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Article Excerpt
THE UNPRECEDENTED MERCER wave observed in the last decade is reshaping the corporate landscape in most countries, in mature and innovative sectors alike. According to Thomson Financial, between 1990 and 2001 there were 54,143 M&As in the major industrial countries, with total value equal to $9,526 billion. A large body of empirical work has investigated the pricing effects of mergers, considering mainly changes in market power and efficiency and the ensuing net variations in average prices induced by the merger (see, e.g., Barton and Sherman 1984, Kim and Singal 1993, Prager and Hannan 1998, Sapienza 2002, Focarelli and Panetta 2003).
However, market power and efficiency are not the only important channels through which M&As can affect the pricing policy of the merging company. In many industries, mergers might change both companies' information sets as well as how they process information. This is likely to be particularly relevant in markets characterized by informational frictions, such as credit and insurance markets, where mergers could modify the ability of, and the incentives for, the merging parties to reduce the informational problems. For example, by acquiring a health insurer, an automobile insurance company might gain information on the health status of its customers, which could be useful in pricing its automobile insurance policies. Even for purely horizontal mergers, the increased volume induced by the merger might justify the adoption of costly improvements in information technology, which enable the consolidated firm to maintain better databases on its customers. On the other hand, mergers could also destroy some knowledge capital of the merging parties, due to corporate cultural differences among the parties, a need to harmonize the way information is processed, and changes in the incentives of the workers to produce and gather information in the wake of the organizational changes arising from the merger.
In this paper we analyze the importance of these informational effects of mergers. We consider a market in which they are likely to be particularly relevant: bank loans, in which borrowers' default risks are an important source of asymmetric information between lenders and borrowers. We identify the informational benefits of mergers by investigating whether mergers improve banks' abilities to screen and assess the unknown default risk of their borrowers. (1) We employ a unique bank-firm matched panel data set from Italy of individual business loan contracts for a nearly complete sample of firms from 1988 to 1998. For each loan contract, we observe the interest rate, the amount borrowed, and the characteristics of the bank, and the firm involved, making it possible to analyze rate changes for different types of borrowers (e.g., according to their default risk) and lenders (e.g., large vs. small banks).
The Italian loan market constitutes a natural laboratory for studying the informational effects of consolidation. First, in the last decade, technological innovation and substantial deregulation prompted an unprecedented merger wave that reduced the number of Italian banks by nearly 25%. Second, the Italian economy is mainly composed of small and unlisted firms, for which the problems posed by asymmetric information are likely to be important, so if mergers did indeed result in informational efficiencies, we are most likely to detect them in this market. Third, Italian companies secure almost all their external financing through credit lines, which are highly homogeneous products and can be meaningfully compared over time and across different banks.
The intuition that underlies our empirical approach is simple: banks with superior screening abilities should have a more precise estimate of a firm's default risk, so that they should charge an interest rate that is more "sensitive" to this risk. Consider a bank with no screening ability: to it, all potential borrowers are identical and should be charged identical interest rates. As the bank improves its screening capacity, it should discriminate among borrowers according to their default risk, charging higher interest rates to riskier borrowers and lower rates to high-quality borrowers. Hence, if mergers lead to informational benefits, one ought to observe a stricter correspondence between the interest rates and default risks of a bank's borrowers after a merger. Therefore, the price impact of these informational benefits might differ considerably across customers. These potential distributional effects of mergers have been overlooked by the empirical literature cited above, which has only analyzed the effect of mergers on average market prices.
One difficulty in implementing our empirical approach is that it requires a measure of a firm's default risk, which is unobserved by banks at the time they extend their loans; however, a crucial feature of our data set is the availability of such a variable, in the form of an independent measure of a firm's default risk (the Z-score of Altman 1968) that, due to accounting rules and data collection requirements, is only made available to banks with a 2-year lag.
We find that after a merger the interest rate curve--the relation between the default probability of each firm and its loan rate--becomes steeper. Thus, while for the low-risk borrowers the loan rates decline, for the riskier borrowers--which before the merger benefited from underpriced loans, due to the informational inefficiencies of their lenders--they actually rise.
We provide evidence that this "increasing slope" finding is larger for lending relationships for which, a priori, the degree of asymmetric information should be higher and, therefore, the scope for merger-related informational gains larger (such as shorter bank-firm relationships, or relationships where the bank supplies a smaller percentage of the borrowing firm's total credit). These findings support our interpretation that M&As improve banks' abilities to screen borrowers. Moreover, we find some support for the hypothesis articulated in Stein (2002) that the "increasing slope" also reflects the fact that consolidated banks price their loans based more on hard information, deemphasizing soft information in the process. We also confirm that the increase in the slope of the interest rate profile does not simply reflect the fact that merged banks are able to better price discriminate due to their increased market power.
Finally, we seek to identify the channels through which the informational benefits from a merger operate. In order to do this, we exploit the fact that Italian firms often borrow from multiple lenders (Detragiache, Garella, and Guiso 2000). We find that the increase in the slope of the interest rate curve is broadly similar both for the companies that before the deal were borrowing from only one of the merging parties and for those that were borrowing from both. This finding suggests that the potential gains from explicit pooling or sharing of firm-specific information--which emerges only when both of the merging banks were lending to the same company before consolidation--is not the relevant channel of informational gains. (2)
We also find little support for the idea that the information benefits arise via a transfer of screening abilities from a more informationally efficient acquiring bank to a less efficient acquired bank. Nevertheless, we uncover an asymmetry in the information improvements between the acquiring and acquired banks: while acquiring banks improve mostly in processing existing information (thus suggesting the importance of managerial improvements in these banks), those taken over become more adept both at using existing information and at gaining new information.
Our results carry important implications for the controversy on the welfare redistributions associated with consolidations. First, we show that mergers may affect different categories of customers in different ways and increase the variance of market prices. This implication, which is likely to hold in other markets as well, implies new challenges for the antitrust authorities because it excludes the possibility of using Paretian criteria to assess the welfare effects of mergers. Second, the simple consideration of average price effects might underestimate the welfare effects of mergers, because information improvements should imply a better allocation of resources. While it is hard to quantify such allocative effects, they are likely to be nontrivial. (3)
The rest of the paper is organized in the following way. In the next section, we analyze the related literature and discuss our empirical approach. In Section 2, we introduce the data. In Section 3, we present and discuss our main empirical findings on the presence and magnitude of informational effects deriving from mergers. In Sections 4 and 5, we consider and test various explanations for these informational effects. We investigate the sources of informational benefits in Section 6. Section 7 concludes.
1. MERGERS, PRICES, AND INFORMATION
A priori the effect of consolidation on market prices is ambiguous. On the one hand, mergers can increase efficiency (through economies of scale and scope or an improvement in managerial x-efficiency), which tends to decrease prices. On the other, if the merging companies have significant market overlap, their market power might increase, leading to adverse price changes for consumers. Several early papers found that mergers increase market power, harming consumers (Kim and Singal 1993, Prager and Hannan 1998). Recent studies relative to the banking sector, however, have found that after taking into consideration important features of the transaction, such as multiproduct firms (Kahn, Pennacchi, and Sopranzetti 1999), the degree of increase in market power (Sapienza 2002), the length of the postmerger period at which the price effects are measured (Focarelli and Panetta 2003) or conceptual problems in measuring service output (Wang 2003), then mergers might actually decrease prices for consumers.
One limitation of these studies is that they only consider the market power and efficiency effects of consolidation, ignoring other factors that might affect the pricing policy of the merged companies. In this paper, we focus on one such factor: information. We consider the market for bank loans. Figure 1, containing plots of the raw data, motivates our empirical analysis. In the upper (lower) graph, we plot average (median) interest rates charged by banks to firms against SCORE, a measure of firms' default risk (with larger values of SCORE corresponding to a higher risk). (4) The two lines in each graph correspond to merged and unmerged banks. Clearly, the lines for the merged banks exhibit a steeper slope; furthermore, the lending rates of the merged banks are lower for the less risky firms (those with a low SCORE measure), but actually higher for riskier firms.
In this paper, we interpret this steeper tilt of the interest-rate/risk relationship after mergers as evidence of informational improvements (improved ability to screen borrowers according to their unknown default risk) stemming from the merger. To see this, consider a lending relationship between bank i and firm j. Firm j's default probability, [p.sub.j], is unknown to the bank and represents a source of asymmetric information between firm j and bank i. Assuming zero expected profits, the interest rate that bank i charges to firm j, [r.sub.ij], satisfies (1 - E{[p.sub.j] | [[OMEGA].sub.i]}) * (1 + [r.sub.ij]) = 1, where [[OMEGA].sub.i] denotes bank i's information about firm j. For default probabilities [p.sub.j] close to zero, this relationship between interest rates [r.sub.ij] and expected default probabilities E{[p.sub.j] | [[OMEGA].sub.i]} is approximately [r.sub.ij] [approximately equal to] E{[p.sub.j] | [[OMEGA].sub.i]}. (5)
Across firms, the default probabilities [p.sub.j] are randomly drawn from a beta distribution with parameters (a, b), so that the average probability of failure in the population is [bar.p] = a/a+b. The information set [[OMEGA].sub.i] consists of [n.sub.i] binary signals s [member of] {h, l}, with Pr{s = l} = [p.sub.j]. Here, [n.sub.i] measures the screening ability of the bank, with larger values of [n.sub.i] indicating that bank i is better informed. Using Bayes rule, the posterior mean (and hence the interest rate) after [n.sub.i] signals and y "l" signals is
[r.sub.ij] [approximately equal to] E{p|[n.sub.i,y]} a+y/a+b+[n.sub.i]. (1)
For a given level of informedness [n.sub.i], the expected number of "l" signals out of [n.sub.i] signals is E{y|[n.sub.i], [p.sub.j]} = [n.sub.i][p.sub.j] so that, on average, bank i charges firm j an interest rate of
E{[r.sub.ij]|[n.sub.i], [p.sub.j]} = a+[p.sub.j][n.sub.i]/a+b+[n.sub.i] = [1 - [alpha]([n.sub.i])][bar.p] + [alpha]([n.sub.i])[p.sub.j], (2)
[FIGURE 1 OMITTED]
where [alpha](n) [equivalent to] a/a+b+n. Expression (2) illustrates how, as more information becomes available, the posterior mean shifts away from the prior mean [bar.p] toward the actual default probability [p.sub.j]. In fact, [alpha](0) = 0, [lim.sub.n[right arrow][infinity]][alpha](n) = 1, and [partial derivative][alpha]/[partial derivative]n = a+b/[(a+b+n).sup.2] > 0. As the screening capability increases, the interest-rate/risk curve shifts down and steepens in slope:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (3)
This equation offers an empirical strategy to detect informational improvements in banks' screening abilities, provided that we have a measure of the actual default probability [p.sub.j] and of banks' screening ability [n.sub.i]. If mergers indeed lead to informational improvements, then a merger event would proxy for increases in screening ability [n.sub.i], so that equation (3) would imply relationships between merger activity, average interest rates, and default probability resembling the graphs in Figure 1. This is the strategy we will follow in our empirical specification, where we will run regressions of the form
[r.sub.ij] = [[beta].sub.0] + [[beta].sub.1] * [MERGE.sub.i] + [p.sub.j]( [[beta].sub.2] + [[beta].sub.3] * [MERGE.sub.i]) + [[epsilon].sub.ij], (4)
where [MERGE.sub.i] is a dummy variable set equal to one if bank i has recently merged and [[epsilon].sub.ij] [equivalent to] E{[r.sub.ij]} - [r.sub.ij] is an orthogonal error. Within the context of this model, the hypothesis that mergers improve information can be modeled by assuming that a merged bank obtains more signals, that is, has a higher [n.sub.i]. (6) Hence, in this case, we expect [[beta].sub.1] in equation (4), in line with the graphs in Figure 1: merged banks should put less weight on the common prior and price more in accordance with the firm's true probability of default. (7)
Needless to say, there could be alternatives to the information-based interpretation of the increased steepness of the interest-rate/risk relationship documented in Figure 1. (8) Hence, it is an empirical question to distinguish our informational interpretation from alternative noninformational explanations, and a substantial portion of this paper focuses on these issues. (9)
2. DATA
We use four main sources of data: (i) interest rate data and data on outstanding loans from the Italian Centrale dei Rischi, or Central Credit Register, (ii) the firm-level balance sheet data from the Centrale dei Bilanci database, (iii) banks' balance-sheet and income-statement data from the Banking Supervision Register at the Bank of Italy, and (iv) data on the mergers and acquisitions from the Census of Banks. By combining these data, we obtain a matched panel data set of borrowers and lenders extending over an 11-year period. We begin with a brief description of the data sources. Specific details regarding the construction of the sample and further descriptive analysis are contained in the Appendix.
The Central Credit Register (hereafter CR) is a database that contains detailed information on all individual bank loans extended by Italian banks. Banks must report data at the individual borrower level on the amount granted and effectively utilized for all loans exceeding a given threshold, (10) with a breakdown by type of the loan (credit lines, financial and commercial paper, collateralized loans, medium and long-term loans and personal guarantees). In addition, a subgroup of around 90 banks (accounting for more than 80% of total bank lending) have agreed to file detailed information on the interest rates they charge to individual borrowers on each type of loan. Summary statistics for these banks are reported in Table 1.
We restrict our attention to short-term credit lines, which have ideal features for our analysis. First, the bank can change the interest rate at any time, while the borrower can close the credit line without notice. This means that (i) a change in the merging banks' ability to process firm-specific information can have almost immediate repercussions on the pricing of the loans, and (ii) differences between the interest rates on loans are not influenced by differences in the maturity of the loan. Second, the loan contracts included in the CR are homogeneous products (e.g., they are not collateralized), so that they can be meaningfully compared across banks and...
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