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Small Business Economics

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01.06.2015

Financial reporting quality and the cost of debt of SMEs

verfasst von: Heidi Vander Bauwhede, Michiel De Meyere, Philippe Van Cauwenberge

Erschienen in: Small Business Economics | Ausgabe 1/2015

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Abstract

This study explores a large and detailed dataset of financial statements of Belgian small and medium-sized enterprises (SMEs) over the 1997–2010 period. Using accruals quality as a proxy for the quality of SMEs’ financial reports, we find that the quality of SMEs’ financial statements is negatively related to those companies’ effective interest cost. This result is also highly economically significant. The findings in this paper are consistent with the idea that earnings are important for creditors in predicting SMEs’ reimbursement capacity (i.e., future cash flows) and that less estimation error in accruals enhances earnings’ ability to predict future cash flows. We deliver evidence of an important economic benefit of financial reporting for SMEs, to wit, the potential to reduce information asymmetry between SMEs and their creditors through higher-quality financial reporting.

Vorheriger Artikel Productivity growth persistence: firm strategies, size and system properties

Nächster Artikel Subsidized start-ups out of unemployment: a comparison to regular business start-ups

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Since debt includes liabilities related to the discounting of accounts receivable, the related costs are added to the interest expense. Not incorporating these costs yields similar results.

Industries are based on the Nace-BEL 2003 sections. See also footnote 10.

Since the dependent variable in the model is working capital accruals, a potential limitation of the model is that non-current accruals, where the cash flow realization typically takes longer than 1 year, are not considered when estimating AQ (Francis et al. 2005). However, this assumption of the AQ model seems very acceptable in this dataset. In almost 98 % of the cases, the length of the operating cycle (days’ accounts receivable + days’ inventory) is smaller than 1 year. Furthermore, the work of Dechow and Dichev (2002) has revealed that working capital accruals and total accruals are highly correlated (0.989, p < 0.01 in this sample), so that focusing on working capital accruals should not be an issue.

In line with prior studies (Francis et al. 2004, 2005; Dechow and Dichev 2002), \({\Delta }{\text{WC}} = ({\Delta }{\text{CA}}-{\Delta }{\text{Cash}})\text{-}({\Delta }{\text{CL}}-{\Delta }{\text{STD}})\), where \(\Delta {\text{CA}}\) is the change in current assets, \(\Delta {\text{Cash}}\) is the change in cash and cash equivalents, \(\Delta {\text{CL}}\) is the change in current liabilities, and \(\Delta {\text{STD}}\) is the change in short-term financial debt and the current portion of long-term debt. The detail in our data was used to explicitly adjust \((\Delta {\text{CA}}\hbox{-}\Delta {\text{Cash}})\) for write-offs on inventories, accounts receivables and other current assets (Ooghe et al. 2012). Results of our analyses are robust to performing this adjustment.

CFO was measured in line with prior studies (Francis et al. 2004, 2005). Taking into account differences in financial statement presentation across countries, in this study CFO was measured as bottom-line net income minus total accruals. Given the detail in the data, total accruals are not limited to depreciation and working capital accruals, but also include accruals related to, among others, write-offs, impairments and provisions (Ooghe et al. 2012). The results are robust to not including these accruals.

Standard deviations of residuals are preferred instead of absolute values since sizeable residuals do not form a serious threat as long as they are consistently large (Francis et al. 2005). Moreover, there might be sound reasons for systematically big residuals (e.g., industry-related factors). In those cases, information risk remains limited. However, as illustrated in the robustness tests in Sect. 5.3, the results are robust to working with the absolute residuals from regression (2).

Please note there are some clear similarities with Francis et al. (2005) as well.

If the natural logarithm of total assets is applied as size measure, the results are qualitatively unchanged.

Whether interest subsidies are taken into account here or not does not influence the results.

The industry dummies were based on the sections of the Nace-BEL 2003 industry classification, i.e., the Belgian 2003 application of the European Nace industry classification. Sections A, B and C, i.e., agriculture, fishing and natural resources, were combined as they include very small numbers of observations (see further Table 3 Panel c). In particular, the following industry dummies were built: primary sector (Nace-BEL A, B and C), manufacturing (Nace-BEL D), construction (Nace-BEL F), hotels and restaurants (Nace-BEL section H), transport (Nace-BEL I) and services (Nace-BEL K).

Age is not winsorized because there is little or no doubt concerning the date of incorporation.

The Cook’s distances are clearly smaller than 1 (max = 0.011), underlining that winsorizing as described above is sufficient to deal with outliers in the data.

A few exemptions, for instance for financial institutions, insurance companies, exchange brokers and hospitals, are made. These special cases produce financial statements in another, generally stricter, format (Huyghebaert et al. 2007).

For Belgian financial statements, a distinction is made between a complete format and an abbreviated format. In this study, companies which submit financial statements in the abbreviated format are not included for two reasons. First, companies which report their accounts in the abbreviated format are not obliged to report their sales figure. This number is, however, needed to ensure proper application of the European Commission’s SME definition further on. Secondly, financial statements in abbreviated format do not report a detailed interest expense number which is recommended in order to compute the cost of debt in an accurate manner.

This sampling yields a rough dataset of 202,046 firm-year observations.

With inconsistent data, we mean firm-year observations with financial statements where at least one account is not in line with one or more other accounts.

21,959 firm-year observations left the dataset because the restructuring criterion could not be verified due to the non-availability of data on total assets for the previous year.

This is due to the five-year standard deviation and the leads and the lags in the Dechow and Dichev (2002) model, and due to the computation of the cash flow from operations, which demands information on the previous year (see Eq. 2).

We ran a pooled OLS model with \({\text{AQ}}_{t}\) as dependent variable and \({\text{AQ}}_{t - 1}\) as independent variable on a subsample with firm-years with two consecutive AQ figures (N = 6093). This way, the estimated coefficient on \({\text{AQ}}_{t - 1}\) can be considered an indication for the stability in the firm-specific AQ measure. We encounter a coefficient of 0.871 (p < 0.01) and interpret this as the AQ measure being subject to a substantial degree of persistence over time. The Pearson correlation figure between \({\text{AQ}}_{t}\) and \({\text{AQ}}_{t - 1}\) (0.860, p < 0.01) confirms the conclusion above that the AQ of a company does not fluctuate a lot over time. Consequently, Fama and MacBeth (1973) regressions were favored instead of fixed effects regressions.

In the Fama and MacBeth (1973) procedure, year-specific OLS regressions are executed and the coefficients from these regressions are then aggregated into coefficients and standard errors across years. The Fama and MacBeth (1973) coefficients are just the unweighted average of the OLS coefficients, and the Fama and MacBeth (1973) standard errors are computed as the standard deviation of the OLS coefficients divided by the square root of the number of years in the estimation sample. The advantage of this method is that it enables to control for time effects in circumstances where panel data techniques cannot be used.

We also estimated regressions in which we replaced the most collinear variables by their orthogonalized values. Orthogonalization was based on the pairwise Pearson correlation coefficients using a modified Gram–Schmidt procedure (Golub and Van Loan 1996). The regression results from those estimations (not reported) are similar to the ones reported in Table 7.

Alternatively, we performed a regression with only two industry dummies, one for each of the two main industries (i.e., retailing and manufacturing), using all other industries as the base case. This confirms that manufacturing has a significantly (p < 0.01) lower cost of debt.

The absolute saving of a one standard deviation gain in AQ is on average € 62,874 or 10.9 % of average operating income.

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Titel: Financial reporting quality and the cost of debt of SMEs
verfasst von: Heidi Vander Bauwhede
Michiel De Meyere
Philippe Van Cauwenberge
Publikationsdatum: 01.06.2015
Verlag: Springer US
Erschienen in: Small Business Economics / Ausgabe 1/2015
Print ISSN: 0921-898X
Elektronische ISSN: 1573-0913
DOI: https://doi.org/10.1007/s11187-015-9645-1

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