Abstract
This work investigates whether local differences in banking competition impact on the amount of bank debt used by Italian small and medium sized manufacturing firms. Sample selection and Double Hurdle models are adopted as the process, which results in the choice of bank financing may differ from that determining its amount. Our main finding is that more competitive banking markets seem to be associated with relatively higher usage of bank debt by less transparent firms. On the other hand, a higher banking competition seems to have no effect on the probability of receiving bank loans.
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Notes
In the following we focus on the research which has directly investigated into the economic effects of banking competition, placing emphasis on the contributions dealing with the relationship between banking competition and credit availability to firms. We do not take explicitly into account the numerous studies that have analyzed the effects on banking competition of the consolidation wave. For reviews of such contributions see—among others—Berger et al. (1999), Carletti et al. (2002), Northcott (2004), Degryse and Ongena (2005).
Petersen and Rajan (1995, p. 418) argue that the Herfindahl index for 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 Sections 1 and 3, this is the case for our sample units.
The specification of this model is close to that used by De Bandt and Davis (2000). On the formal derivation of the H statistic see Panzar and Rosse (1987) and Vesala (1995), whereas for an extensive literature review of the studies that—starting with Shaffer (1981a, b, 1982)—apply this statistic to the banking industry, see Koutsomanoli-Fillipaki and Staikouras (2004).
Fiscal incentives have been warranted by a broad range of laws aiming to favour SME’s investments, investments in depressed geographical areas, investments for technological innovations and the like.
When, prompted by the suggestion of a referee, we considered firm size (proxied by the log of total assets) as an alternative measure of opaqueness, the total assets histogram displays a high concentration of our sample firms around the lower values of the distribution, and the marginal effect of the LBC measures (HHI index and H statistic) is not significant across the values of (the log of) total assets. This confirms our conjecture that using the size as a measure of transparency in our sample might be uninformative, thus corroborating our choice to combine size with other measures of opaqueness by using a Principal Component Analysis.
We consider as outliers the observations for which any of the variables lies beyond 10 standard deviations away from the mean (on the use of this criterion see—for instance—Servèn 2003). However, as a robustness proof we will use a different methodology involving the distribution percentiles (see Subsection 5.1.1) Indeed, as one referee underlined, the use of nonlinear interactions terms may be very sensitive to extreme values.
Results do not change when we take into account other potential control variables such as the riskness of the local banking market (proxied by the ratio of bad loans on total loans, computed at the province level), the branch density (number of branches to total province population, multiplied by 10000), and the local employees. Similarly, results remain substantially unaltered when we replace some controls with other (for instance, we replace the log of total assets with the log of sales), and re-run all regressions by using the lagged values of the measures of banking competition and opaqueness, so as to mitigate any potential simultaneity bias. These results are not reported in order to avoid cluttering, but they are available from the authors upon request.
The Likelihood Ratio test for joint significance of the H statistic and its interaction term is significant at 10% level.
The graphs obtained when using the Double Hurdle model estimates are not reported to economize on space, and also as they lead to the same conclusions.
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”.
Following Carbò Valverde et al. (2003), this index is defined as the mark-up of asset price (PTA) over average cost (ACTA) relative to price or (PTA—ACTA)/PTA , and it is computed by using the methodology described in section 3.1. PTA is given by the ratio of total gross revenue (gross interest revenue plus income from banking services) to total assets, while ACTA is the ratio of total operating costs (inclusive of labor costs) and interest costs to total assets. Values of the Lerner index greater than one or lesser than zero are assumed to be equal to one and zero respectively. Again, the complement to one of this measure is considered, in order to make the Lerner index homogeneous to the H statistic.
The bootstrap method, which “is essentially a Monte Carlo simulation where the observed sample is treated as the population” (Wooldridge 2002 ), has been used to address the generated regressors issue by several authors, such as Knight (2004), Massa and Simonov (2005), Benfratello et al. (2006), Ramos and Schluter (2006).
To save space, and as they are equivalent, we do not report the figures obtained when iterating 500 times. It is worth noticing that the bootstrap method is also applied when considering the H statistic as the LBC measure and, again, the results do not change substantially.
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Acknowledgements
We are grateful to Francesca Gagliardi, Damiano Bruno Silipo, Giovanni Verga and an anonymous referee for their helpful comments and suggestions on a previous version of the paper. We also thank the participants at the workshop ‘‘Concorrenza e informazione nel sistema bancario italiano’’, which was held at the Department of Economics of the University of Calabria on June 2005. Of course, all errors and omissions are ours.
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Agostino, M., Trivieri, F. Banking Competition and SMEs Bank Financing. Evidence from the Italian Provinces. J Ind Compet Trade 8, 33–53 (2008). https://doi.org/10.1007/s10842-007-0005-y
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DOI: https://doi.org/10.1007/s10842-007-0005-y