Organizational structure and earnings quality of private and public firms

Abstract

We examine how heterogeneity in organizational structure affects private firm earnings quality in the European Union. Organizational structure refers to whether the firm is organized as a single legal entity (standalone) or as a business group. Private firms can be organized either way, while public firms are de facto groups. Even though private firms are not affected by market forces, we show that private business groups face greater stakeholder pressure for earnings quality than do standalone firms, while standalone firms have stronger tax minimization incentives. Due to these differences in nonmarket forces, private business groups have higher earnings quality than standalone firms. This heterogeneity among private firms is an important unexplored factor in the study of private firms, affecting the comparison between public and private firm earnings quality. We find that overall, public firms have higher earnings quality than private firms but this relation reverses when we control for nonmarket forces by examining business groups only.

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Fig. 1
Fig. 2

Notes

  1. 1.

    This definition is consistent with the EU legal concept of corporate groups (Windbichler 2000) and with much of the academic work that focuses on corporate groups (Belenzon et al. 2013; Faccio et al. 2010).

  2. 2.

    The terms earnings quality, financial reporting quality, accounting quality, and accruals quality have been used interchangably in the literature.

  3. 3.

    In the European countries we analyze, individual statements are used to determine the tax obligation, and the same tax rules apply for private and public firms (Burgstahler et al. 2006; Leuz and Wüstemann 2003; Pierk 2016; Watrin et al. 2014).

  4. 4.

    Public standalone firms can exist in theory, but we do not observe any in our sample. Moreover, on Compustat, all the U.S. firms provide consolidated financial statements; i.e., they are business groups.

  5. 5.

    In untabulated robustness tests, to test for the effect of tax incentives, Burgstahler et al. (2006) re-run separate analyses either using observations from consolidated financial statement data or observations from unconsolidated (or parent-only) accounts, because the alignment of tax and financial accounting is commonly based on the parent-only accounts. Even though they find a much larger and more significant tax effect, using the subsample of unconsolidated accounts, they do not find evidence of a differential tax effect between public and private firms for either of the two subsamples, which might be due to the substantial decrease in sample size.

  6. 6.

    Data are available here:

    http://databank.worldbank.org/data/reports.aspx?source=2&series=CM.MKT.LDOM.NO&country=#advancedDownloadOptions;https://ec.europa.eu/growth/smes/business-friendly-environment/performance-review_en#sba-fact-sheets.

  7. 7.

    The two-sample Kolmogorov–Smirnov test confirms that the distributions are significantly different from each other.

  8. 8.

    For example, as Watrin et al. (2014, 59) point out, “Corporate income tax is levied at the single-entity level, which means that every parent and subsidiary is obliged to prepare separate tax statements and to pay taxes. The tax laws of member states frequently allow for group taxation within the territory in question, and some allow for losses that occurred in other member states to be deducted. However, income is generally not taxed based on a consolidated European or international income statement. Even under group taxation, taxable income is assessed at the single-entity level and then consolidated for the group. Thus, even in the case of group taxation, the single financial statement is the basis for taxable income in countries with high book-tax conformity.”

  9. 9.

    We have data for fiscal years 2004–2014 and use 2004 to construct lags. This yields a 10-year unbalanced panel.

  10. 10.

    See http://ec.europa.eu/eurostat/web/national-accounts/statistics-illustrated for national statistics in Europe.

  11. 11.

    For a summary of the size thresholds and source references, see Appendix C of Bernard et al. (2018).

  12. 12.

    The relative proportions of public and private business groups, among the different countries, resemble the ones in the work of Coppens and Peek (2005), Table 1. Additionally, in our sample, within each country the industry distribution of private and public firms is similar.

  13. 13.

    As a robustness check, we also use Dechow and Dichev (2002) abnormal accruals and Kothari et al. (2005) performance-matched discretionary accruals with similar results (untabulated).

  14. 14.

    We calculate cash flow from operations using the balance-sheet approach, because U.S. style cash flow statements are generally not available for our sample of private and public European companies.

  15. 15.

    The difference in operating cycles is not due to industry effects, since, as pointed out above, the industry compositions of all three groups are similar.

  16. 16.

    Results are similar if we use Hope et al.’s (2013) research design (untabulated).

  17. 17.

    In Table 5, for each individual country, the mean difference and t-statistic are computed from a firm-year level regression of the stakeholder metric against a dummy variable for business group versus standalone, with industry and year fixed effects and clustering standard errors at the firm level. The aggregate statistics are computed from the mean of the 11 country differences.

  18. 18.

    As a robustness test, we also use the Dechow and Dichev (2002) and Kothari et al. (2005) earnings management proxies, with similar results to those reported in the paper. Results are qualitatively and quantitalively similar using accounts payable scaled by assets instead of Inv_Int as an independent variable.

  19. 19.

    To avoid counfounding effects in this model, we do not include leverage (LEV) as control, as this is also a proxy for stakeholders demand. Nonetheless, we run a sensitivity test (untabulated), and results hold above and beyond the inclusion of such control.

  20. 20.

    Note that, in Table 7, Columns 1 and 2, the dependent variable is unsigned, while in Table 8, the dependent variable is signed.

  21. 21.

    The coefficients of the control variables at the country level are consistent with those in Table 9, Panel A (except, of course, for LEGAL, which is not in the country-level regressions, since it is a constant at the country level).

  22. 22.

    Most studies of U.S. private firms, such as those by Asker et al. (2015), Minnis (2011), Hope et al. (2013), use the Sageworks database, which does not disclose firm names or whether a firm is a business group or a standalone (Asker et al. 2015).

  23. 23.

    When we estimate equation (9) with only leverage or inventory intensity as the sole independent variable, neither is significant. This shows that it is not the other independent variable that is causing leverage and inventory intensity to be insignificant. By contrast, Table 5 showed that business groups and standalone firms differed in their stakeholder pressure, as proxied by inventory intensity and leverage. The different results for inventory intensity and leverage in Table 5 versus Table 11, Panel A, are due to the fact that the different tests flip the independent and dependent variables and consequently have different statistical properties (different coefficients and different error terms) and different interpretations. Despite the fact that standalone firms and business groups have different inventory intensity and leverage ex-post (Table 5), inventory intensity, and leverage are not important determinants of the choice to become a business group ex-ante (Table 11, Panel A).

  24. 24.

    We also replicate the analysis on a country basis (untabulated), and results are quantitatively and qualitatively similar to the ones reported in Table 9, Panel B, using standard OLS.

  25. 25.

    We also use a different specification by including the percentage owned by the three largest shareholders.

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Acknowledgements

We thank seminar participants at Bocconi University, NYU – Stern School of Business, University of Padova, Temple University Accounting Conference, 2017 IAS – AAA Midyear conference in Tampa, 2018 EAA conference in Milan, 2018 Berlin Annual Accounting Conference, and 2018 AAA conference in Washington DC for their constructive feedback. Prior versions of the paper were circulated under the title: “Earnings quality of private and public firms: business groups versus standalone firms.” We are grateful to Donal Byard, Claudia Curi, Lucie Courteau, April Klein, Christian Leuz (our EAA discussant), Garen Markarian, Maurizio Murgia, Annalisa Prencipe, Peter Pope, Ron Shalev, and Hong Xie (our AAA discussant) for helpful comments and suggestions. We are also especially thankful to Claudia Marangoni for her superb research support. Errors remain our own.

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Appendix: Variable definitions

Appendix: Variable definitions

Organizational Structure
Business Group A collection of parent and subsidiary firms that function as a single economic entity
Parent Firm-entity controlling other firms (i.e., controlling subsidiaries).
Subsidiary Firm belonging to a group controlled by a given parent firm. We define subsidiaries as firms directly owned by the parent (Level 1) at a stake higher than 50%.
Standalone Firm that does not belong to a group and is not controlled by any other firm (i.e., no other firm owns more than 20%) and is not controlling any other firm itself (i.e., does not own subsidiaries).
Dependent Variables (Earnings Quality Proxy)
|DeFond| unsigned abnormal working capital accruals, computed as per DeFond and Park (2001).
DeFond_Sign signed abnormal working capital accruals, computed as per DeFond and Park (2001).
|Jones_Mod| unsigned discretionary accruals estimated using the modified Jones Model (Dechow et al. 1995).
Jones_Mod_Sign signed discretionary accruals estimated using the modified Jones Model (Dechow et al. 1995).
EM1 is the number of “small profits” divided by the number of “small losses.” A firm-year observation is classified as small profit (small loss) if positive (negative) net income falls within the range of 1% of lagged total assets, as per Burgstahler et al. (2006).
EM2 is the median ratio of the absolute value of total accruals to the absolute value of cash flow from operations. Total accruals are calculated as follows: (Δtotal current assets - Δcash) - (Δtotal current liabilities - Δshort-term debt) - depreciation expense. Cash flow from operations is equal to operating income minus total accruals, as per Burgstahler et al. (2006).
EM3 is the ratio of the cross-sectional standard deviations of operating income and cash flow from operations (multiplied by −1), as per Burgstahler et al. (2006).
EM4 is the Spearman correlation between the change in total accruals and the change in cash flow from operations (multiplied by −1), as per Burgstahler et al. (2006).
EM aggr is the average percentage rank across all four individual scores, EM1 to EM4. EM scores are constructed such that higher values imply higher levels of earnings management, as per Burgstahler et al. (2006).
Test Variables
LISTED dummy variable equal to 1 if the firm is publicly listed and 0 otherwise.
LEGAL is the mean of three institutional variables from La Porta et al. (1998), as per Burgstahler et al. (2006): efficiency of the judicial system, rule of law, and corruption index.
STAND_ALONE dummy variable equal to 1, if the firm is a standalone, and zero if it is a private business group.
MTR dummy variable taking the value of 1, if firm effective tax rate is above the country statutory tax rate and 0 otherwise (i.e. firm effective tax rate – country average tax rate > 0: dummy equal 1).
Own_Conc Footnote

We also use a different specification by including the percentage owned by the three largest shareholders.

share percentage owned by the single largest shareholder.
Inv_Int ratio of total inventory divided by total assets.
LEV debt-to-equity ratio.
Controls
AGE is the number of years since incorporation.
LEV debt-to-equity ratio.
OP_CYCLE represents the operating cycle (in days) calculated as (yearly average accounts receivable) / (total revenue / 360) + (yearly average inventory) / (cost of goods sold / 360). Cost of goods sold is equal to total revenue minus operating income.
SIZE is the book value of total assets at the end of the fiscal year (natural log).
ROA stands for yearly return on assets and equals net income divided by lagged total assets.
GROWTH is the annual percentage change in revenue.
EXPORT is the ratio of foreign sales over total sales.
QUICK_RATIO is computed as the sum of cash and cash equivalents and current receivables, scaled by current liabilities.

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Bonacchi, M., Marra, A. & Zarowin, P. Organizational structure and earnings quality of private and public firms. Rev Account Stud 24, 1066–1113 (2019). https://doi.org/10.1007/s11142-019-09495-y

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Keywords

  • Private firms
  • Business groups
  • Standalone
  • Stakeholder incentives, Earnings quality
  • Non-market forces

JEL classification

  • D22
  • G15
  • G32 K22
  • M41