Abstract
Using a large panel of Italian firms, spanning the years from 1995 to 2003, this study investigates the relationship between bank debt and non-financial SMEs’ performance, evaluating whether and to what extent this link is affected by the degree of competition characterising the local credit market where firms operate. Controlling for inertia, unobserved heterogeneity and the endogeneity of some performance determinants, we find that the (negative) impact of bank debt on firms’ performance is weaker for firms running in more competitive banking markets. We interpret this result as evidence that a more intense banking competition may lead to better credit conditions for small and medium-sized firms.
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Notes
For a recent empirical investigation on the relationship between leverage and corporate performance, see Weill (2008).
For an overview of theoretical models assuming that banks are identical and competitive, and generating equilibrium (type II) credit rationing, see Parker (2002). After reviewing the empirical evidence on this phenomenon, the author is sceptical of the existence of credit rationing and calls for further investigation of this issue. Also Cressy (2004, p. 26) reckons that: "despite the mountain of theoretical literature suggesting the abstract possibility of credit constraint, it does not appear in general to be an important empirical phenomenon”.
On a theoretical ground, Boot and Thakor (2000) reach a conclusion in contrast to Petersen and Rajan (1995), arguing that competition can increase investments in relationship lending—as these latter represent a way for banks to offer a differentiated product that is less subject to price competition. Empirical support to this result is provided by Elsas (2005).
If a bank can costly use a screening technology which allows to discriminate firms according to their quality, rival banks—though cannot directly observe the outcome of the screening test—can extract information about the screened firms by observing whether the bank extends or denies the loan.
Financing obstacles are measured by a variable having a natural order (from 1 to 4), drawn from a survey question.
The following figures, drawn from the Bank of Italy annual reports (1991−2007), provide an idea of the main transformations that have occurred so far. From 1990 to 2006, the number of banks operating in Italy has dropped from 1,064 to 793, whereas bank branches have grown from 17,721 to 32,337. In the same period, 444 mergers and 205 acquisitions—among domestic credit institutions—were completed (excluding operations that involved the same bank more than once).
Also Angelini and Cetorelli (2003) show that, after the deregulation process, the Italian banking industry has experienced a substantial increase in the degree of competition.
It is worth noticing that local banks have been traditionally prevalent in the Italian banking system (they numbered more than 800 in 1990 and almost 450 at the end of 2006), with a quite homogeneous diffusion in the country. Most of them are cooperative banks (BCC) and Popolari banks. For a detailed discussion on this point, see De Bonis et al. (1994) and Masciandaro (1996).
A large amount of studies on this topic has reached controversial conclusions both on the theoretical and empirical field. According to the Structure-Conduct-Performance paradigm, structural changes leading to concentration in an industrial sector may facilitate collusive behaviour among firms and, therefore, a reduction in the degree of competition. On the other hand, the Efficient-Structure Hypothesis claims that a greater concentration emerges as a consequence of a more vigorous competition in the market, as the most efficient firms might increase their market shares at the expense of their less efficient competitors. For reviews on empirical studies, concerning the banking sector, see Gilbert and Zaretsky (2003) and Berger et al. (2004).
A related analysis concerns bank lending rules (for a discussion see Cressy 2004).
More generally, see Cressy (2004, p. 22) for a discussion on small businesses’ problems with the use of outside equity as alternative source of finance to debt.
Vesala (1995) proves that the same result holds for monopolistic competition without the threat of entry.
For an analysis concerning the problems in measuring market power for dynamic oligopoly models, see Corts (1999).
Indeed, as Panzar and Rosse (1987) clarify, this hypothesis is necessary for the cases of perfect competition and monopolistic competition, while it does not constitute a requirement in the case of monopoly.
For the sake of completeness, other hypotheses on which the H-statistic is based have to be acknowledged. Indeed, it assumes perfect competition in input markets, which may be a restrictive hypothesis in the case of deposits. In fact, there are reasons to doubt that the Italian market for deposits is perfectly competitive (Italian Competition Authority 2007; Cerasi 2007), although—as already mentioned—it is widely documented that banking competition in Italy has heavily increased, as a consequence of the transformations undergone by the sector in the recent past. When applied to the banking sector, the Panzar and Rosse methodology relies also on other restrictive hypotheses—such as the assumption of homogeneity in the cost structure across banks and the hypothesis that the latter are profit maximising single-product firms producing intermediation services (De Bandt and Davis 2000, and Shaffer 2004). Furthermore, the maturity structure of banks’ asset portfolios might imply a downward bias in the estimated elasticities, as fixed rate contracts with longer maturities prevent banks from direct price adjustments. This latter could be a relevant drawback when comparing banking systems of different countries.
The specification of this model is close to that used by De Bandt and Davis (2000).
These latter variables are potentially correlated with past values of the idiosyncratic error, but are not correlated to its present and future values. A strictly exogenous variable is uncorrelated with past, present and future values of the error term. In Eq. 4, if it appears plausible that the current value of a regressor (such as tangible assets) is influenced by past shocks to profitability, that variable is treated as predetermined. When a variable (such as firm’s market power) is likely to be determined simultaneously along with the profitability, it is treated as endogenous. As a result, we treat as exogenous only variables controlling for local market characteristics (population, real per capita growth and bad loans on total loans) and a few variables controlling for firm characteristics (total assets, age and the lag of the expense in research and development). See Sect. 5.1 for a robustness check concerning this choice.
This assumption is verified by two tests for first and second order serial correlation, in the first difference residuals. Indeed, if the errors in level are characterised by lack of serial correlation, the error in differences is expected to display first-order autocorrelation and to be uncorrelated to all other lags. Moreover, it is appropriate to test the overidentifying restrictions through a Sargan test of orthogonality between the extra instruments and the residuals.
In order to account for the presence of potential outliers, we drop, for each variable involved in the econometric analysis, the observations lying in the first and last half percentile of the distribution.
The principal component method allows combining the information contained in our three (highly correlated) measures of profitability. The new variable is the linear combination of the original set of variables, where the weights are chosen so as to maximise the variance explained by the composite index. Prior to the analysis, ROA, ROE and ROS have been standardised in order to avoid that the variable with the highest variance dominates the resulting index.
Although not individually significant, the interaction term is jointly significant with the bank debt variable when implementing an F-test. Such a discrepancy between individual and joint significance is usually interpreted as a symptom of multicollinearity (see Wooldridge 2003 and Brambor et al. 2006) induced by the inclusion of an interaction term. As Brambor et al. (2006) highlight, “even if there really is high multicollinearity and this leads to large standard errors on the model parameters, it is important to remember that these standard errors are never in any sense ‘too’ large—they are always the ‘correct’ standard errors. High multicollinearity simply means that there is not enough information in the data to estimate the model parameters accurately and the standard errors rightfully reflect this”.
It is worth recalling that values of the H-statistic lower than zero and greater than one are equivalent to zero and one, respectively, from an economic perspective.
See Sect. 3.2 for the marginal effect and standard error formulas.
The HHI has been computed as: \( {\hbox {HHI}}_{p} = \sum {\left( {ms_{ip} } \right)}^{2}, \) where \( ms_{ip} = \left( {D_{ip} /D_{p} } \right) \) is the deposit market share for each branch office of bank i in the province p, and \( D_{p} = \sum\nolimits_{i} {D_{ip} }.\) Deposits (D) at the provincial level have been obtained by using the criterion illustrated in Sect. 3.1. It is worth noting that, as Petersen and Rajan (1995, p. 418) argue, the HHI calculated on deposits represents a good proxy for competition in the loan markets if the empirical investigation involves firms that largely borrow from local markets, that is if credit markets are local for the firms under consideration. As we claim in Sects. 1 and 3, this is the case for our sample units.
Another approach to correct for generated regressors is the bootstrap method (see, for instance, Agostino-Trivieri 2008; Benfratello et al. 2006). Here, we take the Jackknife approach because the bootstrap alternative was too time-consuming. Moreover, as Fan and Wang (1996) argue, “the disparity between Jackknife and bootstrap results is primarily affected by the size of a sample to which the two techniques are applied. When the sample is large, the difference from the two approaches is small, or even negligible”.
One reason is that they can be manipulated according to the managers’ (or the owner-managers’) interests.
It is worth mentioning that, when our dependent variable is a proxy of firm size (as for net sales and employees, both divided by total assets), the estimating equations omit the measures of size (log of total assets). In the other two cases, columns 3 and 4 of Table 6, the dependent variable is in log form (log of net sales, and log of the number of employees). Since a lagged dependent variable is always included in the regressors set, we can interpret the regressors coefficients as effects in terms of (sales and employee) growth (see, for instance, Oliveira and Fortunato 2006).
These latter checks are here omitted and available upon request.
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We are very grateful to an anonymous referee for his/her valuable comments and suggestions on a previous version of the paper. We also thanks Francesca Gagliardi.
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Agostino, M., Trivieri, F. Is banking competition beneficial to SMEs? An empirical study based on Italian data. Small Bus Econ 35, 335–355 (2010). https://doi.org/10.1007/s11187-008-9154-6
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DOI: https://doi.org/10.1007/s11187-008-9154-6