Industry concentration and corporate disclosure policy

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Industry concentration and corporate disclosure policy$ Ashiq Ali a,n, Sandy Klasa b, Eric Yeung c a

Naveen Jindal School of Management, University of Texas at Dallas, 800 West Campbell Road, SM-41, Richardson, TX 75080-3021, USA Eller College of Management, University of Arizona, Tucson, AZ 85721-0108, USA c Johnson Graduate School of Management, Cornell University, Ithaca, NY 14853-6201, USA b

a r t i c l e i n f o

JEL classification: L1 M40 M41 Keywords: Industry concentration Corporate disclosures Management forecasts Private placements versus seasoned equity offerings Analyst disclosure ratings Analyst forecast properties

abstract This study examines the association between U.S. Census industry concentration measures and the informativeness of corporate disclosure policy. We find that in more concentrated industries firms' management earnings forecasts are less frequent and have shorter horizons, their disclosure ratings by analysts are lower, and they have more opaque information environments, as measured by the properties of analysts' earnings forecasts. Also, when these firms raise funds they prefer private placements, which have minimal SEC-mandated disclosure requirements, over seasoned equity offerings. Overall, our findings suggest that firms in more concentrated industries disclose less and avoid certain financing decisions that have non-trivial disclosure implications, presumably due to proprietary costs of disclosure. & 2014 Elsevier B.V. All rights reserved.

1. Introduction Verrecchia (1983) predicts that firms with higher proprietary costs of disclosure disclose less than firms with lower proprietary costs of disclosure. A number of prior studies have attempted to test this prediction using industry concentration as a measure for proprietary costs of disclosure. However, the extant evidence is inconclusive (Beyer et al., 2010; Berger, 2011). For instance, Harris (1998) and Botosan and Stanford (2005) show that firms in more concentrated industries are less likely to provide separate business segment disclosures, and Bamber and Cheon (1998) report that firms in more concentrated industries provide less specific management forecasts of their earnings. On the other hand, Verrecchia and Weber (2006) document that firms in more concentrated industries are less likely to request the SEC to withhold proprietary information from their filings. Also, Li (2010) shows that in more concentrated industries, firms are more likely to provide management forecasts. A potential explanation for the mixed prior evidence on the association between

☆ We greatly appreciate the comments of John Core (the editor), Steven Monahan (the referee), Mark Lang and Edward Sul (the discussants), as well as the participants at the 2013 Journal of Accounting and Economics Conference. We also gratefully acknowledge the comments of Aziz Alimov, Steve Baginski, Linda Bamber, John Campbell, Dan Dhaliwal, Bernhard Ganglmair, David Haushalter, Shane Heitzman, Jean Helwege, Bob Holthausen, Kathy Kahle, Chris Lamoureux, Bill Maxwell, Jeff Miller, Ram Natarajan, Hernan Ortiz-Molina, Pradyot Sen, Mike Stegemoller, Tom Stober, Mo Xiao, and seminar participants at Georgetown University, George Washington University, Hong Kong University of Science and Technology, National University of Singapore, Texas A&M University, and the Universities of Arizona, Cincinnati, Georgia, and Notre Dame. We recognize the excellent research assistance provided by Matthew Serfling, Qin Wang, and Weiwei Wang. n Corresponding author. Tel.: þ1 972 883 6360; fax: þ 1 972 883 6811. E-mail addresses: [email protected] (A. Ali), [email protected] (S. Klasa), [email protected] (E. Yeung).

http://dx.doi.org/10.1016/j.jacceco.2014.08.004 0165-4101/& 2014 Elsevier B.V. All rights reserved.

Please cite this article as: Ali, A., et al., Industry concentration and corporate disclosure policy. Journal of Accounting and Economics (2014), http://dx.doi.org/10.1016/j.jacceco.2014.08.004i

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disclosure and industry concentration is that prior studies use industry concentration measures constructed from Compustat data, which do not include data on privately held companies. Ali et al. (2009) show that Compustat based concentration measures are poor proxies for actual industry concentration. They also report that the results of a number of accounting and finance studies that use Compustat based industry concentration measures are sensitive to using U.S. Census measures of industry concentration, which are constructed with data for both publicly traded and privately held companies. Thus, the mixed evidence reported in prior studies on the association between industry concentration and corporate disclosures could be due to the limitations of industry concentration measures constructed with Compustat data (Ali et al., 2009; Berger, 2011). The fact that a number of studies have attempted to examine the association between industry concentration and disclosure underscores the importance of a proper examination of this relation and showing whether the relation is consistent across different disclosure contexts. We do so in this study by using U.S. Census based industry concentration measures in our analyses. We also consider several disclosure contexts that have not been examined in prior papers that study the association between industry concentration and disclosure, in order to enhance the generalizability of our findings. We find that there is a significant negative relation between industry concentration and disclosure across the different disclosure contexts that we consider. We examine the association of industry concentration with the frequency and horizon of management forecasts of earnings and with a financing choice that has significant disclosure implications, specifically, equity financing via private placements versus seasoned equity offerings. We examine these disclosure settings because the related disclosures have the potential to provide rival firms with different types of information. For a more comprehensive examination of whether industry concentration is associated with corporate disclosure, we investigate whether firms in more concentrated industries receive lower disclosure ratings from analysts and have poorer information environments, as measured by properties of analysts' forecasts of earnings. An advantage of these tests is that they do not require us to specify the methods by which firms disclose their private information. We find that the frequency and horizon of management forecasts of annual earnings are negatively associated with industry concentration, suggesting that firms in more concentrated industries disclose less, and when they disclose they are less prompt.1 We also document that when firms sell new common stock, the likelihood of selling shares via a private placement rather than an SEO is positively associated with industry concentration. This finding is consistent with firms in more concentrated industries preferring to sell new equity shares via a private placement, presumably because (i) there are notably fewer Securities and Exchange Commission (SEC)-mandated public disclosure requirements for private placements than for seasoned equity offerings (SEOs), (ii) in most cases firms do not publicly disclose any information about a private placement until several days after the deal takes place, and (iii) in the case of private placements there are typically only a relatively small number of investors who purchase shares in the stock sale and to whom information about the impending sale is disclosed, which reduces the extent to which there is leakage of information to rivals about the sale before it takes place. We also document that firms' disclosure ratings by analysts, obtained from the Report of the Association for Investment Management and Research, are negatively related with industry concentration. Likewise, we show that for firms in more concentrated industries dispersion in analysts' earnings forecasts is higher, analyst earnings forecast errors are larger, and there is a greater volatility of analyst forecast revisions. These results further suggest that firms in more concentrated industries tend to disclose less. Finally, we examine the impact of industry leverage on the relation between industry concentration and disclosure. Chevalier (1995a, 1995b) and Phillips (1995) show that when incumbents in a concentrated industry have greater financial leverage, the intensity of competition from existing rivals is lower because higher leverage limits firms' ability to invest in market share building. Thus, in concentrated industries with lower (higher) debt levels, the probability that proprietary information contained in a firm's disclosures would be used against it by rivals is likely higher (lower). Consistent with the above argument, we find that the associations involving industry concentration that we document in the paper are less pronounced in industries with higher financial leverage. The observed negative relation between U.S. Census based industry concentration and different measures of corporate disclosure could be due to proprietary costs of disclosure if industry concentration proxies for the intensity of industry competition, the level of innovation in an industry, or the extent to which disclosures by firms in an industry provide more substantive and less noisy information about future industry demand. In Section 2, we discuss these plausible explanations in greater detail, but note that the descriptive validity of our arguments are empirical questions and that answering these questions is beyond the scope of our study. Our study makes several contributions. It addresses the concern raised in Beyer et al. (2010) and Berger (2011) that prior accounting studies report mixed evidence on the association between Compustat based measures of industry concentration and corporate disclosure. We document a consistent negative relation between U.S. Census based industry concentration and different measures of corporate disclosure. When we repeat our analyses with Compustat measures of industry concentration in place of the U.S. Census measures of industry concentration, we find that in almost all cases industry

1 Some of the prior studies on corporate disclosures assume that firms incur proprietary costs from providing earnings forecasts (see e.g., Verrecchia, 1983; Bamber and Cheon, 1998; Li, 2010). Accordingly, we consider management earnings forecasts in our analyses. This assumption may not be valid, however, if earnings data are too aggregate to provide any strategic information to rivals.

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concentration does not exhibit a significantly negative association with our corporate disclosure measures. These findings suggest that the use of Compustat based measures of industry concentration is responsible for the mixed evidence in the prior literature on the association between industry concentration and disclosure. An important recent study, Li (2010), investigates in a more comprehensive manner the relation between product market competition and disclosure by considering the effect of competition from both existing rivals and potential entrants. Our study contributes relative to Li (2010) by examining the effect on her results of using U.S. Census industry concentration measures in place of Compustat based industry concentration measures. Li (2010) reports that the frequency and horizon of management forecasts of earnings are negatively related with a composite measure of competition from existing rivals. This composite measure is primarily composed of and is negatively related to Compustat based industry concentration measures, implying that these concentration measures are positively related to the frequency and horizon of management forecasts. In contrast, we find that U.S. Census industry concentration measures are negatively associated with management forecast frequency and horizon. We also contribute beyond Li (2010) by considering several disclosure proxies other than those related to management forecasts and find that their associations with U.S. Census industry concentration measures are always negative. Furthermore, other prior studies examining the effect of proprietary costs of disclosure on the informativeness of corporate disclosure policy consider decisions that are primarily related to information disclosure, e.g., separate business segment disclosures in financial statements (Harris, 1998; Botosan and Stanford, 2005; Bens et al., 2011), management forecasts of earnings (Bamber and Cheon, 1998; Li, 2010), firms requesting the SEC to withhold proprietary information from their filings (Verrecchia and Weber, 2006), and disclosure of information about customers in 10-Ks (Ellis et al., 2012). Our evidence on the association of industry concentration with a firm's decision to sell new equity shares through private placements versus SEOs suggests that proprietary costs of disclosure not only affect disclosure decisions, but also affect other business decisions that have non-trivial disclosure implications. Finally, our paper also contributes to the growing empirical literature on the relation between industry concentration and different types of corporate decisions, such as those related to financing and investing (e.g., Kovenock and Phillips, 1997; Mackay and Phillips, 2005; Haushalter et al., 2007). We show that industry concentration is associated with corporate disclosure decisions. The remainder of the paper is organized as follows. Section 2 discusses empirical predictions of the relation between industry concentration and disclosure based on arguments in prior work. Section 3 discusses data sources and variable definitions. Section 4 presents our empirical results and Section 5 concludes. 2. Predicted relations between industry concentration and disclosure 2.1. Industry concentration and the proprietary costs of disclosure A large body of theoretical work examines managers' incentives to disclose information to outside parties. Grossman (1981) and Milgrom (1981) argue that given adverse selection problems, managers should disclose to capital market participants all value-relevant information. Verrecchia (1983) allows for the existence of proprietary costs of disclosure in his model of discretionary disclosure and arrives at an equilibrium in which some firms do not disclose all value-relevant information. Specifically, he shows that capital market participants will provide firms that have higher proprietary costs of disclosure more discretion in their disclosure practices and that these firms consequently disclose less than firms with lower proprietary costs of disclosure. Proprietary costs of a firm's disclosures are higher the more potentially useful is the disclosed information to the firm's product market rivals and the greater is the extent to which these rivals take advantage of the information at the expense of the disclosing firm. Below, we discuss how the existence of proprietary costs of disclosure leads to empirical predictions with regards to the relation between industry concentration and corporate disclosures. 2.1.1. Industry concentration as a measure of competition Verrecchia (1990) and Clinch and Verrecchia (1997) argue that there is less disclosure in industries in which there is more intense competition among incumbents, because proprietary costs of disclosure are greater in such industries. In these industries, a disclosing firm's rivals are likely to take more aggressive actions in response to its disclosures. Also, Darrough and Stoughton (1990) contend that greater competition in an industry from potential entrants, due to low entry costs, leads to greater disclosure by incumbent firms, because the disclosure of “bad news” by an incumbent can help deter entry. Raith (2003) points out that if product markets vary principally in size or entry costs then low levels of industry concentration are suggestive of intense industry competition. He explains that “competition can also be said to increase when the market size increases or the cost of entry falls, since both changes induce new firms to enter the market, leading to lower prices.” Raith (2003) also argues that if product markets primarily vary in product substitutability or other dimensions of the toughness of competition, high industry concentration levels are indicative of intense industry competition because fewer firms can survive in an industry that is subject to greater competition (e.g., Sutton, 1991). In our empirical models of disclosure, we include as an explanatory variable a measure for competition from potential entrants from Li (2010) and Li et al. (2013). They include this variable in their models to test the Darrough and Stoughton (1990) prediction that firms disclose more when faced with greater competition from potential entrants. The measure for competition from potential Please cite this article as: Ali, A., et al., Industry concentration and corporate disclosure policy. Journal of Accounting and Economics (2014), http://dx.doi.org/10.1016/j.jacceco.2014.08.004i

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entrants is based on proxies for entry costs and market size. Thus, including this measure in our models reduces the likelihood that industry concentration would proxy for these factors, and makes it more likely that industry concentration proxies for product substitutability or other dimensions of the toughness of competition. Under this scenario, competition among existing rivals is greater in more concentrated industries and the arguments in Verrecchia (1990) and Clinch and Verrecchia (1997) predict that firms in more concentrated industries disclose less.2 With regard to the above discussion on industry concentration as a measure of competition, there is an important caveat. Competition is a multidimensional concept and industry concentration is only an indirect and imperfect measure of competition. Thus, any inferences about the effect of competition on disclosure that are drawn from the observed relation between industry concentration and disclosure should be viewed with caution. 2.1.2. Industry concentration as a measure of innovation Firms in more innovative industries run a greater risk of revealing company secrets through their public disclosures. Since rivals can use the disclosed information to take actions that would enhance their profits at the expense of the disclosing firm, proprietary costs of disclosure are likely to be higher for firms in more innovative industries, and they are likely to disclose less (Verrecchia, 1983).3 There is a large body of work that considers the association between industry concentration and innovation; however, theoretical predictions and empirical evidence on this association are mixed (Cohen, 2010).4 Depending on whether the actual relation between industry concentration and innovation is positive (negative), a negative (positive) association between industry concentration and corporate disclosure could be expected. 2.1.3. Industry concentration and informativeness of corporate disclosure about industry demand In more concentrated industries, each firm typically accounts for a large fraction of aggregate industry output. Thus, corporate disclosures in these industries may provide more reliable information about future industry demand than similar disclosures in less concentrated industries. Given that rivals likely use more reliable information on industry demand to revise their strategies to the detriment of the disclosing firm, this argument predicts that proprietary costs of disclosure are larger for firms in more concentrated industries and that there is consequently a negative relation between industry concentration and corporate disclosures. 2.2. Information sharing agreements and collusion Finally, we discuss certain scenarios under which an association between industry concentration and corporate disclosures is not expected, even if industry concentration proxies for competition.5 As discussed by Vives (2008) in his review of the information sharing literature, unilaterally revealing information can be the dominant strategy in certain circumstances.6 Vives (2008) argues that the existence of trade associations is evidence that firms do at times voluntarily pre-commit to share information. If firms commit ex ante to fully disclose most of their private information to competitors, the proprietary costs of ex-post discretionary disclosures resulting from product market competition become unimportant.7 Hence, firms can no longer justify withholding information from the capital markets, and we may not observe any association between industry concentration and discretionary disclosure. Also, if it is possible for firms to collude, the likelihood that firms enter into information sharing agreements will increase and we may not observe any association between industry concentration and discretionary disclosure. However, Kuhn and Vives (1995) and Vives (2006) note that hurdles, such as the threat of being sued by government agencies, often prevents such agreements.8 2 Li (2010) predicts a positive association between industry concentration and disclosure, based on the assumption that higher industry concentration implies less competition among existing rivals. However, as we argue above, with the inclusion of the variable for potential competition, which is based on proxies of entry costs and market size, the incremental variation in industry concentration in our models is more likely due to product substitutability or other dimensions of the toughness of competition. In that case, higher concentration indicates more intense competition among existing rivals (Raith, 2003). 3 In a similar vein, Bamber and Cheon (1998) note that firms in more concentrated industries are more likely to have a “competitive strategic advantage,” which they risk revealing through their disclosures, and would therefore disclose less. Harris (1998) suggests that firms in more concentrated industries enjoy abnormal profits, which they try to hide from rivals, by disclosing less. 4 In his review of this literature, Gilbert (2006) suggests that one should not conclude from the mixed evidence that industry structure does not matter for innovation. He argues that one should consider that there is no one theory of the relationship, but many. Moreover, he points out that empirical research has not controlled for the contingencies highlighted by theorists. 5 We thank the referee for suggesting this point. 6 This theoretical prediction assumes truth-telling, that is, it ignores firms' incentives to disclose misleading information to competitors. See Ziv (1993) for an example of a theoretical analysis on the effect of relaxing the truth-telling assumption on firms' incentives to share information. 7 Verrecchia (2001, p. 146) describes the difference between an ex ante commitment to disclose and ex post discretionary disclosure as follows: “By a discretionary disclosure arrangement, I mean a situation in which managers or firms exercise discretion with respect to the disclosure of information about which they may have knowledge (i.e., ex post). Alternatively, by a pre-commitment arrangement or mechanism, I mean a situation in which managers or firms establish a preferred disclosure policy in the absence of any prior knowledge of the information (i.e., ex ante).” 8 Another hurdle is that in most cases tacit collusion agreements are expected to last for only a short period of time because it is difficult for firms to detect whether a competitor has violated the agreement (e.g., Stigler, 1964; Green and Porter, 1984).

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3. Methodology 3.1. Sample and measurement of industry concentration We study Compustat manufacturing firms (6-digit North American Industry Classification System (NAICS) codes between 311111 and 339999) over the years 1995–2009. We restrict our sample period to these years because data on management forecasts, which we use in some of our tests, are only widely available starting in 1995 and data on the HerfindahlHirschman index and the four-firm ratio, our measures for industry concentration, are collected from Census of Manufactures publications for the 1997, 2002, and 2007 U.S. Census years.9 The Herfindahl-Hirschman index is calculated for a 6-digit NAICS industry within the manufacturing sector by summing the squares of the individual company market shares of the 50 largest public and private companies in the industry or all the public and private companies in the industry, whichever is lower. The U.S. Census Herfindahl-Hirschman index has been used in a large number of prior studies that consider how industry concentration affects corporate policy decisions in the manufacturing sector.10 The four-firm ratio of an industry is the sum of the market shares of the four largest firms in the industry in terms of market share. We use two different variables for industry concentration to ensure that the study's results are not sensitive to how industry concentration is measured. Following prior studies (see e.g., Haushalter et al., 2007; Ali et al., 2009), we assume that the values of the 1997, 2002, and 2007 index are valid for the five-year windows centered on 1997, 2002, and 2007, namely, the 1995–1999, 2000– 2004, and 2005–2009 periods, respectively.11 Panel A in Table 1 presents descriptive statistics of the Herfindahl-Hirschman index and the four-firm ratio. We use the Herfindahl-Hirschman index and four-firm ratio calculated at the 6-digit NAICS level for 356 industries in our multivariate tests. However, given this large number of industries, we provide statistics for broader 3-digit industry groups in this table. For each 6-digit NAICS industry, we first calculate the mean values of the Herfindahl-Hirschman index and the four-firm ratio across the years 1997, 2002, and 2007. Next, for each 3-digit NAICS industry, we report the median value of the Herfindahl-Hirschman index and the four-firm ratio for the 6-digit NAICS industries within the particular 3-digit NAICS industry. This allows us to provide statistics for 21 broader 3-digit NAICS industries. In Panel A of Table 1, the industries are listed in ascending order of the Herfindahl-Hirschman index, and the results show that there is significant variation for industry concentration across our sample. For instance, the printing and related support, wood products, and the fabricated metal products industries are the least concentrated with median 6-digit NAICS industry Herfindahl-Hirschman index (four-firm ratio) values of 98, 217, and 240 (15, 21, and 24). In contrast, the chemicals, beverage and tobacco products, and transportation equipment industries are the most concentrated with median 6-digit NAICS industry Herfindahl-Hirschman index (four-firm ratio) values of 934, 987, and 1033 (53, 62, and 57). Within each of the 3-digit NAICS industry groups, there can also be significant variation in the values of the Herfindahl-Hirschman index across different six-digit NAICS industries. For example, on average over the 1997, 2002, and 2007 Census years, in the transportation equipment industry group (3-digit NAICS code of 336), the automobile manufacturing industry (6-digit NAICS code of 336111) has a Herfindahl-Hirschman index (four-firm ratio) value of 2184 (78.1). Similarly, in the fabricated metal products industry group (3-digit NAICS code of 332), the sheet metal work manufacturing industry (6-digit NAICS code of 332322) has a Herfindahl-Hirschman index (four-firm ratio) value of only 21.3 (5.6). Panel B in Table 1 reports some of the distinguishing characteristics of firms belonging to more versus less concentrated industries. For each of the quintiles sorted by the U.S. Census Herfindahl-Hirschman index reported for 6-digit NAICS industries, this panel reports median values of the total number of firms (public plus private) per industry and median values of firm size for the sample period 1995 to 2009. This table shows that, as expected, industries with higher U.S. Herfindahl-Hirschman index values are populated by fewer and larger firms. Specifically, the median value of the number of firms in a 6-digit NAICS industry is 1011 for the first quintile, which contains the least concentrated industries, and the corresponding value is only 116 for the fifth quintile. Panel B also shows that average firm size as measured by net sales, market capitalization, or book assets, (obtained from the Compustat database) is monotonically increasing from the first to the fifth industry concentration quintiles. Median firm size is about three to four times larger for firms in industries in the highest quintile of industry concentration as compared to firms in the lowest quintile. Specifically, median net sales (market capitalization, book assets) for firms in the lowest and highest quintiles are $296m ($242m, $275m) and $1108m ($749m, $1058m), respectively.

9 We use industry concentration data from only the 1997, 2002, and 2007 U.S. Censuses because prior to 1997 the U.S. Census reported industry concentration measures for 4-digit Standard Industrial Classification (SIC) industries rather than for 6-digit NAICS industries. There are typically more firms in a 4-digit SIC industry than in a 6-digit NAICS industry, and this makes the values of industry concentration higher for 4-digit SIC industries than for 6digit NAICS industries. Thus, U.S. Census measures of industry concentration before and after 1997 are not comparable. 10 For instance, see Aggarwal and Samwick (1999), Mackay and Phillips (2005), Campello (2006), Haushalter et al. (2007), Akdogu and MacKay, 2008, Klasa et al. (2009), and Fresard (2010). 11 As in most work in the product markets area, we assume that there is a one-to-one relationship between the industry classification code assigned to a firm and the product market in which the firm competes. We address the sensitivity of our results to this issue in Section 4.5.1. Also, like other studies that use industry concentration measures provided by the U.S. Census, we use industry concentration measures which are calculated with data from only U.S. firms. Industry concentration measures calculated at the global level are not available.

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Table 1 Industry concentration and industry characteristics. In Panel A, for each 3-digit NAICS industry within the manufacturing sector, we report the median values of the Herfindahl-Hirschman index and the four-firm ratio for the component 6-digit NAICS industries. The Herfindahl-Hirschman index and four-firm ratio for a 6-digit NAICS industry is the average of the 1997, 2002 and 2007 Census year values. In Panel B, quintiles are based on 6-digit NAICS Herfindahl-Hirschman index values. # of firms per industry is the number of public and private firms in a 6-digit NAICS industry as reported by the Census of Manufactures. Net sales is defined as net sales in millions in year t. Market capitalization is defined as the market value of equity in millions at the end of year t. Book assets is the book value of total assets in millions at the end of year t. Net sales, Market capitalization, and Book assets are inflation-adjusted. Median values are reported using firm-year observations over the 1995–2009 sample period. Panel A: Industries sorted by the Herfindahl-Hirschman index

Median 6-digit NAICS industry Herfindahl-Hirschman index and four-firm ratio

3-digit NAICS code

Industry name

Herfindahl-Hirschman index

323 321 332 314 333 313 337 339 326 315 331 327 324 335 334 322 311 316 325 312 336

Printing and related support Wood products Fabricated metal products Textile product mills Machinery Textile mills Furniture and related products Miscellaneous Plastics and rubber products Apparel Primary metal Nonmetallic mineral products Petroleum and coal products Electrical equipment, appliances, and components Computer and electronic products Paper Food Leather and allied products Chemicals Beverage and tobacco products Transportation equipment

Four-firm ratio

97.8 216.8 240.1 320.0 361.7 383.7 387.5 390.2 396.2 537.5 579.6 615.9 658.4 666.4 696.3 758.9 778.1 857.2 933.8 986.6 1033.3

15.4 21.4 24.2 28.4 30.5 30.4 33.5 32.2 32.7 40.4 40.5 42.0 42.2 46.1 44.1 45.8 47.4 49.7 52.9 62.4 56.6

Panel B: Industry characteristics by quintiles of the Herfindahl-Hirschman index Quintile 1 2 3 4 5

Herfindahl-Hirschman index

Four-firm ratio

# of firms per industry

Net sales

Market capitalization

Book assets

108.9 310.6 584.2 991.3 1769.3

17.3 26.1 39.9 51.8 66.5

1011 454 293 158 116

296 520 755 946 1108

242 348 565 747 749

275 459 672 831 1058

3.2. Management and analysts' earnings forecast data Data on management earnings forecasts are obtained from the Company Issued Guidelines (CIG) database maintained by First Call. Data on analysts' earnings forecasts are obtained from the Institutional Brokers' Estimate System (IBES).

3.3. Corporate disclosure ratings data Analysts' overall ratings of a firm's corporate disclosures are hand-collected from the Report of the Association for Investment Management and Research for the years 1995 and 1996, the last two years for which these data are available. These ratings, which range from a score of zero to 100, are determined by analyst subcommittees, organized by industry. They consider the overall quality of a firm's disclosures over a particular year (Lang and Lundholm, 1996; Healy et al., 1999). Specifically, these ratings represent the quality of a firm's disclosures along three dimensions: (1) annual published information, such as annual reports, (2) quarterly and other published information, such as quarterly reports, press releases, and proxy statements, and (3) investor relations and related aspects, such as how responsive companies are to analyst questions, the accessibility of management and their candor in discussing corporate developments, and the frequency and content of presentations to analysts. The industry subcommittees attempt to provide disclosure ratings for the ‘leading’ firms in an industry, resulting in only large firms in an industry being selected for evaluation. Thus, the number of firms with disclosure ratings is relatively small. Please cite this article as: Ali, A., et al., Industry concentration and corporate disclosure policy. Journal of Accounting and Economics (2014), http://dx.doi.org/10.1016/j.jacceco.2014.08.004i

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3.4. Private placements and seasoned equity offerings Data on common stock sales inside the United States through private placements are hand-collected by searching through news stories on the Lexis-Nexis database. Data for SEOs of new common stock that take place in the United States are obtained from Securities Data Corporation's (SDC) Global New Issues database.

3.5. Sample selection bias The U.S. Census provides data on the Herfindahl-Hirschman index only for the manufacturing sector, and therefore we conduct our main tests using a sample of manufacturing firms. Over our sample period, approximately 39.5% of nonfinancial Compustat firms and 33.4% of all Compustat firms are in the manufacturing sector. It is possible that some of the results we obtain in our study may not apply to firms in non-manufacturing industries.12 Moreover, the U.S. Census industry concentration data are only available every five years, hence we use industry concentration ratios of a particular U.S. Census year as a proxy for industry concentration for a five-year window surrounding the U.S. Census year. The resulting measurement error could introduce a bias in our results. Samples of firms that issue management forecasts of earnings, receive analyst disclosure ratings, or sell new equity shares are not random samples, which could also introduce a bias in our results. Further, loss of observations due to data requirements for our explanatory variables may also lead to a bias in our results. Table A1 in the appendix reports how data requirements affect the sample sizes for our different analyses. Furthermore, Table A2 in the appendix compares the mean and median values of the variables for the sample used for a particular analysis with the corresponding mean and median values of the variables for (i) all manufacturing firms on Compustat and (ii) all firms on Compustat. This table shows that for some of our samples, the values for the Herfindahl-Hirschman index and the four-firm ratio are slightly different than those for the sample of Compustat manufacturing firms. Also, firm size tends to be slightly larger for our samples. The larger firm size of our sample firms is the likely reason why the average values of certain variables are different between our sample firms and both the manufacturing firms on Compustat and all firms on Compustat.

4. Empirical findings 4.1. Industry concentration and the frequency and horizon of management earnings forecasts To investigate how industry concentration is related to corporate disclosures, we first examine the association between industry concentration and the frequency of voluntary management earnings forecasts and report the results in Table 2. The frequency of management forecasts is defined as the number of management forecasts made by a firm during a fiscal year for the earnings of that year. We focus on management forecasts of annual earnings following Li (2010), who argues that proprietary costs of disclosure are likely to be more significant for annual forecasts than for quarterly forecasts, because the horizon of an annual forecast is typically longer, providing rivals more time to respond to new information contained in the forecast. Another benefit of using annual forecasts is that this makes our results more comparable to those in Li (2010). In approximately half of our sample firm-years, managers do not make earnings forecasts, so we use one-sided Tobit models.13 Our frequency models contain control variables that are motivated from prior work. We follow Li (2010) and Li et al. (2013) and include a control for competition from potential entrants. This variable is calculated following the approach in Li et al. (2013). We first calculate for each six-digit NAICS industry the size-weighted average of property, plant, and equipment, the size-weighted average of research and development expenses, the size-weighted average of capital expenditures, and total industry sales. Next, we calculate for each industry the average across the decile ranks of these four industry measures. We then multiply this average value by minus one, because competition from potential entrants is less intense when this average value is greater. Bens et al. (2011) argue that public firms competing in an industry with a higher proportion of private firms may disclose less, given that private firms are likely to follow non-disclosure policies. We control for this factor by including the ratio of the number of public to private firms in the firm's 6-digit NAICS industry. Managers who are more uncertain about their firms' future prospects are likely to disclose less (Dye, 1985; Jung and Kwon, 1988). To control for this factor, we include two variables in our models: stock return volatility over the prior year and the current year absolute change in earnings per share (Waymire, 1985; Baginski et al., 2002). To control for management's greater desire to disclose good news than bad news (Miller, 2002; Kothari et al., 2009), we include the current period's market-adjusted stock returns. Analysts are likely to have greater difficulty forecasting earnings of higher R&D firms (Barth et al., 2001) and the managers of these firms may therefore provide more earnings guidance. On the other hand, managers of higher R&D firms may provide fewer forecasts because of a greater concern about their accuracy. We control for these two factors by including R&D expenses. We also include the following previously identified determinants of the frequency of management forecasts: firm size (Kasznik and Lev, 1995), analyst coverage (e.g., Abarbanell et al., 1995; Karamanou and 12 13

We attempt to address this issue in Section 4.5.2. Our results are very similar if we use the Poisson regression models rather than one-sided Tobit models.

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Table 2 Industry concentration and the frequency of management forecasts of earnings. The table reports results of one-sided Tobit regression models. The dependent variable is the number of management forecasts made during a fiscal year for the earnings of that fiscal year. We use a Tobit model because the dependent variable is truncated at zero. The sample consists of firms in the manufacturing sector during the years 1995–2009, with necessary data for the variables used in the regression models. Management forecast data are collected from First Call's Company Issued Guidelines (CIG) database. The industry Herfindahl-Hirschman index is scaled by 10,000. The industry four-firm ratio is scaled by 100. Industry asset-weighted mean of the net-debt-to-asset ratio is for the 6-digit NAICS industry, where net-debt is defined as long-term debt minus cash. To construct the competition from potential entrants variable, we first calculate for six-digit NAICS industries the size-weighted average of property, plant, and equipment, the size-weighted average of research and development expenses, the size-weighted average of capital expenditures, and total industry sales. Next, we calculate for each industry the average across the decile ranks of these four industry measures and then multiply the resulting value by minus one. Ratio of the number of public to private firms in the industry is calculated as the number of firms on Compustat in the 6-digit NAICS industry divided by the difference between this number and the total number of firms in the industry as reported in Census of Manufactures publications. Stock return volatility is calculated with monthly stock return data over the firm's fiscal year. Absolute change in annual earnings per share/ stock price is the absolute value of the annual change in earnings per share deflated by stock price at the beginning of the fiscal year. Market-adjusted stock return is the firm's buy-and-hold 12-month fiscal year stock return minus the CRSP value-weighted stock return over the same period. Research and development expense/book assets is the R&D expense over a fiscal year divided by the book value of assets at the beginning of the fiscal year. Analyst coverage is the average number of analysts making earnings forecasts for the firm over the fiscal year. Institutional ownership data are collected from the Thomson Reuters CDA/Spectrum Institutional (13f) Holdings database and represents institutional holdings at the beginning of the fiscal year. The PostRegulation Fair Disclosure dummy takes the value of one for firm-years from 2001 onwards and zero otherwise. Industry profitability is calculated using principal component analysis following the methodology in Li (2010). Equity or debt issuance dummy takes a value of one if a firm issues public equity or public debt in a subsequent two-year period, and zero otherwise. Leverage is measured at the firm-level as total liabilities minus deferred taxes scaled by total book assets. Standard deviation of earnings is calculated as the standard deviation of earnings before extraordinary items over the prior five years, with a requirement of at least three years of observations. Analyst optimism is measured as the difference between analyst consensus estimation at the beginning of the fiscal year and the actual earnings per share, scaled by the absolute value of actual earnings per share. Litigation industries dummy takes a value of one if the firm operates in an industry facing high litigate risk as defined in Li (2010), and zero otherwise. Year dummies are included in all the models, and the intercept represents the omitted year dummy. z-statistics (reported in parentheses) are based on standard errors clustered by industry. ***, **, and * indicate significance levels for two-tailed tests at the 1%, 5% and 10% levels, respectively. Model Intercept Industry Herfindahl-Hirschman index Industry four-firm ratio Industry Herfindahl-Hirschman index  Industry asset-weighted mean of the net-debt-toasset ratio Industry four-firm ratio  Industry asset-weighted mean of the net-debt-to-asset ratio Industry asset-weighted mean of the net-debt-to-asset ratio Competition from potential entrants Ratio of the number of public to private firms in the industry Stock return volatility Absolute change in annual earnings per share/stock price Market-adjusted stock return Research and development expense/book assets Natural logarithm of market value of equity Analyst coverage Institutional fractional ownership Post-Regulation Fair Disclosure dummy Industry profitability Equity or debt issuance dummy Market-to-book ratio of assets Leverage Standard deviation of earnings Positive earnings change dummy

1

2

3

4

 7.437nnn  7.296nnn  7.327nnn  7.254nnn (  13.20) (  12.58) (  12.83) (  11.95)  5.307nn  6.138nnn (  2.25) (  2.87)  1.302  1.576n (  1.58) (  1.65) 21.734nn (2.42) 3.056 (0.96)  0.533  0.069 (  0.68) (  0.06) 0.839 0.833 0.882 0.823 (1.43) (1.39) (1.51) (1.35)  2.344nn  2.265nn  2.322nn  2.201nn (  2.52) (  2.41) (  2.54) (  2.39)  4.113n  4.396nn  3.670n  3.988n (  1.91) (  1.99) (  1.76) (  1.86)  2.894nnn  2.883nnn  2.868nnn  2.854nnn (  4.82) (  4.78) (  4.79) (  4.78) 0.098 0.098 0.094 0.095 (1.35) (1.33) (1.32) (1.32)  4.322nnn  4.282nnn  4.052nnn  3.995nnn (  3.45) (  3.37) (  3.25) (  3.15) 0.242nnn 0.243nnn 0.238nnn 0.246nnn (2.91) (2.90) (2.91) (2.94) 0.089nnn 0.086nnn 0.092nnn 0.087nnn (2.92) (2.79) (3.03) (2.84) 3.797nnn 3.806nnn 3.819nnn 3.811nnn (11.91) (11.92) (11.90) (11.89) 2.173nnn 2.116nnn 2.201nnn 2.144nnn (5.10) (4.92) (5.17) (5.02) 7.246nnn 7.367nnn 6.838nnn 7.192nnn (5.95) (5.60) (5.95) (5.75)  0.318nn  0.327nn  0.314nn  0.323nn (  2.38) (  2.48) (  2.33) (  2.45) 0.075 0.084 0.072 0.087 (1.05) (1.16) (1.02) (1.22) nnn nnn nn 1.374 1.401 1.053 1.117nnn (3.09) (3.15) (2.48) (2.63)  3.240nnn  3.330nnn  3.129nnn  3.995nnn (  3.96) (  4.04) (  3.93) (  4.02) 0.221n 0.220n 0.232n 0.228n (1.68) (1.67) (1.76) (1.73)

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Table 2 (continued ) Model Analyst optimism Litigation industries dummy R2-adjusted N

1  0.065 (  1.90) 0.599 (1.53) 0.123 20,286

2 n

3 nn

 0.067 (  1.97) 0.597 (1.45) 0.123 20,286

4 n

 0.061 (  1.76) 0.715n (1.82) 0.125 20,286

 0.064n (  1.86) 0.728n (1.72) 0.123 20,286

Vafeas, 2005), institutional ownership (Ajinkya et al., 2005), and an indicator variable for the post-Regulation Fair Disclosure period (Heflin et al., 2003). Finally, we include the following additional control variables that Li (2010, Table 9), uses in her model of management earnings forecast frequency.14 First, she controls for industry profitability, which is estimated using principal component analysis on nine industry-level variables. She argues that this variable may be associated with multiple dimensions of competition. Second, she includes an indicator variable for whether a firm issues public debt or equity in the subsequent two-year period, because firms are likely to disclose more before obtaining external financing to reduce their cost of capital. Third, she includes the market-to-book assets ratio and historical earnings volatility of the firm, because these variables capture forecasting difficulty, which may discourage firms from providing earnings forecasts. Fourth, she also controls for firm-level leverage, noting that leverage is associated with the intensity of competition in an industry. Fifth, she includes a measure for optimism in analysts' earnings forecasts, because managers are likely to “walk down” such forecasts by providing earnings guidance. Finally, she includes an indicator variable for litigation risk, because firms are likely to issue forecasts to disclose bad news sooner to reduce litigation risk. In the first model of Table 2, the coefficient on the Herfindahl-Hirschman index is negative and significant. In the second model, the coefficient on the four-firm ratio is also negative, but is not statistically significant at conventional levels (t-stat ¼  1.58).15 These results suggest that firms in more concentrated industries disclose less.16 The third and fourth models in Table 2 are the same as the first two models in this table, except that they also include as additional variables the 6-digit NAICS asset-weighted industry average of the net-debt-to-assets ratio, calculated for firms on Compustat, and its interaction with the Herfindahl-Hirschman index and the four-firm ratio. We consider these interactions in our models, because Chevalier (1995a, 1995b) and Phillips (1995) report that when incumbents in a concentrated industry have higher financial leverage, the intensity of industry competition is lower because higher leverage limits firms' ability to invest in market share building. Consequently, in concentrated industries with lower (higher) debt levels, the likelihood that proprietary information contained in a firm's disclosures would be used against it by rivals is likely higher (lower).17 In defining the leverage ratio, we subtract cash holdings from debt because corporate cash reserves provide financial flexibility. Also, we use an asset-weighted industry average, because as compared to small companies with low leverage, large companies with low leverage are likely to cause more harm to the disclosing firm when they exploit its disclosed information. In the third and fourth models of Table 2, the coefficients on the Herfindahl-Hirschman index and the four-firm ratio are negative and significant. Also, the coefficient on the interaction variable in the third model is positive and significant, while the coefficient on the interaction variable in the fourth model is positive, but not significant.18 These results suggest that the negative associations we document between industry concentration and management forecast frequency are, as expected, less pronounced for industries with higher leverage. Next, in Table 3 we examine the association between industry concentration and the horizon of management earnings forecasts, defined as the number of days between the date of a management forecast of annual earnings and the firm's fiscal year-end date. For this analysis, firm years with no management earnings forecasts are dropped. The models are the same as those in Table 2, except that the dependent variable is the average horizon of management earnings forecasts. Also, for this analysis we use the OLS procedure.

14 We re-estimate our management forecast frequency models after including only the control variables used by Li (2010, Table 9), and find that the results for our main variables of interest remain very similar. 15 In our regressions in the paper, we either cluster standard errors by year and industry or we include a year indicator variable and cluster by industry, where appropriate. All of our results are robust to clustering by firm in place of clustering by industry. 16 Li (2010, Table 9) reports that management forecast frequency is negatively related to her composite measure of competition from existing rivals. This composite measure is primarily composed of and is negatively related to Compustat based industry concentration measures. Thus, her results suggest that management earnings forecast frequency is positively related to Compustat based measures of industry concentration. We find that management forecast frequency is negatively related to U.S. Census measures of industry concentration. Put together, these findings imply that the association between management forecast frequency and industry concentration is sensitive to using Compustat versus U.S. Census measures of industry concentration. 17 Research in the capital structure area shows that leverage is negatively associated with research and development expenses, one of the measures of innovation (e.g., Hovakimian et al., 2001; Kayhan and Titman, 2007). Thus, in our models of corporate disclosures, the main effect of the industry leverage variable could proxy for innovation in an industry and may capture some of the effect of innovation on disclosure. 18 Given that in the third and fourth models of Tables 2 and 3 we include both firm- and industry-level leverage as explanatory variables, we reestimate these models after dropping the firm-level leverage variable. We find that the results for our main variables of interest remain very similar.

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Table 3 Industry concentration and the horizon of management forecasts of earnings. The table reports results of OLS regression models. The dependent variable is the average number of days between management forecast dates for a firm's fiscal year earnings and the end date of the fiscal year. The sample consists of firms in the manufacturing sector during the years 1995–2009, with necessary data for the variables in the models. Management forecast data are from First Call's Corporate Investor Guidelines (CIG) database. If a firm does not make a management forecast during a given year that firm-year is dropped from the analysis. The industry Herfindahl-Hirschman index is scaled by 10,000. The industry four-firm ratio is scaled by 100. Industry asset-weighted mean of the net-debt-to-asset ratio is for the 6-digit NAICS industry, where net-debt is defined as longterm debt minus cash. To construct the competition from potential entrants variable, we first calculate for six-digit NAICS industries the size-weighted average of property, plant, and equipment, the size-weighted average of research and development expenses, the size-weighted average of capital expenditures, and total industry sales. Next, we calculate for each industry the average across the decile ranks of these four industry measures and then multiply the resulting value by minus one. Ratio of the number of public to private firms in the industry is calculated as the number of firms on Compustat in the 6-digit NAICS industry divided by the difference between this number and the total number of firms in the industry as reported in Census of Manufactures publications. Stock return volatility is calculated with monthly stock return data over the firm's fiscal year. Absolute change in annual earnings per share/stock price is the absolute value of the annual change in earnings per share deflated by stock price at the beginning of the fiscal year. Market-adjusted stock return is the firm's buy-and-hold 12-month fiscal year stock return minus the CRSP value-weighted stock return for the same period. Research and development expense/book assets is the R&D expense for the fiscal year divided by the book value of assets at the beginning of the fiscal year. Analyst coverage is the average number of analysts making earnings forecasts for the firm during the fiscal year. Institutional fractional ownership is institutional holdings at the beginning of the fiscal year. The Post-Regulation Fair Disclosure dummy takes the value of one for years 2001 and beyond, and zero otherwise. Industry profitability is calculated using principal component analysis following the methodology in Li (2010). Equity or debt issuance dummy takes a value of one if a firm issues public equity or public debt in a subsequent two-year period, and zero otherwise. Leverage is measured at the firm-level as total liabilities minus deferred taxes scaled by total book assets. Standard deviation of earnings is calculated as the standard deviation of earnings before extraordinary items over the prior five years, with a requirement of at least three years of observations. Analyst optimism is measured as the difference between analyst consensus estimation at the beginning of the fiscal year and the actual earnings per share, scaled by the absolute value of actual earnings per share. Litigation industries dummy takes a value of one if the firm operates in an industry facing high litigate risk as defined in Li (2010), and zero otherwise. t-statistics (reported in parentheses) are based on standard errors clustered by year and industry. ***, **, and * indicate significance levels for two-tailed tests at the 1%, 5% and 10% levels, respectively. Model Intercept Industry Herfindahl-Hirschman index Industry four-firm ratio

1

2

3

4

5.805 (0.56)  111.238nnn (  3.17)

9.070 (0.83)

0.123 (0.01)  112.138nnn (  3.85)

3.241 (0.29)

 28.320nn (  2.29)

Industry Herfindahl-Hirschman index  Industry asset-weighted mean of the net-debt-toasset ratio Industry four-firm ratio  Industry asset-weighted mean of the net-debt-to-asset ratio Industry asset-weighted mean of the net-debt-to-asset ratio Competition from potential entrants Ratio of the number of public to private firms in the industry Stock return volatility Absolute change in annual earnings per share/stock price Market-adjusted stock return Research and development expense/book assets Natural logarithm of market value of equity Analyst coverage Institutional fractional ownership Post-Regulation Fair Disclosure dummy Industry profitability Equity or debt issuance dummy Market-to-book ratio of assets Leverage Standard deviation of earnings Positive earnings change dummy Analyst optimism

11.366 11.021 (1.13) (1.08) nnn 106.485 107.166nnn (4.33) (4.22)  81.255n  87.611nn (  1.82) (  2.05)  14.823nnn  15.131nnn (  2.58) (  2.60) 4.427nn 4.417nn (2.31) (2.29)  9.851  9.563 (  0.37) (  0.36) 9.162nnn 9.209nnn (5.52) (5.56)  0.976nn  1.066nn (  2.37) (  2.51) 17.656nn 17.200nn (2.08) (2.03) 34.065nnn 33.323nnn (4.43) (4.36) 111.114nnn 114.040nnn (5.86) (5.35) 3.006 2.900 (1.39) (1.33) 3.911nn 4.154nn (2.30) (2.42) 31.008nnn 32.060nnn (4.36) (4.60)  33.876nnn  35.930nnn (  2.80) (  2.79) 8.743nn 8.746nn (2.34) (2.36)  2.619nnn  2.627nnn (  4.22) (  4.29)

306.326nn (2.49)

8.909 (0.66) 1.078 (0.29) 100.830nnn (3.58)  72.344n (  1.66)  14.249nn (  2.49) 4.228nn (2.20)  4.324 (  0.16) 8.511nnn (5.11)  0.898nn (  2.20) 18.990nn (2.27) 35.243nnn (4.53) 108.653nnn (5.11) 3.250 (1.58) 3.968nn (2.33) 21.338nnn (3.21)  32.048nnn (  2.76) 8.846nn (2.37)  2.574nnn (  4.07)

 31.154nn (  2.44)

42.405 (0.84) 17.572 (0.95) 1.412 (0.38) 101.720nnn (3.55)  79.158n (  1.89)  14.351nn (  2.42) 4.267nn (2.21)  3.022 (  0.12) 8.660nnn (5.37)  1.015nn (  2.44) 17.991nn (2.16) 34.532nnn (4.42) 116.743nnn (5.02) 3.108 (1.50) 4.272nn (2.46) 22.762nnn (3.48)  35.115nnn (  2.80) 8.905nn (2.38)  2.575nnn (  4.14)

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Table 3 (continued ) Model Litigation industries dummy 2

R -adjusted N

1

2

3

4

5.747 (0.82) 0.186 7,507

5.915 (0.81) 0.184 7,507

6.556 (0.86) 0.189 7,507

7.264 (0.95) 0.186 7,507

In the first two models of Table 3, the coefficients on the Herfindahl-Hirschman index and the four-firm ratio are both negative and significant.19 The third and fourth models in Table 3 also include as additional variables the 6-digit NAICS asset-weighted industry average of the net-debt-to-assets ratio, and its interaction with the Herfindahl-Hirschman index and the four-firm ratio. In these two models the coefficients on the Herfindahl-Hirschman index and the four-firm ratio are negative and significant. Also, the coefficient on the interaction variable in the third model is positive and significant whereas the coefficient on the interaction variable in the fourth model is positive, but not significant. Overall, the Table 3 results suggest that firms in concentrated industries disclose less, and this association is less pronounced for industries with higher leverage.20 Our tests of the frequency and horizon of management forecasts of earnings use First Call data, which have some shortcomings that limit our inferences from these tests. Chuk et al. (2013) report that several firm characteristics are associated with the coverage and consistency of First Call data on management forecasts of earnings. These determinants of coverage and consistency may be correlated with industry concentration. Given that in our analyses firms that are not covered by First Call are given a value of zero for management forecast frequency, our results of the association between management forecast frequency and industry concentration could be biased. Although our analysis of the association between the horizon of management forecasts and industry concentration is also likely to be affected by coverage bias in the First Call data, potentially the bias is less severe in this case, because we consider only those firms that have data on First Call for at least one forecast in a given year. Given that Chuk et al. (2013) report that the lack of coverage in First Call data is especially severe prior to 1998, we re-estimate the management forecast frequency and horizon models after dropping the years 1995–1997 from our sample period. We find that the results are robust to considering only the post 1997 sample period.

4.2. Industry concentration and the private placement versus seasoned equity offering decision We examine the association between industry concentration and a financing decision that has important disclosure implications. Specifically, we consider the choice of selling new shares via a private placement or an SEO. There are significantly fewer SEC-mandated public disclosure requirements for private placements of new common stock than for SEOs. For instance, firms are required to disclose in their SEO prospectus the proposed use of the funds raised, but for private placements there is no such requirement.21 Also, in private placement transactions, firms are typically not required to disclose that a transaction has taken place and what was the amount of funds raised in the transaction until four business days after closing the deal. Further, in the case of private placements, there are usually only a small number of investors who buy shares in the stock sale and to whom information about the impending stock sale is disclosed. Thus, for these sales there would be a lower risk of leakage of information to rivals about the sale before it takes place. We hand-collect data on common stock sales through private placements from news stories on the Lexis-Nexis database. Our sample years for this analysis consist of the three U.S. Census years in our sample period, 1997, 2002, and 2007, and two additional years, 2000 and 2004.22 We obtain data on SEOs of common stock for these five years from SDC's Global New Issues database. Our sample consists of 60% SEOs and 40% private placements. This distribution is comparable to that reported in prior studies (see e.g., Wu, 2004; Gomes and Phillips, 2012). 19 Li (2010, Table 8) estimates her forecast horizon model at the industry level, and the difference between her results and our results in Table 3 are of similar nature to what we note in the context of management forecast frequency (see Footnote 16). Specifically, Li (2010) reports a negative coefficient on her composite measure of competition from existing rivals. This composite measure is primarily composed of and is negatively related to Compustat based measures of industry concentration, implying that management earnings forecast horizon is positively related to Compustat based industry concentration measures. We observe a negative association between management forecast horizon and U.S. Census measures of industry concentration. These findings imply that the association between management forecast horizon and industry concentration is also sensitive to using Compustat versus U.S. Census measures of industry concentration. 20 We repeat our analyses for the forecast horizon models in the paper with an alternative definition of forecast horizon: the difference between the forecasting date and forecast fiscal year-end, with the earliest forecast used in case of multiple forecasts. Our results are robust to this alternative definition. 21 Walker and Yost (2008) show that abnormal announcement returns are more positive for firms that sell new shares in an SEO when they report in their SEO prospectus specific information about the use of the funds raised in the sale. Their result suggests that disclosures in a firm's SEO prospectus about the proposed use of the funds provides new useful information about the firm. 22 We limited our hand-collection of the data on private placements to only five years, because of the significant costs related to collecting these data. We selected 2000 and 2004 as the two non-Census years for this analysis, because they are centrally located between the three Census years, 1997, 2002, and 2007.

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Table 4 Industry concentration and the private placement versus seasoned equity offering decision. The table reports results of probit regression models. The dependent variable equals one if a firm sells new shares through a private placement and equals zero if it sells new shares through a seasoned equity offering. The sample consists of firms in the manufacturing sector during the years 1997, 2000, 2002, 2004, and 2007 with necessary data for variables in the models. The industry Herfindahl-Hirschman index is scaled by 10,000. The industry four-firm ratio is scaled by 100. Industry asset-weighted mean of the net-debt-to-asset ratio is for the 6-digit NAICS industry, where net-debt is defined as long-term debt minus cash. To construct the competition from potential entrants variable, we first calculate for six-digit NAICS industries the size-weighted average of property, plant, and equipment, the size-weighted average of research and development expenses, the size-weighted average of capital expenditures, and total industry sales. Next, we calculate for each industry the average across the decile ranks of these four industry measures and then multiply the resulting value by minus one. Ratio of the number of public to private firms in the industry is calculated as the number of firms on Compustat in the 6-digit NAICS industry divided by the difference between this number and the total number of firms in the industry as reported in Census of Manufactures publications. Analyst coverage is the average number of analysts making earnings forecasts for the firm during the fiscal year. Industry-adjusted sales growth is calculated as the natural logarithm of the difference between annual sales growth for the firm in the year prior to the event year and the corresponding median sales growth in the firm's 6-digit NAICS industry. Change in industry-adjusted market-to-book equity is the difference between 6digit NAICS industry-adjusted market-to-book equity at the end of the event fiscal year and at the end of the previous fiscal year; it is scaled by 1000. Operating cash flow/book assets is operating income before depreciation divided by lagged book assets. Cash flow volatility is the standard deviation of operating income, calculated with data for at least 12 and up to 20 quarters prior to the event quarter. Altman-Z score, a bankruptcy likelihood measure, is calculated as in Altman (1968), and is scaled by 100. Stock return volatility is calculated with monthly stock return data for the firm's fiscal year. One-year market-adjusted stock return is the firm's stock return net of the value-weighted CRSP index for the year prior to the announcement of the equity issuance. Year dummies are included in all the models, and the intercept represents the omitted year dummy. The table presents estimates of marginal effects. zstatistics (reported in parentheses) are based on standard errors clustered by industry. ***, **, and * indicate significance levels for two-tailed tests at the 1%, 5%, and 10% levels, respectively. Model Intercept Industry Herfindahl-Hirschman index Industry four-firm ratio

1 1.698nnn (4.57) 2.596n (1.75)

2 1.583nnn (4.26)

Natural logarithm of book value of assets Analyst coverage Number of years since a firm's IPO Industry-adjusted sales growth Change in industry-adjusted market-to-book equity Operating cash flow/book assets Cash flow volatility Altman-Z score Stock return volatility One-year market-adjusted stock return The offering takes place within one week of an earnings announcement dummy Pseudo-R2 N

 0.161  0.124 (  0.44) (  0.35)  0.149  0.211 (  0.88) (  1.30) nnn  0.585  0.593nnn (  11.84) (  11.98) 0.038 0.041 (1.07) (1.13) 0.009 0.007 (1.41) (1.16) 0.057 0.059 (0.68) (0.71) nn 0.002 0.002nn (2.22) (2.17)  0.094  0.095 (  0.62) (  0.62) 0.349 0.350 (1.22) (1.21) nn  0.004  0.004nnn (  2.03) (  2.64) 0.384 0.374 (0.66) (0.64)  0.190nnn  0.190nnn (  3.09) (  3.05)  0.518nnn  0.511nnn (  3.70) (  3.72) 0.319 0.319 785 785

4 1.605nnn (4.11)

0.998nn (2.40)  14.488nn (  2.53)

Industry asset-weighted mean of the net-debt-to-asset ratio

Ratio of the number of public to private firms in the industry

1.826nnn (4.88) 1.360 (0.96)

0.938nn (2.27)

Industry Herfindahl-Hirschman index  Industry asset-weighted mean of the net-debt-toasset ratio Industry four-firm ratio  Industry asset-weighted mean of the net-debt-to-asset ratio

Competition from potential entrants

3

0.176 (0.36)  0.114 (  0.32)  0.346n (  1.83)  0.596nnn (  12.30) 0.039 (1.08) 0.012n (1.91) 0.061 (0.71) 0.002n (1.84)  0.104 (  0.67) 0.378 (1.25)  0.004nn (  2.05) 0.338 (0.58)  0.194nnn (  3.11)  0.563nnn (  3.85) 0.328 785

 3.574nn (  2.13) 0.583 (0.83)  0.066 (  0.19)  0.469nn (  2.50)  0.597nnn (  12.36) 0.039 (1.06) 0.011n (1.71) 0.066 (0.76) 0.002n (1.90)  0.096 (  0.61) 0.374 (1.22)  0.004nn (  2.28) 0.325 (0.56)  0.201nnn (  3.22)  0.556nnn (  3.85) 0.328 785

Table 4 reports estimated marginal effects from probit models in which the dependent variable equals one for a private placement and zero for an SEO. As in our other disclosure models, we control for competition from potential entrants. We also control for the ratio of the number of public to private firms in the firm's 6-digit NAICS industry. This variable is expected to capture the issue that a public firm may disclose less if more of its competitors are private with minimal disclosure requirements. Wu (2004) and Gomes and Phillips (2012) show that firms with greater information asymmetry are more likely to sell new shares via a private placement rather than through a seasoned equity offering. To control for Please cite this article as: Ali, A., et al., Industry concentration and corporate disclosure policy. Journal of Accounting and Economics (2014), http://dx.doi.org/10.1016/j.jacceco.2014.08.004i

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information asymmetry, we follow Wu (2004) and include the natural logarithm of book assets, analyst coverage, and the number of years since a firm's IPO. As in Wu (2004), we also control for investors' revisions of firm growth potential, by including industry-adjusted sales growth during the year prior to the sale of new shares and the change in the industryadjusted market-to-book ratio during the year of the stock issue. Following Gomes and Phillips (2012), we include operating cash flow scaled by assets, cash flow volatility, and Altman-Z bankruptcy risk score, as controls for profitability, earning uncertainty, and distress risk, respectively. To control for the possibility that managers of firms in more concentrated industries may disclose less because they are less informed about their firms' future prospects, we include stock return volatility during the year prior to the equity sale. We also include the prior year's market-adjusted stock return to capture firms' tendencies to have an SEO following good stock performance. Finally, we include an indicator variable for whether an SEO takes place within one week of an earnings announcement, because Korajczyk et al. (1991) show that SEOs often take place soon after an earnings announcement that conveys unusually good news about a firm. The results for the first two models in Table 4 show significant positive coefficients on the Herfindahl-Hirschman index and four-firm ratio variables, suggesting that firms in more concentrated industries prefer private placements over SEOs. The third and fourth models in Table 4 also include the industry leverage variable, along with its interactions with the Herfindahl-Hirschman index and the four-firm ratio. The coefficients on both the interaction variables are negative and significant. Overall, the Table 4 results suggest that firms in concentrated industries are more likely to use private placements than SEOs when they sell new shares, and this association is less pronounced in more leveraged industries, in which a firm's rivals would be less likely to aggressively react to information contained in its disclosures. Our finding that industry concentration is associated with the choice of a private placement versus an SEO may be subject to sample selection bias because the sample used for this analysis consists only of firms that sell new equity shares. To address this concern, we first estimate a model of the probability that a firm sells new equity shares, and then include the inverse Mills ratio from this estimation as a control variable in our models of the choice of a private placement versus an SEO. To estimate the probability that a firm sells new equity shares in a given year, we use the DeAngelo et al. (2010) model, which tests the market timing and corporate lifecycle explanations for these sales. The dependent variable in this model takes the value of one if a firm sells new equity shares in a given year, and zero otherwise. The explanatory variables are the ones used in DeAngelo et al. (2010) and we add the firm's industry Herfindahl-Hirschman index or four-firm ratio. The sample used for estimating this model consists of our sample firms that sell new equity shares via private placements or SEOs in 1997, 2000, 2002, 2004, and 2007 and the rest of the manufacturing firms in Compustat and CRSP for which the required data are available for these five years. We find that the correlations between the dependent variable, whether a firm sells equity in a given year, and the Herfindahl-Hirschman index and the four-firm ratio are only  0.002 and 0.008, and the regression results (untabulated) show that the coefficients on the Herfindahl-Hirschman index and the four-firm ratio are insignificant. We include the inverse Mills ratio from the first stage models as an additional control variable in our Table 4 models of the choice of a private placement versus an SEO to sell equity. We find that the coefficients on the inverse Mills ratio variable are insignificant and that our results related to the Herfindahl-Hirschman and four-firm ratio variables remain the same. These results suggest that the associations of these two industry concentration variables with the choice of a private placement versus an SEO are not subject to significant sample selection bias.23 Our conclusion that firms in concentrated industries tend to issue equity through private placements in order to hide information raises the following two concerns. First, firms in more concentrated industries can also achieve the goal of hiding information from their rivals by holding larger cash reserves or by paying out smaller dividends, thereby reducing their reliance on external financing to fund investment. Two prior studies document a positive relation between industry concentration and corporate cash holdings and a negative relation between industry concentration and the amount of dividends a firm pays out. Haushalter et al. (2007) show that firms in more concentrated industries have larger cash reserves. They argue that firms in concentrated industries hold more cash to reduce the risk of predatory behavior by rival firms attempting to increase their market share. Grullon and Michaely (2007) report that firms in more concentrated industries pay out smaller dividends. They contend that this result could imply that corporate payouts are the outcome of external disciplinary forces. We estimate the models in Haushalter et al. (2007) and Grullon and Michaely (2007) for our sample period and confirm their results that firms in more concentrated industries, measured using the HerfindahlHirschman index or the four-firm ratio, hold more cash and pay out smaller dividends. Although these findings are consistent with the explanations provided in these papers, they could also in part be driven by firms in concentrated industries attempting to lower their reliance on external financing to reduce their disclosures. The second concern with our conclusion that firms in concentrated industries prefer to sell equity through private placements in order to hide information is that when external financing is needed, firms in concentrated industries can

23 We also investigate whether the associations of the Herfindahl-Hirschman index and the four firm ratio with the choice of a private placement versus an SEO are robust to using propensity score matching. We find this to be the case. In addition, the results of a Rosenbaum (2002) test for whether this association is sensitive to endogeneity, if it exists, indicate that when using the Herfindahl-Hirschman index (four-firm ratio) as the measure for industry concentration the odds ratio at which a significance level of 0.10 obtains is 1.25 (1.13). This result suggests that the association between industry concentration and the choice of a private placement versus an SEO would be significant (p-value ¼ 0.10) if control firms were actually 1.25 (1.13) times more (rather than equally) likely to be in an industry with low concentration than treatment firms, after conditioning on observable firm characteristics using the propensity score matching.

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avoid public disclosures by borrowing from banks. If that is the case, a firm in a more concentrated industry should have a higher ratio of bank debt to publicly traded debt. Unfortunately, Compustat does not provide the data needed to calculate this ratio, so we proxy for it with the firm's average debt maturity. Harford et al. (2013) document that the maturity of bank debt is markedly shorter than is the maturity of publicly traded debt, implying that average debt maturity is shorter for firms with more bank debt. Following Harford et al. (2013), we measure debt maturity as the fraction of a firm's long-term debt due in the next three years. A higher value for this fraction indicates that the average debt maturity of the firm is shorter. We estimate their debt maturity model for our sample period after including the industry Herfindahl-Hirschman index or the four-firm ratio as additional explanatory variables. We find that the coefficients on the Herfindahl-Hirschman index and four-firm ratio variables are positive and significant, suggesting that firms in more concentrated industries have shorter maturity debt, presumably due to the greater use of bank debt. However, firms in concentrated industries are unlikely to use only bank debt for external financing given that high financial leverage would raise their bankruptcy risk and the risk of predatory actions on the part of rivals (e.g., Chevalier, 1995a, 1995b; Phillips, 1995; Campello, 2003, 2006). Thus, firms in concentrated industries are likely to rely on sales of new equity shares as well when they raise external funds. Overall, our results related to equity financing, corporate cash holdings, dividends, and debt maturity support the proposition that the financing decisions of firms in concentrated industries are affected by their unwillingness to disclose proprietary information. 4.3. Industry concentration and corporate disclosure ratings Our tests so far consider specific disclosures. For a more comprehensive test of the hypothesis that industry concentration is negatively associated with the informativeness of corporate disclosure policy, we examine whether firms in more concentrated industries receive lower disclosure ratings from analysts. These ratings are obtained from the Report of the Association for Investment Management and Research and represent analysts' perceptions of the overall quality of a variety of disclosures that a firm makes (see Lang and Lundholm, 1993). Table 5 provides OLS estimates of a regression of analyst disclosure ratings on industry concentration and control variables. As in our other models, we include the measure for competition from potential entrants and the ratio of the number of public to private firms in the firm's 6-digit NAICS industry. The ease with which analysts can make accurate forecasts of a firm's earnings may impact their perception of the quality of the firm's disclosures. Following Lang and Lundholm (1993), we use the following three variables to proxy for the ease with which analysts can make accurate forecasts: firm size, historical earnings volatility, estimated using earnings data for at least three prior years, and the current year absolute change in earnings per share. Historical earnings volatility and the current year absolute change in earnings per share also control for the possibility that managers disclose less if they are uncertain about their firms' future performance. We also include as controls the book-to-market ratio, return on assets, and one-year sales growth, because a firm's performance can impact analysts' perception of the quality of its disclosures (Lang and Lundholm, 1993; Bens and Monahan, 2004). Following Bens and Monahan (2004), we also include as controls analyst coverage, analyst forecast dispersion, analyst forecast errors, and analyst forecast revision volatility. Finally, we control for a firm's membership in the S&P 500 index as it could positively impact analysts' perceptions of the quality of its disclosures (Bushee and Noe, 2000). The first two models in Table 5 show that firms' analyst disclosure ratings are negatively related with the industry Herfindahl-Hirschman index and the four-firm ratio, implying that firms in more concentrated industries disclose less. The third and fourth models in Table 5 are the same as the first two models, except that they also include the industry leverage variable along with its interaction with the Herfindahl-Hirschman index and the four-firm ratio. The coefficient on the interaction variable in the third model is significant and positive. In the fourth model the coefficient on the interaction variable is positive, but it is not statistically significant (t-stat¼1.33). These results are consistent with the notion that the propensity of firms in concentrated industries to disclose less is less pronounced in industries with higher leverage.24 4.4. Industry concentration and properties of analysts' forecasts of earnings For another comprehensive test of whether industry concentration is associated with the informativeness of corporate disclosure policy, we examine the association between industry concentration and certain properties of analysts' forecasts of earnings. If firms in more concentrated industries exhibit greater forecast dispersion, greater forecast errors, and a greater volatility of revisions in analysts' earnings forecasts, it would suggest that firms in more concentrated industries disclose less (see, e.g., Lang and Lundholm, 1996). We use models of dispersion in analysts' forecasts, analyst forecast errors, and the volatility of analyst forecast revisions that are similar to those in Lang and Lundholm (1996). As in all our other models, we include as controls the measure for competition from potential entrants and the ratio of the number of public to private firms in the firm's 6-digit NAICS industry. We also include variables that prior work argues are related to the ease with which analysts can make forecasts. 24 In the Table 5 models, we include a year indicator variable and cluster standard errors only by industry rather than by both industry and year, because our sample period for the analyses in this table consists of only two years. However, the results are similar if we cluster standard errors both by industry and year.

Please cite this article as: Ali, A., et al., Industry concentration and corporate disclosure policy. Journal of Accounting and Economics (2014), http://dx.doi.org/10.1016/j.jacceco.2014.08.004i

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Table 5 Industry concentration and analyst disclosure ratings. The table reports results of OLS regressions. The dependent variable is analysts' overall ratings of a firm's disclosure practices from the Report of the Association for Investment Management and Research; the rating is scaled by 100. The sample consists of firms in the manufacturing sector during the years 1995 and 1996 with necessary data for the variables in the models. The industry Herfindahl-Hirschman index is scaled by 10,000. The industry four-firm ratio is scaled by 100. Industry asset-weighted mean of the net-debt-to-asset ratio is for the 6-digit NAICS industry, where net-debt is defined as long-term debt minus cash. To construct the competition from potential entrants variable, we first calculate for six-digit NAICS industries the size-weighted average of property, plant, and equipment, the size-weighted average of research and development expenses, the size-weighted average of capital expenditures, and total industry sales. Next, we calculate for each industry the average across the decile ranks of these four industry measures and then multiply the resulting value by minus one. Ratio of the number of public to private firms in the industry is calculated as the number of firms on Compustat in the 6-digit NAICS industry divided by the difference between this number and the total number of firms in the industry as reported in Census of Manufactures publications. Standard deviation of return on equity is computed using annual return on equity of the preceding 10 years, with a minimum of three preceding years of data. Absolute change in annual earnings per share/stock price is the absolute value of the annual change in earnings per share deflated by stock price at the beginning of the fiscal year. Return on assets is earnings before extraordinary items scaled by assets at the beginning of the year. Sales growth is sales divided by lagged sales. Analyst coverage is the average number of analysts making earnings forecasts for the firm during the fiscal year. Analyst forecast dispersion is the 12-month average of the standard deviation of analysts' forecasts, deflated by stock price at the beginning of the fiscal year. Analyst forecast errors is the 12-month average of the absolute values of analyst forecast errors, defined as actual earnings minus median forecast, deflated by stock price at the beginning of the fiscal year. Analyst forecast revision volatility is the standard deviation of forecast revisions deflated by stock price at the beginning of the fiscal year, where forecast revision is defined as current month's median forecast minus previous month's median forecast. The S&P 500 firm dummy takes the value of one if the firm is included in the S&P 500, and zero otherwise. Year dummies are included in all the models, and the intercept represents the omitted year dummy. t-Statistics (reported in parentheses) are based on standard errors clustered by industry. ***, **, and * indicate significance levels for two-tailed tests at the 1%, 5% and 10% levels, respectively. Model Intercept Industry Herfindahl-Hirschman index

1 0.717nnn (3.39)  0.575nn (  2.28)

Industry four-firm ratio

2 0.762nnn (3.43)

 0.247nn (  2.49)

Industry Herfindahl-Hirschman index  Industry asset-weighted mean of the net-debt-to-asset ratio Industry four-firm ratio  Industry asset-weighted mean of the net-debt-to-asset ratio

Ratio of the number of public to private firms in the industry Natural logarithm of market value of equity Standard deviation of return on equity Absolute change in annual earnings per share/stock price Book-to-market-equity Return on assets Sales growth Analyst coverage Analyst forecast dispersion Analyst forecast errors Analyst forecast revision volatility S&P 500 firm dummy R2-adjusted N

4

0.721nnn (3.91)  1.724nnn (  2.73)

7.373nn (2.26)

nn

Industry asset-weighted mean of the net-debt-to-asset ratio Competition from potential entrants

3

 0.336nn (  2.04)  0.244 (  1.47)  0.060n (  1.89) 0.541nnn (2.92)  0.202 (  1.24)  0.069 (  0.50) 0.258 (1.23) 0.108 (1.00) 0.357 (0.94)  10.833nn (  2.10) 0.414 (0.41) 3.112n (1.65) 0.068n (1.87) 0.132 123

 0.338nn (  2.03)  0.289 (  1.52)  0.058n (  1.84) 0.566nnn (3.35)  0.213 (  1.29)  0.076 (  0.53) 0.221 (1.06) 0.117 (1.08) 0.335 (0.88)  10.609nn (  2.00) 0.218 (0.23) 3.172n (1.66) 0.069n (1.88) 0.136 123

 0.795 (  2.32)  0.316nn (  2.07)  0.423nn (  2.32)  0.047n (  1.75) 0.539nnn (3.05)  0.207 (  1.35)  0.011 (  0.10) 0.344nn (2.02) 0.139 (1.45) 0.181 (0.45)  5.556 (  0.84)  0.368 (  0.33) 3.252 (1.48) 0.062n (1.93) 0.157 123

0.832nnn (3.94)

 0.466nn (  2.17)

1.644 (1.33)  0.883 (  1.59)  0.313nn (  2.02)  0.383n (  1.80)  0.050n (  1.77) 0.559nnn (3.33)  0.219 (  1.36)  0.055 (  0.40) 0.280 (1.22) 0.137 (1.32) 0.196 (0.48)  6.423 (  0.96)  0.409 (  0.43) 3.229 (1.58) 0.063n (1.83) 0.140 123

Specifically, we control for the natural logarithm of the market value of equity, R&D expenses, analyst coverage, and the historical correlation of a firm's earnings with its stock returns (Lang and Lundholm, 1996; Barth et al., 2001). To control for the possibility that managers disclose less because they are uncertain about their firms' future performance, we include the following variables: historical earnings volatility, the firm's stock return volatility over the prior year, and the current year absolute change in earnings per share. We also include the current period's market-adjusted stock returns to control for management's desire to disclose good news more promptly than bad news (Miller, 2002; Kothari et al., 2009). Finally, to Please cite this article as: Ali, A., et al., Industry concentration and corporate disclosure policy. Journal of Accounting and Economics (2014), http://dx.doi.org/10.1016/j.jacceco.2014.08.004i

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Table 6 Industry concentration and analyst forecast properties. The table reports results of OLS regressions. The sample consists of firms in the manufacturing sector during the years 1995–2009, with necessary data for the variables in the models. The dependent variable in Models 1 and 2 is the 12-month average of the standard deviation of analysts' forecasts, deflated by stock price at the beginning of the fiscal year. The dependent variable in Models 3 and 4 is the 12-month average of the absolute values of analyst forecast errors defined as actual earnings minus median forecast, deflated by stock price at the beginning of the fiscal year. The dependent variable in Models 5 and 6 is the standard deviation of forecast revisions deflated by stock price at the beginning of the fiscal year, where forecast revision is defined as current month's median forecast minus previous month's median forecast. The industry Herfindahl-Hirschman index is scaled by 10,000. The industry four-firm ratio is scaled by 100. To construct the competition from potential entrants variable, we first calculate for six-digit NAICS industries the sizeweighted average of property, plant, and equipment, the size-weighted average of research and development expenses, the size-weighted average of capital expenditures, and total industry sales. Next, we calculate for each industry the average across the decile ranks of these four industry measures and then multiply the resulting value by minus one. Ratio of the number of public to private firms in the industry is calculated as the number of firms on Compustat in the 6-digit NAICS industry divided by the difference between this number and the total number of firms in the industry as reported in Census of Manufactures publications. Research and development expense/book assets is the R&D expense for the fiscal year divided by book value of assets at the beginning of the fiscal year. Analyst coverage is the average number of analysts making earnings forecasts for a firm during the fiscal year. Correlation between return on equity and stock returns is computed using annual data of the preceding 10 years, with a minimum of three preceding years of data. Standard deviation of return on equity is computed using annual return on equity of the preceding 10 years, with a minimum of three preceding years of data. Stock return volatility is calculated with monthly stock return data for the firm's fiscal year. Absolute change in annual earnings per share/stock price is the absolute value of the annual change in earnings per share deflated by stock price at the beginning of the fiscal year. Market-adjusted stock return is the firm's buy-and-hold 12-month fiscal year stock return minus the CRSP value-weighted stock return for the same period. Average proportion of new monthly forecasts is the fiscal year average of the proportion of analysts' forecasts at the end of a month that are either first-time forecasts or are revised during the month. t-statistics (reported in parentheses) are based on standard errors clustered by year and industry. ***, **, and * indicate significance levels for two-tailed tests at the 1%, 5% and 10% levels, respectively. Model Dependent variable

Intercept Industry Herfindahl-Hirschman index Industry four-firm ratio Competition from potential entrants Ratio of the number of public to private firms in the industry Natural logarithm of market value of equity Research and development expense/ book assets Analyst coverage Correlation between return on equity and stock returns Standard deviation of return on equity Stock return volatility Absolute change in annual earnings per share/stock price Market-adjusted stock return Average proportion of new monthly forecasts R2-adjusted N

1 Analyst forecast dispersion

1.026nnn (5.53) 0.652n (1.75)

 0.212n (  1.80) 0.654nnn (2.89)  0.149nnn (  6.21) 0.675nn (2.31)  0.007 (  1.47)  0.034 (  0.74) 0.527nnn (5.39) 2.765nnn (2.88) 0.631nnn (4.78)  0.284nnn (  5.82) 0.651nn (2.09) 0.318 15,174

2 Analyst forecast dispersion

0.974nnn (5.45)

0.288nn (2.25)  0.192 (  1.61) 0.651nnn (2.87)  0.150nnn (  6.21) 0.675nn (2.33)  0.006 (  1.40)  0.032 (  0.68) 0.530nnn (5.41) 2.765nnn (2.88) 0.629nnn (4.79)  0.285nnn (  5.82) 0.668nn (2.14) 0.319 15,174

3 Analyst forecast errors

4 Analyst forecast errors

4.889nnn (7.75) 2.878nnn (2.99)

4.681nnn (7.77)

 0.428 (  1.46)  0.382 (  0.77)  0.660nnn (  8.52) 0.244 (0.29)  0.006 (  0.29) 0.407nnn (3.59) 1.363nnn (4.42) 9.170nnn (3.27) 2.489nnn (3.96)  1.173nnn (  6.01) 1.863n (1.93) 0.249 17,652

1.187nnn (3.22)  0.358 (  1.22)  0.388 (  0.78)  0.665nnn (  8.43) 0.232 (0.27)  0.005 (  0.22) 0.417nnn (3.59) 1.373nnn (4.45) 9.180nnn (3.26) 2.483nnn (3.97)  1.176nnn (  6.01) 1.932nn (1.98) 0.249 17,652

5 Analyst forecast revision volatility

1.005nnn (7.19) 0.767nnn (3.07)

 0.202nn (  2.43) 0.052 (0.64)  0.189nnn (  8.59) 0.500nnn (3.21)  0.006 (  1.27) 0.048 (1.43) 0.455nnn (6.25) 3.112nnn (3.24) 0.677nnn (5.27)  0.322nnn (  6.14) 2.123nnn (11.58) 0.288 17,525

6 Analyst forecast revision volatility

0.957nnn (7.07)

0.279nnn (2.88)  0.189nn (  2.26) 0.052 (0.61)  0.189nnn (  8.56) 0.502nnn (3.24)  0.006 (  1.20) 0.050 (1.49) 0.459nnn (6.21) 3.112nnn (3.24) 0.676nnn (5.27)  0.323nnn (  6.13) 2.142nnn (11.51) 0.288 17,525

control for the staleness of IBES forecast data, we include the percentage of forecasts at the end of the month that are newly revised or first-time forecasts. Table 6 shows that irrespective of whether industry concentration is measured as the Hefindahl-Hirschman index or the firm-firm ratio, it is significantly positively associated with dispersion in analysts' earnings forecasts, analyst forecast errors, as well as the volatility of analyst forecast revisions. These results suggest that firms in more concentrated industries have inferior information environments, presumably because they disclose less. Table 7 reports the estimates of the six models of Table 6 after including in each model the industry leverage variable and its interaction with the Herfindahl-Hirschman index or the four-firm ratio. The coefficients on all six interactions are negative, and four of these coefficients are significant. These results are consistent with the notion that the tendency of firms in more concentrated industries to disclose less is less pronounced in industries with greater leverage. Please cite this article as: Ali, A., et al., Industry concentration and corporate disclosure policy. Journal of Accounting and Economics (2014), http://dx.doi.org/10.1016/j.jacceco.2014.08.004i

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Table 7 The effect of industry leverage on the associations between industry concentration and analyst forecast properties. The table reports results of OLS regressions. The sample consists of firms in the manufacturing sector during the years 1995–2009 with necessary data for the variables in the models. The dependent variable in models 1 and 2 is the 12-month average of the standard deviation of analysts' forecasts, deflated by stock price at the beginning of the fiscal year. The dependent variable in models 3 and 4 is the 12-month average of the absolute values of analysts' forecasts errors defined as actual earnings minus median forecast, deflated by stock price at the beginning of the fiscal year. The dependent variable in models 5 and 6 is the standard deviation of forecast revisions, deflated by stock price at the beginning of the fiscal year, where forecast revision is defined as current month's median forecast minus previous month's median forecast. The industry Herfindahl-Hirschman index is scaled by 10,000. The industry four-firm ratio is scaled by 100. Industry asset-weighted mean of the net-debt-to-asset ratio is for the 6-digit NAICS industry, where net-debt is defined as long-term debt minus cash. To construct the competition from potential entrants variable, we first calculate for six-digit NAICS industries the size-weighted average of property, plant, and equipment, the size-weighted average of research and development expenses, the size-weighted average of capital expenditures, and total industry sales. Next, we calculate for each industry the average across the decile ranks of these four industry measures and then multiply the resulting value by minus one. Ratio of the number of public to private firms in the industry is calculated as the number of firms on Compustat in the 6-digit NAICS industry divided by the difference between this number and the total number of firms in the industry as reported in Census of Manufactures publications. Research and development expense/book assets is the R&D expense for the fiscal year divided by book value of assets at the beginning of the fiscal year. Analyst coverage is the average number of analysts making earnings forecasts for a firm during the fiscal year. Correlation between return on equity and stock returns is computed using annual data of the preceding 10 years, with a minimum of three preceding years of data. Standard deviation of return on equity is computed using annual return on equity of the preceding 10 years, with a minimum of three preceding years of data. Stock return volatility is calculated with monthly stock return data for the firm's fiscal year. Absolute change in annual earnings per share/stock price is the absolute value of the annual change in earnings per share deflated by stock price at the beginning of the fiscal year. Market-adjusted stock return is the firm's buy-and-hold 12-month fiscal year stock return minus the CRSP value-weighted stock return for the same period. Average proportion of new monthly forecasts is the fiscal year average of the proportion of analysts' forecasts at the end of a month that are either first-time forecasts or are revised during the month. t-Statistics (reported in parentheses) are based on standard errors clustered by year and industry. ***, **, and * indicate significance levels for two-tailed tests at the 1%, 5% and 10% levels, respectively. Model Dependent variable

Intercept Industry Herfindahl-Hirschman index

1 Analyst forecast dispersion

2 Analyst forecast dispersion

1.028nnn (5.52) 0.647 (1.64)

0.965nn (2.34)

Ratio of the number of public to private firms in the industry Natural logarithm of market value of equity Research and development expense/book assets Analyst coverage Correlation between return on equity and stock returns Standard deviation of return on equity Stock return volatility Absolute change in annual earnings per share/stock price Market-adjusted stock return Average proportion of new monthly forecasts R2-adjusted N

4 Analyst forecast errors

4.859nnn (7.42) 2.787nnn (2.61)

4.578nnn (7.45)

0.309nn (2.34)

Industry four-firm ratio Industry Herfindahl-Hirschman index  Industry asset-weighted mean of the net-debt-to-asset ratio Industry four-firm ratio  Industry asset-weighted mean of the net-debt-to-asset ratio Industry asset-weighted mean of the net-debt-toasset ratio Competition from potential entrants

3 Analyst forecast errors

0.108 (1.26)  0.208n (  1.74) 0.729nnn (2.95)  0.149nnn (  6.26) 0.644nn (2.10)  0.007 (  1.49)  0.030 (  0.65) 0.550nnn (5.24) 2.633nnn (2.77) 0.669nnn (4.63)  0.281nnn (  5.84) 0.669nn (2.06) 0.314 15,174

 0.006 (  1.60) 0.273 (1.62)  0.191 (  1.57) 0.731nnn (2.95)  0.151nnn (  6.27) 0.645nn (2.13)  0.007 (  1.42)  0.027 (  0.58) 0.555nnn (5.29) 2.628nnn (2.76) 0.666nnn (4.63)  0.282nnn (  5.84) 0.689nn (2.11) 0.315 15,174

0.994nnn (6.82) 0.742nnn (2.78)

1.325nnn (3.32)  6.607nn (  2.28)

 1.204 (  1.22)

5 Analyst forecast revision volatility

0.577nn (2.31)  0.428 (  1.35)  0.081 (  0.34)  0.655nnn (  8.18) 0.201 (0.23)  0.008 (  0.34) 0.401nnn (3.45) 1.455nnn (4.49) 8.709nnn (3.16) 2.807nnn (4.33)  1.116nnn (  6.05) 1.888n (1.87) 0.254 17,652

6 Analyst forecast revision volatility 0.928nnn (6.60)

0.311nnn (3.12)  1.865nn (  2.52)

 3.359nnn (  2.75) 1.533nnn (2.76)  0.365 (  1.18)  0.094 (  0.17)  0.661nnn (  8.14) 0.195 (0.22)  0.006 (  0.28) 0.411nnn (3.39) 1.476nnn (4.56) 8.695nnn (3.14) 2.797nnn (4.34)  1.167nnn (  6.05) 1.961nn (1.92) 0.255 17,652

0.164nnn (2.57)  0.203nn (  2.31) 0.095 (1.08)  0.186nnn (  8.20) 0.496nnn (3.07)  0.007 (  1.30) 0.047 (1.42) 0.475nnn (6.08) 2.974nnn (3.14) 0.725nnn (5.07)  0.319nnn (  6.11) 2.147nnn (11.57) 0.288 17,525

 0.902nnn (  2.90) 0.414nnn (2.92)  0.193nn (  2.20) 0.102 (1.15)  0.187nnn (  8.14) 0.493nnn (3.11)  0.006 (  1.23) 0.050 (1.45) 0.481nnn (6.08) 2.974nnn (3.14) 0.724nnn (5.08)  0.320nnn (  6.11) 2.169nnn (11.29) 0.288 17,525

Please cite this article as: Ali, A., et al., Industry concentration and corporate disclosure policy. Journal of Accounting and Economics (2014), http://dx.doi.org/10.1016/j.jacceco.2014.08.004i

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4.5. Robustness checks 4.5.1. Sensitivity of the results to changes in model specification A potential concern with our use of the U.S. Census industry concentration measures is that for multi-segment firms on Compustat, the industry concentration value we assign to a given firm is the value that corresponds to its segment with the greatest sales. As a result, we do not consider the industry concentration of other business segments in which a multi-segment firm operates. Further, some multi-segment firms whose primary industry is in the manufacturing sector may have other business segments that are not in the manufacturing sector. Panel A of Table A2 in the appendix shows that for Compustat manufacturing firms over our 1995–2009 sample period, the mean and median values of the fraction of a firm's sales that are manufacturing related are 98.1% and 100%. Likewise, the mean and median values of the fraction of a firm's sales that relate to its primary industry are 91.4% and 100%, while the mean and median values of the fraction of a firm's manufacturing sales that relate to its primary industry are 93.1% and 100%. These results suggest that the error in our industry concentration measure arising from firms operating in multiple industries is not likely to be very large. Nevertheless, we use the following approaches to address this measurement error. First, in all of our models we include the following variables: the fraction of a firm's sales that relate to manufacturing and the fraction of its manufacturing sales that relate to its primary industry. For our tests that consider the association between industry concentration and disclosure, in 21 of the 25 cases where we found significant negative associations between industry concentration and disclosure, we continue to find the significant negative associations. Also, in eight of nine instances where we found that the negative association between industry concentration and disclosure is less pronounced in more leveraged industries, we continue to find that to be the case. Second, we estimate all of our models after excluding sample firms with less than 50% of their sales in manufacturing. For our tests that examine the association between industry concentration and disclosure, in 22 of the 25 cases where we found significant negative associations between industry concentration and disclosure, we continue to observe significant negative associations. Further, in eight out of nine cases where we found that the negative association between industry concentration and disclosure is less pronounced in more leveraged industries, the results continue to hold. Third, we estimate all our models after replacing a firm's industry Herfindahl-Hirschman index or four-firm ratio with a sales-weighted measure of the Herfindahl-Hirschman index or the four-firm ratio. The sales-weighted measure is defined as the fraction of the firm's sales in a business segment multiplied by the segment's industry Herfindahl-Hirschman index or four-firm ratio, added across all the firm's segments. A benefit of using this composite measure of industry concentration is that it considers the concentration of all the industries in which a firm operates. However, a potential concern is that the linear weighting with sales may not be the optimal way to construct the measure, given that it is not clear how the proprietary costs of disclosure associated with each of the firm's non-primary segments affect the firm's overall disclosure practices. On using the sales-weighted U.S. Census concentration measures, we find that in 21 of the 25 cases where we found significant negative associations between industry concentration and disclosure, we continue to find significant negative associations. Also, of the nine instances where we found that the negative association between industry concentration and disclosure is less pronounced in more leveraged industries, the results continue to hold for two of these instances. Finally, for our sample firms, we examine the sensitivity of our results to using the Compustat-based HerfindahlHirschman index and four-firm ratio in place of the U.S. Census Herfindahl-Hirschman index and four-firm ratio. On using the Compustat-based concentration measures, of the 25 cases where we found significant negative associations between industry concentration and disclosure, we continue to find significant negative associations in only four cases. Also, of the nine cases where we found that the negative association between industry concentration and disclosure is less pronounced in more leveraged industries, the result does not hold in any of the cases. Overall, our results are very sensitive to using the Compustat based industry concentration measures in place of the U.S. Census-based measures. 4.5.2. Sample consisting of manufacturing and non-manufacturing firms In addition to providing data on the Herfindahl-Hirschman index and the four-firm ratio for the manufacturing sector, the U.S. Census provides data on the four-firm ratio for most of the non-manufacturing industry sectors for the years 1997, 2002, and 2007.25 For our tests that require only machine-readable data, namely management earnings forecasts and analysts' earnings forecasts related tests, we examine whether the results for the manufacturing sample, reported earlier, are robust to including nonmanufacturing firms in the sample. Leverage can differ markedly across industry sectors for a variety of reasons. Consequently, in the sample consisting of firms from multiple industry sectors, it is difficult to assess whether rivalry in a concentrated industry is less or more intense based on whether industry leverage is high or low relative to that of other industries. Thus, for this sample, we do not test how industry leverage affects the association between industry concentration and our proxies for corporate disclosure. Table 8 shows that for the sample of manufacturing and non-manufacturing firms, industry concentration is significantly negatively associated with the frequency and horizon of management forecasts. Likewise, Table 9 documents that for the sample of 25 The U.S. Census does not report industry concentration data for the following 2-digit NAICS industries, (i) agriculture, forestry, fishing and hunting (2-digit NAICS 11), (ii) mining, quarrying, and oil and gas extraction (2-digit NAICS 21), (iii) construction (2-digit NAICS 23), and (iv) management of companies and enterprises (2-digit NAICS 55). Also, the U.S. Census does not report Herfindahl-Hirschman index values for any of the non-manufacturing industry sectors.

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Table 8 Industry concentration and the frequency and horizon of management forecasts of earnings – all industries sample. This table presents the results of the management forecast frequency and horizon models. The dependent variable in the first model is the number of management forecasts made during a fiscal year for the earnings of that fiscal year. We use a Tobit regression for this model because the dependent variable is truncated at zero. The dependent variable in the second model is the average number of days between management forecast dates for a firm's fiscal year earnings and the end date of the fiscal year, and for this model, we use the OLS procedure. The sample consists of firms in Compustat during the years 1995–2009, with necessary data for the variables in the models. For the second model if a firm does not make a management forecast during a given year that firm-year is dropped from the analysis. Management forecast data are from First Call's Corporate Investor Guidelines (CIG) database. The industry four-firm ratio is scaled by 100. To construct the competition from potential entrants variable, we first calculate for six-digit NAICS industries the sizeweighted average of property, plant, and equipment, the size-weighted average of research and development expenses, the size-weighted average of capital expenditures, and total industry sales. Next, we calculate for each industry the average across the decile ranks of these four industry measures and then multiply the resulting value by minus one. Ratio of the number of public to private firms in the industry is calculated as the number of firms on Compustat in the 6-digit NAICS industry divided by the difference between this number and the total number of firms in the industry as reported by the U. S. Census. Stock return volatility is calculated with monthly stock return data over the firm's fiscal year. Absolute change in annual earnings per share/stock price is the absolute value of the annual change in earnings per share deflated by stock price at the beginning of the fiscal year. Market-adjusted stock return is the firm's buy-and-hold 12-month fiscal year stock return minus the CRSP value-weighted stock return for the same period. Research and development expense/book assets is the R&D expense for the fiscal year divided by the book value of assets at the beginning of the fiscal year. Analyst coverage is the average number of analysts making earnings forecasts for the firm during the fiscal year. Institutional fractional ownership is institutional holdings at the beginning of the fiscal year. The Post-Regulation Fair Disclosure dummy takes the value of one for years 2001 and beyond, and zero otherwise. Industry profitability is calculated using principal component analysis following the methodology in Li (2010). Equity or debt issuance dummy takes a value of one if a firm issues public equity or public debt in a subsequent two-year period, and zero otherwise. Leverage is measured at the firm-level as total liabilities minus deferred taxes scaled by total book assets. Standard deviation of earnings is calculated as the standard deviation of earnings before extraordinary items over the prior five years, with a requirement of at least three years of observations. Analyst optimism is measured as the difference between analyst consensus estimation at the beginning of the fiscal year and the actual earnings per share, scaled by the absolute value of actual earnings per share. Litigation industries dummy takes a value of one if the firm operates in an industry facing high litigate risk as defined in Li (2010), and zero otherwise. The first model includes year dummies, and the intercept for this model represents the omitted year dummy. In the first model, z-statistics (reported in parentheses) are based on standard errors clustered by industry. In the second model, t-statistics (reported in parentheses) are based on standard errors clustered by year and industry. ***, **, and * indicate significance levels for two-tailed tests at the 1%, 5% and 10% levels, respectively. Model Dependent variable Intercept Industry four-firm ratio Competition from potential entrants Ratio of the number of public to private firms in the industry Stock return volatility Absolute change in annual earnings per share/stock price Market-adjusted stock return Research and development expense/book assets Natural logarithm of market value of equity Analyst coverage Institutional fractional ownership Post-Regulation Fair Disclosure dummy Industry profitability Equity or debt issuance dummy Market to book ratio of assets Leverage Standard deviation of earnings Positive earnings change dummy Analyst optimism Litigation industries dummy R2-adjusted N

1 Forecast frequency

2 Forecast horizon

 7.936nnn (  13.02)  1.616nn (  2.01) 0.588 (1.09)  3.266nn (  2.49)  5.465nnn (  2.86)  2.906nnn (  6.46) 0.103n (1.73)  2.991nn (  2.00) 0.162nn (1.97) 0.153nnn (6.91) 0.730 (1.47) 4.586nnn (8.37) 2.632nnn (2.62)  0.099 (  0.77) 0.108n (1.72) 0.029 (0.05)  2.908nnn (  4.00) 0.117 (1.25)  0.093nnn (  3.80) 0.723nn (2.17) 0.100 41,208

 14.998 (  0.82)  62.578nn (  2.28) 23.226 (1.38) 86.749n (1.80)  150.311nn (  2.57)  30.807nnn (  3.25) 5.774n (1.68)  149.376nnn (  3.05) 19.027nnn (6.40)  1.196 (  1.55) 60.360nnn (5.28) 81.346nnn (8.42) 42.596nn (2.44) 5.186 (1.31) 7.500nnn (3.90) 88.249nnn (6.41)  31.115 (  1.56) 15.822nnn (3.33)  4.086nnn (  4.55) 10.276 (1.07) 0.189 14,069

Please cite this article as: Ali, A., et al., Industry concentration and corporate disclosure policy. Journal of Accounting and Economics (2014), http://dx.doi.org/10.1016/j.jacceco.2014.08.004i

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Table 9 Industry concentration and analyst forecast properties – all industries sample. The table reports results of OLS regressions. The sample consists of firms in Compustat during the years 1995–2009, with necessary data for the variables in the models. The dependent variable in model 1 is the 12-month average of the standard deviation of analysts' forecasts, deflated by stock price at the beginning of the fiscal year. The dependent variable in model 2 is the 12-month average of the absolute values of analysts' forecasts errors defined as actual earnings minus median forecast, deflated by stock price at the beginning of fiscal year. The dependent variable in model 3 is the standard deviation of forecast revisions deflated by stock price at the beginning of the fiscal year, where forecast revision is defined as current month's median forecast minus previous month's median forecast. The industry four-firm ratio is scaled by 100. To construct the competition from potential entrants variable, we first calculate for six-digit NAICS industries the size-weighted average of property, plant, and equipment, the size-weighted average of research and development expenses, the size-weighted average of capital expenditures, and total industry sales. Next, we calculate for each industry the average across the decile ranks of these four industry measures and then multiply the resulting value by minus one. Ratio of the number of public to private firms in the industry is calculated as the number of firms on Compustat in the 6-digit NAICS industry divided by the difference between this number and the total number of firms in the industry as reported by the U.S. Census. Research and development expense/book assets is the R&D expense for the fiscal year divided by book value of assets at the beginning of the fiscal year. Analyst coverage is the average number of analysts making earnings forecasts for a firm during the fiscal year. Correlation between return on equity and stock returns is computed using annual data of the preceding 10 years, with a minimum of three preceding years of data. Standard deviation of return on equity is computed using annual return on equity of the preceding 10 years, with a minimum of three preceding years of data. Stock return volatility is calculated with monthly stock return data for the firm's fiscal year. Absolute change in annual earnings per share/stock price is the absolute value of the annual change in earnings per share deflated by stock price at the beginning of the fiscal year. Marketadjusted stock return is the firm's buy-and-hold 12-month fiscal year stock return minus the CRSP value-weighted stock return for the same period. Average proportion of new monthly forecasts is the fiscal year average of the proportion of analysts' forecasts at the end of a month that are either first-time forecasts or are revised during the month. t-Statistics (reported in parentheses) are based on standard errors clustered by year and industry. ***, **, and * indicate significance levels for two-tailed tests at the 1%, 5% and 10% levels, respectively. Model Dependent variable

Intercept Industry four-firm ratio Competition from potential entrants Ratio of the number of public to private firms in the industry Natural logarithm of market value of equity Research and development expense/book assets Analyst coverage Correlation between return on equity and stock returns Standard deviation of return on equity Stock return volatility Absolute change in annual earnings per share/stock price Market-adjusted stock return Average proportion of new monthly forecasts R2-adjusted N

1 Analyst forecast dispersion

2 Analyst forecast errors

3 Analyst forecast revision volatility

1.175nnn (4.48) 0.587nnn (4.04)  0.034 (  0.21) 0.239 (1.32)  0.183nnn (  5.88) 1.554nn (2.39)  0.014nn (  2.01) 0.017nn (2.08) 1.050nnn (5.19) 5.815nn (1.99) 1.056nnn (5.68)  0.590nnn (  5.99) 0.940 (1.57) 0.216 34,736

8.522nnn (5.66) 2.681nnn (4.02)  0.188 (  0.39)  3.774nnn (  3.76)  1.233nnn (  7.63)  0.752 (  0.36)  0.010 (  0.22) 1.225nnn (2.60) 3.890nnn (4.95) 18.178n (1.90) 6.186nnn (5.04)  3.209nnn (  5.74) 2.985 (0.78) 0.159 40,794

1.269nnn (5.47) 0.551nnn (3.74)  0.096 (  0.95)  0.269nnn (  3.96)  0.264nnn (  6.07) 1.114nnn (3.81)  0.013 (  1.56) 0.264nn (2.51) 0.985nnn (6.44) 4.471n (1.88) 1.163nnn (5.15)  0.685nnn (  5.70) 3.111nnn (4.74) 0.187 40,424

manufacturing and non-manufacturing firms, industry concentration is significantly positively associated with analyst forecast dispersion, analyst forecast errors, and analyst forecast revision volatility. These findings suggest that our conclusion that firms in more concentrated industries disclose less is robust to using samples consisting of manufacturing and non-manufacturing firms. 5. Conclusion Several prior studies have attempted to examine the effect of proprietary costs of disclosure on corporate disclosure policy by using industry concentration as a measure for proprietary costs of disclosure. However, the extant evidence on the association between industry concentration and disclosure is inconclusive (Beyer et al., 2010; Berger, 2011). This mixed evidence could be due to the use in prior work of Compustat based industry concentration measures, which are constructed with data from only publicly traded firms (Berger, 2011). In this paper, we examine the association between industry concentration and disclosure using U.S. Census based industry concentration measures, which are constructed with data from both publicly traded firms and private firms. We also consider several disclosure contexts that have not been considered by prior studies that examine the relation between industry concentration and disclosure, and in doing so, increase the generalizability of our findings. Overall, we find that firms in more concentrated industries have less informative disclosure practices across a variety of disclosure settings. Please cite this article as: Ali, A., et al., Industry concentration and corporate disclosure policy. Journal of Accounting and Economics (2014), http://dx.doi.org/10.1016/j.jacceco.2014.08.004i

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First, we show that the frequency of management earnings forecasts is lower and the horizon of these forecasts is shorter in more concentrated industries. Second, we document that when firms in more concentrated industries sell new shares, they are more likely to do so via private placement deals rather than SEOs. This result is consistent with firms in more concentrated industries trying to avoid SEC-mandated public disclosure requirements for SEOs. Third, we find that firms in more concentrated industries are characterized by lower analyst disclosure ratings. Fourth, we show that firms in these industries have greater dispersion in analysts' earnings forecasts, greater analyst earnings forecast errors, and a higher volatility of analyst forecast revisions. These findings suggest that firms in more concentrated industries disclose less and consequently have inferior information environments. Finally, we find that the associations involving industry concentration that we document in the context of different disclosure settings are generally less pronounced in more leveraged industries. These results are consistent with the arguments in Chevalier (1995a, 1995b) and Phillips (1995) that among firms in concentrated industries, those that are in industries with higher leverage are expected to face less intense industry rivalry. The negative relations we document between U.S. Census industry concentration and different measures of corporate disclosure could be due to industry concentration proxying for the intensity of competition between existing industry rivals, for the level of innovation in an industry, for the extent to which disclosures by firms in an industry provide more substantive and less noisy information about future industry demand, or for some other factors. We leave it for future research to determine what factors are responsible for the observed negative relation between industry concentration and disclosure. Appendix A See Tables A1 and A2. Table A1 The effect of data requirements on sample sizes. Compustat/CRSP/IBES initial sample refers to the number of firms in the manufacturing sector over our sample period, 1995–2009, that are included on Compustat, CRSP, and IBES. For Table 4, the initial sample consists of firms in the manufacturing sector that sold new equity via private placements or seasoned equity offerings in 1997, 2000, 2002, 2004, or 2007 and which are included on Compustat, CRSP, and IBES. Only the year(s) in which a firm sells new shares is (are) included in this initial sample. For Table 5, the initial sample consists of firms in the manufacturing sector whose disclosure practices are rated in the Report of the Association for Investment Management and Research in 1995 or 1996, and which are in Compustat and IBES. Only the year(s) for which a firm receives a disclosure rating is (are) included in this initial sample. N/A stands for not applicable. For Tables 8 and 9 Compustat/CRSP/IBES initial sample refers to the number of firms over our sample period, 1995–2009, that are included on Compustat, CRSP, and IBES.

Dependent variable

Compustat/CRSP/IBES initial sample Number of firms dropped due to missing IBES data items Number of firms dropped due to missing data on institutional ownership Number of firms dropped due to missing data to calculate the correlation between return on equity and stock returns Number of firms dropped due to missing data for remaining variables Number of firms dropped due to no management forecasts Final sample

Table 2

Table 3

Table 4

Table 5

Tables 6 and 7

Tables 6 and 7

Management forecast frequency

Management forecast horizon

Private placement vs. SEO

Disclosure rating

Analyst forecast dispersion

Analyst forecast error

Analyst forecast revision volatility

32,551 4,139

32,551 4,139

1,144 115

130 0

29,369 5,702

29,369 3,224

29,369 3,351

6,351

6,351

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

4,029

4,029

4,029

1,775

1,775

244

7

4,464

4,464

4,464

N/A

12,779

N/A

N/A

N/A

N/A

N/A

20,286

7,507

785

123

15,174

17,652

17,525

Dependent variable

Compustat/CRSP/IBES initial sample Number of firms dropped due to missing IBES data items Number of firms dropped due to missing data on institutional ownership Number of firms dropped due to missing data to calculate the correlation between return on equity and stock returns Number of firms dropped due to missing data for remaining variables Number of firms dropped due to no management forecasts Final sample

Tables 6 and 7

Table 8

Table 8

Table 9

Table 9

Table 9

Management forecast frequency

Management forecast horizon

Analyst forecast dispersion

Analyst forecast errors

Analyst forecast revision volatility

85,031 20,130 19,587

85,031 20,130 19,587

85,031 31,859 N/A

85,031 25,801 N/A

85,031 25,431 N/A

N/A

N/A

9,123

9,123

9,123

4,106

4,106

9,313

9,313

9,313

N/A 41,208

27,139 14,069

N/A 34,736

N/A 40,794

N/A 40,424

Please cite this article as: Ali, A., et al., Industry concentration and corporate disclosure policy. Journal of Accounting and Economics (2014), http://dx.doi.org/10.1016/j.jacceco.2014.08.004i

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Table A2 Descriptive statistics of the variables in the models in Tables 2–7, for our sample firms, and also for all manufacturing Compustat firms and all Compustat firms. Manufacturing firms consist of all manufacturing sector firms, for which necessary data to construct the relevant variables are available on Compustat. All Compustat firms consist of all firms for which necessary data to construct the relevant variables are available on Compustat. Descriptive statistics of the variables are computed for the sample periods used for estimating the models. For the ‘Manufacturing Firms’ and ‘All Compustat Firms’ samples, within each panel the number of observations varies for each variable depending on data availability. Sample firms

Panel A: Frequency of management forecasts (Table 2) Dependent variable: Number of management forecasts Independent variables: Industry Herfindahl-Hirschman index (scaled by 10,000) Industry four-firm ratio (scaled by 100) Industry asset-weighted mean of the net-debt-to-asset ratio Competition from potential entrants Ratio of the number of public to private firms in the industry Stock return volatility Absolute change in annual earnings per share/stock price Market-adjusted stock return Research and development expense/book assets Natural logarithm of market value of equity Analyst coverage Institutional fractional ownership Post-regulation fair disclosure dummy Industry profitability Equity or debt issuance dummy Market to book ratio of assets Leverage Standard deviation of earnings Positive earnings change dummy Analyst optimism Litigation industries dummy Variables used in robustness tests discussed in Section 4.5.1 Fraction of a firm's sales related to manufacturing Fraction of a firm's sales related to its primary industry Fraction of a firm's manufacturing sales related to its primary industry Sales-weighted Herfindahl-Hirschman index (scaled by 10,000) Sales-weighted four-firm ratio (scaled by 100) Compustat Herfindahl-Hirschman index (scaled by 10,000) Compustat four-firm ratio (scaled by 100) Panel B: Horizon of management forecasts (Table 3) Dependent Variable: Horizon of management forecasts Independent Variables: Industry Herfindahl-Hirschman index (scaled by 10,000) Industry four-firm ratio (scaled by 100) Industry asset-weighted mean of the net-debt-to-asset ratio Competition from potential entrants Ratio of the number of public to private firms in the industry Stock return volatility Absolute change in annual earnings per share/stock price Market-adjusted stock return Research and development expense/book assets Natural logarithm of market value of equity Analyst coverage Institutional fractional ownership Post-Regulation Fair Disclosure dummy Industry profitability Equity or debt issuance dummy Market to book ratio of assets Leverage Standard deviation of earnings Positive earnings change dummy Analyst optimism Litigation industries dummy Variables used in robustness tests discussed in Section 4.5.1. Fraction of a firm's sales related to manufacturing Fraction of a firm's sales related to its primary industry

Mean

0.719

Median

Manufacturing firms Mean

Median

All compustat firms

Mean

Median

0.000

0.273

0.000

0.289

0.000

0.074 0.059 0.405 0.409 0.038 0.020 0.807 0.861 0.118 0.047 0.033 0.017 0.124 0.034 0.079  0.077 0.096 0.039 6.095 5.880 6.088 3.923 0.376 0.336 0.096 0.039  0.050  0.034 0.740 1.000 2.241 1.633 0.449 0.437 0.109 0.049 0.564 1.000 0.698 0.125 0.372 0.000

0.079 0.434 0.101 0.556 0.034 0.045 0.571 0.053 0.091 5.337 4.046 0.202 0.454  0.051 0.742 2.264 0.446 0.135 0.550 0.554 0.376

0.060 0.420 0.099 0.556 0.013 0.020 0.046  0.116 0.035 5.140 2.000 0.000 0.000  0.035 1.000 1.592 0.421 0.056 1.000 0.005 0.000

N.A. 0.352 0.176 0.500 0.025 0.039 0.527 0.049 0.046 5.461 4.107 0.168 0.459  0.174 0.719 1.943 0.541 0.103 0.576 0.502 0.324

N.A. 0.312 0.094 0.500 0.005 0.015 0.040  0.082 0.000 5.315 2.000 0.000 0.000  0.095 1.000 1.327 0.536 0.033 1.000 0.000 0.000

0.981 0.914 0.931 0.069 0.400 0.334 0.782

1.000 1.000 1.000 0.055 0.386 0.237 0.818

N.A. 0.923 N.A. N.A. N.A. 0.276 0.687

N.A. 1.000 N.A. N.A. N.A. 0.181 0.724

0.987 0.925 0.936 0.073 0.403 0.286 0.742

1.000 1.000 1.000 0.059 0.399 0.200 0.761

Mean

Median

Mean

101.778

86.500

199.960

0.076 0.057 0.406 0.406 0.050 0.047 0.798 0.861 0.084 0.036 0.026 0.014 0.063 0.027 0.039  0.059 0.059 0.033 6.635 6.507 7.839 5.900 0.528 0.614 0.654 1.000  0.028  0.020 0.699 1.000 2.036 1.591 0.456 0.461 0.074 0.037 0.554 1.000 0.765 0.121 0.320 0.000

0.079 0.434 0.101 0.556 0.034 0.045 0.571 0.053 0.091 5.337 4.046 0.202 0.454  0.051 0.742 2.264 0.446 0.135 0.550 0.554 0.376 0.981 0.914

0.987 0.913

1.000 1.000

Median

200.60

Mean

Median

203.86

201.85

0.060 0.420 0.099 0.556 0.013 0.020 0.046  0.116 0.035 5.140 2.000 0.000 0.000  0.035 1.000 1.592 0.421 0.056 1.000 0.005 0.000

N.A. 0.352 0.176 0.500 0.025 0.039 0.527 0.049 0.046 5.461 4.107 0.168 0.459  0.174 0.719 1.943 0.541 0.103 0.576 0.502 0.324

N.A. 0.312 0.094 0.500 0.005 0.015 0.040  0.082 0.000 5.315 2.000 0.000 0.000  0.095 1.000 1.327 0.536 0.033 1.000 0.000 0.000

1.000 1.000

N.A. 0.923

N.A. 1.000

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Table A2 (continued ) Fraction of a firm's manufacturing sales related to its primary industry Sales-weighted Herfindahl-Hirschman index (scaled by 10,000) Sales-weighted four-firm ratio (scaled by 100) Compustat Herfindahl-Hirschman index (scaled by 10,000) Compustat four-firm ratio (scaled by 100) Panel C: Private placement vs. seasoned equity offering decision (Table 4)

0.925 0.078 0.406 0.311 0.769 Mean

1.000 0.060 0.401 0.222 0.801 Median

Dependent Variable: Firm sells new shares through private placement instead of seasoned equity offering 0.357 0.000 Independent Variables: Industry Herfindahl-Hirschman index (scaled by 10,000) 0.071 0.066 Industry four-firm ratio (scaled by 100) 0.413 0.429 Industry asset-weighted mean of the net-debt-to-asset ratio  0.023  0.061 Competition from potential entrants 0.843 0.889 Ratio of the number of public to private firms in the industry 0.211 0.134 Natural logarithm of book value of assets 4.751 4.454 Analyst coverage 1.887 0.000 Number of years since a firm's IPO 10.043 7.000 Industry-adjusted sales growth 0.095 0.000 Change in industry-adjusted market-to-book equity 0.078  0.209 Operating cash flow/book assets  0.066 0.037 Cash flow volatility 0.144 0.036 Altman-Z score 10.503 3.351 Stock return volatility 0.077 0.035 One-year market-adjusted stock return 0.547 0.413 The offering takes place within one week of an earnings announcement 0.126 0.000 Variables used in robustness tests discussed in Section 4.5.1. Fraction of a firm's sales related to manufacturing 0.955 1.000 Fraction of a firm's sales related to its primary industry 0.825 1.000 Fraction of a firm's manufacturing sales related to its primary industry 0.862 1.000 Sales-weighted Herfindahl-Hirschman index (scaled by 10,000) 0.068 0.061 Sales-weighted four-firm ratio (scaled by 100) 0.413 0.427 Compustat Herfindahl-Hirschman index (scaled by 10,000) 0.281 0.193 Compustat four-firm ratio (scaled by 100) 0.689 0.510 Panel D: Disclosure ratings (Table 5) Dependent Variable: AIMR disclosure rating Independent Variables: Industry Herfindahl-Hirschman index (scaled by 10,000) Industry four-firm ratio (scaled by 100) Industry asset-weighted mean of the net-debt-to-asset ratio Competition from potential entrants Ratio of the number of public to private firms in the industry Natural logarithm of market value of equity Standard deviation of return on equity Absolute change in annual earnings per share/stock price Book-to-market-equity Return on assets Sales growth Analyst coverage Analyst forecast dispersion Analyst forecast errors Analyst forecast revision volatility S&P 500 firm dummy Variables used in robustness tests discussed in Section 4.5.1. Fraction of a firm's sales related to manufacturing Fraction of a firm's sales related to its primary industry Fraction of a firm's manufacturing sales related to its primary industry Sales-weighted Herfindahl-Hirschman index (scaled by 10,000) Sales-weighted four-firm ratio (scaled by 100) Compustat Herfindahl-Hirschman index (scaled by 10,000) Compustat four-firm ratio (scaled by 100) Panel E: Analyst forecast properties (Tables 6 and7) Dependent Variables: 12-month average of the standard deviation of analysts' forecasts 12-month average of the absolute values of analyst forecast errors Standard deviation of forecast revisions Independent Variables:

0.931 0.069 0.400 0.334 0.782

1.000 0.055 0.386 0.237 0.818

N.A. N.A. N.A. 0.276 0.687

N.A. N.A. N.A. 0.181 0.724

Mean

Median

Mean

Median

N.A.

N.A.

N.A.

N.A.

0.081 0.437 0.097 0.553 0.037 5.154 4.011 15.185 0.034 0.251 0.004 0.195 7.352 0.044 N.A. N.A.

0.061 0.427 0.098 0.556 0.014 4.884 2.000 10.000 0.000 0.047 0.094 0.018 3.543 0.020 N.A. N.A.

N.A. 0.353 0.211 0.500 0.025 5.708 4.067 13.337 0.037 0.302 0.004 0.305 7.418 0.037 N.A. N.A.

N.A. 0.312 0.094 0.500 0.005 5.615 2.000 9.000 0.000 0.114 0.075 0.023 3.207 0.014 N.A. N.A.

0.985 0.931 0.945 0.072 0.406 0.332 0.781

1.000 1.000 1.000 0.057 0.398 0.233 0.818

N.A. 0.925 N.A. N.A. N.A. 0.274 0.686

N.A. 1.000 N.A. N.A. N.A. 0.182 0.720

Mean

Median

Mean

Median

Mean

Median

69.340

71.220

N.A.

N.A.

N.A.

N.A.

0.081 0.428 0.137 0.905 0.128 8.525 0.111 0.064 0.412 0.086 1.096 20.370 0.005 0.011 0.005 0.554

0.049 0.356 0.111 0.944 0.088 8.281 0.095 0.037 0.382 0.073 1.089 13.583 0.002 0.004 0.002 1.000

0.063 0.437 0.124 0.568 0.030 4.721 0.230 0.377 0.525  0.046 1.193 3.662 0.009 0.038 0.010 0.058

0.053 0.427 0.126 0.583 0.012 4.532 0.137 0.046 0.432 0.043 1.112 1.778 0.004 0.009 0.004 0.000

N.A. 0.331 0.119 0.500 0.027 4.720 0.223 0.532 0.558  0.041 1.185 3.787 0.009 0.041 0.009 0.056

N.A. 0.283 0.127 0.500 0.005 4.533 0.131 0.047 0.458 0.035 1.099 1.750 0.003 0.008 0.003 0.000

0.955 0.825 0.862 0.078 0.406 0.256 0.661

1.000 1.000 1.000 0.047 0.387 0.130 0.604

0.985 0.930 0.943 0.062 0.379 0.315 0.772

1.000 1.000 1.000 0.053 0.379 0.240 0.822

N.A. 0.952 N.A. N.A. N.A. 0.262 0.667

N.A. 1.000 N.A. N.A. N.A. 0.168 0.706

Mean

0.008 0.027 0.009

Median

0.003 0.009 0.004

Mean

0.012 0.049 0.014

Median

0.004 0.010 0.004

Mean

0.011 0.050 0.013

Median

0.003 0.008 0.003

Please cite this article as: Ali, A., et al., Industry concentration and corporate disclosure policy. Journal of Accounting and Economics (2014), http://dx.doi.org/10.1016/j.jacceco.2014.08.004i

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A. Ali et al. / Journal of Accounting and Economics ] (]]]]) ]]]–]]]

Table A2 (continued ) Industry Herfindahl-Hirschman index (scaled by 10,000) Industry four-firm ratio (scaled by 100) Industry asset-weighted mean of the net-debt-to-asset ratio Competition from potential entrants Ratio of the number of public to private firms in the industry Natural logarithm of market value of equity Research and development expense/book assets Analyst coverage Correlation between return on equity and stock returns Standard deviation of return on equity Stock return volatility Absolute change in annual earnings per share/stock price Market adjusted stock return Average proportion of new monthly forecasts Variables used in robustness tests discussed in Section 4.5.1. Fraction of a firm's sales related to manufacturing Fraction of a firm's sales related to its primary industry Fraction of a firm's manufacturing sales related to its primary industry Sales-weighted Herfindahl-Hirschman index (scaled by 10,000) Sales-weighted four-firm ratio (scaled by 100) Compustat Herfindahl-Hirschman index (scaled by 10,000) Compustat four-firm ratio (scaled by 100)

0.075 0.058 0.404 0.406 0.057  0.044 0.805 0.861 0.118 0.045 6.212 6.003 0.090 0.037 6.322 4.077 0.209 0.268 0.210 0.137 0.031 0.016 0.123 0.033 0.080  0.066 0.288 0.282

0.079 0.434 0.101 0.556 0.034 5.337 0.091 4.046 0.231 0.225 0.045 0.571 0.053 0.279

0.060 0.420 0.099 0.556 0.013 5.140 0.035 2.000 0.151 0.292 0.020 0.046  0.116 0.273

N.A. 0.352 0.176 0.500 0.025 5.461 0.046 4.107 0.234 0.197 0.039 0.527 0.049 0.256

N.A. 0.312 0.094 0.500 0.005 5.315 0.000 2.000 0.297 0.117 0.015 0.040  0.082 0.273

0.986 0.920 0.932 0.073 0.403 0.292 0.747

0.985 0.931 0.945 0.072 0.406 0.332 0.781

1.000 1.000 1.000 0.057 0.398 0.233 0.818

N.A. 0.925 N.A. N.A. N.A. 0.274 0.686

N.A. 1.000 N.A. N.A. N.A. 0.182 0.720

1.000 1.000 1.000 0.058 0.392 0.205 0.771

References Abarbanell, J.S., Lanen, W.N., Verrecchia, R.E., 1995. Analysts' forecasts as proxies for investor beliefs in empirical research. J. Account. Econ. 20, 31–60. Aggarwal, R.K., Samwick, A.A., 1999. Executive compensation, strategic competition, and relative performance evaluation: Theory and evidence. J. Financ. 54, 1999–2043. Ajinkya, B., Bhojraj, S., Sengupta, P., 2005. The association between outside directors, institutional investors, and the properties of management earnings forecasts. J. Account. Res. 43, 343–376. Akdogu, A., MacKay, P., 2008. Investment and competition. J. Financ. Quant. Anal. 43, 299–330. Ali, A., Klasa, S., Yeung, E., 2009. The limitations of industry concentration measures constructed with Compustat data: Implications for finance research. Rev. Financ. Stud. 22, 3839–3871. Altman, E.I., 1968. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. J. Financ. 23, 589–609. Baginski, S., Hassell, J., Kimbrough, M., 2002. The effect of legal environment on voluntary disclosure: Evidence from management earnings forecasts issued in U.S. and Canadian markets. Account. Rev. 77, 25–50. Bamber, L.S., Cheon, Y.S., 1998. Discretionary management earnings forecast disclosures: Antecedents and outcomes associated with forecast venue and forecast specificity choices. J. Account. Res. 36, 167–190. Barth, M., Kasznik, R., McNichols, M., 2001. Analyst coverage and intangible assets. J. Account. Res. 39, 1–34. Bens, D.A., Monahan, S.J., 2004. Disclosure quality and the excess value of diversification. J. Account. Res. 42, 691–730. Bens, D.A., Berger, P.G., Monahan, S.J., 2011. Discretionary disclosure in financial reporting: An examination comparing internal firm data to externally reported segment data. Account. Rev. 86, 1–33. Berger, P.G., 2011. Challenges and opportunities in disclosure research - A discussion of ‘the financial reporting environment: Review of the recent literature.’. J. Account. Econ. 51, 204–218. Beyer, A., Cohen, D., Lys, T., Walther, B., 2010. The financial reporting environment: Review of the recent literature. J. Account. Econ. 50, 296–343. Botosan, C.A., Stanford, M., 2005. Managers' motives to withhold segment disclosures and the effect of SFAS No. 131 on Analysts' Information Environment. Account. Rev. 80, 751–771. Bushee, B.J., Noe, C.F., 2000. Corporate disclosure practices, institutional investors, and stock return volatility. J. Account. Res. 38, 171–202. Campello, M., 2003. Capital structure and product market interactions: evidence from business cycles. J. Financ. Econ. 68, 353–378. Campello, M., 2006. Debt financing: Does it boost or hurt firm performance in product markets? J. Financ. Econ. 82, 135–172. Chevalier, J.A., 1995a. Capital structure and product-market competition: Empirical evidence from the supermarket industry. Am. Econ. Rev. 85, 415–435. Chevalier, J.A., 1995b. Do LBO supermarkets charge more? An empirical analysis of the effects of LBOs on supermarket pricing. J. Financ. 50, 1095–1112. Chuk, E., Matsumoto, D., Miller, G.S., 2013. Assessing methods of identifying management forecasts: CIG vs. researcher collected. J. Account. Econ. 55, 23–42. Clinch, G., Verrecchia, R.E., 1997. Competitive disadvantage and discretionary disclosure in industries. Aust. J. Manage. 22, 125–138. Cohen, W., 2010. ‘Fifty years of empirical studies on innovative activity and performance.’. In: Hall, Bronwyn, Rosenberg, Nathan (Eds.), Handbook of Economics of Innovation, vol 1. , North Holland, Amsterdam, pp. 129–213. Darrough, M.N., Stoughton, N.M., 1990. Financial disclosure policy in an entry game. J. Account. Econ. 12, 219–243. DeAngelo, H., DeAngelo, L., Stulz, R.M., 2010. Seasoned equity offerings, market timing, and the corporate life cycle. J. Financ. Econ. 95, 275–295. Dye, R., 1985. Disclosure of nonproprietary information. J. Account. Res. 23, 123–145. Ellis, J.A., Fee, C.E., Thomas, S.E., 2012. Proprietary costs and disclosure of information about customers. J. Account. Res. 50, 685–727. Fresard, L., 2010. Financial strength and product market behavior: The real effects of corporate cash holdings. J. Financ. 65, 1097–1122. Gilbert, R., 2006. ‘Looking for Mr. Schumpeter: Where are we in the competition-innovation debate?’. In: Jaffe, A., Lerner, S., Stern, S. (Eds.), Innovation Policy and the Economy, vol. 6. , MIT Press, Cambridge, MA, pp. 159–215. Gomes, A., Phillips, G.M., 2012. Why do public firms issue private and public securities? J. Financ. Intermed. 21, 619–658. Green, E., Porter, R., 1984. Non-cooperative collusion under imperfect price information. Econometrica 52, 87–100. Grossman, S.J., 1981. The role of warranties and private disclosure about product quality. J. Law Econ. 24, 461–483. Grullon, G., Michaely, R., 2007. Corporate payout policy and product market competition (Working Paper). Rice University. Harford, J., Klasa, S., Maxwell, W.F., 2013. Refinancing risk and cash holdings. J. Financ.. (forthcoming). Harris, M.H., 1998. The association between competition and managers' business segment reporting decisions. J. Account. Res. 36, 111–128. Haushalter, G.D., Klasa, S., Maxwell, W.F., 2007. The influence of product market dynamics on a firm's cash holdings and hedging behavior. J. Financ. Econ. 84, 797–825.

Please cite this article as: Ali, A., et al., Industry concentration and corporate disclosure policy. Journal of Accounting and Economics (2014), http://dx.doi.org/10.1016/j.jacceco.2014.08.004i

A. Ali et al. / Journal of Accounting and Economics ] (]]]]) ]]]–]]]

25

Healy, P.M., Hutton, A.M., Palepu, K.G., 1999. Stock performance and sustained increases in disclosure. Contemp. Account. Res. 16, 485–510. Heflin, F., Subramanyam, K.R., Zhang, Y., 2003. Regulation FD and the financial information environment: Early evidence. Account. Rev. 78, 1–37. Hovakimian, A., Opler, T., Titman, S., 2001. The debt-equity choice. J. Financ. Quant. Anal. 36, 1–24. Jung, W.O., Kwon, Y.K., 1988. Disclosure when the market is unsure of information endowment of managers. J. Account. Res. 26, 146–153. Karamanou, I., Vafeas, N., 2005. The association between corporate boards, audit committees, and management earnings forecasts: An empirical analysis. J. Account. Res. 43, 453–486. Kasznik, R., Lev, B., 1995. To warn or not to warn: Management disclosures in the face of an earnings surprise. Account. Rev. 70, 113–134. Kayhan, A., Titman, S., 2007. Firms' histories and their capital structures. J. Financ. Econ. 83, 1–32. Klasa, S., Maxwell, W.F., Ortiz-Molina, H., 2009. The strategic use of cash holdings in collective bargaining with labor unions. J. Financ. Econ. 92, 421–442. Korajczyk, R.A., Lucas, D.J., McDonald, R.L., 1991. The effect of information releases on the pricing and timing of equity issues. Rev. Financ. Stud. 4, 685–708. Kothari, S.P., Shu, S., Wysocki, P.D., 2009. Do managers withhold bad news? J. Account. Res. 47, 241–276. Kovenock, D., Phillips, G.M., 1997. Capital structure and product market behavior. Rev. Financ. Stud. 10, 767–803. Kuhn, K., Vives, X., 1995. Information exchange among firms and their impact on competition. European Commission Document, Luxembourg: Office for Official Publications of the European Communities. Lang, M.H., Lundholm, R.J., 1993. Cross-sectional determinants of analyst ratings of corporate disclosures. J. Account. Res. 31, 246–271. Lang, M.H., Lundholm, R.J., 1996. Corporate disclosure policy and analyst behavior. Account. Rev. 71, 467–492. Li, X., 2010. The impacts of product market competition on the quantity and quality of voluntary disclosures. Rev. Account. Stud. 15, 663–711. Li, F., Lundholm, R., Minnis, M., 2013. A measure of competition based on 10-K filings. J. Account. Res. 51, 399–436. Mackay, P., Phillips, G.M., 2005. How does industry affect firm financial structure? Rev. Financ. Stud. 18, 1433–1466. Milgrom, P., 1981. Good news and bad news: representation theorems and applications. Bell J. Econ. 12, 380–391. Miller, G., 2002. Earnings performance and discretionary disclosure. J. Account. Res. 40, 173–204. Phillips, G.M., 1995. Increased debt and industry product markets, an empirical analysis. J. Financ. Econ. 37, 189–238. Raith, M., 2003. Competition, risk, and managerial incentives. Am. Econ. Rev. 93, 1425–1436. Rosenbaum, P.R., 2002. Observational Studies, 2nd Edition Springer Series in Statistics, Berlin. Stigler, G., 1964. A theory of oligopoly. J. Polit. Econ. 72, 44–61. Sutton, J., 1991. Sunk Costs and Market Structure. M.I.T. Press, Cambridge. Verrecchia, R.E., 1983. Discretionary disclosure. J. Account. Econ. 5, 179–194. Verrecchia, R.E., 1990. Endogenous proprietary costs through firm interdependence. J. Account. Econ. 12, 245–250. Verrecchia, R.E., 2001. Essays on disclosure. J. Account. Econ. 32, 97–180. Verrecchia, R.E., Weber, J., 2006. Redacted disclosure. J. Account. Res. 44, 791–814. Vives, X., 2006. ‘Information sharing: Economics and antitrust’, in The pros and cons of information sharing. Swedish Competition Authority, Stockholm. Vives, X., 2008. ‘Information sharing among firms’, in The New Palgrave: A Dictionary of Economics. Palgrave Macmillan, New York. Walker, D., Yost, K., 2008. Seasoned equity offerings: What firms say, do, and how the market reacts. J. Corp. Financ. 14, 376–386. Waymire, G., 1985. Earnings volatility and voluntary management forecast disclosure. J. Account. Res. 23, 268–295. Wu, Y., 2004. The choice of equity-selling mechanisms. J. Financ. Econ. 74, 93–119. Ziv, A., 1993. Information sharing in oligopoly: the truth-telling problem. Rand J. Econ. 24, 455–465.

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