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Quantity and quality of information and SME financial structure

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Abstract

We test whether the amount and/or quality of financial statement information affects the financial structure of small and medium-sized enterprises (SMEs). Belgian SMEs are used, because there are important differences in disclosure and audit requirements among them. Consistent with the traditional view that asymmetric or incomplete information restricts access to external funds, our results indicate that both the amount and quality of financial statement information are positively related to SME leverage. In addition, we find that leverage is positively related to asset structure, growth (prospects) and median industry leverage, and negatively related to firm age and profitability.

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Notes

  1. Note that a negative relationship is also consistent with firms signalling future results by using more or less debt (Ross 1977).

  2. As a sensitivity check, we also considered truncation at 1% and 99%. In addition, we considered an alternative cutoff. More specifically, we also winsorized and truncated observations that were more than two standard deviations away from the mean value. Results were insensitive to these robustness checks.

  3. As a robustness check, we considered alternative proxies for firm size, asset structure and industry. Firstly, we also considered the natural logarithm of the number of employees as a measure of firm size. Secondly, asset structure was also measured as the ratio of net fixed assets to total assets [inspired by the belief that fixed assets are more secure than current assets (see e.g. Van der Wijst and Thurik 1993)]. Finally, we also employed industry dummies to capture the industry effect. Results were robust to these alternative specifications.

  4. Moreover, variance inflation factors (VIFs) are not indicative of multicollinearity problems related to the inclusion of firm size in our model.

  5. Results for estimating Eq. 2 are presented in Appendix.

  6. Results for estimating Eq. 2 are presented in Appendix.

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Acknowledgments

The authors would like to thank Eric Vandenbroele and Graydon Belgium NV for providing us with data employed in the current study. The insightful comments of two anonymous reviewers and the participants of the 2010 Finance & Corporate Governance Conference (FCGC) in Melbourne are also gratefully acknowledged.

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Authors and Affiliations

  1. Hogeschool-Universiteit Brussel, Universiteit Antwerpen (Antwerp Management School), Stormstraat 2, 1000, Brussel, Belgium

    Tom Van Caneghem

  2. Hogeschool-Universiteit Brussel, Voluntary Researcher, K.U. Leuven, Stormstraat 2, 1000, Brussel, Belgium

    Geert Van Campenhout

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  1. Tom Van Caneghem
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Correspondence to Tom Van Caneghem.

Appendix

Appendix

1.1 Estimating the likelihood of voluntarily hiring an external auditor (having a Big4 auditor)

In the first stage, we estimate the likelihood of the firm voluntarily appointing an auditor based on a probit regression. For these purposes, we rely on three alternative theories: agency theory, signalling theory and insurance theory. Firstly, it is typically argued that an external FS audit reduces agency costs (i.e. a FS audit serves as a monitoring device). Based on this view, the likelihood of a voluntary FS audit increases as agency problems become more severe. It is generally recognized that agency problems increase with firm size (e.g. managerial ownership typically decreases as firm size increases), and similar to prior studies (see e.g. Chow 1982; Weets 1999; Niemi et al. 2009), we include firm size (i.e. SIZE) as a proxy for agency costs. In addition, as argued by Chow (1982), there are fixed (start-up) costs of providing audits, and therefore audit costs decline with firm size. Analogous to Niemi et al. (2009), we include a dummy variable that captures the presence of financial debt (i.e. FINDEBT). That is, Chow (1982) observed that the likelihood of voluntarily opting for a FS audit is positively related to being subject to accounting-based debt covenants (which are related to financial debt). As argued by Abdel-Khalik (1993), actions of employees are more difficult to observe as their number increases, and an auditor can be used to reduce this ‘loss of control’. Similar to Weets (1999), we therefore include the natural logarithm of the number of employees in our model (i.e. NOEMPL). Inspired by signalling theory, we include firm profitability (i.e. PROF) and growth (i.e. PGROWTH) in our model. That is, a voluntary FS audit is likely to add credence to the signal that is sent out by highly profitable and/or high-growth firms. FS users may suffer losses due to material misstatements, and the probability of recovering these losses is larger when the company has an auditor (i.e. stakeholders are able to sue the auditor). Similar to prior studies, we therefore include some variables related to financial distress in our model. Based on Weets (1999), we include the ‘quick ratio’ as a liquidity proxy (i.e. QUICK), and based on Niskanen et al. (2009), we include a dummy variable that captures the presence of (sequential) losses (i.e. LOSS). More specifically, we include a dummy variable that takes the value of 1 if the firm has a loss for the current accounting period and retained losses on the balance sheet, and 0 otherwise. Finally, we also control for industry using the Campbell (1996) industry classification (cf. infra).

For those firms having their FS audited, we use the same independent variables to estimate the likelihood of the firm hiring a Big4 auditor (versus a non-Big4 auditor); that is, the three aforementioned theories also apply to auditor selection. A high-quality audit (i.e. a Big4 audit) is likely to reduce agency costs, is likely to add credence to the signal that is sent out by highly profitable and/or high-growth firms, and increases the probability of recovering losses that are suffered because of material errors in the FS (i.e. the ‘deep pockets’ hypothesis in the auditing literature).

The estimates from the probit regression are then used to compute the inverse Mills ratio (IMR). In the second stage, we model leverage including the IMR as a proxy for FS quality [i.e. QUAL2-IMR (model IV) and QUAL5-IMR (model X), respectively].

Probit regression results

 

Probit regression

 

Likelihood of voluntarily hiring an external auditor

Likelihood of hiring a Big4 auditor

Intercept

−4.943 (50.44)***

3.342 (24.56)***

SIZE

0.473 (35.74)***

0.034 (2.14)**

FINDEBT

−0.482 (15.67)***

−0.621 (19.13)***

NOEMPL

0.013 (12.29)***

0.004 (6.64)***

PROF

−0.343 (3.00)***

0.075 (0.63)

PGROWTH

0.650 (3.45)***

0.794 (2.90)***

QUICK

0.006 (1.38)

−0.002 (0.04)

LOSS

0.239 (6.29)***

0.234 (4.61)***

% of correct predictions

97.38

71.01

Number of obs.

67165

8771

  1. SIZE ln(total assets); FINDEBT dummy variable that takes a value of 1 if the firm employs financial debt, and 0 otherwise; NOEMPL the natural logarithm of the number of employees; PROF earnings before interest and taxes/total assets; PGROWTH geometric average of the yearly growth in total assets over the last 3 years; QUICK the quick ratio; LOSS dummy variable that takes a value of 1 if the firm has a loss for the current accounting period and retained losses on the balance sheet, and 0 otherwise
  2. Absolute values of t-statistics are reported in parentheses
  3. * Statistically significant at the 10% level
  4. ** Statistically significant at the 5% level
  5. *** Statistically significant at the 1% level

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Van Caneghem, T., Van Campenhout, G. Quantity and quality of information and SME financial structure. Small Bus Econ 39, 341–358 (2012). https://doi.org/10.1007/s11187-010-9306-3

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