Have estimates of cost stickiness changed across listing cohorts?


While the discussion of changes in financial accounting properties over time is already well-established, there is a lack of evidence whether changing firm compositions in empirical samples might bias cost stickiness research. We document that with each additional listing cohort, the U.S. public firm universe becomes more knowledge-intensive and, at the same time, more cost sticky. Higher reliance on temporary labor by newer listing cohorts partly mitigates this development. Our results call for the use of listing cohort-specific slopes to allow for cohort-specific estimates of cost stickiness in future research.

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  1. 1.

    We attribute the higher value for cost increases to our longer sample period which ends in 2014, as compared to 1998 for Anderson et al. (2003)‘s original contribution.

  2. 2.

    Refer to Table 7 for additional results.

  3. 3.

    We refer to the intangible asset definition by Lev et al. (2009) which builds on four concepts (p. 276): (1) Discovery/learning intangibles, largely attributable to R&D; (2) Customer-related intangibles, related to brands, trademarks, distribution channels and advertising expenses; (3) Human-resource intangibles, associated with specialist employees, training, and compensation systems; and (4) Organization capital, which refers to unique business processes and corporate cultures allowing the firm to create abnormal profits.

  4. 4.

    The Fama–French industry 48 is a compound item for firms which are not included in one of the other 47 categories. More specifically, these include firms providing sanitary services, steam and air conditioning supplies, irrigation systems and cogeneration power producers.

  5. 5.

    We test this by considering the descriptive results presented in Table 1 for each industry, separately. As an example, we exhibit the results for the manufacturing industry in “Appendix B”.

  6. 6.

    Industry- and year-fixed effects control for unobserved factors. Robust standard errors, clustered at the firm-level, control for autocorrelation and heteroscedasticity.

  7. 7.

    All variables are defined in detail in “Appendix A”.

  8. 8.

    SGA cost include all costs related to the running the operations, with the exception of production (e.g., spending on advertising, training, business travel and sales commissions).

  9. 9.

    Even for the seasoned and 1970s cohorts the effect should be minimal since at most, for a firm already listed in 1970, five firm-year observations are pre-SFAS 2. The only exception to the immediate expensing of R&D outlays are selective development costs for in-house software, following SOP98-1 (AICPA 1998). This should—if at all—work against finding an increase in R&D intensity.

  10. 10.

    Employing OC, COGS and core expenses as our cost category does not alter the presented conclusions. Only for noncore expenses the results are generally insignificant and not in line with the expectations. We presume that this is in line with the multivariate results presented above (Table 5). Noncore expenses include many components unrelated to increasing intangible capital (e.g., audit and legal fees, restructuring expenses etc.).

  11. 11.

    In line with extant literature in financial accounting (e.g., Roychowdhury 2006), we set R&D expenses to zero if the value is missing. This presumes that firms which, in fact, undertake material R&D activities also disclose related expenditures in their financial reports. This procedure results in a median of zero R&D expenses and unequal subsamples.

  12. 12.

    Alternatively, we also employ Tobin’s Q/the market-to-book (MTB) ratio as a proxy for knowledge intensity in our mediation analysis. While the coefficient for cost stickiness is slightly more negative for the above median MTB-subsample, the difference between the coefficients for both subsamples is highly insignificant (t = 0.14). Nonetheless, the MTB ratio proxies for much more than just knowledge intensity, such as, but not limited to, growth prospects, accounting conservatism and underinvestment (e.g., Dybvig and Warachka 2015).


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Part of this research was conducted while Loy was visiting the University of Nebraska-Lincoln. We thank Marcus Bravidor, Benedikt Downar, Brigitte Eierle, Thomas W. Günther (the editor), Sally Widener, and two anonymous referees for their insights and suggestions. All remaining errors are our own.

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Correspondence to Thomas R. Loy.


Appendix A

Table 10 Variable definitions

Appendix B

Table 11 Magnitude of (de-)listing for each listing cohort for the manufacturing industry

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Loy, T.R., Hartlieb, S. Have estimates of cost stickiness changed across listing cohorts?. J Manag Control 29, 161–181 (2018). https://doi.org/10.1007/s00187-018-0263-3

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  • Asymmetric cost behavior
  • Listing cohorts
  • Adjustment costs

JEL Classification

  • D22
  • M41