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Product market competition and conditional conservatism

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Abstract

Based on pooled cross-sectional analysis, we find a robust positive relation between product market competition and conditional accounting conservatism. We also find evidence of an inter-temporal increase in conditional conservatism following industry deregulation and increased antitrust case filings. Distinguishing further between two dimensions of competition, we find conditional conservatism is greater when there is a higher threat of new entrants as well as stiff existing competition. Moreover, we find these results largely hold for industry followers as opposed to industry leaders, suggesting that strategic considerations shape the extent to which product market competition affects conditional conservatism.

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Notes

  1. There are many reasons why anti-trust actions vary across time. One could be the philosophy of the administration in place. Alternatively, budget constraints and other regulatory issues may reduce the resources needed for anti-trust enforcement.

  2. A competing view to this argument pertains to the presence of proprietary costs. This view also stresses competition among existing rivals (Verrecchia 1983). In the presence of proprietary costs, a firm may be less forthcoming with respect to firm-specific information out of concern that it will affect its competitive position. The upshot of this argument is that competitive pressures impose proprietary costs that may induce firms to recognize gains (losses) less (more) quickly. These effects may be more pronounced for industry followers because they are prone to greater competitive pressures than industry leaders.

  3. Chhaochharia et al. (2009) provide additional evidence on the governance role of product market competition by examining its relation to formal governance mechanisms in place. They find firms in less competitive industries use more formal governance mechanisms such as having less anti-takeover provisions, greater pay-for-performance sensitivity, and greater managerial equity ownership. They find the converse to be true for firms in industries that face more intense product market competition.

  4. Several studies have exploited this change in anti-takeover laws to examine its implication on financial reporting. Mehta (2010) finds financial reporting quality limits potential managerial excesses due to the passage of the anti-takeover laws. Other studies examine the impact of the adoption of the anti-takeover laws on financial reporting quality. For instance, Armstrong et al. (2012) find an improvement in financial statement informativeness following the passage of these laws. Other studies (e.g., Callen et al. 2010; Jayaraman and Shivakumar 2013) also find that conditional accounting conservatism increased significantly after the passage of state anti-takeover laws. Both studies argue that improvements in financial reporting serve to offset the potential weakening of corporate governance due to the stifling of the market for corporate control. In our context, the implication of this argument is that product market competition should reduce the demand for conservatism to the extent that it plays an effective governance role.

  5. http://www.rhsmith.umd.edu/industrydata/.

  6. 1977 is the first calendar year when segment data were available for all firms under SFAS No. 14, which became effective in 1976. We restrict our sample to the year 2005 because of the unavailability of the product market competition variable obtained from the Hoberg-Phillips data library for 2005 and thereafter.

  7. Approximately 10 % of our firm-years’ segment SIC codes (three digit) did not match with the SIC codes in the Compustat annual file. To reduce the number of mismatches, we also matched the segment SIC codes with the SIC codes in the CRSP database and found even larger number of mismatches, which is consistent with Guenther and Rosman (1994), who find large differences between SIC codes assigned to companies by Compustat and CRSP and suggest using Compustat for SIC codes. As a result, we followed Li's (2010) approach of matching with the Compustat annual file instead of the CRSP database.

  8. Ball et al. (2012) demonstrate that when firm-specific effects in earnings are taken into account, estimates of asymmetric timeliness do not exhibit the bias identified in Patatoukas and Thomas (2011) and are statistically and economically significant (though smaller in magnitude).

  9. Specifically, they adopt a two-step method to create the fitted HHI. First, they regress the census HHI on three variables: the HHI calculated from Compustat data for those manufacturing companies, the average number of employees for each industry (including both public and private firms) using employee data from the Bureau of Labor Statistics, and the number of employees per firm for public companies in each industry using Compustat data. In addition, they also include interaction terms of each of the two employee size variables with the Compustat HHI. Then, they use the parameter estimates derived from the regression to estimate the fitted HHI for all industries.

  10. While the Ali et al. (2009) and Hoberg and Phillips (2010) product market competition measure are improvements over the measure based on Compustat data, they are still incomplete in the sense that they do not include competition that can arise from not-for-profit and governmental entities or foreign firms. The omission of not-for-profit and governmental entities results in product market competition measures that may not completely reflect the extent of competition that exists in the marketplace. The measurement error is likely to be more severe for healthcare and education industries, in which non-profits dominate. To address this issue, we exclude healthcare and education industries from our sample and reestimate our regression models. Untabulated results indicate that the tenor of our results continue to hold after excluding these industries. To address the omission of foreign firms, we approximate competition from foreign firms by using import data by industry following Hui et al. (2011) and include it as an additional control variable in our empirical models; our inferences with respect to our test variable remain unchanged. However, we acknowledge that we still cannot completely rule out this measurement error and that it is important to recognize this limitation in interpreting our results.

  11. Beaver and Ryan (2009) show that conservatism is overestimated in the presence of debt, suggesting an additional reason to control for leverage.

  12. As a sensitivity test, we used the litigation risk metric developed by Kim and Skinner (2012), who supplement indicator variables for industry membership with measures of firm characteristics (such as size, growth, and stock volatility) to predict litigation risk. Untabulated results using Kim and Skinner’s (2012) measure yielded inferences with respect to our test variables that were similar to those reported in the paper.

  13. Untabulated correlations for the sample based on Li’s (2010) methodology exhibit similar patterns and are not reported for brevity.

  14. In terms of economic magnitude, when we multiply the standard deviation of 1.763 for PMC in the industry follower sample with the coefficient on NEG*RET*PMC (0.009), we get 0.016, which represents about a 2 % increase relative to the coefficient on NEG*RET of 0.763. Untabulated F-statistics to test for the difference in the incremental timely loss recognition coefficients for industry leaders and followers indicate a more pronounced effect for industry followers than leaders.

  15. Under the political costs hypothesis, industry leaders might expect to be differentially scrutinized and therefore report more conservatively. Our results are consistent with the arguments in Basu (2005) that political costs/regulation are more likely to be circumvented by unconditional conservatism.

  16. Our analysis focuses on the 10-year period surrounding the deregulation of the three industries covered in our study. We drop the year of deregulation from our sample and focus on the five years prior to and the five years after deregulation.

  17. For the airline industry, the mean values of PMC2(Sale) are calculated based on three years before and after the deregulation year (1978) because the sample for our PMC2(Sale) measure is not available before 1975.

  18. Analyses of the Compustat universe indicate that there is about a 10 % increase in firms with BIG N auditors during the same period.

  19. Following Garcia Lara et al. (2009), we introduce the variable LEADER to capture political costs that create incentives for firms under regulators’ scrutiny to shift income to periods with a lower public visibility, inducing conditional conservatism.

  20. We obtain the number of antitrust (government and private) case filings from Table 2 of Lin et al. (2000) with supplemental-years data provided by the Antitrust Division of the U.S. Department of Justice (Source: Maguire 2010; http://www.albany.edu/sourcebook/pdf/t5412010.pdf). We use the natural logarithm of the number of antitrust case filings each year to proxy for the level of antitrust enforcement. Because the antitrust data cases are not disaggregated into three-digit SIC codes, we use aggregated data only.

  21. We include HIGH_LIT in the model to control for the effect of high litigation regimes because our antitrust variable may simply reflect different litigation regimes (Basu 1997; Ball et al. 2000; Holthausen and Watts 2001; Talley 2009).

  22. We acknowledge that this finding could also be viewed as being consistent with political cost argument, in that more timely loss recognition during greater antitrust enforcement lowers political costs (Watts 2003).

  23. One argument is that firms in concentrated industries may adopt conservative accounting methods to avoid regulatory scrutiny. This political cost argument implies that firms in concentrated industries, i.e., industries with limited product market competition, may adopt accounting methods that lead to lower reported earnings. We assess the validity of this claim by evaluating the relation between product market competition and unconditional conservatism in the form of accounting method choices. Principally, we examine whether firms in concentrated industries adopt conservative accounting methods in the form of accelerated depreciation and LIFO cost flow assumption. When we correlate these two accounting method choices with our product market competition variables, we find evidence supporting the argument that firms in concentrated industries tend to adopt accelerated depreciation methods (but not LIFO) that contribute to lower reported earnings.

  24. One caveat for such categorical analysis is that the accelerated depreciation argument applies only when firms are growing and for LIFO when prices and inventory levels are not decreasing. Following Lev et al. (2005), we recode the two indicator variables to capture the depreciation and inventory cost flow-related accounting method choices under these specific scenarios. The first variable is set to be equal to 1 if a firm uses an accelerated depreciation method and when its depreciation expense growth exceeds its earnings growth and 0 otherwise. Similarly, the second variable takes a value of 1 if the firm adopts LIFO and when its growth in cost of goods sold is higher than its earnings growth and 0 otherwise. Untabulated results yield qualitatively similar inferences.

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Acknowledgments

We gratefully acknowledge the thoughtful comments and suggestions of Lakshmanan Shivakumar (the editor), the referee, and John Wang. We also acknowledge the comments of workshop participants at the Florida International University, University of Technology-Sydney, University of Queensland, and Macquarie University.

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Authors and Affiliations

  1. Eller College of Management, University of Arizona, Tucson, AZ, 85721, USA

    Dan Dhaliwal

  2. Arizona State University, Tempe, AZ, 85287, USA

    Shawn Huang

  3. Robert J. Trulaske, Sr. College of Business, University of Missouri-Columbia, Columbia, MO, 65211, USA

    Inder K. Khurana & Raynolde Pereira

Authors
  1. Dan Dhaliwal
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  2. Shawn Huang
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  3. Inder K. Khurana
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Corresponding author

Correspondence to Inder K. Khurana.

Appendices

Appendix 1: Variable definitions

Dependent Variable:

NI:

Net income after extraordinary items deflated by the beginning of the fiscal-year market value of equity

Independent Variables:

RET:

12-month fiscal year buy-and-hold return

NEG:

1 if RET < 0, 0 otherwise

Test Variable:

PMC:

A proxy for the extent of product market competition

PMC1(Sale):

(−1) times Herfindahl–Hirschman Index (Hj) measured as the sum of squared market shares of all firms on Compustat in an industry based on three-digit SIC code. Market share is calculated based on the ratio of firm i’s sales to industry j’s total sales

PMC2(Sale):

= (−1) times the fitted Herfindahl–Hirschman Index (Hj) computed by Hoberg and Phillips (2010), which combines the Compustat data with private firm data from the U.S. Department of Commerce and employee data from the U.S. Bureau of Labor Statistics

Control Variables:

SIZE:

Natural logarithm of market value of equity at the beginning of the fiscal year

LEV:

Debt-to-asset ratio at the beginning of the fiscal year

BM:

Book-to-market ratio computed as book value of common equity scaled by market value of common equity at the beginning of the fiscal year

LIT:

A firm-specific measure of litigation risk at the beginning of the fiscal year as modeled by Rogers and Stocken (2005). Specifically, LIT is equal to −5.738 + 0.141*(Size) + 0.284*(Turn) + 0.012*(Beta)−0.237*(Returns) −1.34*(Std_Ret) + 0.011*(Skewness) −3.161* (Min_Ret) −0.025*(Biotechnology) + 0.378* (Computer Hardware) + 0.075* (Electronics)−0.034*(Retailing) + 0.211*(Computer Software), where Size is the natural log of the average market value of equity; Turn is the average daily share volume divided by the average shares outstanding; Beta is the slope coefficient from regressing daily stock returns on the CRSP equal-weighted stock returns; Returns is the cumulative buy-and-hold returns; Std_Ret is the standard deviation of the daily returns; Skewness is the skewness of the daily returns; and Min_Ret is the minimum of the daily returns. Industry dummies are used to control for high-risk industries including biotechnology (SIC 2833 to 2836), computer hardware (SIC 3570 to 3577), electronics (SIC 3600 to 3674), retailing (SIC 5200 to 5961), and computer software (SIC 7371 to 7379)

BIG_N:

1 if the auditor is a Big 4/6 firm for the current fiscal year and 0 otherwise

Other Variables:

Leaders:

1 for a firm-year classified as industry leader if its sales rank is in the top quartile in an industry based on three-digit SIC code and 0 otherwise

Appendix 2

See Table 8.

Table 8 Summary of the principal component analysis to extract measures of competition from existing rivals and competition from potential entrants
Full size table

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Dhaliwal, D., Huang, S., Khurana, I.K. et al. Product market competition and conditional conservatism. Rev Account Stud 19, 1309–1345 (2014). https://doi.org/10.1007/s11142-013-9267-2

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