targets, and bidders). On the average, the bidder banks have a return on equity (ROE) higher than that of those of the “status quo” and “target” banks – respectively 14.07%, 7.84%, and 1.74%. In the same way, they are capital intensive, that is, they are large banks. With regard to contextual variables, the results have a certain similarity for both target and bidder banks. However, in spite of the efforts made by the European regulators in regard to legal harmonization, we still note a certain divergence among countries. The “Superv” variable varies between 0 (Sweden) and 6 (Austria). The “Transp” variable takes values ranging between 1 (Austria) and 3 (Finland, Ireland, Spain Italy, the Netherlands and United Kingdom). “ECap” varies between 2 (Greece) and 6 (Austria, France, Belgium, Italy, the Netherlands and GerM&Any). “Restric” and “DivLiq” take values between 1 (Germany) and 2.33 (Greece, Belgium and Italy), and 1 (Sweden, Greece, Spain, Italy, the Netherlands and United Kingdom) and 3 (Luxembourg, Austria, France, Ireland and Belgium), respectively. Lastly, concerning the variables related to lines of activity, we joined the remark of Laeven et al. (2007) with respect to the superiority of variables built on the basis of the balance sheet (ActivSheet, ActivSheetInc and DivSheet). The interpretation of ActivInc and DivInc is rather delicate as they take negative values. Moreover, the correlation between ActivSheet and ActivInc is about 13.18% (Table 4). This weak correlation means that the two variables do not inform about the same phenomenon. In the same way for DivSheet and DivInc, the correlation is about 5.51%.

[Insert tables 3 and 4 here] 4. 2. Estimation of multinomial Logit model By using the maximum likelihood method, we estimated two specifications of the Logit multinomial model. In the first one, we introduced the financial and contextual variables as well as control variables. In the second one, we added the variables related to the lines of activity. However, the estimates of the multinomial Logit model have problems on the level of precision of the estimates when we have a small number of observations from one modality compared to another (Palepu 1986). To overcome this problem of inaccuracy, we will proceed like Pasiouras et al. (2007b) by weighting the data to compensate for differences in the sample16.

16 This method consists of weighting the observations in order to represent their contribution to the whole population. The weight of each group of observations is represented as follows: (1/N0) * [(N0 +N1+N2) /3] for group 0 (non-involved), (1/N1) * [(N0 +N1+N2) /3] for group 1 (targets) and (1/N2) * [(N0 +N1+N2) /3] for group 2 (purchasers); with: N0, N1 and N2 respectively represent the number of observations of the non-

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The results of these two specifications are represented in Table 517. They are related to the estimate of the difference between the two modalities (target and bidder) compared to the modality of reference (non-involved). The estimation of the Logit model allows us only the interpretation of the relation between the dependent variable and the independent variables, without giving a measurement of the influence between them. Consequently, we deferred the effects of the explanatory variables on the odds. The measurement of the total significance of the model is carried out by several criteria among which are X², Pseudo R,² and the likelihood. These criteria are well adapted to compare specifications which do not have the same dimension. Table 5 shows that the two specifications present significant X² (i.e., we reject the assumption of the nullity of the coefficients of the models). In the same way, the Pseudo R² and the Log likelihood increase respectively by 0.339 and -5082.9 (specification I) to 0.421 and -4446.6 (specification II). The adjustment of the model is much better than Pseudo R² and Log likelihood is stronger. We can conclude that the addition of the variables related to the lines of activity improves the quality of robustness of the model.

[Insert table 5 here] The variables ROE, IndGRR and Size are significant for the targets as well as for the bidders. The ROE variable presents a negative sign (positive) for the targets (bidders). We can thus conclude that the activity of the target banks (bidder) is often less (more) profitable than that of the bidder (target). Targets belong to countries where the banking systems have on average a level of return on capital relatively higher than that of the countries of the target banks (Focarelli et al. (2001)). The assumption of the inadequacy between the resources and growth for the target banks seems not well verified. The IndGRR variable is significantly negative for the two types of banks. According to the construction of this variable18, we can conclude that target banks as well as bidder banks do not have this inadequacy. The factor Size is a determining factor for the targets, as well as for the bidders. The Size variable is associated with a negative sign for targets and with a positive sign for bidders. In fact, large banks can best issue options to buy other banks. The target banks are generally small. Moreover, all former works have reached this result. The contribution of the legal environment with regard to the identification of the targets and the bidders is measured through the variables Superv, Transp, ECap, Restric, and DivLiq. These variables are determining variables, especially for the bidder banks. Only Restric and DivLiq have a significant impact on the profile of the targets and the bidders at the same time. The signs associated with these variables show that these banks operate in financial systems which restrict the activities of the banks but do not pose constraints regarding liquidity. This result explains the strategies of geographical diversification developed by the banks for a reduction of the risks’s objective, in a context of restriction of activities. The significant signs associated with Superv, Transp, and Ecap for the profile bidder suppose that the latter

involved, the targets and the purchasers. In our study N0= 7020, N1=64, and N2=112. For more discussions on the method used, see King and Zeng (2001). 17 We used the Stata 10.0 software fort the estimation. 18 See Table 2

13

operates in an environment characterized by a high level of supervision and transparency and by a weak requirement for legal capital. The contribution of our work is that we introduced a new dimension of analysis with respect to the identification of the targets and the bidders, which is related to lines of activity. The principal activity of a bank is expressed through three measurements. A high level of these measurements announces that the bank specializes in detail activity. While a low level informs about specialization in whole activities, i.e. IFB. All measurements related to the activity of the banks were significant for the bidder’s profile. The signs associated with these measurements suppose that the bidder banks tend to develop a whole activity, which is non-intermediation. The significant positive sign of ActivSheetInc suggests that the bidder banks gain more in terms of output of their intermediation activity. In other words, their total operating income is formed mainly by the intermediation revenue. As for the target banks, the ActivSheet variables and ActivSheetInc are respectively negative and positive. This means that these banks also develop the same activities as those of the bidders and that both have the same revenue structure. The level of the diversification of the business portfolios is understood through DivSheet and DivInc. These measurements indicate whether the bank specializes in detail activities or investment or whether it develops several activities at the same time. A low level of these two measurements announces that the bank has a specialized portfolio, while a high level announces that the latter is diversified. In our study, the first measurement is significant for the two types of banks. The respective signs inform about a specialized portfolio for the target and a diversified portfolio for the bidder. The second measurement confirms the result for the target banks. It is not significant for the bidders. The consideration of these two notions of activities (measurement of activities and diversification of the portfolio) allows us to conclude that the target banks tend to have the portfolio specialized in the IFB activities, while the bidder banks develop mainly the same activities but have diversified portfolios.19 Lastly, by introducing control variables, we wanted to establish the various macroeconomic characteristics to characterize the environment of the banks. Two variables of control (CRPOP and FDI) among the five introduced, are significant for the two types of banks. The results indicate that the targets and the bidders tend to operate in countries with weak direct foreign investments (FDI) and with high population growth. Variable GDPP is significant only for the profile bidder. It supposes that the latter operates in countries with weak distribution of income per capita, while CRGDP and INFL are significant only for the target profile. It inform of economic outlooks that are favorable to the target country. These results were expected. Economic growth is a good sign for foreign investors. It announces a favorable economic conjuncture to the target’s country. In our study, the variables Restric, ActivSheetInc, CRGDP, and CRPOP present the most significant influences on the odds for the target: the probability to become target rather than non-involved. However, for the bidder profile, the ROE, Transp, Restric, ActivSheetInc, DivSheet, and CRPOP variables are those which present the most important odds.20

19 These results join the conclusions of Saïdane (2007) where he noted, “The cross-border consolidations, even if they remain less important within the EU, seem more advanced in the whole bank activity than in the detail bank”. 20 Refer to paragraph 3.2 for a definition of the odds.

14

5. Conclusion Into our study, we introduced three dimensions of analysis regarding the identification of targets and initiators of M&As. The introduction of the dimension “analysis by line of activity” constitutes the originality of our work. On the basis of a sample made up of 1071 banks of the EU15, over the period 1999-2005, we established the main characteristics of the target and the bidder banks. The results show a determination of each dimension of analysis. As to the financial specificities, the results show that the activities of the target banks (bidders) are often less (more) profitable that those of the bidders (targets). Moreover, the two types of banks do not have an inadequacy between their resources and their growth. Lastly, the large banks can best issue purchase options on other banks. With regard to legal specificities, we found that the target and bidder banks operate in financial systems which restrict the activities of the banks but do not pose constraints regarding liquidity. Moreover, for the bidders, the results suppose that the bidders operate in an environment characterized by a high level of supervision and transparency and by a weak requirement for legal capital. The consideration of the “line of activity” dimension permits to conclude that the target banks tend to have portfolios specialized in activities of market investment, while the bidder banks develop mainly the same activity but have diversified portfolios. Lastly, we will be able to compare, in works to come, the results of the Logit multinomial model with those of other methods allowing to overcome its parametric nature and its statistical restrictions like the models of survival, neural networks, and the multicriteria approach.

15

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Pasiouras, F. and Gaganis, Ch., (2006), “Are the determinants of banks' acquisitions similar across EU countries?, Empirical evidence from the principal banking sectors”, Working paper, Financial Engineering Laboratory, Technical University of Crete. Pasiouras, F. and Gaganis, Ch., (2007), “Financial Characteristics of Banks Involved in Acquisitions: Evidence from Asia”, Applied Economics, Vol. 17, No. 4, pp. 329-341. Pasiouras, F., Tanna, S. and Zopounidis, C., (2007a), “The identification of acquisition targets in the EU banking industry: An application of multicriteria approaches”, International Review of Financial Analysis, Volume 16, Issue 3, Pages 262-281 Pasiouras F., Tanna S., and Gaganis Ch., (2007b), “What drives acquisitions in the EU banking industry? The role of bank regulation and supervision on framework, bank-specific and market-specific factors”, Coventry University, Economics, Finance and Accounting Applied Research, Working Paper Series, No. 2007-3. Pasiouras, F. and Zopounidis, C.,, (2008), “Consolidation in the Greek Banking Sector: Which Banks are Acquired?”, Managerial Finance, Vol. 24, No. 3, pp. 198-213. Powell, R. G., (1997), “Modelling takeover likelihood”, Journal of Business Finance and Accounting 24, pp. 1009–1030. Powell, R., G., (2001), “Takeover prediction and portfolio performance: A note”, Journal of Business Finance & Accounting 28, pp. 993–1011. Powell, R. G., (2004), “Takeover Prediction Models and Portfolio Strategies: A Multinomial Approach”, Multinational Finance Journal, vol. 8, pp. 35–72. Rossi, S., and Volpin, P. F. (2004), “Cross-country determinants of mergers and acquisitions”, Journal of Financial Economics, 74, 277−304. Saïdane, D., (2007), « L'industrie bancaire : mondialisation des acteurs et des marchés », Ed Revue Banque. Slowinski, C. Zopounidis and A. Dimitras, (1997), “Prediction of company acquisition in Greece by means of the rough set approach”, European Journal of Operational Research 100, pp. 1–15. Stevens, (1973), “Financial characteristics of merged firms: A multivariate analysis”, Journal of Financial and Quantitative Analysis 00, pp. 149–158. Trimbath, S., Frydman, H., and R. Frydman (2001), “Cost Inefficiency, Size of Firms and Takeovers.” Review of Quantitative Finance and Accounting, Vol. 17, no. 4: 397-420. Valkanov E and Kleimeier, S., (2007), “The role of regulatory capital in international bank mergers and acquisitions”, Research in International Business and Finance, Vol. 21, issue 1, p50-68.

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Vander Vennet, R. (2002), “Cost and profit efficiency of financial conglomerates and universal banking in Europe”, Journal of Money, Credit and Banking, No. 34(1), pp. 254-282, February. Walter, R. M. (1994), “The usefulness of current cost information for identifying takeover targets and earning above-average stock returns”, Journal of Accounting, Auditing & Finance, 9, Issue 2, p378-380. Wheelock, D.C. and Wilson, P.W., (2004), “Consolidation in US banking: Which banks engage in mergers?”, Review of Financial Economics 13, 7–39.

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Appendices

Table 1: Repartition of the M&A between the Countries of the EU15

N : number of non-involved T : number of targets B : number of bidders Source: authors calculations from the Zephyr database.

Year 2000 2001 2002 2003 2004 2005 2006 Total

Country N T B N T B N T B N T B N T B N T B N T B N T B

Germany 570 1 3 576 1 3 587 3 2 589 2 0 589 0 0 586 5 0 589 2 0 4086 14 8 Austria 43 0 1 43 1 1 50 0 2 50 0 4 51 0 3 52 0 2 50 1 3 339 2 16

Belgium 19 1 4 25 0 2 27 0 2 25 1 2 27 0 1 25 2 1 25 0 1 173 4 13 Denmark 23 1 0 23 1 0 25 0 0 25 0 0 25 0 0 25 0 0 24 0 1 170 2 1

Spain 5 1 2 9 1 1 12 0 0 10 0 2 9 2 1 10 1 0 7 2 1 62 7 7 Finland 2 0 0 2 0 0 5 0 0 5 0 0 5 0 0 5 0 0 5 0 0 29 0 0 France 106 3 0 111 2 1 120 1 2 123 0 1 122 0 1 119 0 1 118 0 3 819 6 9 Greece 6 0 1 9 0 0 10 0 0 8 0 2 8 1 1 8 1 1 9 0 1 58 2 6 Ireland 14 0 0 14 0 0 14 1 1 16 0 0 16 1 0 15 1 1 17 0 0 106 3 2

Italy 17 0 0 14 2 1 13 0 4 13 0 4 15 4 1 16 1 2 15 3 1 103 10 13 Luxembourg 50 2 1 53 2 1 55 2 1 56 0 1 55 0 2 54 1 2 55 0 0 378 7 8 Netherlands 14 1 0 14 0 1 16 0 2 15 1 3 16 1 1 16 0 1 14 0 3 105 3 11

Portugal 5 0 0 6 0 1 6 0 1 5 1 1 7 0 0 4 0 2 4 0 1 37 1 6 United Kingdom 55 0 2 57 0 0 64 0 1 62 2 1 67 1 2 70 0 1 70 0 1 445 3 8

Sweden 13 0 1 14 0 1 16 0 1 17 0 0 17 0 0 16 0 1 17 0 0 110 0 4

Total 942 10 15 970 10 13 1020 7 19 1019 7 21 1029 10 13 1021 12 15 1019 8 16 7020 64 112

20

Table 2: Construction of Variables Variables Category Descriptions Sources Financial variables

ROE

Return on average equities Net income/average equities Bankscope

IndGRR

The inadequacy between the resources and the growth 1 if the bank presents one of the following cases: considerable low growth/strong resources or strong growth/poor resources 0 otherwise

Bankscope

with growth resources

The growth rate of ratio total operating income/net loans Net loans/total assets

Bankscope Bankscope

Size Size Total assets / total assets of all bank s in a country Bankscope Contextual Variables

Superv

Index of supervision Index of toughness of banking supervisors that has been computed as the sum of 1-0-dummies capturing the following aspects: (1) Are supervisors legally liable for their actions?, (2) Can the supervisory agency supercede bank shareholder rights and declare bank insolvent?, (3) Can the supervisory agency order directors/management to constitute provisions to cover actual/potential losses?, (4) Can the supervisory agency suspend dividends?, (5) Can supervisory agency suspend bonuses?, (6) Can supervisory agency suspend management fees?

World Bank Database, Barth et al. (2001)

Transp

Transparency index Index of disclosure requirements in the banking industry that has been computed as the sum of 1-0-dummies capturing the following aspects: (1) Are consolidated accounts covering bank and any non-bank financial subsidiaries required?, (2) Do regulations require credit ratings for commercial banks?, (3) Must banks disclose risk management procedures to the public?, (4) Are off-balance sheet items disclosed to the public?


ECap

Requirement on capital This variable takes values between 0 and 6, with higher values indicating grater stringency. It is determined by adding 1 if the answer is yes and 0 otherwise, for each one of the following seven questions: (1) Is the minimum required capital asset ratio risk-weighted in line with Basle guidelines? (2) Does the ratio vary with market risk, (3) Is subordinated debt allowable (required) as part of capital? (4) Are market value of loan losses not realized in accounting books deducted? (5) Are unrealized losses in securities portfolios deducted? (6) Are unrealized foreign exchange losses deducted?


Restric

Level of the restriction of activities The score for this variable is determined on the basis of the answers to three questions (1) What is the level of regulatory restrictiveness for bank participation in securities activities (the ability of banks to engage in the business of securities, underwriting, brokerage, dealing, and all aspects of the mutual fund industry?) (2) What is the level of regulatory restrictiveness for bank participation in insurance activities (the ability of banks to engage in insurance underwriting and selling)? (3) What is the level of regulatory restrictiveness for bank participation in real estate activities (the ability of banks to engage in real estate investment, development, and management)? The answer to each one of the above questions is quantified on a scale of 1 to 4, depending on whether the answer is: Unrestricted =1: full range of activities can be conducted directly in the bank; Permitted = 2: full range of activities can be conducted, but some or all must be conducted in subsidiaries; Restricted = 3: less than the full range of activities can be conducted in the bank or subsidiaries; and Prohibited = 4: the activity cannot be conducted in either the bank or the subsidiaries. In this study, we use an overall index by calculating the average value over the three categories. Obviously, a higher value indicates greater restrictiveness.


21

DivLiq Level of diversification and the liquidity authorized This variable captures the degree to which banks are encouraged or restricted with respect to liquidity as well as asset and geographical diversification. The index is based on the following three questions: (1) Are there explicit, verifiable, and quantifiable guidelines for asset diversification? (2) Are banks prohibited from making loans abroad? (3) Is there a minimum liquidity requirement? The score is calculated on the basis of yes/no questions, by adding 1 to yes for questions (1) and (3) and no for question (2) since this response is associated with greater diversification. The variable takes values between 0 and 3, with a higher value indicating greater liquidity and diversification.


Variables related to the lines of activities

ActivSheet

The measure of activity in basis of the elements of sheet Net loans/ total earning assets Bankscope

ActivInc

The measure of activity in basis of the elements of income statement

Net interest revenue/total operating income Bankscope

ActivSheetInc

The measure of activity in basis of the elements of sheet and income statement income) operating total

assets earning total

loansnet ( ×Ln

Bankscope

DivSheet

The level of diversification of the banks portfolio in basis of the elements of sheet

assets earning Total

assets earningother - loansNet 1−

Bankscope

DivInc The level of diversification of the banks portfolio in basis of the elements of income statement

income operating total

revenues operatingother - revenueinterest Net 1−

Bankscope

Control variables GDPP

Gross Domestic Product per Capita (log) gross domestic product per Capita World Bank Database

CRGDP Growth rate of the GDP (GDPt-GDP(t-1))/GDP(t-1) World Bank Database

CRPOP

The growth rate of population (P) (Pt-P(t-1))/P(t-1) World Bank Database

INFL

The inflation deflated by the GDP The inflation deflated by the GDP World Bank Database

FDI Direct Foreign Investments (Log) Direct Foreign Investments World Bank Database

22

Table 3: Descriptive Statistics

Non-involved Target Bidder Variable Mean Median SE Min Max Mean Median SE Min Max Mean Median SE Min Max ROE (%) 7,842 6,030 14,910 -354,660 384,630 1,747 6,170 22,734 -95,220 37,990 14,079 13,775 8,149 -12,310 45,920 IndGRR 0,612 1,000 0,487 0,000 1,000 0,531 1,000 0,503 0,000 1,000 0,500 0,500 0,502 0,000 1,000

Size 0,011 0,000 0,050 0,000 0,841 0,020 0,003 0,057 0,000 0,341 0,180 0,155 0,160 0,000 0,663 Superv 2,641 2,000 1,213 0,000 6,000 2,750 3,000 1,369 1,000 6,000 3,134 3,000 1,742 0,000 6,000 Transp 2,064 2,000 0,396 1,000 3,000 2,266 2,000 0,512 1,000 3,000 2,161 2,000 0,651 1,000 3,000

ECap 5,193 5,000 0,673 2,000 6,000 5,109 5,000 0,838 3,000 6,000 5,054 5,000 1,114 2,000 6,000 Restric 1,226 1,000 0,352 1,000 2,333 1,635 1,333 0,517 1,000 2,333 1,711 1,333 0,479 1,000 2,333 DivLiq 2,142 2,000 0,596 1,000 3,000 2,063 2,000 0,794 1,000 3,000 2,054 2,000 0,899 1,000 3,000

ActivSheet 0,572 0,623 0,237 0,000 1,000 0,525 0,528 0,294 0,024 1,000 0,512 0,544 0,177 0,000 0,991 ActivInc 0,716 0,758 0,354 -2,428 13,000 -0,097 0,664 5,904 -46,500 1,956 0,542 0,574 0,182 0,000 1,052

ActivSheetInc 10,401 10,313 1,596 0,728 17,077 10,472 10,990 2,437 5,208 15,544 13,901 14,422 2,174 4,420 16,891 DivSheet 0,580 0,625 0,264 0,000 1,000 0,498 0,477 0,303 0,000 0,998 0,731 0,796 0,230 0,000 0,996

DivInc 0,419 0,457 0,591 -24,000 1,000 -1,095 0,434 11,680 -93,000 0,968 0,705 0,771 0,227 -0,104 0,996 GDPP 10,234 10,150 0,213 9,315 11,084 10,214 10,169 0,281 9,315 10,916 10,174 10,147 0,308 9,315 10,916

CRGDP (%) 1,815 1,250 1,595 -0,743 10,720 2,635 2,666 2,124 0,000 8,440 2,282 2,010 1,505 0,037 8,440 CRPOP (%) 0,290 0,150 0,397 -0,407 2,200 0,600 0,468 0,535 -0,334 1,800 0,506 0,458 0,415 -0,407 1,800

INFL (%) 1,316 1,200 1,164 -0,680 5,500 2,136 2,021 1,393 -0,680 5,500 2,252 2,028 1,269 -0,680 5,500 FDI 24,296 24,287 1,053 19,580 26,094 24,075 23,977 0,858 21,286 26,071 23,931 23,988 1,403 19,580 26,094

N. Obs. 7020 64 112

23

Tableau 4: Correlation Analysis (Number of Observations = 7196)

ROE IndGRR Size Superv Transp ECap Restric DivLiq ActivSheet ActivInc ActivSheetInc DivSheet DivInc GDPP CRGDP CRPOP INFL FDI ROE 1,0000

IndGRR -0,0295 1,0000

Size 0,1121 -0,0373 1,0000

Superv 0,0968 -0,1489 0,0535 1,0000

Transp 0,0386 -0,0290 0,1186 -0,2868 1,0000

ECap 0,0158 -0,0968 -0,0944 0,6159 -0,1202 1,0000

Restric 0,1148 -0,0778 0,3381 0,2417 0,2436 -0,0317 1,0000

DivLiq 0,0544 -0,1014 -0,0540 0,5001 -0,5799 0,4892 0,0585 1,0000

ActivSheet -0,0492 0,4577 -0,0475 -0,2984 -0,0707 -0,1726 -0,0953 -0,1624 1,0000

ActivInc -0,0459 0,0308 -0,0415 -0,0719 -0,0619 -0,0388 -0,0524 -0,0061 0,1328 1,0000

ActivSheetInc 0,0479 0,1087 0,4810 -0,1235 0,0332 -0,0922 0,1094 -0,1172 0,4105 0,0065 1,0000

DivSheet -0,1251 -0,1181 0,0805 -0,1117 -0,1616 -0,0233 -0,1852 -0,0275 -0,0247 0,0747 0,2535 1,0000

DivInc 0,0146 0,0070 0,0444 0,0246 -0,0399 0,0133 -0,0635 0,0311 -0,0081 0,5665 0,1260 0,0551 1,0000

GDPP 0,0789 -0,0984 -0,0384 0,3047 -0,0131 -0,0481 -0,0129 0,1616 -0,2054 -0,0267 -0,1418 -0,0992 0,0225 1,0000

CRGDP 0,1411 -0,0768 0,1304 0,3222 0,1809 0,0593 0,3348 0,2022 -0,1908 -0,0688 -0,0990 -0,1678 -0,0023 0,2442 1,0000

CRPOP 0,1315 -0,1073 0,1650 0,4055 0,1525 0,1956 0,4845 0,3743 -0,2269 -0,0800 -0,0625 -0,2201 0,0229 0,1414 0,4990 1,0000

INFL 0,0971 -0,0958 0,2047 0,3681 0,3037 0,1301 0,5276 0,0833 -0,2021 -0,0790 -0,0140 -0,2071 -0,0018 0,1283 0,2149 0,5969 1,0000

FDI 0,0196 -0,0573 -0,1202 -0,1077 0,2667 -0,0044 -0,1605 -0,0394 -0,1272 -0,0185 -0,1035 -0,0466 0,0068 0,0614 0,2721 0,0999 -0,1826 1,0000

24

Table 5: Estimations of Multinomial Logit Model

Specification I Specification II

Target Bidder Target Bidder

Financial variables Coefficient cote Coefficient cote Coefficient cote Coefficient cote

ROE -0,0281*** 0,9723*** 0,0116*** 1,0117*** -0,0289*** 0,9716*** 0,0155*** 1,0156***

(-0,0026) (0,0025) (0,0034) (0,0035) (0,0028) (0,0027) (0,0042) (0,0042)

IndGRR -0,6107*** 0,5430*** -0,6423*** 0,5261*** -0,3970*** 0,6724*** -0,0657 0,9364

(0,0698) (0,0379) (0,0850) (0,0447) (0,0790) (0,0531) (0,1022) (0,0957)

Size 4,2288*** 68,6326*** 19,9495*** 4,61E+08*** -3,0041*** 0,0496*** 1,4754** 4,3729**

(0,9424) (64,6818) (0,8745) (4,03E+08) (0,7344) (0,0364) (0,6543) (2,8613)

Contextual variables

Superv -0,0033 0,9967 0,6912*** 1,9961*** -0,0352 0,9654 0,6695*** 1,9533***

(0,0484) (0,0482) (0,0568) (0,1135) (0,0494) (0,0477) (0,0636) (0,1243)

Transp 0,4871*** 1,6276*** 0,6640*** 1,9426*** 0,1558 1,1686 0,2827* 1,3267*

(0,1320) (0,2148) (0,1600) (0,3107) (0,1336) (0,1562) (0,1712) (0,2271)

ECap 0,0176 1,0178 -0,6452*** 0,5246*** 0,0894 1,0935 -0,7924*** 0,4528***

(0,0739) (0,0752) (0,0885) (0,0464) (0,0764) (0,0835) (0,0963) (0,0436)

Restric 1,4441*** 4,2378*** 1,1266*** 3,0851*** 1,4240*** 4,1539*** 1,6491*** 5,2025***

(0,1041) (0,4411) (0,1188) (0,3667) (0,1119) (0,4646) (0,1398) (0,7273)

DivLiq -0,1856** 0,8306** -0,1031 0,9020 -0,4417*** 0,6429*** -0,6144*** 0,5410***

(0,0917) (0,0762) (0,1099) (0,0991) (0,0969) (0,0623) (0,1282) (0,0693) Variables related to the lines of activities

ActivSheet -2,0100*** 0,1340*** -6,6726*** 0,0013***

(0,2149) (0,0288) (0,3658) (0,0005)

ActivInc -0,2083 0,8120 -0,7075*** 0,4929***

(0,1545) (0,1255) (0,2018) (0,0995)

ActivSheetInc 0,2865*** 1,3317*** 0,8697*** 2,3861***

(0,0249) (0,0332) (0,0337) (0,0804)

25

DivSheet -0,7506*** 0,4721*** 0,9127*** 2,4910***

(0,1477) (0,0697) (0,2152) (0,5361)

DivInc -0,3431*** 0,7096*** -0,1116 0,8944

(0,1012) (0,0718) (0,1308) (0,1170)

Control variables

GDPP 0,2939 1,3417 -1,9286*** 0,1453*** 0,2810 1,3245 -1,8733*** 0,1536***

(0,1849) (0,2481) (0,2346) (0,0341) (0,1842) (0,2439) (0,2436) (0,0374)

CRGDP 0,2224*** 1,2491*** -0,0191 0,9811 0,2201*** 1,2461*** -0,0105 0,9896

(0,0255) (0,0318) (0,0288) (0,0282) (0,0264) (0,0329) (0,0339) (0,0335)

CRPOP 0,6641*** 1,9427*** 0,3221** 1,3800** 0,7752*** 2,1711*** 0,6953*** 2,0043***

(0,1232) (0,2394) (0,1458) (0,2012) (0,1308) (0,2839) (0,1664) (0,3335)

INFL -0,1438*** 0,8661*** -0,1173** 0,8893** -0,1200*** 0,8869*** -0,0634 0,9385

(0,0418) (0,0362) (0,0474) (0,0421) (0,0431) (0,0382) (0,0540) (0,0507)

FDI -0,3356*** 0,7149*** 0,0458 1,0469 -0,3624*** 0,6960*** -0,2037*** 0,8157***

(0,0384) (0,0274) (0,0447) (0,0468) (0,0401) (0,0279) (0,0503) (0,0411)

Intercept 2,1616 16,2854*** 2,6194 16,8634

(2,0409) (2,4233) (2,1094) (2,6214)

X2 5212,92*** 6485,34***

Pseudo R2 0,3390 0,4217

Log likelihood -5082,9003 -4446,6945

By using the method of maximum likelihood, we estimated two specifications of the multinomial Logit model. In the first one, we introduced the financial and contextual variables, as well as the variables of control. In the second one, we added the variables related to the lines of activities. *significant at 10%, ** significant at 5%, *** significant at 1% (the values between brackets express the standard errors).

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