Extant literature on cost stickiness has focused on how firm-specific characteristics affect the asymmetric cost behavior. In this paper, we explore how a firm’s operating environment affects the firm’s cost stickiness. Specifically, we examine the effect of product market competition on cost stickiness since a firm’s investment and cost retention decisions partly depend on how the firm interacts with its rival firms in the product markets. Using two firm-level text-based product market competition measures extracted from management disclosures in firms’ 10-K filings (Li et al. in J Account Res 51(2):399–436, 2013; Hoberg and Phillips in Rev Financ Stud 23(10):3773–3811, 2010; J Polit Econ, 2015), we find strong evidence consistent with cost asymmetry increasing in competition after controlling for known economic determinants of cost stickiness. In additional analyses, we also find that the effect of product market competition on the degree of cost stickiness increases in firms’ financial strength, likely because management in financially stronger firms has more resources for investment expenditures in spite of a sales fall. We also find that cost stickiness is increasing in competition if management is optimistic about future demand, whereas competition is not associated with cost asymmetry if management is pessimistic about future demand. Finally, we find that the relationship between competition and cost stickiness, although statistically insignificant at conventional levels, is more pronounced for single-segment firms relative to multi-segment firms.
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The investment concept in the real options literature encompasses a broad scope of the types of expenditures. Some of these expenditures may be in the form of capital expenditures, but under the current accounting standards many of these expenditures are treated as period costs including but not limited to research and development, advertisement, and costs incurred to improve product image, delivery services, or customer relations.
The LLM data is available from Professor Feng Li’s webpage at http://webuser.bus.umich.edu/feng/.
Under the FIC, firms can be grouped into any number of industries based on product similarity. Hoberg and Phillips (2015) consider 50–800 industries in increments of 50. However, they focus on the classification of 300 industries (hereafter, FIC300) because the number of industries in three-digit SIC and four-digit NAICS is close to 300. We therefore use FIC300 when the FIC classification is used in the tests.
Rauh and Sufi (2012) identify firms’ product market competitors listed in the 10-Ks and also find that SIC classifications do not accurately capture competitors.
The FIC and THHI data are available from the Hoberg-Phillips Data Library at http://hobergphillips.usc.edu/industryclass.htm.
As discussed in the literature review, TNIC relaxes the membership transitivity property so that a firm may enter multiple product markets. As such, we use the FIC rather than TNIC industry classifications as industry dummies.
We obtain the data on materials cost, wage, and oil price from the Federal Reserve Bank of St. Louis. Specifically, they are from the producer price index for crude materials, average hourly earnings of production and nonsupervisory employees, and crude oil prices databases, respectively.
The import tariff data are available on Professor Peter K. Schott’s website at http://faculty.som.yale.edu/peterschott/sub_international.htm.
We limit the impact of a large tariff cut in the year of, as well as the two years following, the cut because moving away from the short event window will allow other factors to enter the equation. In order to ensure our results are not limited to the choice of the number of years impacted by the tariff cuts, we repeat our analysis using the year of, as well as the one year following, the cut and, alternatively, the year of, as well as the three years following, the cut. The coefficient on ΔlnSALE i,t × DEC i,t × COMP i,t remains negative and statistically significant at conventional levels.
Since the data do not allow us to identify when in the year the tariff cut occurs, the impact of the tariff cut in the event year is ambiguous. As such, we reexamine the analysis by excluding the year of the tariff cut from the sample. The results are qualitatively similar.
We determine the median value of each financial strength measure using the full sample. Therefore, the numbers of observations in the two sub-samples are not even.
In our first set of analyses, we limit our sample period to 1998 through 2009 due to change in segment reporting requirement under SFAS No. 131 which was issued in 1997. In sensitivity analyses, we include years 1996 and 1997 and the results are qualitatively similar.
When we use the LLM sample, the p value (one-tailed) from the test of difference in coefficient on ΔlnSALE i,t × DEC i,t × COMP i,t is 0.47. When we use the THHI sample, the p value (one-tailed) from the test of difference in coefficient on ΔlnSALE i,t × DEC i,t × COMP i,t is 0.11, close to the conventional level of statistical significance.
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We are grateful to the editor Professor Cheng-Few Lee and two anonymous referees for insightful comments and suggestions which significantly improve the quality of this paper.
See Table 7.
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Li, WL., Zheng, K. Product market competition and cost stickiness. Rev Quant Finan Acc 49, 283–313 (2017). https://doi.org/10.1007/s11156-016-0591-z