Connections and performance in bankers’ turnover

European Economic Review 56 (2012) 470–487

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Connections and performance in bankers’ turnover$ Erich Battistin a,b, Clara Graziano c,d, Bruno M. Parigi a,d,n a

University of Padova, Italy IRVAPP, Italy c University of Udine, Italy d CESifo, Germany b

a r t i c l e in f o

abstract

Article history: Received 28 February 2010 Accepted 29 November 2011 Available online 14 December 2011

In this paper we study the impact of the connections of the top executives (Presidents, CEOs and General Managers) of Italian banks on their turnover and on bank performance. We measure managers’ connections by the kilometer distance between the province of the bank’s headquarter and the manager’s province of birth. We show that top managers tend to be local in the sense that the distribution of this distance is heavily skewed towards zero. On the basis of this evidence we investigate whether connections affect the duration of the appointment at the bank, and whether connections entrench managers at the expense of the bank’s performance. We find that connections generally decrease the probability of bank manager’s turnover, and that the positive effect of performance on tenure is strongly attenuated once connections are taken into account. Furthermore we find that for any bank type performance does not increase with connections. On the contrary, we show that having connected managers hurts performance in Mutual, Cooperative and Rural banks. Overall these findings suggest that connections are collusion devices to share and maintain rents at the expenses of bank performance. & 2011 Elsevier B.V. All rights reserved.

JEL classification: J40 J63 G21 G34 Keywords: Corporate governance Executive turnover Commercial and Cooperative banks Panel data analysis Survival analysis Social networks

1. Introduction In this paper we study the impact of the connections of the top executives (Presidents, CEOs and General Managers) of Italian banks on their turnover and on bank performance, controlling for observable and unobservable characteristics by exploiting longitudinal information on bank-manager appointments. It has been widely documented that connections and social networks play a crucial role on labor outcomes by affecting information about employment opportunities, turnover rates, chances of obtaining a higher salary, and labor market participation (see for example Goyal, 2007, Cingano and Rosolia, 2012, and the survey by Ioannides and Loury, 2004). Connections and network effects are particularly important for positions and industries where what matters is information

$ The authors wish to thank two anonymous referees for their very constructive suggestions, the Italian Ministry of University and Research for financial support, and the Padova Chamber of Commerce for access to their data. The paper has benefited from comments by audiences at the EEA Conference 2007, the EARIE Conference 2007, the ASSET Conference 2007, the 2008 Australasian Finance and Banking Conference, the CESifo 2008 Applied microeconomics conference, several seminars, and from helpful discussions with Cinzia Baldan, Alberto Galasso, Luigi Guiso, Adriano Paggiaro, Alberto Pozzolo, Enrico Rettore, and Klaus Schaeck. Thomas Ciatto, Roberto Piazza, Enrico Pizzolato, and Flaviano Vrech provided valuable research assistance. The usual disclaimer applies. n Corresponding author at: Department of Economics, University of Padova, Italy. Tel.: þ 039 049 827 4062. E-mail address: [email protected] (B.M. Parigi).

0014-2921/$ - see front matter & 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.euroecorev.2011.11.006

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about the person’s quality and the information that he/she may have acquired about the business. Such effects are of substantial importance for top managers. Banking is of particular interest to investigate the above issues for two reasons. First, it is information-intensive chiefly for small and medium-sized enterprises (SME) where ‘‘soft’’ borrower-specific information matters (Bhattacharya and Chiesa, 1995; Petersen and Rajan, 1994; Degryse and Ongena, 2005). Second, local connections between the bank management and local firms, and the spatial organization of banks are very important elements of the bank-firm relationship. On the whole, the credit market is not spatially-neutral (Klagge and Martin, 2005) and the geographic relationships of banks with the socio-institutional environment matter for regional development (Pike, 2006). Two related aspects of the spatial dimension of banking have been identified in the literature: functional distance and geographic distance. The distance between hierarchical layers in the bank (Stein, 2002; Alessandrini et al., 2010), belongs to the first category, while the distance between the bank’s headquarters and the borrower (Mistrulli and Casolaro, 2008), and between the lending branch and its customers (Petersen and Rajan, 2002; Degryse and Ongena, 2005; Carling and Lundberg, 2005) shares traits of both functional and geographic distance. In this paper we add a further notion of geographic distance to measure the strength of a manager’s local connections: the kilometer distance between the capital city of his/her province of birth and the capital city of the province of the bank headquarter—an Italian province being a territory administratively similar to a US county. According to this measure, the greater the distance, the lower the manager’s connections.1 The Italian banking industry offers a good environment to study these issues for a number of reasons. First, banks provide the largest share of outside funds, in particular to SME. In Italy the proximity of banks’ decisional centers to local areas (close hierarchical levels inside banks’ organizational structure) has been shown to affects SME’s access to credit (Alessandrini et al., 2009), interest rates (Mistrulli and Casolaro, 2008), and firms’ innovation adoption (Alessandrini et al., 2010). Second, the Italian population is local: according to the 2001 Census (ISTAT, 2001), 86% of Italians live in the region of birth, 71% in the province of birth, and 45% in the township of birth. This suggests that the bulk of the Italian population stays very close to its birthplace over time, with the potential of developing local connections. Third, the Italian banking industry has undergone major changes which have simultaneously increased the importance of market forces and of local forces. Before 1990 the banking industry was heavily protected by legislation enacted during the Depression years to enhance bank stability both through severe restrictions on competition (Guiso et al., 2006), and through government ownership of banks. New laws passed in the 1990s allowed banks formerly chartered as public institutions to be transformed into joint stock companies, which paved the way to their privatization and to an increase in competition. The resulting waves of M&As have increased both the functional distance and the geographic distance between banks’ headquarters and firms thus potentially lowering the importance of connections. However, the controlling blocks of many newly-privatized banks were allocated to Bank Foundations, which are accountable only to the local communities and have close ties with particular cities.2 This has increased the importance of local, as opposed to nationwide, factors in bank governance. The research question we address in this paper is thus if and how local connections of the top executives affect their turnover and bank performance. We formulate two competing hypotheses in this respect. Hypothesis I. A locally connected banker may have a better knowledge of the local economy and of the local business community, and this information can be useful for the bank. Knowledge of the local economy may be useful precisely because distance matters in banking, and being a locally connected banker is part of the notion of functional proximity between bank and firm. Evidence that social networks and connections facilitate information flow is found in the mutual funds industry, where managers invest more in firms within their network and they earn higher returns (Cohen et al., 2008). Analysts with school ties to a company’s senior officers outperform on their stock recommendations (Frazzini et al., 2008). Similarly, geographically proximate US analysts possess an information advantage which translates into better performance (Malloy, 2005). When officers of bank and firm show some personal connections (social, professional, school ties) firm borrowing costs decline and the firm after loans performance improves, thus suggesting that these connections mitigate agency problems (Engelberg et al., 2012). Political connections, too, may help performance. In a cross-country study, Faccio (2006) has analyzed the widespread presence of politically-connected controlling shareholders and/or top officers and has shown that firm’s stock market valuation increases when the CEO or the controlling shareholder is elected in the Parliament. Hypothesis II. An alternative and less benign hypothesis is that banks have the power to distribute large amounts of benefits, monetary, private and political, among those that share some connections. Connections matter because, whether consciously or not, they facilitate mutual understandings with others who share similar characteristics and experiences. 1 Braggion (2011) constructs a similar measure of connections using a dummy variable for the manager working in the same place where born to capture the relationship of the manager with other individuals in his environment. 2 For example, of the 16 directors of the board of the Foundation that controls Banca Monte dei Paschi di Siena, one of the largest Italian banks by assets, 8 are appointed by the Township of Siena, 5 by the Province of Siena, 1 by the Tuscany Region, 1 by the University of Siena, and 1 by the Archbishop of Siena.

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Several studies show that social, business, and political connections boost salary, lower the turnover–performance sensitivity, and the pay–performance sensitivity, thus indicating that the connected managers enjoy benefits at the expense of their companies. Hwang and Kim (2009) show that social connections – regional origin, ties from school, military service, academic discipline – between members of the board of directors and the CEO soften the monitoring role of the board and thus tend to increase the level of compensation of the CEO, lower his/her pay–performance sensitivity, and his/her turnover–performance sensitivity. Similarly, Subrahmanyam (2008) shows that members of board are not effective monitors of the CEO as they are likely to belong to the CEO’s social network and they want to preserve their ‘‘social capital’’. The benefits from belonging to the networks of the French business e´lite have been documented by two studies. Nguyen-Dang (2006) shows that when the CEO and some board members belong to the same network of alumni of the Grandes Ecoles, turnover-performance sensitivity is lower, and if the connected CEO is ousted he/she is more likely to find a new job. Kramarz and Thesmar (2007) provide similar evidence: socially connected French CEOs have a higher probability of being hired, once hired they determine the composition of their boards, and this helps them retain their jobs after poor performance. In the case of political connections the rents accrue to the winning parties as shown by Sapienza (2004) who has analyzed political influence on state-owned Italian banks. Other papers have addressed the issue of the interplay between connections and firm performance. In a study on Freemasonry secret network in companies listed on the London Stock Exchange around 1900 Braggion (2011) shows that listed companies run by Freemasons had lower profits and lower market valuations and that non-listed companies enjoyed higher access to credit through the Freemasonry network. Interlocking directorship is another business network that might affect firm performance. Drago et al. (2010) and Non and Franses (2007) investigate the view that board interlocks reflect a strong social cohesion and that interlocked directors form a business e´lite that share some beliefs and values. The former paper looks at Italian listed firms, the latter uses a sample of Dutch firms; both find that interlocking directorship has a negative impact on firm performance. The relationship between connections and firm efficiency is studied also by Bandiera et al. (2009) within a managers–workers organization. They find that managers favor high-ability workers regardless of their connections only in the presence of high-powered incentives; otherwise they favor connected workers regardless of their ability. In a related paper Bandiera et al. (2008) analyze the incentive structure faced by Italian managers. They find that firms where managers are expected to implement faithfully the owner’s wishes have lower return on capital and lower growth than firms that hire managers on the basis of expected performance. Finally, the recent financial crisis offers anecdotal evidence that banks underperform when connected board members are involved in bank executives’ appointments (Hau and Thum, 2009). The power of banks to distribute monetary, private and political benefits develops from collusion amongst their top managers. The theory of social capital maintains that individuals tend to establish social links on the basis of affinities. Therefore it is reasonable to expect that collusion is more likely to be established and maintained among members of the same network and that locally connected managers are more likely to belong to the same network. For example, Albareto et al. (2010) suggest that the high turnover rate of bank branch managers in Italy may arise to prevent collusion with local borrowers. More generally collusion among members of the same network is a manifestation of the phenomenon of ‘‘amoral familism’’ (Bainfield, 1958), namely the belief that only family members are trustworthy. Indeed the literature on social capital and development argues that in areas of low social capital there is a more intense reliance on transactions within networks such as families and friends (Guiso et al., 2004). The aim of our empirical analysis is twofold. First, we investigate the relative importance of connections and performance as determinants of managerial turnover, and of the resulting tenure at a bank. Second, we test the competing hypotheses that connections have an information value that helps bank performance (measured by ROE, EBITDA over Total Assets and Non-Performing Loans over Total Loans), or that connections foster collusion among top managers and hurt bank performance. The two hypotheses deliver testable implications both in terms of the relationship between connections and bank performance and in terms of the relationship between connections and turnover. Hypothesis I implies that: – we should observe a positive relationship between local connections and bank performance; – by increasing bank performance local connections lower a banker’s probability of turnover.

Hypothesis II implies that: – we should observe a negative relationship between local connections and bank performance; – local connections entrench managers, hence lowering their probability of turnover.

We built a unique dataset integrating information from different sources to obtain data on the universe of Italian banks for the period 1993–2001, as well as on a set of characteristics of their top managers (Presidents, CEOs and General Managers). This enabled us to study the job tenure of top managers by exploiting matched bank/manager data. In particular, for each bank we were able to determine the identity and number of top managers in place, and to follow them throughout their career at the bank.

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We classify banks in two groups according to their voting mechanism: Commercial and Saving banks, with voting based on the number of shares owned, and Mutual, Cooperative and Rural banks, with per-capita voting. To anticipate the discussion that follows, the top managers in the latter group face less outside interference since, in addition to per-capita voting, Mutual, Cooperative and Rural banks are more likely to be independent, and less likely to be listed on the Italian Stock Exchange than Commercial and Saving banks. Furthermore, Mutual, Cooperative and Rural banks are more local, in the sense that they tend to be headquartered in towns which are not province capital, and have more geographically concentrated operations. The profound regulatory changes that took place over our sample period could in principle be exploited as an exogenous source of variation. However, in practice it is very difficult to trace the effects of this process on managerial turnover because it resulted from overlapping waves of changes whose implementation was spread over time. For this reason, our identification strategy does not rely on instrumental variables defined in a natural experiment setting; rather, it will exploit the availability of panel data. Our empirical analysis consists of the following steps. First, we model the probability of turnover conditional on the tenure of managers at the bank, thus taking into account the dynamics that lead to exit from the position. We let such probability depend upon a large set of observable characteristics as well as on bank/manager unobservables, which are match-specific. This approach allows us to study whether manager survival probabilities differ on average across bank types or whether they vary with manager characteristics. It also allows us to examine how the dynamics that generates turnover is affected by such characteristics and, in particular, by connections. Second, we exploit longitudinal information on bank-manager appointments to study the impact of manager connections on bank performance, controlling for unobserved bank fixed effects. We are not able to control for the endogenous match creation between managers and banks, which would be only possible with a larger extent of mobility of managers across banks. The determinants of this match are modeled, on the manager’s side, using only age and education (which is the only information available from the dataset that we constructed). To preview our main results, we find that local connections matter in various ways for Italian bankers. Bankers are local in the sense that the distribution of the variable distance is heavily skewed towards zero kilometers. This holds true for all the positions considered and regardless of the type of bank, and it is stronger for Mutual, Cooperative and Rural banks. We move from these findings to confront the two competing hypotheses on the role of connections. Having more connections on average decreases the turnover probabilities for Presidents, CEOs and General Managers by around 12%, 5% and 2%, respectively. At the same time it increases survival probabilities at bank for Presidents, CEOs and General Managers by around 27%, 12% and 4%, respectively. Importantly, once connections are accounted for, the relationship between bank performance and tenure is strongly attenuated and, in some cases, disappears. This finding is not per se in contrast with evidence from other studies that document a positive relationship between bank or firm performance and tenure (see for example Barro and Barro, 1990, and Brunello et al., 2003). Rather, it points to a weaker incidence of performance on tenure net of our indicator of connections, and thus calls for further discussion about the interplay between connections of managers and bank performance. We find no evidence that local connections improve the performance of any type of banks. On the contrary, we find evidence that connections hurt the performance of some types of banks. In particular, the probability of performing below the median of any bank performance measure increases with the fraction of connected top managers for the Mutual, Cooperative and Rural banks. These findings are consistent with the hypothesis that connections are collusion devices to share and maintain rents, but not with the hypothesis that connections convey information that helps a bank’s performance. Two sets of factors seem to be at work: first, Mutual, Cooperative and Rural banks tend to be more local and collusion is easier to establish and maintain in local networks; second, their governance shields their top managers more and leaves them more autonomy than in Commercial and Saving banks. The remainder of the paper is organized as follows. Section 2 presents the main characteristics of the information available, while Section 3 presents descriptive statistics. In Section 4 we illustrate the empirical strategy used to investigate the causal effects of connections on the tenure of top managers as well as on bank performance. The results are presented and discussed in Section 5, while Section 6 concludes. In Appendix A we report both the list of available variables and their sources and the regression results of the survival analysis. 2. Data First, we show how we combined data from complementary administrative archives to obtain information on both banks and managers for the time period considered. We then present the bank and manager characteristics used in our specification. 2.1. Data sources We made use of data from three sources: a dataset that contains mainly bank-level information (Bilbank), and two datasets on the characteristics of managers appointed at banks (Annuario ABI and Telemaco). Bilbank is managed by the Italian Bank Association (ABI) which provides bank-level information on the balance sheets, group affiliations and major operations (like M&As) of all the banks operating in Italy. Annuario ABI (ABI Yearbook) provides information on the

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identities and the level of education of CEOs, Presidents and other members of the boards of directors of Italian banks. The Italian Chamber of Commerce’s Telemaco data bank provides information about bank managers’ dates and places of birth. In addition to these sources, information on the number of branches per province was gathered through the Bank of Italy. Our reference population comprises all banks (Commercial, Saving, Mutual, Cooperative and Rural) operating in Italy during the period 1993–2001. It thus covers a time span in which the sweeping regulatory reforms that took place in Italy during the 1990s displayed their major effects. We obtained longitudinal information on each bank from Bilbank by following banks over the time window considered. Each bank operating in Italy is identified by its ABI code. Bank information such as balance sheets was available on a yearly basis, so that a panel of banks for up to nine years could be defined. We were able to obtain information about Presidents, who have a supervisory role but in small banks may also have an executive role, CEOs, who have an executive role, and General Managers, for each bank and in each year, and this information was used to merge managers’ characteristics into our data using Annuario ABI and Telemaco. 2.2. Bank characteristics As anticipated we classify banks in two groups according to their voting mechanisms. Although bank types had different origins reflecting their initial specializations, the distinction among them became blurred over time with the gradual repeal of the 1936 Italian banking law, so that they eventually came to perform the same functions. The first group includes banks with a voting mechanism based on the number of shares owned, i.e. Commercial banks and Saving banks converted into joint stock companies. The second group comprises Mutual, Cooperative and Rural banks, which are all cooperatives with per-capita voting. The fact that the members of these cooperatives are often employees of the bank themselves and local residents, and the legal restrictions that until 1986 prevented these banks from branching outside a narrow geographical area, make them essentially local banks (Guiso et al., 2006). To reinforce this notion, the charter of some Rural and Cooperative banks establishes that they must make at least 50% of their loans to their members and reinvest at least 70% of their profits in the local community. For each bank we know the province and city of the bank’s headquarter. Using information on the number of branches per province, we constructed an index of the branch concentration of each bank defined as the fraction of branches in the province of the bank’s headquarter over the total number of branches of that bank (if this index assumes value one, the bank operates only in one province). We measured bank size by the value of total assets. We know whether the bank is independent or affiliated to a Bank Holding Company (BHC), whether it is the head of a BHC, and whether it is listed on the Italian stock exchange. In the period considered, only 20% of Commercial and Saving banks and 2% of Mutual, Cooperative, and Rural banks were listed. No bank is state-owned. We have no systematic information on the identity of the bank’s shareholders. As very few Italian banks are listed, we decided to focus only on accounting measures of performance, despite the drawback that they may be manipulated by the executives themselves, e.g. by smoothing earnings over time. The performance indicators that we considered are the return on equity (ROE), earnings before interest, taxes, depreciation and amortization (EBITDA) divided by total assets, and the fraction of loans at risk (Non-Performing Loans) divided by total loans. The very limited number of listed banks prevents us from using market valuation metrics such as Tobin’s q, e.g. market value over book value. 2.3. Geographic distance as measure of local connections We measure the strength of a bank manager’s local connections by the (negative of the) kilometer distance between his/her province of birth and the province of the bank headquarter. More specifically, we calculate the spherical distance (i.e. corrected for the earth surface curvature) measured in kilometers between the geographical coordinates (in degrees, minutes, and seconds) of the capital city of the province of the bank’s headquarter and of the capital city of the manager’s province of birth. We interpret this variable in the sense that the greater the distance, the fewer and the weaker the manager’s connections, and the lower the scope for collusion with other top managers of the same bank. In principle this measure of connections could pick up other effects related to social capital that may affect the integrity and honesty of the managers and thus his/her propensity to collude. To determine if this is true we considered first two widely used indicators of civic participation and social capital, namely blood donations per province (blood bags per million inhabitants in 1995 from the Italian association of voluntary blood donors), and electoral participation by province (average voter turnout for all referenda between 1946 and 1989 from the Italian Ministry of Interior). We refer to Guiso et al. (2004) for a description of these variables and their interpretation. If collusion is associated with low social capital we should expect a high and negative correlation between our measure of connections and both blood donations and electoral participation. As a further attempt to quantify integrity and honesty we use a measure of the intensity of the evasion of a tax called IRAP, computed by the Italian Ministry of Economics and Finance (2006).3 If collusion is also associated with low 3 IRAP, which is on top of the income tax companies pay, was introduced in 1997 and it is proportional to company sales. Tax evasion per province is computed taking the difference between the tax base for IRAP from the national accounts and the tax base from the company tax returns for the years 1998–2002. This data is then divided by the tax base from the company tax returns to obtain the intensity of tax evasion.

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social capital, we should expect a high and negative correlation between our measure of distance and the intensity of tax evasion. We studied the explanatory power of these three indicators, which exhibit a great deal of variability and don’t simply reflect a North/South mapping of Italian provinces, in predicting a measure of localness defined at the same level of aggregation. To this end, we first considered an indicator for whether all managers employed at the bank are local (all with zero distance). We then computed the mean of this indicator across all banks and time periods by province, thus obtaining an index of the intensity of localness at the province level.4 Localness was finally regressed on blood donations, electoral participation, and intensity of tax evasion using 98 out of the 103 Italian provinces (this because of missing information for some of the dimensions). Overall, localness as defined in our analysis is orthogonal to the dimensions of civicness and social capital spanned by the indicators considered. These variables proved jointly insignificant in the regression, which is characterized by a value of the Adjusted R-squared of about 3%. This result suggests that our measure of connections reflects other than social capital. Ideally we would like to complement our measure of distance with other indicators of social connections like school ties, military service, or academic discipline. Unfortunately we have no way to gather such information. Similarly, we have no way to determine if a close relative of the bank officer was himself a banker, a potentially relevant phenomenon in Italy where anecdotal evidence of nepotism is ample. In principle a source of ties among managers is interlocking directorships, a well documented phenomenon among financial and non-financial Italian banks and firms (Bianco and Pagnoni, 1997). However, in our sample contemporaneous membership in the board of more than one bank is extremely rare (only 8 managers). This is not surprising because interlocks are less frequent among managers with executives positions: two of the positions considered (CEO and General Manager) are executives, and in Mutual, Cooperative and Rural banks also the President often performs executive functions.5 We have information also on managers’ age and education, which we use as our main measure of manager’s human capital (see the top panel of Table A1). 2.4. Information on manager’s appointments Only limited information on a manager’s appointment at the bank was available. In particular, we were only able to reconstruct the manager’s position at the bank and (but only for a much smaller sample) the starting date of the appointment. The number of positions considered varies according to the type of bank. Most Commercial and Saving banks have a President, a CEO, and a General Manager. In most Mutual, Cooperative and Rural banks the President also performs the functions of the CEO, the latter being observed in very few instances. Our data provide information about the top bankers at the survey time of each year, June 30.6 Using this information we were able to follow them throughout their careers at their banks over the period 1993–2001; in each year we defined their job tenure as the number of years spent in the same position, and turnover as the exit from that position. Unfortunately, our data do not allow us to identify the causes of the turnover, e.g. forced resignation, voluntary quit, death, illness or retirement. Furthermore, we have no information on whether the bank charter mandates a retirement age. Job durations can be right censored because we do not have information about board memberships after 2001 (our last survey year). Note that also left-censored job durations may appear in the data, because for 1993 we observe the stock of managers in place in the universe of the banks operating in Italy, but we don’t know when their position at the bank had started. We partially overcame this problem by exploiting additional information on the starting dates of jobs, which we were able to retrieve for listed banks only. However, the number of left-censored observations remained non-negligible. In particular, left censoring appears to be more problematic for Mutual, Cooperative and Rural banks, where around 54% of managers are left censored compared to 34% in Commercial and Saving banks. Presidents are on average more likely to have left-censored durations (49%) than General Managers and CEOs (around 35%). To analyze more in detail the relationship between left censoring on one hand and manager characteristics on the other we run a probit regression (not reported for brevity) where the dependent variable is a dummy for left-censored observations on the variables described in Table A1. The regression reveals that left censoring is strongly associated with being employed in banks that are geographically more concentrated. This reflects in the lower mobility of managers at Mutual, Cooperative and Rural banks compared to Commercial and Saving banks. For example, in the former case around 37% of Presidents who were observed on the board in 1993 remain in the same position throughout the time window covered by our analysis. For Commercial and Saving banks this number decreases to 5%. The difference between these figures is less pronounced for General Managers and CEOs. Moreover, we find that the propensity to have left-censored 4 As a robustness check we experimented with alternative definitions of localness, for example replicating the specification considered in the analysis of Table 5 where banks are grouped according to the proportion of local managers among the top three managers. The results proved informationally equivalent to those reported in what follows, and are omitted for brevity. 5 Common board membership between banks and non-banks is more frequent but, unfortunately, this information is only available for listed banks that, as noted before, represent a small fraction of our sample. 6 Thus we have no information on spells on boards completed in between two consecutive survey years. Whilst this may mean that turnover is underestimated, anecdotal evidence indicates that instances of top executives resigning or being fired after only a few months in the job are rare.

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appointments varies greatly along several bank and manager dimensions, and in particular that is positively associated with the age and connections of the manager. It is worth bearing in mind that non-negligible sample selection may come into play as a result of ignoring left censoring from the analysis.

3. Descriptive analysis The aim of this section is to discuss the sample selection criteria that led to the final sample used in the analysis, as well as to provide descriptive statistics about bank and manager characteristics. Here and in what follows the observations will refer to matched bank/manager data: that is, for each bank over the window considered we have repeated observations over time for the same manager, and observations for all the top managers appointed in each year. We applied the following minor selection criteria. First, we excluded observations referring to managers for whom we were unable to retrieve information about their place of birth, and thus the measure of local connections (this number proved to be negligible). Second, since in almost all Mutual, Cooperative and Rural banks the President also performs the function of the CEO (as discussed in the previous section), for this group of banks we limited observations only to Presidents and General Managers. In the turnover analysis we considered the top executive turnover resulting from acquisitions but not from mergers, because after a merger turnover occurs with probability one by definition, as the ABI code of the target bank disappears. As a result of these steps our sample for executive turnover consists of 8371 observations referred to 739 banks and 1736 managers. As explained below, in studying survival probabilities we restrict the sample only to managers whose tenure is not left censored. In this case the sample size drops to 3872 observations, of which 2439 for Presidents, 372 for CEOs and 1061 for General Managers. Table 1 reports summary statistics for bank characteristics by bank type. Mutual, Cooperative and Rural banks are less likely than Commercial and Saving banks to be headquartered in a provincial capital (8% vs. 72%). Importantly, Mutual, Cooperative and Rural banks are more likely than Commercial and Saving banks to be independent, that is not part of a Bank Holding Company (95% vs. 40%), and less likely to be head of a BHC (3% vs. 25%). As expected, Mutual, Cooperative and Rural banks have a higher fraction of their branches concentrated in the province of the bank’s headquarter (92% vs. 66%). No clear difference emerges between the two types of banks as far as the geographical location of the headquarter is concerned: more than half banks in both types are in Northern Italy; Commercial and Saving banks have a slightly larger (smaller) fraction of banks in Central (Southern) Italy than Mutual, Cooperative and Rural banks.

Table 1 Bank characteristics. Commercial, and Saving banks. Voting mechanism based on the number of shares owned (number of observations 3532). Fraction of banks which are: City bank Independent bank Listed Head of bank holding company Located in Central Italy Located in Southern Italy Assets (in log of million Italian Lira) ROE EBITDA/Total Assets Non-Performing Loans/Total Loans Branch concentration Mutual, Cooperative, and Rural banks. Per-capita voting mechanism (number of observations 4839). Fraction of banks which are: City bank Independent bank Listed Head of bank holding company Located in Central Italy Located in Southern Italy Assets (in log of million Italian Lira) ROE EBITDA/Total Asset Non-Performing Loans/Total Loans Branch concentration For a description of the variables and their sources see Table A1.

Mean

Standard deviation

0.72 0.40 0.20 0.25 0.20 0.23 2.72 0.006 0.016 0.043 0.66

0.45 0.49 0.40 0.44 0.40 0.42 0.13 0.669 0.013 0.049 0.29

Mean

Standard deviation

0.08 0.95 0.02 0.03 0.15 0.27 2.52 0.082 0.015 0.041 0.92

0.27 0.21 0.17 0.16 0.36 0.44 0.12 0.076 0.008 0.053 0.17

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Table 2 Distance. Distribution of the distance between the province of the bank’s headquarters and the manager’s province of birth, for Mutual, Cooperative, and Rural banks, and for Commercial and Saving banks by position of the manager; 1993–2001. Distance is defined as the kilometer distance between the geographical coordinates of the capital cities of two provinces. For example, the entry 90.59 below indicates that at least 75% of the General Managers of Mutual, Cooperative, and Rural banks were born in provinces whose capital is at a distance r 90.59 km from the province capital of the bank’s headquarters. 5%

25%

Mutual, Cooperative, and Rural banks (number of observations 4839) President 0.00 0.00 General Manager 0.00 0.00 Commercial and Saving banks (number of observations 3532) President 0.00 0.00 CEO 0.00 0.00 General Manager 0.00 0.00

50%

75%

95%

0.00 0.00

0.00 90.59

112.45 625.23

0.00 96.32 75.70

83.47 365.40 249.77

500.83 787.69 709.60

The distribution of ROE across bank types appears markedly different, with Commercial and Saving banks presenting smaller values on average. In fact, we found that the same result holds at all percentiles of the ROE distribution, thus producing a distribution for Mutual, Cooperative and Rural banks skewed towards larger values of this indicator. On the other hand, the distributions of EBITDA over Total Assets and Non-Performing Loans over Total Loans do not vary across bank types. Table 2 reports percentiles of the distribution of the distance by bank type and position. The distribution is markedly skewed towards zero for both types of banks, though to a lower extent for Commercial and Saving banks. Overall, managers in Mutual, Cooperative and Rural banks are more local than managers in Commercial and Saving banks. Presidents are on average the most local and CEOs the least local. It is worth noting that bank managers are on average as local as the general population, despite the low geographic mobility of Italians and despite the broader market, higher salaries and higher relocation benefits that bank managers enjoy.7 From Table 2 we observe that more than 75% of Presidents and between 50% and 75% of General Managers of Mutual, Cooperative and Rural banks work in the Province of birth. The corresponding percentages for Commercial and Saving banks are suggestive of more variability in the distribution. As a benchmark for the geographic mobility of Italians observe that, as mentioned, 71% of the general population from the 2001 Census lives in Province of birth (ISTAT, 2001). Similarly, 45% of married couples less than 65 remain to live within one kilometer from at least one parent’s home (ISTAT, 2006). For a small group of managers, which we labeled ‘‘movers’’, we observe multiple working episodes across different banks. This group is potentially very important because by observing the same manager across different banks we should be able to gain information on his/her propensity to develop local connections and to distinguish how much of the demand for localness comes from the preferences of the manager and from the bank. We will come back again to this issue in the next section where we explain our empirical strategy. However, given the small size of this group (92 managers) we preferred to use information on movers just for descriptive purposes. The only result of our analysis worth reporting is that, on average, they are less local than ‘‘not-movers’’, and that they do not necessarily have subsequent spells in the banking industry. To establish a link with the executive-turnover literature, we modeled the occurrence of turnover as a function of bank and manager characteristics. To this end from pooled data we estimated a probit regression of the binary indicator for the occurrence of turnover on the variables in Table A1 (excluding distance). We find that turnover remains relatively stable over the sample period but with markedly different levels for the three positions considered. On average, the yearly rate of turnover is 9%, 16% and 15% for Presidents, CEOs and General Managers, respectively, with higher values in Commercial and Saving banks. The results (not reported for brevity) also show that an increase in the accounting-based performance of the bank negatively affects the probability of turnover regardless of the measure of performance considered. Similar conclusions have been drawn for example by Brunello et al. (2003), who showed that for the CEOs of Italian non-financial listed firms the performance/turnover relationship is negative when the CEO is not the controlling shareholder; by Barro and Barro (1990), who found a negative relationship between CEOs turnover and stock returns for U.S. commercial banks; and by Houston and James, 1995, who found that the frequency of manager turnover among poorly-performing US commercial banks was about the same as that in poorly-performing non-banks. The overall picture that emerges from our data is thus very much in line with the one documented by other studies on top executives turnover. In Table 3 we report the average tenure for the three positions considered, restricting the sample to observations that are not left censored and accounting for right censoring by assuming that the underlying distribution of tenure in our data is lognormal (see, for example, Lancaster, 1990). The differences across positions and bank types are clear-cut. Presidents on average stay longer at banks than managers in the other positions, and this holds particularly true for Mutual,

7 We also considered the correlation between localness and key demographic indicators such as age and education. We do not report the results here for brevity, as they are not suggestive of any neat differential association across increasingly higher degrees of localness.

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Table 3 Average tenure (in years) of Presidents, CEOs and General Managers by bank category and connections. Results were obtained by fitting to the distribution of tenure a lognormal parametric model and taking right-censored employment spells into account. Separate models were estimated for the three positions considered. The differences between Commercial and Saving banks and Mutual, Cooperative, and Rural banks are statistically significant at the conventional level. Managers by connections

President

CEO

General Manager

Average of all positions

Commercial and Saving banks Connected managers (distance¼ 0) Not connected managers (distance40) All managers

8.12 6.15 7.09

6.90 5.22 5.53

8.00 4.47 5.27

7.97 5.27 6.15

Mutual, Cooperative, and Rural banks Connected managers (distance¼ 0) Not connected managers (distance40) All managers

11.29 6.09 9.99

Na Na Na

7.93 4.33 3.86

10.99 5.32 9.10

Table 4 Survival probabilities by position. All banks. Kaplan Meier estimates for connected (distance ¼0) and not connected (distance 40) managers. For example, the number 0.66 in the column of not connected Presidents means that 66% of not connected Presidents survive at bank at least three years. Years in the position

1 2 3 4 5 6 7 Log–Rank test for equality of survival probabilities

Presidents

CEOs

General Managers

Not connected

Connected

Not connected

Connected

Not connected

Connected

0.86 0.76 0.66 0.48 0.38 0.32 0.27 p-value: 0.000

0.94 0.85 0.74 0.68 0.62 0.57 0.48

0.93 0.78 0.61 0.43 0.37 0.29 0.29 p-value ¼ 0.2727

1.00 0.87 0.67 0.50 0.50 0.50 0.50

0.84 0.64 0.52 0.37 0.28 0.26 0.23 p-value ¼0.005

0.86 0.77 0.69 0.60 0.53 0.53 0.48

Cooperative and Rural banks, where Presidents have an average tenure of 10 years compared to 7 in Commercial and Saving banks (these differences are statistically significant at the conventional level). Given the evidence provided in Table 2, we also investigated whether tenure is different by the level of connections. To this end we computed tenure for ‘‘connected’’ managers, which we define as those whose measure of distance is zero, and for ‘‘not connected’’ managers, those with positive values of the variable distance. Within each category of banks connected managers have the longest tenure. By looking at the differences across bank types we notice that the tenure of the not connected managers is about the same in the two types of banks, while the tenure for connected Presidents is longer in Mutual, Cooperative and Rural banks (these differences are statistically significant at the conventional level). We also consider how job tenure relates to turnover. Table 4 studies the relationship between tenure at a bank and our measure of connections by reporting estimates of the survival probabilities for two groups of managers. In the remainder of this paper we often refer to quantities such as ‘‘survival probabilities’’ or ‘‘hazard probabilities’’. In the former case, we simply refer to the percentage of managers whose tenure at bank is greater than a certain value. In the latter case, we refer to the probability that a manager leaves the bank at time t, conditional on still being in place at time t1.8 It is worth mentioning that this definition differs from that of ‘‘turnover probability’’, which is usually employed in the managerial-turnover literature. Our definitions allow us to shed light into issues related to job duration dependence at the bank, i.e. whether having longer tenure affects positively or negatively the probability of turnover. Thus we are able to analyze the dynamics that lead to exit from the position. Given the evidence provided in Tables 2 and 3, we estimate separate models for the survival probability of connected and not connected managers. We find that connected managers have statistically significant higher survival probabilities than their not connected peers, except for the CEOs for whom the difference is not statistically significant. This finding is consistent with both the collusion hypothesis, since banks will oust local managers only when the problems are so acute that it cannot be solved by the incumbents, and with the hypothesis that connections entail better local knowledge since banks, ceteris paribus, are more reluctant to part with valuable local managers.

8 For example, the survival probability after three years represents the percentage of managers whose tenure at the bank is longer than three years. The hazard probability during the third year at bank is instead defined as the percentage of managers leaving the bank during their third year, which intrinsically controls for them having tenure of at least two years.

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4. Methods 4.1. Identification problem Ideally, we would like to estimate the following relationship: ybt ¼ at þ bxbt þ zb þ ebt ,

ð1Þ

where ybt is the outcome of interest for bank b at time t, which may be thought as our preferred measure of bank performance, xbt denotes a set of observable regressors, which may vary across banks as well as over time. The variable at is a time effect, and can be modeled by year dummies or a flexible polynomial in time. The remaining variables in the equation are unobservable error components. The term zb represents an idiosyncratic effect of the bank, and summarizes the set of characteristics which are relevant to ybt but cannot be included as regressors because they are not observable. The zb thus captures unobserved heterogeneity of the bank, which is fixed over time such as long term strategy and propensity towards better governace. Finally, the term ebt is a random error, which we assume uncorrelated with the included regressors. The identification of b, the marginal effect of xbt on ybt , is of central interest in our analysis. However, estimates of this parameter obtained from the ‘‘feasible’’ regression of ybt on xbt may encounter a simultaneity problem. For example, propensity towards better governance might simultaneously affect bank performance and the scope for collusion among top managers. If this were the case, we would not be able to disentangle how much of the negative association between collusion and performance depends solely on collusion itself, and how much of it is actually caused by confounding unobservable effects, which reflect good governance. The specification in Eq. (1) explicitly allows for the presence of such confounding effects, in that it allows for omitted regressors, which are bank-specific and remain constant over time. Standard panel data analysis takes such (fixed) effects into account by exploiting the longitudinal dimension of the data. In principle using micro-data on banks and managers over time, one could estimate Eq. (1) by allowing for bank and manager fixed effects, and thus testing for the presence of confounding effects along both dimensions. Intuitively, for such a strategy to work one would need a sufficient degree of mobility of the same manager across banks over the time period considered. In the extreme case of no mobility of managers, we would not be able to disentangle the manager’s fixed effect from the bank fixed effect.9 However, as noted, for the case at hand the degree of mobility in the data is relatively low, in particular for connected managers and for those employed in small banks and in banks with high branch concentration. In the end since we cannot use information on ‘‘movers’’, we decided to rely on standard panel data analysis controlling solely for bank unobserved characteristics and netting off manager effects through the observable dimensions in the data (age and education). 4.2. Estimation The discussion in the previous section helps clarify that unobserved heterogeneity of banks and managers plays a key role to shed light on the research question addressed in this paper. In what follows, we at first model the hazard probabilities of managers, and then the effects of connections on bank performance. In either case the analysis corrects for confounding effects by exploiting the longitudinal dimension of our data (i.e. banks and managers employed at bank over time). 4.2.1. Connections and tenure We start by modeling the relationship between tenure and connections separately for the three positions considered and by bank type. Hazard probabilities are identified by following the bank/manager match over time from the first to the last year at the bank. As mentioned we limited our analysis to complete or right-censored job durations by excluding from the analysis bank-manager matches for which we were unable to determine the starting date of the appointment (see the discussion in Section 3). Although this is clearly a major source of selection in our working sample, we made this choice because accounting for left censoring would require assumptions on the starting time of the appointment at the bank, which we found difficult to motivate in the context of this paper. We present the results from a maximum likelihood estimation of mixed hazards so as to take manager unobserved heterogeneity into account (see, for example, van den Berg, 2001). As a robustness check we experimented with different assumptions about the distribution of the unobserved component, all of which led to qualitatively similar conclusions. For this reason, in what follows we present the results from a complementary log–log model where the baseline hazard is modeled via a quadratic polynomial in (logged) time and the individual effect is normally distributed (see Jenkins, 2005).10 The results of this analysis are required to derive the graphs of Fig. 1. 9 The setup discussed here closely resembles the identification problem dealt with by Abowd et al. (1999) in the case of matched employer/employee data (see also Kramarz and Thesmar, 2007). 10 As expected, unobserved heterogeneity comes significantly into play in modeling hazard probabilities: all tests referring to its significance rejected the null hypothesis of no effect at the conventional levels. Our approach to modeling the probability of turnover closely resembles that of Denis et al.

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Commercial and Saving Banks Presidents percentage of survivors

not connected managers

connected managers

1 0.8 0.6 0.4 0.2 0 0

1

2

3 4 5 6 tenure at bank

7

8

0

p-value for the equality of curves: 0.4285

1

2

3 4 5 6 tenure at bank

7

8

p-value for the equality of curves: 0.2580

low performing banks

high performing banks

Graphs by degree of connections of the manager performance defined from quartiles of Non-Performing Loans

Mutual, Cooperative and Rural Banks Presidents not connected managers

connected managers

percentage of survivors

1 0.8 0.6 0.4 0.2 0 0

1

2

3 4 5 6 tenure at bank

7

8

p-value for the equality of curves: 0.3520

0

1

2

3 4 5 6 tenure at bank

7

8

p-value for the equality of curves: 0.0467

low performing banks

high performing banks

Graphs by degree of connections of the manager performance defined from quartiles of Non-Performing Loans Fig. 1. Survival probabilities of connected (distance¼ 0) and not connected (distance40) managers for low and high performing banks (bottom 25% and top 25% of the distribution of Non-Performing Loans/Total Loans). Figures obtained from a complementary log–log model where the baseline hazard is modeled through a quadratic polynomial in logged tenure and the unobserved heterogeneity term is normally distributed (see Section 4.2). Tenure at bank in years.

In these graphs we present the predicted survival probabilities that result from our model for groups of observations defined by: (a) connected and not connected managers, (b) Commercial and Saving banks, and Mutual, Cooperative and Rural banks, and (c) ‘‘high’’ and ‘‘low’’ performing banks, these being banks performing, respectively, in the top or the bottom 25% of the distribution of our measures of accounting-based bank performance. In all the regressions considered, we allowed for non-linearities in the effect of performance by adopting a specification in which performance affects

(footnote continued) (1997), who include tenure amongst the determinants of turnover and thus implicitly model the hazard rate. Similarly to their approach, our procedure estimates a discrete time hazard rate but explicitly models unobserved heterogeneity.

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survival probabilities through dummies referred to quartiles of the performance distribution. Following standard results from the duration (or survival) literature on proportional models, tests for the difference amongst survival probabilities for the above groups can be obtained from suitably defined linear combinations of the coefficients of the variables entering the hazards (see, for example, Jenkins, 2005).11 Moreover, time effects are controlled for throughout our analysis by adding time dummies to all specifications considered. The analysis also allows for interactions of our measure of connections with performance, degree of branch concentration of the bank, bank type, and position of the manager. 4.2.2. Connections and bank performance We then move to model the relationship between connections and bank performance. Differently from before, the analysis now makes use of longitudinal information collapsed at the bank level and thus considers only one observation per bank at each point in time. We purposively consider a specification, which is flexible enough to allow for differential effects of connections across percentiles of the distribution of bank performance. Thus we are able to assess whether having connected managers pushes the ranking of the bank towards lower values when compared to performance across banks in the population. To ease estimation we decided to adopt the following strategy. First, we defined dummies for the bank performing at different quantiles of the distributions of ROE, EBITDA over Total Assets and Non-Performing Loans over Total Loans. In particular, we considered two dummies for performing in the top 50% and top 25% of the performance distribution, and we used them as the outcome variable ybt . Second, we explicitly allowed for some dynamics in the relationship between connections and bank performance and constructed the percentage of connected top managers employed at bank in the current and the previous two years. For each time period we classified banks depending on the value of this percentage, and defined three categories associated with increasing degrees of localness: at least one third of, at least two thirds of, and only connected top managers employed in the current and the previous two years. We then constructed the full set of incremental dummies for these three categories that we used jointly as regressors xbt . Third, we considered as additional regressors all time varying bank characteristics listed in Table A1, as well as average age and average education of the top managers employed at bank in the current and the previous two years. This approach closely resembles the idea of quantile regression, which we decided to avoid as fixed effects estimation in this context would be feasible but computationally more demanding. To ease interpretation, results are derived from a linear probability model that ignores the binary nature of the outcome variable and is estimated using standard panel data analysis (i.e. within group estimation) clustering standard errors at the bank level. If connections had a negative impact on bank performance, we should observe that they increase ceteris paribus the likelihood of performing in the lower part of the distribution of bank performance. The results for the incremental dummies of the degree of localness from these regressions are reported in Table 5. We run the analysis separately for Commercial and Saving banks, and for Mutual, Cooperative and Rural banks to study possible differential effects of connections in the two groups. Since there is a one-to-one map between the bank fixed effects and the bank geographical location, it is worth noting that our analysis also takes into account unobservable province level characteristics (e.g. local unemployment rate) that might spuriously affect the relationship under investigation. 5. Results 5.1. Does performance matter for manager’s survival? The profiles of predicted manager’s survival probabilities are obtained as explained in Section 4.2 for high and low performing banks, separately for Commercial and Saving banks, and Mutual, Cooperative and Rural banks, by position of the manager at bank, and by the manager’s degree of connection (connected and not connected). The reason for the split by bank type is that Mutual, Cooperative and Rural banks are more ‘‘local’’ in the sense that they have smaller asset size, a higher branch concentration, are less frequently headquartered in a provincial capital (Table 1), and have a higher fraction of top managers with distance equal to zero (Table 2). To reinforce the local nature of these banks they are less frequently listed (Table 1), they are less likely to be head of BHC (Table 1), and per-capita voting allows their members (depositors, borrowers, employees) to wield a disproportionate influence on bank’s governance. Furthermore since Mutual, Cooperative and Rural banks are more likely to be independent than Commercial and Saving banks (Table 1) the actions of their top managers are subject to weaker outside monitoring and less interference. Therefore connections may come into play for bank performance – in a positive or in a negative fashion – more for the Mutual, Cooperative and Rural banks than for the Commercial and Saving ones. The comparison of survival probabilities across groups allows us to shed light on the ceteris paribus effects of connections and performance on the job tenure of managers at banks. For brevity we present the results for Presidents only and only for Non-Performing Loans/Total Loans (see Fig. 1).12 The right- and left-hand side panels of Fig. 1 refer to predicted profiles for connected and not connected Presidents, respectively. p-values for the equality of curves within each panel are also presented. 11 Throughout our analysis standard errors are robust to heteroskedasticity and take into account the fact that repeated measurements for bank/ manager matches are available. 12 The results not reported here are available from the authors upon request.

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Table 5 Bank Accounting Performance and Connections of the top managers. The independent variables reported (1/3 connected, 2/3 connected and All connected) refer to incremental dummies, denoting whether the percentage of top executives with distance¼ 0 employed at bank at times t, t-1 and t-2 is at least 1/3, at least 2/3 or 100%, respectively. The first column for each performance variable reports the coefficients from a within-group regression with bank fixed effects. In the second and third columns the dependent variable is a dummy for the bank performing in the top 50% or in the top 25% of each performance measure. Banks in the Top 25% of the distribution of ROE, and EBITDA/Total Assets (Non-Performing Loans/Total Loans) are the best (worst) performers. All regressions include a set of bank characteristics (total assets, total assets squared, branch concentration, branch concentration squared) and a set of managers characteristics (average age of the top managers, average age squared, percentage of top managers with college degree). See Section 4.2 for details. We topcoded the top and bottom 1% of the distributions of performance to limit the effects of outliers. Clustered standard errors in brackets. Dependent variable ROE

Dummy for ROE in

Top 50%

EBITDA/ Total Assets

Top 25%

Dummy for EBITDA/ Total Assets in Top 50%

Top 25%

Mutual, Cooperative, and Rural banks (N. Obs. 2903) 1/3 Connected  0.006  0.103  0.003  0.000 (0.006) (0.068) (0.056) (0.001) n 2/3 Connected  0.001  0.110 0.009  0.000 (0.001) (0.061) (0.045) (0.001) All Connected  0.004 0.046 0.024 0.001 (0.006) (0.057) (0.051) (0.001)

0.101 (0.072)  0.125nn (0.053) 0.057 (0.052)

 0.005 (0.059) 0.009 (0.044) 0.065 (0.052)

Commercial and Saving banks (N. Obs. 908) 1/3 Connected 0.038 0.069 0.030 (0.036) (0.057) (0.054) 2/3 Connected  0.015  0.034  0.002 (0.009) (0.069) (0.055) All Connected 0.020  0.004 0.046 (0.026) (0.074) (0.065)

0.006 (0.049)  0.015 (0.077) 0.081 (0.101)

 0.053 (0.047) 0.014 (0.067) 0.092 (0.070)

 0.001 (0.001)  0.000 (0.001) 0.002 (0.002)

Non-Perf. Loans/ Total Loans

Dummy for Non-Performing Loans/ Total Loans in Top 50%

Top 25%

 0.000 (0.003) 0.006nn (0.002)  0.004 (0.003)

 0.067 (0.067) 0.208nn (0.069)  0.091 (0.065)

 0.052 (0.045) 0.085nn (0.037)  0.050 (0.037)

0.000 (0.004) 0.009 (0.005) 0.001 (0.006)

 0.095 (0.089)  0.004 (0.070) 0.120 (0.077)

0.009 (0.050)  0.026 (0.038) 0.018 (0.047)

p o 0.05. po 0.1.

nn n

One striking finding is that the survival probabilities of all managerial positions considered in low and high performing banks are only marginally different once connections are accounted for. In particular the difference between the two survival curves is never statistically significant for CEOs for any measure of performance, and it is only marginally significant for Presidents and General Managers in few instances. For example in Fig. 1 we show that the difference is significant only for connected Presidents in Mutual, Cooperative and Rural banks, although with a p-value, which is borderline at the conventional level. Therefore we find that performance per se has only a weak effect on the survival probability of the manager, once connections are accounted for.

5.2. Do connections affect manager’s survival? To assess whether connections affect manager’s survival at bank amounts to testing whether the dotted (continuous) lines in the left-hand side panel (not connected managers) of each graph of the survival probabilities for the Presidents (see Fig. 1), CEOs and General Managers differ on average from the dotted (continuous) lines in the right-hand side panel (connected managers). We run this test by looking at the coefficients of a regression model according to the following procedure. First, we pooled the estimated hazard functions underlying all the survival probabilities of the three positions, the two types of banks, and the three performance measures, thus obtaining 8 point estimates (from the first to the eighth year) for 60 curves for a total of 480 observations. We then specified the regression equation: h ¼ b0 þ b1 C þ b2 logðTÞ þ b3 logðTÞ2 þ b4 C  logðTÞ þ b5 C  logðTÞ2 þ u:

ð2Þ

Eq. (2) models the hazard, h, as a function of a quadratic polynomial in log tenure, T, interacted with a dummy for the degree of connections, C. We thus allowed the curves to have different shapes for connected and not connected managers (C¼1 and C¼0) by letting the coefficient of log(T) depend on the connections of managers. As additional regressors we considered group dummies for the two types of banks and high and low performing banks, using the same group definition used in Fig. 1. The specification (2) was chosen to produce a good fit of the results obtained as discussed in Section 4 and provides a synthetic way of presenting the results. To gain efficiency, we modeled differences in duration dependence across positions at bank by adding interactions of all terms in Eq. (2) with dummies for Presidents, CEOs and General Managers.

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Presidents

483

CEOs

0.3 0.2

hazard

0.1 0 0

2

4

6

8

General Managers 0.3 0.2 0.1 0 0

2

4

6

8 tenure at bank

not connected

connected

profiles estimated from equation (2) Fig. 2. Probability of turnover (hazard) at bank, for the three positions considered. Reported here are the estimated profiles obtained from Eq. (2), allowing for differences in the effect of connections on the shape and on the level of the probability. The average difference of the probability of turnover between not connected and connected is 12%, 5% and 2% for Presidents, CEOs and General Managers, respectively. p-values for the joint significance of effect of connections calculated as b1 ¼ b4 ¼ b5 ¼ 0, i.e. that connections do not matter for the probability of manager turnover are equal to 0.0000 for all three positions. Tenure at bank in years. The regression results are reported in Table A2.

The regression yielded an R2 of around 60%, and pointed to a highly significant effect of connections on levels and shapes on the probability of turnover for all top positions considered. We strongly rejected the hypothesis b1 ¼ b4 ¼ b5 ¼ 0 that connections do not matter for the probability of turnover. The estimated profiles for the probability of turnover obtained from Eq. (2) are graphed in Fig. 2, and the regression results are reported in Table A2. We find that connections lower on average the probability of turnover for Presidents, CEOs and General Managers of about 12%, 5% and 2%, respectively. This figure is the average difference of the predicted hazard from the first to the eighth year, out of an average probability of turnover of about 18% for not connected Presidents, 15% for not connected CEOs, and 14% for not connected General Managers. With the exception of not connected General Managers, the probability of turnover flattens for all positions beginning from the third or fourth year of tenure, remaining almost constant after then. For all positions considered, the gap caused by connections does not diminish with tenure; or, to put it differently, tenure does not offer the Presidents, CEOs and General Managers the advantages in terms of lower probability of turnover provided by local connections. Higher hazards probabilities for not connected managers mechanically translate into statistically lower survival probabilities. Thus, connected Presidents, CEOs and General Managers have higher survival probabilities at a bank than their not connected peers. In particular, using predicted values from Eq. (2) we calculated that having connections increases survival probabilities for Presidents, CEOs and General Managers by around 27%, 12% and 4% on average, respectively.

5.3. Do connections affect bank performance? Having established that connected managers are more likely to be hired and retained, a finding consistent with both HP I and II, the issue that remains to be addressed to discriminate between the two hypotheses is whether connections have a negative or a positive impact on bank performance. Estimation results about this relationship are reported in Table 5, separately for Mutual, Cooperative and Rural banks, and Commercial and Saving banks. We report only the coefficients of the three dummies for the extent of localness, which were defined to model the incremental effect on bank performance of increasing the fraction of connected managers in the top managerial positions. We present the results from within group regressions using as outcome variable the performance indicator (first column) and dummy variables for whether the bank performs in the top 50% or 25% of the distribution (second and third columns, respectively). We topcoded the top and bottom 1% of the distributions of performance to limit the effects of outliers. All regressions control for bank fixed effects, and thus results are robust to the effect of unobserved bank characteristics. Moreover, standard errors were made robust to heteroskedasticity and clustered by bank allowing for serial correlation.13 13 As discussed in Section 4.2, our procedure amounts to estimating a quantile regression by means of a linear probability model. The results of this procedure prove qualitatively similar to those obtained from fixed effect logit regression.

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We find that a higher percentage of local top managers generally points to lower bank performance for all performance measures in Mutual, Cooperative and Rural banks. Furthermore there is no significant coefficient offering evidence of a favorable effect of connections on bank performance. Having a higher fraction of connected managers in the top managerial positions decreases the probability for Mutual, Cooperative and Rural banks of being above the median for ROE and EBIDTA over Total Assets, and increases the probability of being above the median for Non-Performing Loans over Total Loans (see the top panel). However, the effects of connections are non-linear. Having at least two thirds of connected top managers decreases the probability of being above the median by 11% (with respect to having at least one third of connected managers) if the performance measure is ROE and by 12% if the performance measure is EBITDA over Total Assets. Having at least two thirds of connected managers increases by 20% the probability of being above the median and by 8% the probability of being in the top 25% (the bottom quartile) for Non-Performing Loans over Total Loans, with respect to having at least one third connected managers. It is worth noting that the coefficients for all connected managers are never statistically significant. A possible explanation is that most Mutual, Cooperative and Rural banks employ only two top managers, President and General Manager. In this case, the coefficient for the incremental dummy 2/3 connected managers could already capture the effect of having only connected managers. Managerial connections play no detectable role on the performance of Commercial and Saving banks (see the bottom panel), their effects being always insignificant for all performance measures. In sum we find no evidence that connections improve the performance of any type of banks. On the contrary we find non-conflicting evidence that connections have a negative impact on the performance of Mutual, Cooperative and Rural banks. These results are inconsistent with the hypothesis that connections boost bank performance because of better local knowledge. This is even more so because, as noted, the alleged better knowledge of the local economy should benefit more Mutual, Cooperative and Rural banks, which are more local. Connections display their negative impact on performance in two ways: through inferior skills, since the person owes his/her position also to the fact that he/she is local; and by protecting the managers after poor performance, thus offering connected managers the perks of longer tenure and lower turnover (Table 3), and higher survival probability (Table 4 and Fig. 2), hence potentially hurting also future bank performance. The question is then why the negative impact of local connections on bank performance shows up only among Mutual, Cooperative and Rural banks. We conjecture that two sets of reinforcing factors may be at work: the more local inclination of these banks, and their governance. First, collusion is easier to establish and maintain in the more local banks, and these banks are more local as discussed above. Second, some observable characteristics of their governance may facilitate collusion. On the one hand, per-capita voting shields these banks from market pressures, and entrenches their top managers more than those of Commercial and Saving banks. This is reinforced by the fact that only 2% of Mutual, Cooperative and Rural banks are listed (versus 20% of the Commercial and Saving banks). On the other hand, 95% of Mutual, Cooperative and Rural banks are independent, that is, are not part of any Bank Holding company, versus 40% of Commercial and Saving banks (Table 1). Bank independence allows more scope for collusion among the top managers as they have no outside supervisors. Furthermore not being part of any Bank Holding Company entails a more informal governance with fewer internal controls which reinforces the scope for collusion. As a sensitivity check we have also conducted the performance-connections analysis stratifying the banks on the basis of their independence and not on the basis of their voting mechanism, thus replicating the analysis in Table 5. Not surprisingly given that almost all Mutual, Cooperative and Rural banks are independent we have obtained qualitatively similar results that we do not report for brevity.

6. Conclusions We have studied the effects of the local connections of the Italian bankers on their turnover and the performance of the Italian banks. We believe that our findings have a general interest since banking is worldwide an information-sensitive industry, relationship banking makes connections potentially relevant, and spatial dimensions are important in banking. A large literature has shown that the distance between banks and firms matters. We showed that also the distance between the bank and its bankers matters. Our research also complements the existing literature on banker’s turnover by moving in a new direction where studies on social networks are integrated with the more traditional investigation of corporate governance in banking. We have measured the degree of local connections of bankers by the kilometer distance between the province of the bank’s headquarter and the banker’s province of birth. The top managers of Italian banks tend to be local in the sense that their distance is rather small, and in fact was zero for the large majority of observations in our sample. The degree of connections varies greatly for different types of manager, with Presidents being more local especially in Mutual, Cooperative and Rural banks where their executive role gives them more power. We find that connections generally increase the survival probabilities and the tenure for all positions considered, and that the positive effect of accounting-based performance on tenure (which has been widely documented by the executive-turnover literature) weakens once connections are accounted for. There is no evidence of positive returns for any type of banks to having connected top managers. We instead find evidence that connections reduce bank accounting performance in Mutual, Cooperative and Rural banks. A bank of this type employing more connected managers most likely performs in the lower quartiles of the distribution of performance, vis-a -vis a peer bank employing not connected managers.

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Since Mutual, Cooperative and Rural banks are the most local and therefore the ones that could in principle take more advantage from the managers’ alleged better knowledge of the local economy, our finding is inconsistent with the hypothesis that connections entail better local knowledge. We interpret this finding as an indication that connections do not provide any advantage to the bank. However, since connections do provide advantages to the managers themselves in terms of longer tenure and higher survival probabilities, our evidence suggests that connections are a collusion device to share rents in the more local banks.

Appendix A See Tables A1 and A2. Table A1 List of variables used in the analysis. Variable name

Description

Source

Manager characteristics Age Education Position at bank Place of Birth Distance

Age of the manager High School Graduate, College Graduate President, CEO, General Manager Province of birth Degree of connection

Telemaco ABI Yearbook ABI Yearbook Telemaco http://www.stata.com/users/brising

Bank characteristics Total Assets City bank Bank type Head Independent bank Listed Headquarter area Branch concentration EBITDA ROE Non-performing loans

Asset values at current prices Dummy for banks headquartered in a provincial capital Dummy for Commercial and Saving banks Dummy for banks head of a bank holding company Dummy for banks not part of any bank holding company Dummy for listed banks Dummies for Northern, Central and Southern Italy Fraction of branches in the province of the bank’s headquarter Earnings before interests, taxes, depreciation, and amortization at current prices Return on Equity Loans in or near default

Bilbank ISTAT Bilbank Bilbank Bilbank Bilbank Bilbank Bank of Italy Bilbank Bilbank Bilbank

Table A2 Estimated probability of turnover (hazard) from Eq. (2). Log(T) ¼log tenure. h is the hazard function. Robust standard errors in parentheses. Variables

h

Log(T)

0.102nnn (0.039)

Log(T) squared

 0.012 (0.020)

Dummy for connected managers times log(T)

 0.062 (0.049)

Dummy for connected managers times log(T) squared

0.011 (0.023)

Dummy for CEOs times log(T)

0.014 (0.048)

Dummy for General Managers times log(T)

 0.112nn (0.054)

Dummy for CEOs times log(T) squared

 0.013 (0.025)

Dummy for General Managers times log(T) squared

0.036 (0.029)

Dummy for connected CEOs

0.050nn (0.025)

Dummy for connected General Managers

0.063n (0.037)

Dummy for connected CEOs times log(T)

0.018 (0.058)

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E. Battistin et al. / European Economic Review 56 (2012) 470–487

Table A2 (continued ) Variables

h

Dummy for connected General Managers times log(T)

0.085 (0.079)

Dummy for connected CEOs times log(T) squared

 0.001 (0.028)

Dummy for connected General Managers times log(T) squared

 0.033 (0.037)

Dummy for CEOs

 0.119nnn (0.019)

Dummy for General Managers

 0.023 (0.021)

Dummy for connected managers

 0.071nnn (0.024)

Dummy for low performance

0.004 (0.007)

Dummy for ROE

0.011 (0.007)

Dummy for NPL/Total Loans

 0.018nn (0.008)

Dummy for bank type

0.127nnn (0.008)

Constant

0.044nn (0.018)

Number of observations

480

R-squared

0.603

p o 0.01. p o 0.05. po 0.1.

nnn nn n

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