What drives cross-segment diversity in returns and risks? Evidence from Japanese and U.S. firms

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The International Journal of Accounting 45 (2010) 44 – 76

What drives cross-segment diversity in returns and risks? Evidence from Japanese and U.S. firms☆ Pontus Troberg, Juha Kinnunen ⁎, Harri J. Seppänen Helsinki School of Economics, Department of Accounting and Finance, Finland

Abstract The usefulness of segment reporting is grounded on the presumption of diversities of returns and risks across reported segments. We examine the effect of country-specific factors, reporting incentives, and choices on an ANOVA-based measure of cross-segment diversities (CSD) in risk and returns for a sample of Japanese and U.S. multi-segment firms. We find that, in contrast to our expectations, Japanese firms exhibit greater CSD than U.S. firms. Moreover, we find that in both countries CSD is driven especially by reporting incentives associated with profitability and foreign sales, but not by proprietary costs. Further, the manager's choice of the number of reported segments is an important factor affecting CSD. © 2010 University of Illinois. All rights reserved. JEL classification: M41; G14; D81 Keywords: Risk and return; Segment reporting; Business segment; Operating segment; Proprietary costs; Japanese keiretsu

1. Introduction Diversity in returns and risks across reported segments is a key characteristic of segment reporting that helps users of financial statements understand a firm's past performance and better assess its future. In its 1993 position paper, Financial Reporting in the 1990s and Beyond, the Association of Investment Management Research (AIMR) states that segment data is fundamental and integral to the investment analysis process because analysts need to know and understand how the various components of a multifaceted enterprise behave ☆ This paper has benefited from presentations at the Accounting Research Workshop of the HSE (Helsinki School of Economics, Finland) and the 17th Asian-Pacific Conference on International Accounting Issues (Wellington, New Zealand). ⁎ Corresponding author. E-mail address: [email protected] (J. Kinnunen).

0020-7063/$ - see front matter © 2010 University of Illinois. All rights reserved. doi:10.1016/j.intacc.2010.01.003

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economically. Different segments will generate dissimilar streams of cash flows to which are attached disparate risks and which bring about unique values. Consequently, without disaggregation, there is no sensible way to predict the overall amounts, timing, or risks of future cash flows of an enterprise as a whole (AIMR, 1993). Similarly, diversities in performance across segments have been of major concern to international standard-setters — the Financial Accounting Standards Board (FASB) in the United States and International Accounting Standards Board (IASB) worldwide. For example, the predecessor of IASB, the International Accounting Standards Committee (IASC), recognized this in defining a business segment as “a distinguishable component of an entity that is … subject to risks and returns that are different from those of other business segments” (IASC 14, revised 1997). Also the current U.S. standard (SFAS 131, FASB (1997)) was allegedly aimed at improving disclosures of the diversity of a firm's operations through segment reporting (see Ettredge, Kwon, Smith, & Stone, 2006). The views of these standard-setters and professional bodies, thus, unequivocally indicate that the need and usefulness of segment disclosures arise from the dissimilarities, not similarities, of the reported segments. Given the inherent importance of performance diversity across reported segments, and inspired by recent research on the effects of the revised segment-reporting standard in the United States (Botosan & Stanford, 2005; Ettredge et al., 2006; Berger & Hann, 2007, among others), we examine the importance of country-specific factors, firm-level reporting incentives, and reporting choices as drivers of cross-segment diversity (CSD henceforth) in returns and risks exhibited by Japanese and U.S. multi-segment firms during the five-year period from 1999 to 2003. We focus on these two countries because they represent largely different institutional settings with different requirements on segment reporting. In contrast to the United States, we expect that the importance of the keiretsu form of business organizations (Miyashita & Russel, 1996; Lincoln & Gerlach, 2004) and more intense use of private debt financing affect the quality of segment reporting and thereby cross-segment diversities in Japan. A keiretsu affiliation is likely to discourage a firm's financial disclosures to outsiders in general, including segmental disclosures to competitors affiliated with other keiretsu clusters. Proprietary costs from revealing profitable segments to competitors, coupled with heightened reputation costs from unveiling poorly performing operations to outsiders, are also expected to provide incentives to segment aggregation, thereby decreasing crosssegment diversities in Japanese firms (cf. Mande & Ortman, 2002). In addition, compared to the 75% rule mandated by SFAS 131 in the United States, Japanese segment-reporting rules are less stringent in requiring only 50% of total sales or operating income to be disclosed under reported segments, thus providing more ample opportunities for segment aggregation and decreased diversities across reported segments. For measuring CSD in returns and risks, we develop an adjusted R-square statistic based on the one-factor ANOVA (Analysis of Variance) test with reported segments as the grouping variable. This measure captures the differences in returns (risks) between reported segments resulting in a high R-square statistic when the cross-segment variation in returns (risks) is high relative to the variation within segments. Our descriptive results indicate significant variation in the sample firms' CSD, both within and across the two countries. In the aggregate sample, the mean CSD (the adjusted

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R-square) is 0.39 for returns and 0.27 for risks. These findings suggest that, on average, clearly less than half the total variation in segment returns and risks is attributable to crosssegment differences, the rest being attributable to within-segment variation. They also suggest that CSD in returns is higher than in risks. Contrary to our expectation, we document that cross-segment diversities in returns and risks tend to be higher in Japanese firms (with mean CSD of 0.47 and 0.30 for returns and risks, respectively) than in U.S. firms (0.28 and 0.23). Significant differences in CSD between the two countries pertain even after controlling for the effect of relevant firm-level factors, such as the number of reported segments, the degree of segment aggregation, and the profitability spread of the industries underlying the firm. A plausible explanation for this finding is that, while we are able to find a significantly higher degree of segment aggregation in Japanese firms, the number of and underlying diversity across the nonaggregated (i.e., reported) segments remain sufficiently large so that higher aggregation does not result in lower CSD in Japan. Furthermore, our additional tests of the Japanese sample suggest that, while firm affiliation to a vertical keiretsu significantly reduces CSD in returns, differences between horizontal keiretsu and independent Japanese firms are insignificant. This finding, coupled with the small proportion of firms with vertical keiretsu affiliation in our Japanese sample suggests that the negative impact of the Japanese keiretsu setting on cross-segment diversities may, in retrospect, be relatively small. In addition to differences of CSD between the countries, we document a significant impact of management's disclosure incentives, after controlling for other relevant factors. Consistent with our expectation, we find that higher CSD in returns is significantly related to higher overall profitability (ROA). This finding is consistent with the view that highly profitable firms and, hence, firms with more good news, provide better information in general (e.g., Verrecchia, 1983; Lang & Lundholm, 1993). These firms do not seemingly consider disclosing diversity in returns across segments as being harmful to their potential competitive position. The finding is also in line with the argument that agency and/or reputation costs provide incentives to conceal unprofitable segments through aggregation (cf. Berger & Hann, 2007). Moreover, the findings indicate a significant negative relation between CSD in returns and firm leverage. This is consistent with the view that firms with higher financial leverage may use (private) debt financing to protect their proprietary information and thus have less incentive to provide more diverse segment information publicly (cf. Verrecchia, 1983; Healy & Palepu, 1993; Yosha, 1995). We find that the level of foreign sales is negatively associated with crosssegment diversity. This suggests that when operating in foreign markets, firms seemingly judge political risk arising from informing host government as exceeding the benefits from informing capital markets about differences in its performance drivers (segments). Furthermore, we document that CSD (the adjusted R-square statistic) is significantly related to the number of reported segments. Consistent with one of the objectives behind the change in the U.S. segment-reporting standard (SFAS 131, 1997) we find that crosssegment diversity in returns increases with the number of reported segments. However, it seems to increase only to a certain point because the findings indicate that CSD in returns is the highest when the number of reported segments is four. Finally, our empirical results do not lend support to the hypothesis that the strengths of the impacts of segment-reporting incentives and choices on cross-segment diversities are

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systematically different between Japan and the United States. This finding is based on the generally insignificant differences of the slope coefficients of the reporting incentive and choice variables between the two countries. We contribute to prior research literature on segment reporting (reviewed in the following section) in three primary ways. First and foremost, our sample provides a comparison between two major countries that differ with respect to their institutional and economic backgrounds and segment-reporting standards. Unlike prior related literature, we are, therefore, able to provide new empirical evidence on international differences in segment-reporting practices. Further, in addition to country-specific factors, our paper provides evidence on the importance of management-reporting incentives and choices as drivers of segment-reporting practices in different institutional settings. Finally, we introduce a new empirical method for measuring diversity in returns and risks across reported segments that has not previously been used in related prior literature. Our method is based on the simple ANOVA test design with reported segments as the grouping variable and, hence, provides an operational and easily interpretable measure analogous to the adjusted R-square of the ordinary multiple regression. The remainder of this paper is organized as follows. Next, we review relevant prior literature (Section 2) and develop hypotheses on the relation between disclosed segment diversity and country-specific factors as well as reporting incentives (Section 3). Thereafter, we explain our methods (in Section 4) and sample selection (Section 5). After reporting the empirical results in detail (Section 6), the paper concludes with a summary and discussion of main findings (Section 7). 2. Review of relevant prior literature A FASB (1993) study of 6935 public companies found that, during the years 1985–1991, 75% of these companies operated in one industry segment only. At the same time, 43% or 1051 of the companies, had sales exceeding $ 1 billion. Similarly an IASC background paper Reporting Financial Information by Segments (1994) showed that 38% of 1062 large companies from 32 countries reported only one industry segment. Considering the fact that users of financial information, specifically financial analysts, regard segment reporting as a key piece of financial information for making financial decisions (Balakrishnan, Harris & Sean, 1990; Boatsman, Behn & Patz, 1993; Ijiri, 1995; Prather-Stewart, 1995; Epstein & Palepu, 1999), the Financial Accounting Standards Board and the International Accounting Standards Committee, urged by the Association for Investment Management Research and the American Institute of Certified Public Accountants, reacted to the then existing segment-reporting practice and published new revised standards in 1997 (SFAS 131 and IAS 14, respectively). Since these changes in the standards, research of segment reporting has concentrated on three main areas: (1) descriptive research of segment-disclosure practices, (2) the effect of segment-reporting practices on analysts' valuations and forecasts, i.e., the relevance of segment information, and (3) disclosure incentives and other factors affecting segment-reporting behavior. The trade-off between the benefits from informing capital markets through segmental disclosures (capital-market incentives) and the benefits from withholding such information to avoid proprietary and/or agency costs is central to this paper.

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2.1. Segment-reporting practices Much of the recent descriptive segment-reporting research has focused on the effects of the adoption of the current SFAS 131 in the United States. Empirical evidence has shown that it has been followed by an increase in the number of reported segments, and by an improved congruence between segment reports and management's operating review (Street, Nichols & Gray, 2000; Herrmann & Thomas, 2000; Bar-Yosef & Venezia, 2000; Berger & Hann, 2003). In addition, Herrmann and Thomas (2000) document that the adoption of SFAS 131 resulted in two-thirds of the selected 100 sample firms making a change in how they defined reportable operating segments, implying that, the remaining one-third already defined segments consistent with the internal management of operations of the company under the predecessor SFAS 14. Furthermore, a number of studies show that disclosure practices are partly determined by many fundamental factors, such as firm industry, size, country of domicile, international-listing status, and having a Big 5/4 auditor (e.g., Meek, Roberts & Gray, 1995; Street & Bryant, 2000; Street & Gray, 2001; Prather-Kinsey & Meek, 2004). 2.2. Relevance of segment information The growing importance of industry and country risks heightens the need for information disaggregated along those dimensions in order to provide more useful data for investment decisions (Balakrishnan et al., 1990; Boatsman et al., 1993; Ijiri, 1995; Prather-Stewart, 1995; Epstein & Palepu, 1999). In general, segment reporting improves forecasting ability and stock market investors find this information useful (Roberts, 2000). Maines, McDaniel and Harris (1997) find that analysts have more confidence in segment reporting when externally reported segments correspond to the internally reported segments (management approach) as prescribed in SFAS 131. The effects of the adoption of SFAS 131 on analyst earnings forecasts and market expectations are documented by a number of empirical studies (Venkataraman, 2001; Behn, Nichols & Street, 2002; Berger & Hann, 2003; Ettredge, Kwon, Smith, & Zarowin, 2005). Botosan and Stanford (2005), however, conclude that while SFAS 131 increased analysts' reliance on public data, this may have taken place at the cost of a marginal increase in overall uncertainty. Interestingly, Ettredge et al. (2006) report an increase in cross-segment variability of segment earnings and in the association between reported and inherent cross-segment variability of earnings attributable to the adoption of SFAS 131 in multiple segment U.S. firms. 2.3. Disclosure incentives Consistent with the proprietary-cost hypothesis, managers have clearly expressed their concerns about potential competitive disadvantages of segment information (Mautz, 1968; AICPA, 1994; Sanders, Alexander & Clark, 1999; Deppe & Omer, 2000). Segment reporting gives details about the company's operating margins, return on assets, and growth rates in different lines of business. This information can reveal the existence of weaknesses or opportunities to be exploited by competitors and other parties (like customers). For example, Ettredge, Kwon and Sarin (2002) report that 86% of the (self-selected) industrial

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firms that commented on the exposure draft of the current U.S. segment-reporting standard (SFAS 131) were opposed to the new standard on the grounds that “it would put them on a competitive disadvantage.” Under the proprietary-cost hypothesis, a major factor affecting the extent of disclosure is the degree of competition in the product markets, often measured with the number and size of competitors. Analytical studies indicate that, under severe competition, diversified companies try to hide more profitable activities by reporting a single segment (Hayes & Lundholm, 1996). These results are in congruence with Darrough and Stoughton's (1990) as well as Harris' (1998) conclusion that as the number and size of competitors increase, disclosure becomes more costly and voluntary provision of proprietary information is discouraged. An interesting dimension in the discussion about the impact of competitiveness is Botosan and Stanford's (2005) finding that pre-SFAS 131 single-segment firms that initiated segment disclosure with the new standard had used the latitude in SFAS 14 to hide profitable segments operating in less competitive industries than their primary operations. However, the literature suggests that there is a trade-off between the benefits of informing the capital markets about firm value and the proprietary cost of aiding the rival. In the United Kingdom, a study of the preparers' view on segment reporting under the British standard SSAP 25 showed a clear reduction from 55 to 32% of companies being concerned about competitive disadvantage, as compared to the situation prior to the introduction of SSAP 25 (Edwards & Smith, 1996). Furthermore, concern was seen to arise due to geographical rather than business segment disclosures. Thirty-one percent of the respondents indicated that the issue of competitive disadvantage was of no importance. The initial experience of segment disclosure may have reduced some preparers' concern. In a recent study, Berger and Hann (2007) examine proprietary and agency costs as competing drivers of concealing segments with abnormal earnings. While proprietary costs provide an incentive to withhold segment disclosures when the revelation of segments with high abnormal earnings attracts competition, agency costs motivate management to withhold segments with low abnormal earnings. Under the agency cost hypothesis, withholding these low-performing segments is attributable to unresolved conflicts of interest between management and shareholders. Unlike Botosan and Stanford (2005), who do not find evidence of pre-SFAS 131 single-segment firms masking poor performance under the old standard, Berger and Hann (2007) provide evidence consistent with the agency-cost hypothesis. They document that within the subsample of firms with at least one “inefficient” segment, new segments reported under SFAS 131 tend to have lower abnormal earnings than those of the old segments. Regarding the proprietary-cost hypothesis, however, the results reported by Berger and Hann (2007) are inconclusive. 3. Hypotheses development 3.1. Country effects: the role of institutional factors It is reasonable to assume that cross-segment diversities in returns and risks exhibited by firms are jointly driven by the institutional (and economic) factors underlying the reported segments and the reporting practices followed by the firms in preparing their segmental disclosures. Regarding the former, the institutional settings where multi-segment firms

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operate in the countries examined are different. As the legal origins (code vs. common law), enforcement systems and corporate governance arrangements differ between Japanese and Anglo-Saxon settings (see La Porta, Lopez-De-Silanes, Shleifer, & Vishny, 1997, among others), it is likely that these idiosyncrasies are also reflected in segment-reporting practices and their drivers. For example, unlike their Anglo-Saxon counterparts, Japanese firms are often affiliated with a keiretsu representing firms from diverse sectors and industries of the economy, with close financial and control ties with each other (Miyashita & Russel, 1996; Lincoln & Gerlach, 2004). In keiretsu settings, external financing of firms is often obtained through private debt from banks and other financial institutions under the same keiretsu umbrella. Therefore, a keiretsu affiliation can be expected to discourage firm financial disclosures to outsiders in general and segmental disclosures to competitors affiliated with other keiretsu in particular. Accordingly, in anticipation of proprietary costs attributable to revealing highly profitable segments to outsiders, Japanese firms expressed markedly strong opposition towards disclosing segment information when segment-reporting requirements were introduced in Japan in the late 1980s (Ozu & Gray, 1997; Mande & Ortman, 2002). In addition, it is well-known that the avoidance of losing reputation publicly is deeply rooted in the Japanese culture. Consistent with this, Japan ranks clearly higher than the United States in terms of masculinity (i.e. preference for achievement and financial success) as a cultural value (Hofstede, 1984). Empirical evidence also suggests that the mitigation of reported losses by applying so-called cosmetic earnings management is more significant in Japan than in Anglo-Saxon countries, such as the United States (Kinnunen & Koskela, 2003). In conclusion, because of higher reputation costs compared to their U.S. colleagues, managers of Japanese multi-segment firms can be expected to face strong incentives to conceal poorly performing operations by aggregating them with other (profitable) segments. Such aggregation may have a direct, negative impact on the observed diversity in returns and risks across reported segments. 3.2. Country effects: the role of segment-reporting standards A review of the segment-reporting standards in the United States and Japan reveals some important differences. In the United States, the adoption of SFAS 131 in 1997 meant disclosing management's insight into management strategy (see Reason, 2001, among others). It highlights the opportunities and risks management believes are important. The ultimate rationale behind replacing the line-of-business approach with the management approach was based on the expectation that, under this approach, user assessment of risks and returns is likely to produce more efficient allocation of capital. Operating segments may be aggregated into a single operating segment (only) if the segments have similar economic characteristics and the nature of products or services, production processes, types or class of customer, and methods of distribution are similar (SFAS 131, 1997). According to the Japanese Business Accounting Council (BAC, 1988), information is to be segmented by business activity and by business unit. Segmentation by business activity is further specified as segmentation by business division (product line), by location of parent company and its subsidiaries, and by market segment. The Japanese GAAP give rather detailed guidance on segmentation by business division. Business divisions are to be

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determined with regard to similarity among product types, product characteristics, product manufacturing methods, and similarities in the destination of the product, so as to disclose information that reflects actual conditions and the actual diversification of management (BAC, 1988; CESR, 2005). As was already noted, Japanese firms have strongly opposed disclosing segment data in fear of proprietary costs (Ozu & Gray, 1997; Mande & Ortman, 2002). However, the Japanese Ministry of Finance mandated some degree of segment reporting in 1990. The Japanese segment-reporting rules were complemented by the Japanese Institute of Certified Public Accountants (JIPCA, 1995), which issued Accounting Techniques for Disclosing Segment Information in 1995. Compared to the American standard, Japanese rules are less stringent; they require that only 50% of total sales or operating income be disclosed in the form of reportable segments while the corresponding U.S. requirement is 75%. This could imply that the degree of segment aggregation in Japanese firms is higher than in U.S. firms. Furthermore, Mande and Ortman (2002) contend that Japanese segment-reporting can result in far greater aggregation of financial data and loss of information content because Japanese firms generally disclose segment data according to major industry grouping only. In conclusion, differences in the institutional settings as well as in the segment-reporting standards between Japan and the United States give rise to the expectation that a firm's country of domicile has an impact on the diversity in returns and risks across reported segments. We hypothesize: H1. After controlling for relevant industry and firm-level factors, diversity in returns and risks across reported segments is related to the firm's country of domicile. More specifically, cross-segment diversities are expected to be lower in Japanese firms than in the United States. 3.3. The role of reporting incentives: the impact of agency and reputation costs Presumably, there is a trade-off between the costs of revealing proprietary information, and the potential benefits from informing capital markets in order to reduce information asymmetries. To avoid information asymmetries attributable to adverse selection, highly profitable firms may benefit more from informing capital markets about the drivers (segments) of their outstanding overall performance than from withholding such information on the grounds of potential competitive harm (cf. Verrecchia, 1983; Lang & Lundholm, 1993). Under such circumstances, we can expect a positive relation between firm overall profitability and the degree of cross-segment performance diversity. Moreover, to the extent that agency and reputation costs induce management to conceal lowperforming segments (Berger and Hann, 2007), we expect a positive relation between firm overall profitability and the observed cross-segment diversity. The rationale for such an expectation is twofold. First, the existence of a poorly performing (loss-making) line-of-business in a multi-segment firm has a deteriorating effect on the firm's overall performance (profitability). In addition, when the low-performing line-of-business is aggregated with other more profitable segments to avoid agency and/or reputation costs, this aggregation has a direct, negative impact on cross-segment performance diversity. Assuming that the aggregation per se does not affect a firm's overall performance, we can then observe deteriorating overall

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performance coupled with decreasing cross-segment diversity. This, in turn, translates into a positive relation between firm overall performance and cross-segment diversity. In conclusion, putting together these arguments about agency and reputation costs, we have sufficient grounds to hypothesize a positive relation between firm profitability and cross-segment diversity in returns and risks. In view of the differences in segment-reporting incentives and reporting requirements between Japan and the United States discussed above, we also expect that the strength of the hypothesized association differs between the two countries. Such a difference is also to be expected in light of existing international evidence which suggests that the association between firm profitability and discretionary reporting of segment information varies across countries.1 However, we do not have sufficient grounds for any particular sign expectation for this difference. H2. Ceteris paribus, diversity in returns and risks across segments reported by a firm increases with its overall profitability. The strength of this relation differs between Japan and the United States. 3.4. The role of reporting incentives: the impact of financial leverage Regarding the impact of capital structure, prior empirical evidence of the relation between firm leverage and segment disclosure is inconclusive. While some studies have documented a positive relation (for example, Mitchell, Chia and Loh, 1995; Giner, Ruiz, Cervera, & Arce, 1997; Prencipe, 2004) some others have found an insignificant impact (McKinnon & Dalimunthe, 1993; Kelly, 1994; Leuz, 2004). As already discussed, companies that have more to lose by increased disclosure are more likely to provide aggregated information. This is typically the case for highly leveraged companies, especially if the leverage problem is not counterbalanced by high profitability. The use of relatively high levels of debt financing, particularly in the form of private debt (which is possible especially in the Japanese keiretsu setting), may be attributable to firms' incentives to withhold public information of a proprietary nature from capital markets and thereby also from competitors. Private debt financing requires less extensive public disclosure and relies more on private communication with banks and debt investors. Consequently, a high level of debt suggests that firms may use debt financing to protect their proprietary information and these firms have less incentive to provide detailed segment information. (cf. Verrecchia, 1983; Healy & Palepu, 1993; Yosha, 1995). On the basis of these arguments, we posit H3. Consistent with H2 above, we expect differences between the two countries, but do not impose any particular sign expectation for the impact of a firm's country of domicile on this relation.

1

Internationally the evidence on the association between profitability and discretionary reporting of segment information is mixed. Positive and generally significant relations are found by Giner et al. (1997) for Spain, Saada (1998) for France, and Prencipe (2004) for Italy, whereas negative and generally significant relations are documented by Kelly (1994) for Australia, Leuz (2004) for Germany, and Harris (1998) and Piotroski (2003) for the U.S. It is noteworthy that instead of cross-segment diversities, these prior studies examine other properties of segment reporting, such as the amount of information disclosed in segmental reports in the countries examined.

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H3. Ceteris paribus, cross-segment diversity in returns and risks declines with increasing financial leverage of the firm. The strength of this relation is different between Japan and the United States. 3.5. The role of reporting incentives: the impact of political risk Finally, we expect that firms operating in foreign markets do not want to draw any unnecessary attention to their activities because this could undermine successful operations in these markets. Segment reporting could reveal sensitive information to the host government and local competitors who may view the foreign firm as a threat to local firms, which could potentially lead to hostile actions by the host government against the foreign firm, thereby increasing political risk and related costs (Van Horne & Wachowicz, 2001; Pike & Neale, 2003; Watson & Head, 2007). Consequently, we hypothesize the following H4. As with H2 and H3, we expect a country effect on the hypothesized relation. H4. Ceteris paribus, cross-segment diversity in returns and risks declines with an increase in foreign sales. In addition, the strength of this relation differs between Japan and the United States. 3.6. The role of reporting incentives: the impact of proprietary costs On the basis of prior literature and research findings, proprietary costs suggest that firms have disincentives to disclose segments with abnormally high performance (Mautz, 1968; AICPA, 1994; Sanders et al., 1999; Deppe & Omer, 2000; among others). Due to the asserted proprietary costs in the form of competitive harm, companies have more to lose by revealing high-performing segments than by not disclosing them. Accordingly, the degree of competition measured by the number and size of competitors is likely to discourage voluntary disclosure of proprietary information since the costs of disclosure exceed the benefits (Darrough & Stoughton, 1990; Harris, 1998). The expected impact of proprietary costs is summarized in H5 below. Consistent with the previous hypotheses, we once again consider potential country effect on the hypothesized relation. H5. Ceteris paribus, cross-segment diversity in returns and risks declines as the number and size of competitors increase. The strength of this relation is different between Japan and the United States. 3.7. The role of reporting choices: the impact of segment aggregation and the number of reported segments In addition to the reporting incentives examined above, a firm's actual segment-reporting choices through segment aggregation as well as the number of reported segments are expected to have an effect on observed cross-segment diversities, as hypothesized in H6. In line with the above, we consider potential country effect but do not posit any particular direction for it.

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H6. Ceteris paribus, cross-segment diversity in returns and risks declines with segment aggregation and increases with the number of reported segments. The strengths of these relations differ between Japan and the United States. 4. Methods 4.1. Measuring cross-segment diversity in returns and risks The general objective promulgated by the accounting standard-setting bodies for segment reporting is that it should provide relevant information of the return and risk profiles of the reported segments and thereby facilitate informed judgments about the enterprise as a whole. Accordingly, we define cross-segment diversity as the degree to which the returns and risks differ across the segments reported by a firm. In a recent study, Ettredge et al. (2006) use the range (the difference between maximum and minimum) of segment earnings to capture variability of profitability across reported segments. Our approach extends the method used by Ettredge et al. (2006) in two important ways. First, applying the one-factor ANOVA design with reported segments as the grouping variable, we are able to segregate between-segment and within-segment performance variability from each other. Second, we explicitly consider segment returns and risks as distinct measures of segment performance. Arguably, such distinction is very important in view of the general objectives of segment-reporting standards and the usefulness of segment information reported by firms in practice. To measure segment returns for a given firm, we use the return on assets (ROAst) measure (subscript s denotes segment and t period): ROAst =

Operating profitst : Total assetsst

ð1Þ

Correspondingly, as the risk measure related to Eq. (1) we compute the absolute deviation of ROAst from its segment mean ROA s :: RISKst = ABSðROAst −ROA s :Þ 1 n t

where segment mean ROA s : = ∑ROAst :

ð2Þ

Thus, for a firm with, say, three reported segments over the five-year period, we have 15 observations of segment returns (ROAst), coupled with 15 observations of risks (RISKst). Next, we employ the one-factor ANOVA (Analysis of Variance) test design in decomposing the total variation in these performance measures into their within-segment and between-segment components. Accordingly, cross-segment diversity (CSD) is then measured separately for returns and risks by the proportion of total variation in the performance measure attributable to differences between segments. This proportion, which

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is analogous to the adjusted R-square in an ordinary multiple regression, is given by the following expression (see the Appendix A for a formal derivation):   nT −1 SSWS ; CSD = Adj: R = 1− nT −r SSTO 2

ð3Þ

where SSWS SSTO r nT

sum of squares within segments total sum of squares number of segments reported by the firm total number of observations for the firm (number of segments × number of periods per segment).

The intuition behind Expression (3) is that the larger the variation in the performance (return or risk) measure attributable to separately reported segments relative to corresponding variation within segments, the smaller is SSWS relative to SSTO on the right-hand side, and hence the larger the CSD. The adjustment factor in the brackets is needed to make firms with different numbers of reported segments comparable with each other. The general idea behind our diversity measure is illustrated by Fig. 1A through D. They exhibit the behavior of segment profitability (ROAst) for four firms which report two segments during 1999–2003. Fig. 1A for Bob Evans Farms Inc. is an illustrative example of a case where CSD is high both in terms of returns (the mean ROA of the reported segments) and risks (the variability of returns around segment means) which are clearly different between the two reported segments.2 The cross-segment diversity in Bob Evans Farms Inc. is indicated by our CSD measures (Expression (3)). When computed, they turn out to be as high as 0.727 for returns and 0.650 for risks in this firm. In contrast, Fig. 1B for Colgate-Palmolive Co. exhibits different returns but similar risks across the reported segments. The CSD measure for returns proves to be as high as 0.884, whereas it is only 0.065 for risks. Correspondingly, Fig. 1C exhibits the case of Sealed Air Corporation for which the mean segment returns are similar but their variability differs across the segments. Accordingly, the CSD measure is only 0.095 for returns but as high as 0.634 for risks in this firm. Finally, Fig. 1D for Steinway Musical Instruments illustrates an example where cross-segment diversity is low for returns as well as for risks. In this firm, the CSD measures, when adjusted for the number of reported segments, are as low as − 0.090 and − 0.086 for returns and risks, respectively.3 2

For segment 1 of Bob Evans Farms Inc., the mean ROA during the five-year period is 13.3%, whereas the corresponding mean for segment 2 is 29.1%. Similarly, segment risks measured by the mean absolute deviations of ROA from the segment means are 0.6% and 5.8% for segments 1 and 2, respectively. 3 Note that, analogously to ordinary multiple regression where R-squares adjusted for the number independent variables can take negative values, our CSD measure adjusted for the number of reported segments can take negative values when the number of reported segments (denoted by r in Expression (3)) is sufficiently large.

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Fig. 1. A–D: Illustrative examples of cross-segment diversities (CSD) in returns and risks.

4.2. Regression model To test the hypothesized drivers of CSD, we estimate the following cross-sectional regressions separately for returns and risks. In addition to an indicator variable for a firm's country of domicile (Japan vs. the United States), we include interactions of the country indicator with segment-reporting incentives and choices in order to consider differential slope coefficients for reporting incentives and choices between the two countries: CSD = β0 + βd Firm0 s country of domicile + Σβi Segment reporting incentives + Σβid Segment reporting incentives × Firm0 s country of domicile + Σβc Segment reporting choices + Σβcd Segment reporting choices × Firm0 s country of domicile + Σo βo Control variables + e;

ð4Þ

where the dependent CSD variable is given by Expression (3), e is the regression residual, and the independent variables representing the firm's country of domicile, segment-

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57

reporting incentives, segment-reporting choices, and other (control) factors are as defined in Exhibit 1. In addition to the main test variables, we control for the effect of three blocks of underlying factors: (1) underlying intra-firm diversity, (2) firm industry sector, and (3) firm size. It is noteworthy that since we do not have sufficient grounds to expect that our dependent variables (cross-segment diversities in returns and risks) are monotonically increasing (or decreasing) functions of the number of reported segments or firm size, we use dummy indicator variables rather than continuous variables to measure the effects of these factors. 5. Sample selection Our analysis retrieves the sample of firms and data on variables from Thomson ONE Banker (hereafter TOB). TOB is an online database with financial data available from Worldscope among other financial databases such as IBES and Datastream. The sampleselection criteria used in this study aim to enhance the power of the statistical tests, while maintaining sufficient generalizability of the results, through the following criteria. The results of the sample selection are reported in Table 1. First, we first select all “active” firms (i.e., firms that are listed and exist) with annual sales in excess of 100 USD millions from all world regions (Americas, Asia Pacific, Europe and Africa, Emerging Markets, and North America) as classified in TOB in year 2002 (sample formation date is March 10, 2004). Active firms are selected in order to ensure sufficient time-series data as our primary analysis uses data from the reporting years 1999– 2003. The lower boundary of the sample period in this study is limited by the changes in the segment-reporting standards in the United States (SFAS 131 was enforced in 1998).4 Moreover, we exclude firms which were members of financial services industry (Worldscope SIC starting with six) and firms that are considered governmental or quasigovernmental (Worldscope SIC starting with nine) at the firm or group level.5 These primary sample-selection criteria provide us with 9154 firm observations. Second, in order to measure cross-segment diversity of returns and risks of a firm, (Expressions (1) and (2)), we calculate the return on asset (ROA) metric for each segment in each sample year. Consequently, for each of our sample firms, we require complete segment data on operating income and assets for each reported segment for five consecutive years, 1999–2003.6 ROA is calculated as the segment's annual operating income divided by the segment assets at the end of the reporting year. The exception to this criterion is that firms that had missing ROA segment data only for the last reported segment in TOB database are 4

No changes in segment-reporting standards took place in the United States, United Kingdom and Japan during the sample period 1999–2003. 5 The initial sample screening employed through the TOB website was not effective in all respects. In particular, in our sample there are some firms left with SIC starting with 6 or 9. These firms were eliminated after the inspections of the data show that they use the TOB variable “WS.PrimarySIC2”. Moreover, the sample selection procedure using the initial screening criteria and devices available in TOB website generate several duplicate observations of the sample firms. To avoid duplicate observations we eliminate firms with the same name (“EntityName”), firms with the same Worldscope identification code (“EntityKey”), and/or firms with both nonADR and ADR (identified by “WS.ADRIndicator”) coded data. 6 We also eliminate all ROA observations where a segment's reported assets in any year are zero.

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Exhibit 1 Definitions of the independent variables. A. Country of domicile (U.S. as a benchmark) Japan Indicator variable = one, if the firm's country of domicile is Japan, otherwise zero. B. Segment-reporting incentives Firm profitability Average return on assets (ROA) of the firm during 1999–2003. Financial leverage Average percentage of total debt per total assets of the firm during 1999–2003. Foreign sales Average percentage of foreign sales per net sales of the firm during 1999–2003. Number of competitors Number of firms in the firm's primary two-digit SIC industry in its country of domicile included in Thomson Worldscope database. Industry concentration Industry concentration measured by the Herfindahl index, based on the firm's primary two-digit SIC code and average net sales during 1999–2003 of all firms available from Thomson Worldscope database for the firm's country of domicile. C. Segment-reporting choices Segment aggregation Number of four-digit SIC codes for the firm in Thomson Worldscope database divided by the number of reported segments. Three reported segments Indicator variable = one, if the number of segments reported by the firm during 1999–2003 is three, otherwise zero. Four reported segments Indicator variable = one, if the number of segments reported by the firm during 1999–2003 is four, otherwise zero. Five or more segments Indicator variable = one, if the number of segments reported by the firm during 1999–2003 is at least five, otherwise zero. D. Control variables (1) Underlying intra-firm diversity Industry profitability spread Range of the median returns on assets in the firm's underlying industries during 1999–2003. For each firm, the industry profitability spread is measured by the difference between the maximum and minimum industry ROA, based on the median ROA of all firms included in Thomson Worldscope database for the firm's two-digit SIC industries in its country of domicile. (2) Firm industry sector (mining and construction as a benchmark) Manufacturing Indicator variable = one, if the firm's primary first-digit SIC is two or three, otherwise zero. Transportation etc. Indicator variable = one, if the firm's primary first-digit SIC is four, otherwise 0. Trade Indicator variable = one, if the firm's primary first-digit SIC is five, otherwise 0. Services Indicator variable = one, if the firm's primary first-digit SIC is seven or eight, otherwise zero. (3) Firm size (net sales below 500 million USD as a benchmark) Size class 2 Indicator variable = one, if the firm's average net sales during 1999–2003 is between 500 and 1000 million USD, otherwise zero. Size class 3 Indicator variable = one, if the firm's average net sales during 1999–2003 is between 1000 and 2500 million USD, otherwise zero. Size class 4 Indicator variable = one, if the firm's average net sales during 1999–2003 is more than 2500 million USD, otherwise zero.

included in the sample. For these firms, however, the last incomplete segment information was eliminated. Moreover, we require that a firm in our sample should have at least two segments as reported in TOB database. These eliminations leave us with a total of 1399 firms. Third, we exclude from the analysis firms that changed their fiscal year end during the sample period. This criterion is used to eliminate observations in which the reporting-period

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Table 1 Sample selection. Selection criteria

Number of firms

All nonfinancial (the first SIC digit other than 6) and nongovernmental (the first SIC digit other than 9) firms with segment data for any year during 1999–2003 in the Thomson/Worldscope database as of August 2004 Firms with incomplete segment data during 1999–2003 Firms with operating income and assets data available for at least two reported segments in each year 1999–2003 Firms with changes in the accounting period end or the accounting standards or the number of segments reported during 1999–2003 Firms with usable accounting data during 1999–2003 Firms from countries with 100 firms or less per country Japanese firms following U.S. GAAP Firms with the first SIC digit 0 (agriculture, forestry and fishing) Firms with missing data for some relevant firm-specific variables Final sample Firms from Japan Firms from United States

9154

− 7755 1399 − 121 1278 − 309 −8 −5 −1 955 537 418

100.0% 56.2% 43.8%

length deviates from the 12-month reporting period. Deviation distorts ROA thereby affecting the measurement of its variability across reporting years. Fourth, we employ two criteria to identify firms that potentially experienced changes in their segment-reporting practices. We eliminate firms that changed their accounting standards during the sample period. A change in the standard was defined to have taken place if there was a change in a firm's accounting standard between IFRS, U.S. GAAP, or local standard.7 This is to ensure that there are no changes in the reporting standards followed by the firm that could affect a sample firm's segment-reporting practices. In addition, we also exclude firms that made changes in the number of their reported segments. We recognize that these two are coarse measures to eliminate the effect of the potential changes in segment-reporting practices, because they do not necessarily capture the effect of the changes in the nature of reported business lines.8 After these eliminations we have 1278 firms in our sample. Finally, we eliminate countries with segment data for 100 firms or fewer, thereby leaving firms only from Japan and the United States in the sample. In addition, inspection of the

7

We have no mechanism to detect the changes in local standards or local segment-reporting standards for each sample firm and for each sample country during the sample period beyond the U.S. GAAP, U.K. GAAP and Japanese GAAP. 8 A complementary mechanism to identify potential changes in sample firms' segment-reporting practices would be to identify changes in segment classifications across years, examining whether there is a change in segment description from one year to another using the segment description reported in TOB, and/or identifying changes in the segment's SIC code across reporting years. However, as the data on these are not complete for all of the firms in the TOB database we do not employ these criteria.

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remaining firms in these countries reveals that eight Japanese firms reported financial information using U.S. GAAP and five firms had the first digit of the SIC code as zero (Agriculture, Forestry and Fishing). Moreover, one firm had missing data on relevant firmspecific variables such as net sales. The employment of all the above criteria results in our final sample of 955 firms, 537 (56.2%) of which are from Japan, and 418 firms (43.8%) from the United States. 6. Empirical results 6.1. Descriptive statistics The descriptive characteristics of the sample firms by country are reported in Table 2. Based on the medians of the average net sales during 1999–2003, the U.S. firms are larger (USD 1070 millions) than the Japanese firms (USD 864 millions) in the sample. Similarly, the U.S. sample firms are larger, based on the average market capitalization during 1999–2003 (USD 880 millions) than their Japanese (USD 340 millions) counterparts. In contrast, Japanese firms as measured by the number of four-digit SIC codes have more industries (mean number of industries is 5.7) than the United States (3.8). The statistics also show that the average number of segments reported by firms was higher in Japan (3.9) than in the United States. The differences in these characteristics between the two countries are statistically significant except for the average net sales on the Kruskall– Wallis test. The table also shows a significant difference in the degree of segment aggregation measured by the ratio of the number of firm's four-digit SIC industries to the number of reported segments (see the column on the right in the table). In particular, it turns out that the mean (median) ratio is 1.55 (1.50) in Japanese firms, whereas the corresponding mean (median) is 1.36 (1.25) in the U.S. sample. Consistent with our expectation, these statistics suggest that the degree of segment aggregation is higher in Japanese multi-segment firms than in their U.S. counterparts. Summary statistics of our cross-segment diversity measures appear in Panels A and B of Table 3.9 Overall, the measures indicate large variation of CSD across the firms in both countries. In our total sample of 955 observations, the mean (median) CSD is 0.389 (0.371) in returns and 0.266 (0.256) in risks. As these statistics fall well below 0.5, they suggest that, on average, clearly less than half of the total variation in segment returns and risks is attributable to cross-segment differences, the rest of the variation being caused by segmentspecific (within-segment) factors. Further, they also are consistent with the view that, on average, CSD is somewhat higher for returns than for risks. In (nontabulated) paired sample tests, the difference turns out to be very significant. The parametric F-value as well as the nonparametric Chi-square statistics reported on the bottom of Panel A are very significant both for returns and for risks, indicating that 9

To eliminate the effect of potential outliers on our CSD measures, we winsorize segment ROA data to the 0.5% and 99.5% percentiles. Accordingly, approximately 200 segment ROA observations (i.e., 1% of the total sample of about 20,000 segment ROA observations) are adjusted to the top and bottom half percent limits which were +197.8% and − 171.4%, respectively, in the raw data.

537 22.6 1358.4 340.1 92,119.8 4721.4 418 4.5 5229.3 880.4 274,702.8 20,876.4 955 4.5 3052.6 538.3 274,702.8 14,377.7 17.33 (0.000) 69.96 (0.000)

Average market capitalization (million USD) 1999–2003 537 2.0 5.7 6.0 8.0 1.5 418 1.0 3.8 3.0 8.0 1.7 955 1.0 4.9 5.0 8.0 1.9 358.7 (0.000) 263.1 (0.000)

Number of SIC 4 industries

F-values and KW (Kruskall–Wallis) Chi-squares significant at 10% or lower are in boldface. a Segment aggregation is measured by ratio of the number of firm's SIC 4 industries to the number of reported segments.

F-value (prob) KW Chi-square (prob)

Total

U.S.A.

537 113.8 2441.1 864.1 120,945.5 6382.3 418 72.7 4531.8 1069.5 210,671.0 18,513.9 955 72.7 3356.2 941.2 210,671.0 13,182.8 5.94 (0.015) 1.64 (0.200)

Japan

Nobs Minimum Mean Median Maximum Std. deviation Nobs Minimum Mean Median Maximum Std. deviation Nobs Minimum Mean Median Maximum Std. deviation

Average net sales (million USD) 1999–2003

Country

Table 2 Descriptive statistics of the sample firms.

537 2.0 3.9 4.0 8.0 1.2 418 2.0 2.9 3.0 7.0 1.0 955 2.0 3.5 3.0 8.0 1.2 194.6 (0.000) 179.8 (0.000)

Number of reported segments

537 0.33 1.55 1.50 4.00 0.55 418 0.25 1.36 1.25 4.00 0.67 955 0.25 1.47 1.33 4.00 0.61 23.95 (0.000) 41.64 (0.000)

Segment aggregation a

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Table 3 Descriptive statistics of cross-segment diversity in returns and risks. Panel A: CSD in returns and risks by country Country Japan

U.S.A.

Total

Nobs Minimum Mean Median Maximum Std. deviation Nobs Minimum Mean Median Maximum Std. deviation Nobs Minimum Mean Median Maximum Std. deviation

F-value (prob) KW Chi-square (prob)

CSD in returns

CSD in risks

537 −0.173 0.470 0.494 0.995 0.340 418 −0.180 0.284 0.221 0.986 0.317 955 − 0.180 0.389 0.371 0.995 0.343 74.67 (0.000) 68.75 (0.000)

537 −0.191 0.295 0.281 0.930 0.236 418 −0.173 0.229 0.220 0.925 0.248 955 −0.191 0.266 0.256 0.930 0.244 17.79(0.000) 18.62 (0.000)

Panel B: CSD in returns and risks by number of reported segments Number of reported segments

CSD in returns

CSD in risks

2

208 − 0.125 0.257 0.148 0.966 0.336 321 − 0.166 0.408 0.379 0.995 0.356 245 − 0.173 0.449 0.468 0.994 0.330 181 − 0.180 0.426 0.438 0.973 0.305

208 −0.125 0.174 0.105 0.884 0.257 321 −0.161 0.258 0.236 0.930 0.246 245 −0.173 0.303 0.292 0.925 0.217 181 −0.191 0.338 0.328 0.898 0.225

3

4

≥5

Nobs Minimum Mean Median Maximum Std. deviation Nobs Minimum Mean Median Maximum Std. deviation Nobs Minimum Mean Median Maximum Std. deviation Nobs Minimum Mean Median Maximum Std. deviation

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Table 3 (continued ) Number of reported segments

CSD in returns

CSD in risks

Total

955 − 0.180 0.389 0.371 0.995 0.343 14.46 (0.000) 44.03 (0.000)

955 − 0.191 0.266 0.256 0.930 0.244 18.10 (0.000) 57.06 (0.000)

Nobs Minimum Mean Median Maximum Std. deviation

F-value (prob) KW Chi-square (prob)

Cross-segment diversity (CSD) is measured by Expression (3). F-values and KW (Kruskall–Wallis) Chi-squares significant at 10% or lower are in boldface.

cross-segment diversity differs across the countries examined. On average, CSD for returns proves to be clearly higher in Japanese firms (the mean is 0.470), as compared to U.S. firms (0.284). As regards CSD in risks, the differences between the countries are much smaller. However, Japanese firms still take the lead (the mean is 0.295) in comparison to U.S. firms (0.229). Panel B of Table 3 shows cross-segment diversities by the number of reported segments.10 Overall, we find very significant differences in CSD across the numbers of reported segments, as indicated by the F-value and Chi-square statistics. For returns, the mean CSD turns out to be highest (0.449) when the number of reported segments is four. It is clearly higher than the mean for firms reporting three segments (0.408), and also higher than for firms with five or more reported segments (0.426). Thus, the findings fall in line with the view that cross-segment diversity in returns is not a monotonically increasing function of the number of segments reported by the firm. Instead, it seems that CSD in returns peaks around four reported segments after which no increase can be found.11 Unlike the mean (and median) CSD in returns, the corresponding statistics for risks are suggestive of monotonically increasing CSD with the number of reported segments. The mean CSD peaks for firms with five or more reported segments (0.338) where the crosssegment diversity is significantly higher than in firms that report only two segments (0.174). 6.2. Regression results The estimation results of our main regressions (Eq. (4)) appear in Panels A and B of Table 4. 10 Because the numbers of firms reporting six segments (42 firms), seven segments (12 firms) and eight segments (2 firms) are relatively small in our sample, these firms are combined with firms reporting five segments (125 firms), thereby providing a reasonable number of observations (181) for the last firm group. 11 The mean CSD in returns for firms with five or more segments are the following: 0.445 (firms reporting five segments); 0.380 (six segments); 0.435 (seven segments), and 0.183 (eight segments). However, as the numbers of observations are very small especially for firms reporting six, seven or eight segments (see the preceding footnote), these mean statistics should be interpreted cautiously.

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Overall, the findings from Panel A of Table 4 fall in line with our hypotheses that firm's country of domicile (H1) and reporting incentives proxied by a firm's overall profitability (H3), leverage (H3), and foreign sales (H4) are statistically significant drivers of cross-segment diversity in returns. Regarding the country effect, however, we find a significant positive coefficient for the country-indicator variable (Japan), suggesting that CSD in returns is higher in Japanese than U.S. firms. While this result

Table 4 Regression results of cross-segment diversity. Panel A: CSD in returns (dependent variable) Independent variables Exp. sign β (Intercept) Country of domicile Japan Segment-reporting incentives Firm profitability ×Japan Financial leverage ×Japan Foreign sales ×Japan Number of competitors ×Japan Industry concentration ×Japan Segment-reporting choices Segment aggregation ×Japan Three reported segments ×Japan Four reported segments ×Japan Five or more rep. segments ×Japan Control variables Industry profitability spread Firm industry-sector indicators Firm size indicators Model F-value (prob) Adj. R-square

t-value

β

t-value

β

t-value

?

0.333

7.22

0.204

3.15

0.152

1.90

−

0.184

7.83

0.222

7.38

0.239

1.86

1.691

7.78

− 0.169

−2.89

− 0.131

−2.07

0.000

0.08

0.056

0.36

1.455 0.965 − 0.061 − 0.111 − 0.152 0.018 0.000 0.000 0.291 − 0.388

5.99 1.81 −0.66 −0.93 −1.94 0.14 0.64 −0.32 1.25 −1.22

− 0.013

− 0.63

0.119

3.89

0.124

3.41

0.088

2.01

0.010 − 0.050 0.060 0.168 0.125 0.056 0.165 − 0.039

0.37 −1.18 1.63 2.43 2.53 0.70 2.54 −0.41

+ ? − ? − ? − ? − ? − ? + ? + ? + ? + ? ?

0.007 0.04 Included Included 9.87 (0.000) 0.077

− 0.040 − 0.22 Included Included 11.26 (0.000) 0.162

− 0.075 −0.40 Included Included 8.36 (0.000) 0.172

Panel B: CSD in risks (dependent variable) (Intercept) Country of domicile Japan Segment-reporting incentives Firm profitability ×Japan

?

0.232

6.90

0.200

4.13

0.137

2.28

−

0.072

4.19

0.052

2.30

0.110

1.14

0.451

2.77

0.463 − 0.116

2.53 −0.29

+ ?

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Table 4 (continued ) Independent variables

Exp. sign

Financial leverage ×Japan Foreign sales ×Japan Number of competitors ×Japan Industry concentration ×Japan Segment-reporting choices Segment aggregation ×Japan Three reported segments ×Japan Four reported segments ×Japan Five or more rep. segments ×Japan Control variables Industry profitability spread Firm industry-sector indicators Firm size indicators Model F-value (prob) Adj. R-square

− ? − ? − ? − ?

β

t-value

− ? + ? + ? + ? + ? ?

− 0.062 −0.45 Included Included 4.42 (0.000) 0.031

β

t-value

0.014

0.32

− 0.114

−2.41

0.000

0.30

0.017

0.14

− 0.029

−1.88

0.069

2.99

0.096

3.53

0.118

3.60

− 0.071 − 0.51 Included Included 5.03 (0.000) 0.071

β

t-value

0.067 − 0.104 − 0.076 − 0.065 0.000 0.000 0.146 − 0.309

0.97 −1.16 −1.30 −0.71 1.11 −1.46 0.84 −1.29

− 0.011 − 0.026 0.056 0.082 0.055 0.121 0.071 0.123

−0.54 −0.81 2.01 1.58 1.47 2.01 1.45 1.72

− 0.106 −0.75 Included Included 3.90 (0.000) 0.076

Cross-segment diversity (CSD) is measured by Expression (3). F-values and t-values (two-tailed test) significant at 10% or lower are in boldface. Number of observations is 955 in all regressions.

holds even after controlling for the effect of relevant firm and industry-level factors, it is contrary to our expectation (H1). A plausible explanation for this intriguing finding is attributable to the underlying intra-firm performance diversity in large Japanese conglomerates. This diversity may be large enough to counteract the significantly higher degree of segment aggregation documented for Japanese firms in Table 2 (after controlling for other factors, however, the negative impact of segment aggregation remains insignificant in Panel A of Table 4). Another explanation is that our variables measuring intra-firm diversity, i.e., the number of reported segments and the profitability spread of firm's lines of business, are noisy measures of true underlying heterogeneity of firm operations and therefore do not adequately control for these firm-level differences. Further, we do not find statistically significant support for our hypothesis that CSD is driven by proprietary costs measured by the number of competitors and industry concentration (H5). Instead, we document that the number of reported segments has a significant effect on CSD, even after controlling for other relevant factors. Consistent with Panel B of Table 3, the coefficient of the indicator variable measuring the effect of four reported segments (0.124) is higher than the corresponding coefficients for three and for

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five or more segments (0.119 and 0.088, respectively). Thus, after controlling for other factors, cross-segment diversity in returns is not monotonically increasing with the number of reported segments, but peaks at four reported segments. Further, the results show that our measure of segment aggregation has a negative but statistically insignificant coefficient. Regarding country-specific differences in the impact of the examined factors, the slope coefficients of the interactions with the country-indicator variable (Japan) are generally insignificant. We thus do not find clear support for the expectation that the strengths of the examined relations are different between Japan and the United States. Notable exceptions are firm profitability and the number (three) of reported segments for which the t-values indicate different slope coefficients between the two countries. Finally, it is noteworthy that the regression R-square reported in Panel A of Table 4 is 7.7% when the country-indicator (Japan) and control variables are included in the regression, and it increases to 16.2% when the variables measuring segment-reporting incentives and choices are added to the model. Country interactions (×Japan) are able to increase the adjusted R-square only marginally (to 17.2%). However, model F-values are very significant in all regressions. Corresponding regressions of cross-segment diversity in risks are shown in Panel B of Table 4. Overall, the regression of cross-segment diversity in risks yields clearly weaker results than the corresponding results for returns in Panel A.12 Although the model F-values still are very significant, the adjusted R-squares are substantially lower, ranging from 3.1% to 7.6%. After controlling for other factors, the regression coefficients estimated for the country indicator (Japan) are significant, as are also the coefficients of firm profitability and foreign sales with expected signs. Unlike in Panel A, however, firm leverage remains insignificant but segment aggregation becomes significant (t-value − 1.88) with the expected sign. Consistent with the findings in Panel A, the numbers of reported segments once again have significant coefficients suggesting that CSD in risks increases when firms report more than two segments. Finally, consistent with Panel A, the results from augmenting country (Japan) interactions to the model do not yield significant results. 6.3. Additional tests 6.3.1. The impact of Japanese keiretsu affiliation In the Japanese network economy, the most significant players in the field have affiliations to horizontal or vertical keiretsu which form very important clusters of industrial, commercial, and financial corporations in the country. Lincoln and Gerlach (2004, p. 15) define these clusters as “independently managed firms maintaining close and stable business ties, cemented by governance mechanisms such as presidents' councils, partial cross-ownership, and interlocking directorates.” The most famous horizontal keiretsu (the Mitsui Group, the Mitsubishi Group, the Sumimoto Group, the Fuyo Group,

12

A plausible explanation for these weaker results is that compared to segment return, the concept of segment risk is ambiguous in practice, and our measure of it (see Expression (2)), therefore, may be biased and noisy.

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the Sanwa Group, and the Dai-Ichi Kangyo Group, also known as the “Big Six”) are diversified constellations of corporations where each firm typically represents a separate industry sector, including financial services. It is important to note that, unlike in common economics parlance, the term horizontal does not here refer to relations among rivals within the same industry, because these horizontal clusters are quite diverse in terms of their industry makeup (Lincoln & Gerlach, 2004). In contrast, vertical keiretsu are “pyramids” of firms, on the visible top of which are large manufacturers (such as Toyota, Hitachi, and Matsushita Electric). Beneath the top, these manufactures have their suppliers, and their suppliers' suppliers. In addition to these production keiretsu, another type of vertical keiretsu are clusters based on distribution where a manufacturer (such as Matsushita, Shiseido, and Fuji Photo Film) moves products out to market through a network of wholesalers and retailers that depend on the parent firm for its goods or services (Miyashita & Russel, 1996). To gain insight into the impact of keiretsu affiliation on cross-segment diversities in our Japanese sample, we employ the keiretsu classification used in Gramlich, Limpaphayom and Rhee (2004), which is based on the Dodwell Marketing Consultants' list of Industrial Groups in Japan. In our total sample of 537 Japanese multi-segment firms, we identify 47 firms (8.8%) with a vertical keiretsu affiliation, 162 firms (30.2%) with a horizontal keiretsu affiliation, and 207 firms (38.5%) which are independent with no keiretsu affiliation.13 We expect that, because of the close ties and interrelations within keiretsu clusters, which are supported by appropriate governance mechanisms, the general informativeness of financial reporting, and thereby also the return and risk diversities across reported segments, may be lower in Japanese keiretsu firms than in their independent counterparts with no such affiliation. The descriptive statistics shown in Table 5 indicate that Japanese firms with vertical keiretsu affiliation indeed have significantly lower cross-segment diversity in returns compared to their horizontal keiretsu and independent counterparts. The mean (median) CSD in returns is only 0.370 (0.295) in vertical keiretsu whereas it is 0.488 (0.512) and 0.477 (0.507) in horizontal keiretsu and independent firms, respectively. While these differences are statistically significant in parametric and nonparametric tests, corresponding differences of CSD in risks remain insignificant at conventional levels (see the bottom section of Table 5). To control for the effect of variables measuring segment-reporting incentives, reporting choices, and other underlying factors, we also estimate multivariate regressions to see the impact of keiretsu affiliation. The findings reported in Table 6 below are in all material respects consistent with those in Table 5. While firms with a vertical keiretsu affiliation have significantly lower cross-segment diversities in returns compared to independent firms, there is no significant difference between horizontal keiretsu and independent firms in this respect even after taking into account the above mentioned background factors. Overall, these findings fall in line with the notion that Japanese horizontal keiretsu clusters represent diverse industries instead of being rivals in the same industry.

13

For the remaining 121 Japanese firms (22.5%) in the sample we are unable to define keiretsu status from Dodwell, and these firms are therefore left out from subsequent tests.

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Table 5 Descriptive statistics of cross-segment diversity in returns and risks for Japanese keiretsu and independent firms. Firm affiliation Vertical keiretsu

Horizontal keiretsu

Independent

Total

Vertical vs. horizontal F-value (prob) KW Chi-square (prob) Vertical vs. independent F-value (prob) KW Chi-square (prob) Horizontal vs. independent F-value (prob) KW Chi-square (prob)

Nobs Minimum Mean Median Maximum Std. deviation Nobs Minimum Mean Median Maximum Std. deviation Nobs Minimum Mean Median Maximum Std. deviation Nobs Minimum Mean Median Maximum Std. deviation

CSD in returns

CSD in risks

47 − 0.088 0.370 0.295 0.971 0.317 162 − 0.158 0.488 0.512 0.995 0.333 207 − 0.173 0.477 0.507 0.994 0.349 416 − 0.173 0.469 0.495 0.995 0.340

47 − 0.153 0.265 0.228 0.802 0.226 162 − 0.191 0.310 0.307 0.822 0.219 207 − 0.125 0.279 0.252 0.930 0.242 416 − 0.191 0.290 0.273 0.930 0.232

4.68 (0.032) 4.46 (0.035)

1.54 (0.216) 2.60 (0.107)

3.71 (0.055) 3.45 (0.063)

0.14 (0.712) 0.18 (0.667)

0.10 (0.751) 0.07 (0.785)

1.62 (0.204) 1.82 (0.178)

Cross-segment diversity (CSD) is measured by Expression (3). F-values and KW (Kruskall–Wallis) Chi-squares significant at 10% or lower are in boldface.

6.3.2. The impact of a firm's ownership structure and the number of analysts following Besides the factors discussed above, we consider the impact of firm's ownership structure and the number of analysts following as additional proxies for segment-reporting incentives. Regarding a firm's ownership structure, measured by the percentage of closely held shares,14 we have grounds for two opposite expectations. On the one hand, under concentrated ownership a firm's insiders may have incentives to conceal detailed performance information from outsiders (e.g., Leuz, Nanda & Wysocki, 2003), thus decreasing cross-segment diversity

14

As an empirical measure, we use the average percentage of closely held shares during 1999–2003 available from Thomson Worldscope database.

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Table 6 Regression results of cross-segment diversity for Japanese keiretsu and independent firms. Independent variables

Exp. Dependent variable sign CSD in returns β

(Intercept) Keiretsu affiliation Vertical Horizontal Segment-reporting incentives Firm profitability Financial leverage Foreign sales Number of competitors Industry concentration Segment-reporting choices Segment aggregation Three reported segments Four reported segments Five or more rep. segments Control variables Industry profitability spread Firm industry-sector indicators Firm size indicators Model F-value (prob) Adj. R-square

? − −

CSD in risks

t-value β 0.668

9.08

−0.119 −2.16 0.006 0.17

t-value β 0.602

4.32

− 0.102 −1.92 0.012 0.34

+ − − − −

2.500 − 0.276 − 0.097 0.000 − 0.233

− + + +

− 0.069 −1.76 0.233 3.28 0.183 2.37 0.119 1.36

+ ? ?

0.164 0.31 Included Included 1.92 (0.041) 0.022

t-value β 0.295

5.82

− 0.011 −0.30 0.036 1.45

2.68

− 0.029 − 0.77 0.025 1.03 − 0.388 − 0.064 − 0.142 0.000 − 0.097

3.69 −2.90 − 0.80 − 0.04 − 0.60

0.117 0.23 Included Included 4.14 (0.000) 0.126

t-value 0.263

− 0.81 − 0.95 −1.66 − 0.35 − 0.36

− 0.037 − 1.34 0.106 2.12 0.157 2.88 0.184 2.98 − 0.117 −0.33 Included Included 0.82 (0.605) − 0.004

− 0.347 − 0.97 Included Included 2.39 (0.001) 0.060

Cross-segment diversity (CSD) is measured by Expression (3). F-values and t-values (two-tailed test) significant at 10% or lower are in boldface. Number of observations is 416 in all regressions.

of reported segments. On the other hand, firms with concentrated ownership structures and high percentages of closely held shares may have an incentive to decrease agency problems, and information asymmetry, and, thereby their cost of capital through increased disclosure (e.g., Healy & Palepu 2001). As regards the number of analysts following,15 our expectation is that firms with investors who take a larger amount of interest and pay attention to the firm's activities face larger pressures for increased disclosure and hence have increased incentives for showing diversity among reported segments. However, augmenting our regressions with these two independent variables does not prove to have any material effects (not reported in detail). Regression coefficients of these variables are generally insignificant, and their impact on the R-squares is marginal. Thus, the findings do not indicate that these two factors have any significant effect on crosssegment diversities.

15

The number of analysts following is measured by the number of analysts' earnings forecast for the firm available in the Thomson IBES History database.

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6.3.3. The impact of last reported segment In many firms with at least three segments, the earnings and assets information for the last reported segment may represent a group accounting or consolidation residual rather than describe the performance and resources of an actual business segment. In such cases, the role of the last segment is just to reconcile the financial-statement items of the individual business segments with those of the whole group or business combination.16 In order to see the impact of the last reported segment, we re-compute our CSD measures without the last reported segment for all firms with more than two reported segments.17 We expect that in a firm where the last segment represents an accounting residual rather than an actual business segment, its “performance” in terms of returns and risks deviates significantly from the rest of the firm's segments, thus leading to an increase in cross-segment performance diversity. In our data, the mean CSD in returns without the last segment is 0.310. As expected, it is clearly lower than the corresponding mean (0.389) when all reported segments are considered (the bottom section in Panel A of Table 3). Correspondingly, the mean CSD in risks without the last segment is 0.194, which is also lower than the corresponding mean (0.266) for all reported segments. These findings fall in line with the view that, for many firms, the returns and risks of the last reported segment differ from those of the other segments. Nevertheless, the crosssectional correlations of our CSD measures with and without the last segment turn out to be as high as 0.730 for returns and 0.697 for risks. Thus, although our CSD measures are affected by the last segment, its impact on the relative standings of firms is not very large; the same firms tend to show high (or low) cross-segment diversity in returns and risks irrespective of whether the last segment is considered or not. Finally, we regress our CSD measures adjusted for the effect of the last reported segment (by leaving it out from the computation of CSD for all firms with at least three segments) on a firm's country of domicile, segment-reporting incentives, reporting choices, and on the control variables. The overall results (not reported in detail) turn out to be weaker than those in Table 4 where all reported segments are included. Although the model F-values remain very significant, the adjusted R-square for returns decreases from 0.172 (Panel A of Table 4) to 0.097 when the last reported segments are excluded. This decrease is reflected also in the tvalues of the estimated regression coefficients which generally become less significant. In fact, only the country effect of Japanese firms and the effect of a firm's profitability remain significant at conventional levels. Corresponding results for risks are similar in the sense that the adjusted R-square decreases (from 0.076 to 0.066) and the t-values become less significant. In conclusion, it seems that in many firms the last reported segment can be an important driver of cross-segment diversity in returns and risks. A plausible explanation for this is that the last segment may in these firms represent a group accounting or consolidation residual

An illustrative example of such a case is Stora Enso for which sales of the last segment in 2003 is − 2185 million €, suggesting that this segment is a consolidation residual rather than an actual business segment. A look in the annual report confirms this suspicion because the last segment is called “Other and elimination” in the report. 17 Note that for firms with only two reported segments, the second (i.e. the last) segment must, by definition, be an actual segment rather than a consolidation residual. 16

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rather than a genuine business segment, thereby increasing the cross-segment diversity of reported segments. 7. Summary and discussion Given the inherent importance that diversities in returns and risks across reported segments have for the usefulness of segmental disclosures, we examine the extent to which a firm's reported segments convey information about cross-segment diversity in returns and risks. We expect that a firm's institutional setting and management-reporting incentives are important drivers of these diversities. For empirical measure, we develop an adjusted R-square statistic based on the one-factor ANOVA (Analysis of Variance) test with reported segments as the grouping variable. Using segment data from Japanese and U.S. firms, we document that, in general, there is a large variation of cross-segment diversity in returns and risks among the sample companies. Our results indicate that these diversities tend to be larger for returns than for risks. This finding suggests that profitability levels (average returns) differentiate reported segments from each other more effectively than segment risks (variability of returns). A plausible explanation for this is that, compared to more ambiguous risks, segment returns are conceptually and empirically easier to define from a management perspective, and therefore they play a more important role in management decisions concerning the segmentation of firm operations. Surprisingly, we document that, even after controlling for the effect of relevant industry and firm-level factors, cross-segment diversity in returns is significantly higher in Japanese than in U.S. multi-segment firms. This finding implies that the management approach for segmentation, as mandated in SFAS 131, does not necessarily by itself result in more diversity across reported segments. In contrast, higher cross-segment diversity in Japanese firms, even after controlling for other relevant underlying factors, unambiguously suggests that factors other than the management approach mandated in the United States can result in higher cross-segment diversity. While this finding is intriguing and anomalous to our expectation based on the institutional differences between the Japanese keiretsu setting and the United States, it may have at least three explanations. First, if the underlying intra-firm performance diversity in large Japanese conglomerates is larger than in their U.S. counterparts, then these diversities can be large enough to counteract the impact of a higher degree of segment aggregation documented in Japanese firms. While aggregation of business lines reduces the number of reported segments and thereby cross-segment diversities, this reduction does not necessarily imply that the remaining cross-segment diversities in Japanese firms are low. In fact, our descriptive statistics (reported in Table 2), showing a larger number of underlying industries and reported segments as well as a higher degree of segment aggregation in Japanese firms, fall in line with this explanation. It is also consistent with our additional tests, indicating insignificant differences of cross-segment diversities in returns and risks between horizontal keiretsu and independent firms in Japan. Another plausible explanation for the documented country effect relates to differences in reporting standards. While the Japanese segment-reporting standards are less stringent in some respects, they may still be effective enough in restricting segment aggregation to the point where the observed cross-segment diversity remains at a relatively high level. This may well be the case, for example, considering the guidance of Japanese GAAP on

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segmentation by business division, which differs from the U.S. in its level of detail as discussed in Section 3. Finally, it is possible that our control variables (number of reported segments and profitability spread of a firm's lines of business) are inadequate measures of the true underlying heterogeneity of firm operations, thereby leaving room for this heterogeneity to be reflected in our measure of cross-segment diversity. In addition to the economic and institutional settings related to the firm's country of domicile, we document that cross-segment diversities have significant relations with segment-reporting incentives measured by firm profitability, leverage, and the amount of foreign sales. These findings are consistent with the argument that capital-market information needs, agency and/or reputation costs, as well as a firm's political risks in foreign markets are significant drivers of cross-segment diversities. Contrary to our expectation, however, we are not able to find evidence that cross-segment diversity has a significant association with proxies of proprietary costs, such as the number of competitors and industry concentration in the two countries. Neither do we find evidence that the impacts of the segment-reporting (dis)incentives examined have systematic differences between the countries examined. Finally, our results from multiple regressions also suggest that the number of reported segments is an important factor in explaining cross-segment diversity in returns and risks. It seems, however, that cross-segment diversity in returns is not monotonically increasing with the number of reported segments. For managers making decisions on segmentation, this finding has interesting implications. In particular, the results indicate that when the number of reported segments is increased from two to four, a significant increase in cross-segment diversity in returns can be achieved. However, the empirical results from the aggregate sample do not indicate any significant increase in the diversity of returns from further disaggregation of the firm's operations into five or more segments. Acknowledgements The authors are grateful to Hannu Ojala, Seppo Ikäheimo and Teemu Malmi (HSE), for their helpful comments, to Joni Virtanen (HSE) for excellent research assistance, to Kazuo Hiramatsu, (Kwansei Gakuin University, Japan), Koji Kojima (Kwansei Gakuin University, Japan) and Kazuo Kobayashi (Financial Accounting Standards Foundation, Japan) for providing background information on national segment-reporting requirements, and to Jeffrey Gramlich and Ghon Rhee for their help in providing data on Japanese keiretsu classification. Constructive feedback from associate editor In-Mu Haw and an anonymous reviewer, as well as financial support from the HSE Foundation for this project, is gratefully acknowledged. Appendix A. Deriving measures of cross-segment diversity in returns and risks (CSD) To estimate the proportion of the total variation in the return and risk measures (Expressions (1) and (2)) attributable to differences between segments, we apply the onefactor ANOVA (Analysis of Variance) test with reported segments as the grouping variable (for a general description of the one-factor ANOVA, see, for example, Neter, Kutner, Nachtsheim, & Wasserman, 1996). This approach allows us to decompose the total variation of performance measure Mst (return or risk) into its between-segment and within-

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segment components as follows (subscripts s and t denote segment and period, respectively): Mst −M:: = M s :−M :: + Mst −M s : where

ðA1Þ

Mst −̅ M.. = deviation of the performance measure Mst for segment s and period t around the overall mean ̅ M.. across all segments and periods ̅ Ms· −̅ M..= deviation of the estimated segment performance mean ̅ Ms· around the overall mean ̅ M.. Mst −̅ Ms. = deviation of the performance measure Mst for segment s and period t around the segment mean ̅ Ms· Squaring Eq. (A1) and then summing over all segments and periods yields (note that the cross-product terms on the right-hand side drop out): ∑ ∑ðMst PM ::Þ2 = ∑ ns ðM s :PM ::Þ2 + ∑ ∑ðMst −M s :Þ2 s

t

s

s

t

ðA2Þ

where ns is the number of observations (periods) per segment. Denoting 2 SSTOðtotal sum of squaresÞ = ∑∑ ðMst PM ::Þ ;

SSBSðsum of squares between segmentsÞ = ∑ns ðM s :PM ::Þ2 ; and s

SSWSðsum of squares within segmentsÞ = ∑ ∑ðMst PM s :Þ2 ; s

t

Expression (A2) can be rewritten more concisely: ðA3Þ

SSTO = SSBS + SSWS:

Based on the above ANOVA design, cross-segment diversity (CSD) can now be measured with the following R-square statistic, adjusted for the number of reported segments (cf. Neter et al., 1996, 230–231), and computed separately for segment returns and risks: 2

CSD = Adj: R =

SSWS nT −r 1− SSTO nT −1

  nT −1 SSWS = 1− nT −r SSTO

ðA4Þ

where r nT

number of segments in the firm total number of observations for the firm (number of segments × number of periods per segment). Our measure for cross-segment diversity is analogous to the adjusted R-square of an ordinary multiple regression where the coefficient of determination is based on the ratio of

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