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Lasting lending relationships and technical efficiency. Evidence on European SMEs

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Abstract

Linking the theoretical predictions of the research on lending relationships with those of the literature on managerial incentives, we investigate whether the duration of credit relationships impacts on SMEs’ technical efficiency. Our hypothesis is that the balance between costs and benefits of enduring banking relationships might have heterogeneous effects on managers’ incentives depending on the level of firms’ indebtedness. Using a large sample of European SMEs, observed in the period 2001–2008, and adopting both parametric and non-parametric measures of efficiency, we find that the positive impact of longer lending relationships on efficiency decreases as indebtedness increases, suggesting that moral hazard problems may endanger firms’ technical efficiency.

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Notes

  1. Focused initially on the issue of the separation between ownership and control in large companies, this theory has found important applications also in the SMEs’ literature (e.g.: Ang 1992; McMahon 2004). In this context, the primary agency relationship is not between owners and managers, as these roles often overlap, but “between insiders and outside suppliers of funds” (McMahon 2004, p.123). For this reason, in the following, when using the term managers we mean owner-managers.

  2. Nonetheless, the multiple borrowing practice does not preclude Italian firms from having strong ties with an individual bank, especially in the case of SMEs (e.g.: Carmignani and Omiccioli 2007).

  3. Vice versa, efficiency may also be defined as the ability to minimise the amount of inputs required to produce a given output level. An output orientation is commonly adopted when it is fair to assume that firms seek to maximize output for given input combinations (as in the manufacturing case; see for instance Milana et al. 2013). By contrast, when producers have a statutory obligation to meet demand, and they also have to guarantee certain quality levels, it is proper to assume that firms attempt to minimise input costs for given output levels.

  4. Schmidt and Wan (2002) recall that the two-step SFA approach is biased if the efficiency determinants and the inputs are correlated. Furthermore, the assumption that the inefficiency term is independently and identically distributed in the first step is at odds with the second step, where the efficiency terms are assumed to be normally distributed and dependent on some explanatory variables.

  5. Banker and Natarajan (2008) show that the OLS estimator in the second-stage regression is statistically consistent under some circumstances, providing theoretical justification for its use. Yet, Simar and Wilson (2011) highlight that this consistency depends crucially on quite restrictive assumptions on the production process, that should not be expected to hold in general (on this issue, see also Badin et al. 2014).

  6. To corroborate our expectation, we run a nonparametric output-based test of returns to scale (Simar and Wilson 2002). In each sector/year, the null hypothesis that the technology is globally CRS (versus VRS) is always rejected, with rare exceptions.

  7. The output is approximated by the sales, deflated by producer prices. Analogously, capital and raw materials volumes are approximated by their respective costs, deflated by inputs prices. Finally, labour is measured by the number of employees. In order to interpret the deflated values as real aggregates, since we lack data on inputs and outputs prices at the firm level, as described in the data section, we employ deflators that are as close as possible to the prices of the group of goods we employ.

  8. Different stochastic frontier models have been proposed for panel data (we refer to Greene, 2008, and Kumbhakar et al. 2014, for some reviews). The model most commonly adopted is Battese and Coelli (1995), which allows for a time-varying inefficiency term. However, it does not capture firm-specific latent heterogeneity, thus firm effects are mixed with inefficiency. The Greene (2005) “true” fixed effects (TFE) or “true” random effects (TRE) models have the merit of disentangling these components. In the TFE model, the producer-specific effects can be correlated with the regressors. However, we discard this model, as it is plagued by an incidental parameters problem when the time span of the panel is limited compared to the number of units, as it is the case in our analysis. Indeed, Belotti et al. (2013) recall that the maximum-likelihood dummy variable (MLDV) approach does not appear appropriate when the length of the panel is T ≤ 10.

  9. Within the TRE model, by parameterizing the variance, one not only allows the inefficiency term uit to be heteroskedastic, but also can investigate the determinants of the average inefficiency. Indeed, the mean of the inefficiency term ui is in direct proportion to its standard deviation, for all commonly adopted distributions of the inefficiency term. Hence, a factor influencing the variance of uit is bound to influence its mean in the same direction.

  10. To preserve the anonymity of the firms surveyed, the EFIGE dataset provides information on industrial sectors in the form of a randomized identifier ranking from 1 to 11.

  11. The preceding question is “What % of your firm’s total bank debt is held at your main bank?”

  12. Incidentally, the EFIGE survey does not provide the identity of a firm’s main bank, and information concerning other lending relationships’ characteristics—such as the percentage of the firm’s total bank debt held by the main bank, and the number of lending banks—is available for 2009 only.

  13. It should also be recalled that the EFIGE dataset neglects firms with less than ten employees, thus implying that our results may not be generalised to the smallest of firms. Furthermore, accounting information refers to firms that are surveyed in 2010, thus defaulted entities are excluded. Hence, our findings are conditional on survival (as in other works based on the same source of data, such as Barba Navaretti et al. 2014; Agostino and Trivieri 2018).

  14. For instance, if the quantile on which the firm is efficient (in the output orientation) is 0.95, this means that there are 5% (1−0.95 = 0.05) of the firms in the comparison set (firms employing at most the same level of inputs) which outperform the considered firm by producing more output. Thus, order-α partial frontiers allow for super-efficient units, i.e. in the output-oriented (input-oriented) case scores may fall short of (exceed) the value of 1.

  15. Following Simar (2003), if we considered as potential outliers all DMUs with super-efficiency higher than 10%, we would have eliminated about 15% of observations.

  16. The choice of α = 99% has been made after applying the following procedure: for each sector in each year, we run series of order-α efficiency analyses using increasing values for α. Subsequently, we plotted the share of super-efficient firms as a function of increasing values of α. In the large majority of sectors/years, we noticed a smooth decrease of the share, which is considered as a symptom of absence of outliers. We also considered three rules for detecting points of discontinuity (two based on either rough or smoothed series of differences in differences, the other based on the BIC detection rule). Yet, the share of outliers based on the most conservative of these rules was too frequently higher than any reasonable upper bound.

  17. The marginal effect of DURAT, and the relative standard error are computed conditional on the level of LEV, accounting for the non-linear nature of the model. We omit the others graphs for the sake of conciseness, making them available upon request.

  18. Given monitoring costs and regulatory constraints, banks may prefer to minimise the counterparty risk by diversifying their exposures with a higher number of firms (Carletti, 2004). Also, banks may tend to act as free riders: believing that other banks will bear the cost of monitoring the firm, if information is a public good no one has incentive to do costly monitoring activities. On the other hand, firms may be inclined to establish multiple relations with banks to hide their effective financial situation avoiding a careful monitoring by banks (Foglia et al.1998).

  19. We do not add the percentage of graduate employees, proxy of human capital, as defined on a limited number of observations (even after imputing).

  20. The Z-score is the sum of return on assets plus the capital asset ratio divided by the standard deviation of return on assets, the latter being computed over 3-year rolling time windows.

  21. We cannot emphasise the results of this test for two reasons: first, we do not consider the other determinants included in the efficiency equation. Moreover, we encounter computational problems in several sectors/years, as the splitting above described reduces the number of observations available. Therefore, we proceed applying the test to each sector, separately for each country, pooling observations over the years.

  22. For the sake of conciseness, we omit the estimates concerning the frontier. Incidentally, in the benchmark model, the estimated output elasticities at the mean of the inputs are 0.02 for capital, 0.26 for labour and 0.58 for raw materials.

  23. Karakaplan and Kutlu (2017) suggest a one-step maximum likelihood-based methodology that allows to account for endogenous variables both in the frontier and the inefficiency model. Further, it provides a test of endogeneity, relying “on ideas similar to the standard Durbin-Wu-Hausman test for endogeneity” (Karakaplan and Kutlu 2017, p. 6).

  24. This result is based on instrumental variables defined in 1936 at regional level: the number of branches per million inhabitants (p.m.i); the number of saving banks (p.m.i).; the number of mutual cooperative banks (p.m.i).; the share of branches owned by cooperative Popolari banks, and the share of branches owned by large banks.

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Acknowledgements

We are very grateful to three anonymous reviewers, and the Editor for their precious comments and suggestions. We also wish to thank the participants to the RSA-SIE 2017 conference (Università della Calabria, Rende), where an earlier version of the paper was presented. Any remaining errors are solely our responsibility.

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  1. Department of Economics, Statistics and Finance “Giovanni Anania”, University of Calabria, Rende, Italy

    Mariarosaria Agostino, Sabrina Ruberto & Francesco Trivieri

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  1. Mariarosaria Agostino
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Correspondence to Francesco Trivieri.

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Agostino, M., Ruberto, S. & Trivieri, F. Lasting lending relationships and technical efficiency. Evidence on European SMEs. J Prod Anal 50, 25–40 (2018). https://doi.org/10.1007/s11123-018-0532-z

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