3.1 Dependent variables: organizational features
Organizational data are drawn from a survey conducted by the Bank of Italy on a non- random sample of about 300 banks, depicting lending practices at the end of 2006 and representing around 80 per cent of the Italian banking system’s total loans to firms. Intermediaries specialized in consumer credit, leasing and factoring financing were excluded, as well as all Italian branches of foreign banks. The survey covers almost the whole spectrum of banks above a minimum threshold in terms of size and organizational complexity, including virtually all large, medium and small banks, and excluding very small intermediaries. The accuracy of the data collected is reasonably high.6
In order to compare two homogenous groups of intermediaries, we have excluded from the sample those banks that underwent a merger between 2000 and 2005. Therefore, the econometric analysis compares banks that have experienced an acquisition, either as bidder or target, and banks that did not take part in any deal during the same period. We have also excluded outlier observations and banks that only finance long-term investment projects.
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6 Preliminary interviews with bankers helped in organizing the questions properly, avoiding potential ambivalences; ex post interviews helped to fill missing information and to fix erratic responses. For more details, see Albareto et al. (2008).
Bank acquisitions and decentralization choices 81
The survey focuses on the organizational design of business lending and, secondly, on the implementation of rating systems. The information about bank organizational structure is very analytical. Banks were asked to give information about: the number of hierarchical layers involved in the decision to grant loans to SMEs, the amount of finance up to which each hierarchical level may lend autonomously (from the branch loan officer up to the board of directors), the type of information required (soft versus hard information), the frequency of loan officer turnover, the importance of credit scoring and internal rating methodologies in the assessment of credit.
Our delegation indexes are equal to the amount of loans the local manager and the CEO are respectively allowed to grant to SMEs (logarithms).7 The first indicator provides information about
the power of the “periphery” to grant credit autonomously, the latter informs about the authority of the top management. We have chosen the General Manager to embody the CEO because this hierarchical level is always present. The branch loan officer and the top management can respectively represent the “agent” and the “principal” indicated in the theoretical literature (Stein, 2002).8
The second step involved drawing information from the survey sample on loan officers’ turnover to assess whether the length of tenure managing a branch (in terms of the number of months) is affected by involvement in M&As. On the one hand, establishing close and long-lasting relations with opaque borrowers should be directly linked to the stability of local managers. On the other hand, the longer the time a manager works in the same branch, the higher the likelihood of moral hazard behaviour. The loan officer’s turnover is intrinsically related to relationship lending, but it is also a device for the top management to reduce monitoring costs, which increase when power must be delegated. This is the case, for instance, in financing opaque SMEs, for which information is not easily transferable upwards.
Descriptive statistics on the sample employed for the baseline estimates are presented in Table 1, with variable definitions. On average, a branch manager can grant loans up to €136,000 autonomously; the median is considerably lower (€70,000), since the upper bound is lower for small and mutual banks. The power delegated to the CEO is about €2.4 million; again the median value is significantly lower (€0.5 million). Local officers stay in charge for an average of 46 months; the distribution is relatively narrow: the first quartile is 35 months, the third is 60.
Table 2 reports descriptive statistics on organizational features across the banks involved (either as bidder or target) or not involved in acquisitions. By and large, acquiring banks are large intermediaries; their delegation indexes are higher than the sample mean. Their local officers show a faster turnover, most likely because of the extent of their branch network. Target banks in acquisition deals are larger than sample mean, too. Their officers have considerable delegated power (albeit, in median, less than the power of officers in bidder banks) and a fast turnover. Banks not involved in the consolidation process are largely small and local: this contributes to explain the lower degree of power delegated to their local officers and their slower turnover, too.
3.2 Dummies accounting for bank acquisitions
In order to capture systematic differences in lending strategy following an acquisition, we build a dummy for each kind of dealing partner (bidder or target banks); it is equal to 1 for each ——————
7 The threshold under which a borrower can be classified as an “SME” varies from bank to bank; the most frequent classification term is the total of net sales.
8 In some unreported estimates, we have also experimented an index of internal decisional decentralization, calculated for each bank i, as the ratio between the maximum amount of finance up to which a branch loan officer may lend autonomously and the CEO can lend (“internal decentralization”).
bank involved in a deal, and zero otherwise. Information on acquisitions is drawn from the Bank of Italy’s Register of Banking Groups.9
We try to overcome the difficulties of trying to capture a dynamic phenomenon in a cross-section estimate by building two sets of dummies: these distinguish recent acquisitions (between 2003 and 2005, new) from older ones (between 2000 and 2002, old). Disentangling operations over time allows us to verify whether there are temporary effects or not: we expect different restructuring results depending on the time these deals took place.
Another interesting feature to investigate is the different effect of deals among geographically contiguous banks from those involving distant intermediaries. With this aim, we interact the dummy identifying a target bank with dummy variables measuring the geographical reach between dealing partners. The first (Overlap) refers to the overlapping in branch networks of the target and its bidder: it is equal to 1 if more than 50 per cent of the branches of the target is located in provinces in which the bidder has also branches,10 and zero otherwise; at the same time
NoOverlap is its orthogonal dummy in order to split target banks into two homogenous groups. A
second set of dummies, Inmkt and its orthogonal Outmkt, refers to the legal headquarters of dealing partners: dividing Italy into two areas (North and Centre; South), the dummy Inmkt is equal to 1 if the target is headquartered in the same area of the bidder, and zero otherwise. It is the opposite for the dummy Outmkt.
Finally, to control if the effects of an acquisition are different for large and small banks, we interact the dummy “target bank” with a dummy Small (Large) bank, which is equal to 1 if at the end of 2006 the total assets of the bank were less (more) than €1.3 billion, and zero otherwise.
3.3 Other controls
In our econometric exercise, we control for other bank-level features (average loan, portfolio risk, profitability, banks belonging to banking groups, geographical location of the headquarters, presence in industrial districts), potentially able to affect bank organization.
The log of total assets is our size variable; as already seen, banks involved in acquisitions (either as bidder or target) are larger than others (Table 2). Our specialization index in small business lending is measured as the average size of loans to firms and households; for this feature, there does not seem to be a strong variability between the different groups of banks. The ratio between profits before taxes and total assets (ROA) informs us about bank profitability: as expected, the profitability of acquiring banks is higher than both target and independent banks. The share between non-performing loans and total loans is our proxy for the bank portfolio credit risk; also in this respect, bidders perform better than targets, and rivals are in the middle. Data used to build previous bank-level characteristics are drawn from the Bank of Italy Supervisory Reports.
Our control for local environment is the “district index”, constructed for each bank as the share of branches located in municipalities classified as industrial districts according to Istat taxonomy. Our hypothesis is that banks with wide activity in district areas will be encouraged to set up close relationships with local SMEs; this may enhance the importance of soft information and promote decentralized organizations. The index is on average 36 per cent, and it is higher for stand- alone banks.
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9 Information on merged banks, instead, is drawn from the Census of Banks (SIOTEC) and it is employed to cut them out of the sample.
10 The distribution of this ratio is polarized between very small and very large values; the threshold of 50 per cent seems to represent a satisfactory cut-off to split the sample into the two groups.
Bank acquisitions and decentralization choices 83
A control for the bank’s geographical location is provided by the dummy South, that is equal to 1 if the intermediary’s headquarters are located in the South of Italy. With respect to governance, the dummy Group is equal to 1 if, at the beginning and at the end of the sample period, the bank was part of a bank group and it has not been acquired in the meantime.
Other variables contribute to define bank organizational design in lending to SMEs. First of all, organizational complexity depends on the ability to process information. For this purpose, we distinguish between banks that have adopted a credit scoring system and intermediaries which have not.11 Moreover, using the pecking order on sources of information about borrowers (financial
statements, credit registers, qualitative data, etc.), we operate a distinction between intermediaries giving great importance to soft information and banks for which this kind of information is less important.12