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The effects of SFAS 131 geographic segment disclosures by US multinational companies on the valuation of foreign earnings

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Abstract

Foreign operations are becoming increasingly important for US companies. We investigate whether the market's valuation of foreign earnings is a function of the firm's geographic segment disclosures. Specifically, we examine the effects of an increase in the number of geographic segments disclosed and the inclusion of earnings measures in geographic segment disclosures following the adoption of SFAS 131. We find strong evidence that our proxies for increased disclosure are positively associated with the valuation of foreign earnings. Our results are robust to a number of sensitivity analyses. Taken together, our results suggest that the pricing of foreign earnings is associated with important aspects of the firm's information environment.

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Notes

  1. International business theory defines internationalization as an incremental process of adjustment to the environment where firms learn as they get more involved with foreign markets and such learning helps them to increase their commitment to internationalization and vice versa (Johanson & Vahlne, 1977, 1990).

  2. Foreign earnings of US companies totaled $315 billion in 2004, an increase of 78% over the past decade (Hilsenrath, 2005). Of the 10 largest US companies listed on the NYSE, nearly one-half of their revenues are generated from foreign operations (Meek & Thomas, 2004).

  3. A simple real-world example will make this issue clear. In the year prior to implementation of SFAS 131, Procter & Gamble disclosed four foreign geographic segments: (1) Africa/Middle East/Europe; (2) Asia; (3) South America/Mexico; and (4) International. In the year of adoption of SFAS 131, Procter & Gamble reported only a single foreign segment (International), even though approximately 50% of its sales were from foreign sources. In contrast, Corning Inc. disclosed three foreign geographic segments in the year prior to implementation of SFAS 131: (1) Asia/Japan/Pacific; (2) Europe/Great Britain/France/Germany; and (3) South America/Mexico/Other Foreign. In the year of adoption, Corning Inc. reported 11 foreign geographic segments: (1) Germany; (2) France; (3) Brazil; (4) Mexico; (5) Japan; (6) China; (7) Korea; (8) Other Europe; (9) Other Latin America; (10) Other Asia Pacific; and (11) All Other. As a practical matter, some firms continued their geographic reporting practices similar to those prior to adoption of SFAS 131.

  4. Paragraph 45 of SFAS 131 includes the following statement: “There is no disagreement among AIMR members that segment information is totally vital to their work. There also is general agreement among them that the current segment reporting standard, Financial Accounting Standard No. 14, is inadequate.” Likewise, Epstein and Palepu (1999) report that many sell-side analysts consider segment disclosures as the most useful data for their investment recommendations.

  5. Materiality is not specifically defined for enterprise-wide disclosures. According to Herrmann and Thomas (2000), many companies use 10% as a threshold. Doupnik and Seese (2001), however, find that many firms use quantitative thresholds less than 10%. In addition to providing information by individual material country, SFAS 131 indicates that “an enterprise may want to provide subtotals of geographic information about groupings of countries” (paragraph 38).

  6. Prior literature suggests that information asymmetry is especially severe for foreign operations (e.g., Callen et al., 2005; Duru & Reeb, 2002; Herrmann et al., 2008; Hope, Kang, Thomas, & Vasvari, 2008; Hope & Thomas, 2008; Khurana, Pereira, & Raman, 2003; Reeb, Kwok, & Baek, 1998; Thomas, 1999).

  7. Segment information provided at a less aggregated level should be at least as useful as that provided at a more aggregated level. This is an application of the fineness (or Blackwell) theorem from information economics. For example, Piotroski (2003) finds that segment reporting fineness is negatively associated with information asymmetry about future earnings realizations. However, there are conditions under which the fineness theorem, may not hold. For example, if segment data are measured or reported with error, decisions using the finer data need not be as accurate as decisions using consolidated data alone (e.g., Givoly et al., 1999).

  8. Our empirical tests control for variations in the growth of foreign operations and for differential earnings persistence.

  9. The debate on whether information risk is diversifiable or not remains open. While Easley and O'Hara (2004) suggest that firms with less public and more private information have greater information risk that cannot be diversified, Lambert et al. (2007) argue that information risk can be diversified when the number of traders is large. As an empirical example of this debate, Francis, LaFond, Olsson, and Schipper (2005) proxy for information risk using accrual quality and find that information risk is a priced risk factor. However, Core, Guay, and Verdi (2008) find no evidence that accruals quality is a priced risk factor.

  10. Given that SFAS 131 changed not only the number of reported segments but also the definition of segments, it is conceivable that for some firms (and under certain circumstances) a decrease in reported segments could in fact yield a more informative system. However, we would consider this to be the exception rather than the rule. The prime intent of SFAS 131 was for firms to disaggregate their segment information (e.g., Ettredge et al., 2005; FASB, 1997). In addition, as described above, both the AICPA Special Committee (AICPA, 1994) and the AIMR committee (AIMR, 1993) explicitly called for more detailed segment information (i.e., more disaggregated information). This suggests that both standard setters and user groups view segment disaggregation as useful for investors.

  11. Inferences are not affected if we instead scale by lagged or average total assets.

  12. As alternative specifications we have used (1) raw returns, (2) value-weighted market-adjusted returns, and (3) size-adjusted returns. We have also required 60 months of returns for the market model estimation. Results are similar with these alternative specifications of annual returns.

  13. If a firm has December fiscal year end, then the post-SFAS 131 period starts with fiscal year 1998; otherwise the post period starts with fiscal year 1999.

  14. We delete these observations to ensure that our results are not driven by extreme observations. However, results are not sensitive to this filter.

  15. We have a sample of 4536 observations (716 firms) for the pre-SFAS 131 period. We apply the same filters for the pre- as for the post-SFAS 131 period.

  16. Seven percent of the firms decrease the number of geographic segments and 69% have no change.

  17. Untabulated statistics show that the median (mean) number of geographic segments disclosed increases from 2 (2.63) in the pre period to 3 (3.44) in the post period. Both the increase in the median and the mean are significant at the 1% level, suggesting that SFAS 131 brought about significant increases in geographic segment disclosure.

  18. We have computed variance inflation factors (VIF) for all regressions presented. The highest VIF is 2, suggesting that multicollinearity is not an issue in our estimation.

  19. Also consistent with Bodnar and Weintrop (1997), the estimated coefficient on ΔForEarn is significantly larger than the coefficient on ΔDomEarn, suggesting that the value of the firm is on average more sensitive to changes in foreign income than it is to changes in domestic income.

  20. The choice of the number of geographic segments to disclose is affected by the mandates of SFAS 131 as well as by management's strategic considerations. Our view is that the number of geographic segments disclosed is largely a voluntary decision.

  21. Untabulated results show that the effect on the foreign ERC is even greater for firms that increase their reported geographic segments by two or more. This result provides further support for the contention that increased disaggregation is valued by investors.

  22. ΔGSEG and GEARN are defined the same way in the pre-SFAS 131 period as in the post-SFAS 131 period. Specifically, firms that eventually increase their number of reported geographic segments and firms that include geographic earnings after implementation of SFAS 131 are assigned a value of 1 for ΔGSEG and GEARN, respectively (0 otherwise).

  23. The adjusted R2 is higher in the post- than in the pre-SFAS 131 period. This difference is driven primarily by the higher significance of the fixed year effects in the post period.

  24. Specifically, the coefficient on GEARN × ΔForEarn is significant at the 1% level both with and without the interaction term with ΔDomEarn. The coefficient on ΔGSEG × ΔForEarn has (two-sided) p-values of 0.061 and 0.042 excluding and includes the interaction with ΔDomEarn, respectively. After including control variables, ΔGSEG × ΔForEarn is significant at the 5% level in both specifications (see next section).

  25. Our findings are also consistent with the findings of the empirical literature that links disclosure quality with the ability of financial analysts and investors to predict firm performance, both with respect to overall disclosure quality (e.g., Gelb & Zarowin, 2002: Lang & Lundholm, 1996; Lundholm & Myers, 2002) and with respect to geographic segment disclosures (e.g., Balakrishnan, Harris, & Sen, 1990; Herrmann, 1996; Nichols, Tunnell, & Seipel, 1995).

  26. Recall that we control for both differential growth and persistence in our tests (i.e., alternative numerator effects).

  27. Our results are also consistent with those of Thomas (1999). He finds that investors discount the value of foreign (but not domestic) earnings, and conjectures that this may be caused by poor disclosure related to foreign operations.

  28. Although all tests control for domestic and foreign profitability by including ΔDomEarn and ΔForEarn, as an additional test we add main and interaction effects for domestic and foreign profit margins. Untabulated results show that no inferences are affected from the inclusion of these additional controls.

  29. In untabulated tests we further control for variations in earnings persistence. Specifically, we estimate domestic and foreign earnings persistence using annual cross-sectional regressions and then add these estimates as control variables to our main tests. We do not employ firm-specific estimates of persistence as there are a limited number of time-series observations (foreign earnings are not provided on a quarterly basis). We find that results are nearly identical to those reported.

  30. We have re-run this test (1) using 10% and 40% as alternative cut-offs and (2) employing the Ettredge et al. (2005) technique of using Compustat data items to identify mergers, acquisitions, or divestitures. No inferences are affected.

  31. We estimate the probit models cross-sectionally every year post-SFAS 131 adoption to compute annual inverse Mills ratios. A complete description of this research methodology, including the computation of the inverse Mills ratio, can be found in Maddala (1983).

  32. We do not include variables that are derived from market returns, since returns are the dependent variable in the second stage.

  33. We have also considered the probability of informed trade (PIN) as a proxy for the information asymmetry among investors. The PIN was developed by Easley, Kiefer, and O'Hara (1997) and has been widely used in recent research (e.g., Botosan, Plumlee, & Xie, 2004; Easley, Hvidkjaer, & O'Hara, 2002). However, the PIN data are only available through 2001 (from www.smith.umd.edu/faculty/hvidkjaer). Untabulated results show that no inferences are affected if we include PIN and end our sample period in 2001.

  34. As alternative measures of liquidity, we use (1) working capital divided by total assets and (2) cash flow from operating activities divided by net sales, and obtain similar results.

  35. Results are consistent if we instead estimate regressions (3a) and (4a).

  36. The reported first-stage results are for probit models corresponding to the year of SFAS 131 adoption. Results for the other post-SFAS 131 years are similar and are available upon request. The second-stage results are robust to including the control variables used above in “Controlling for Other Factors that Might Affect the Pricing of Foreign Earnings.”

  37. The sample size is smaller than that used in the main tests because of data availability for first-stage variables (especially I/B/E/S coverage).

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Acknowledgements

We appreciate helpful comments from Gauri Bhat, Donal Byard, Gus De Franco, Peter Easton, Yuyan Guan, Don Herrmann, Justin Jin, Yoo Yong Keun, Kin Lo, Lee Radebaugh (Editor), Eddie Riedl, Gord Richardson, Mary Stanford, Terry Warfield, three anonymous reviewers, and seminar participants at the Amsterdam graduate Business School, Baruch College, University of Cyprus, Harvard Business School, McMaster University, McGill University, Nanyang Business School, Norwegian School of Economics and Business Administration, Norwegian School of Management, University of Notre Dame, Singapore Management University, Texas Christian University, Tilburg University, University of Toronto, University of Waterloo, Wharton, Wilfrid Laurier University, University of Wisconsin, and the American Accounting Association International Section Midyear Meeting (San Antonio), European Accounting Association Annual Meeting (Gothenburg), University of Oklahoma Accounting Research Conference, Canadian Academic Accounting Association Annual Meeting (Quebec City), HKUST Summer Symposium (Hong Kong), Columbia Accounting Symposium (New York), and American Accounting Association Annual Meeting (San Francisco) on earlier versions of this paper. Hope gratefully acknowledges the financial support of the Deloitte Professorship and the Social Sciences and Humanities Research Council of Canada.

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Authors and Affiliations

  1. Rotman School of Management, University of Toronto, Toronto, Canada

    Ole-Kristian Hope

  2. Barry Kaye College of Business, Florida Atlantic University, Davie, USA

    Tony Kang

  3. Michael F. Price College of Business, University of Oklahoma, Norman, USA

    Wayne B Thomas

  4. London Business School, London, UK

    Florin Vasvari

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  1. Ole-Kristian Hope
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Correspondence to Wayne B Thomas.

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Accepted by Lee Radebaugh, Area Editor, 27 December 2007. This paper has been with the authors for two revisions.

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Hope, OK., Kang, T., Thomas, W. et al. The effects of SFAS 131 geographic segment disclosures by US multinational companies on the valuation of foreign earnings. J Int Bus Stud 40, 421–443 (2009). https://doi.org/10.1057/jibs.2008.72

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