Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
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.
References
Anderson M, Banker R, Janakiraman S (2003) Are selling, general, and administrative costs “sticky”? J Account Res 41(1):47–63
Balakrishnan R, Gruca T (2008) Cost stickiness and core competency: a note. Contemp Account Res 25(4):993–1006
Balakrishnan R, Petersen M, Soderstrom N (2004) Does capacity utilization affect the “stickiness” of cost? J Account Audit Finance 19(3):283–300
Banker R, Byzalov D, Chen L (2013) Employment protection legislation, adjustment costs and cross-country differences in cost behavior. J Account Econ 55(1):111–127
Banker R, Byzalov D, Ciftci M, Mashruwala R (2014) The moderating effect of prior sales changes on asymmetric cost behavior. J Manag Account Res 26(2):221–242
Bernard A, Jensen B, Schott P (2006) Trade costs, firms and productivity. J Monetary Econ 53(5):917–937
Chen C, Lu H, Sougiannis T (2012) The agency problem, corporate governance, and the asymmetric behavior of selling, general, and administrative costs. Contemp Account Res 29(1):252–282
Dierynck B, Landsman W, Renders A (2012) Do managerial incentives drive cost behavior? Evidence about the role of zero earnings benchmarks for labor cost behavior in private Belgian firm. Account Rev 87(4):1219–1246
Dixit A, Pindyck R (1994) Investment under uncertainty. Princeton University Press, Princeton
Feenstra R (1996) U.S. imports, 1972–1994, data and concordances. NBER Working Paper 5515
Feenstra R, Romalis J, Schott P (2002) U.S. imports, exports and tariff data, 1989–2001. NBER Working Paper 9387
Feng S, Ho CY (2015) The real option approach to adoption or discontinuation of a management accounting innovation: the case of activity-based costing. Rev Quant Finance Account. doi:10.1007/s11156-015-0522-4
Fresard L (2010) Financial strength and product market behavior: the real effects of corporate cash holdings. J Finance 65(3):1097–1122
Grenadier S (2002) Option exercise games: an application to the equilibrium investment strategies of firms. Rev Financ Stud 15(3):691–721
Hadlock C, Pierce J (2010) New evidence on measuring financial constraint: moving beyond the KZ index. Rev Financ Stud 23(5):1909–1940
Harford J, Mikkelson W, Partch M (2003) The effect of cash reserves on corporate investment and performance in industry downturns. Working paper
Hoberg G, Phillips G (2010) Product market synergies and competition in mergers and acquisitions: a test-based analysis. Rev Financ Stud 23(10):3773–3811
Hoberg G, Phillips G (2015) Text-based network industries and endogenous product differentiation. J Polit Econ. doi:10.2139/ssrn.1520062
Holzhacker M, Krishnan R, Mahlendorf M (2015) The impact of changes in regulation on cost behavior. Contemp Account Res 32(2):534–566
Kama I, Weiss D (2013) Do earnings targets and managerial incentives affect sticky costs? J Account Res 51(1):201–224
Kim C, Mauer D, Sherman A (1998) The determinants of corporate liquidity: theory and evidence. J Financ Quant Anal 33(3):335–359
Lee KJ, Shyu DS, Dai MJ (2009) The valuation of information technology investments by real options analysis. Rev Pac Basin Financ Mark Pol 12(4):611–628
Li F, Lundholm R, Minnis M (2013) A measure of competition based on 10-K filings. J Account Res 51(2):399–436
Martzoukos S (2001) Hysteresis models of investment with multiple uncertainties and exchange rate risk. Rev Quant Finance Account 16(3):251–268
McDonald R, Siegel D (1986) The value of waiting to invest. Q J Econ 101(4):707–728
Mikkelson W, Partch M (2003) Do persistent large cash reserves hinder performance? J Financ Quant Anal 38(2):275–294
Noreen E, Soderstrom N (1997) The accuracy of proportional cost models: evidence from hospital service departments. Rev Account Stud 2(1):89–114
Phillips G (2013) Discussion of a measure of competition based on 10-K filings. J Account Res 51(2):437–447
Pindyck R (1988) Irreversible investment, capacity choice, and the value of the firm. Am Econ Rev 78(5):969–985
Rauh J, Sufi A (2012) Explaining corporate capital structure: product markets, leases, and asset similarity. Rev Finance 16(1):1–41
Schott P (2010) U.S. manufacturing exports and imports by SIC and NAICS category and partner country, 1972–2005. Working paper
Trigeorgis L (1996) Real options: managerial flexibility and strategy in resource allocation. MIT Press, Cambridge
Whited T, Wu G (2006) Financial constraints risk. Rev Financ Stud 19(2):531–559
Acknowledgments
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.
Author information
Authors and Affiliations
Corresponding author
Appendix
Rights and permissions
About this article
Cite this article
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
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11156-016-0591-z