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Audit firms face downward-sloping demand curves and the audit market is far from perfectly competitive

Review of Accounting Studies volume 22, pages 1582–1594 (2017)Cite this article

Abstract

We discuss the discrete choice demand estimation methods applied by Guo et al. (2017) in the audit setting. We then review insights into audit market competition that demand estimation has already provided. We conclude that the audit market is far from perfectly competitive.

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Notes

  1. In this setting, the change in consumer surplus can be interpreted as the aggregate dollar amount that audit clients would be willing to pay to not be subject to a joint audit regime. This measure does not capture changes in producer surplus or externalities imposed on entities outside the market. Thus it does not capture the full welfare effects of such a regulatory change.

  2. There are a wealth of resources explaining the basic concepts and practices of discrete choice demand estimation. For example, Anderson et al. (1992) and chapter 3 of Train (2009) provide thorough explanations of these methods.

  3. For a thorough review of this literature, see Hay et al. (2006).

  4. For a detailed discussion of the interpretation of hedonic price regressions like the standard audit fee regression, see Rosen (1974).

  5. Note that he assumes that the Big 8 market and non-Big 8 markets are segmented and that the non-Big 8 market is perfectly competitive.

  6. We are not the first to make this point in the auditing setting. See, for example, Gaver & Gaver (1995), Copley et al. (1995), and Deis & Hill (1998), and Hay et al. (2006).

  7. An indirect utility function substitutes the optimal quantity—which is trivially equal to one in a discrete choice framework (the firm either hires the audit firm or it doesn’t)—back into the standard utility function.

  8. In their model of lowballing, Kanodia & Mukherji (1994) similarly assume that clients choose audit firms based on client-level utility.

  9. Hence this approach is sometimes referred to as “conditional logit.” The benefit of the Type I extreme value distribution is that its integral has a closed-form expression, leading to the straightforward functional form in Eq. 6. By contrast, we could assume that the error terms had some other distribution (multivariate normal for example) but would in this case have to use numerical integration. Assumptions about the 𝜖 i j distribution do place restrictions on clients’ substitution patterns, as we discuss below.

  10. As discussed by Train (2009), the absolute level of utility can be pinned down only to within a constant. Changes in utility can, however, be calculated because the constant drops out.

  11. Similarly, Eq. 5 can be used to compute the expected changes in consumer surplus from entire sets of choices using formulas given by Anderson et al. (1992), among others.

  12. Because fees are only directly observed in the data for the audit firm that a client actually chooses, predicted fees must be used in the demand estimation for the audit firms that are not chosen. Several different approaches might be used for this, including machine-learning type predictive models. See Gerakos & Syverson (2015) for one example of such an approach and details on its implementation.

  13. For a discussion of how to calculate changes in consumer surplus from discrete choice models, see McFadden (1999).

  14. While often referred to as the “brand effect” in the demand estimation literature, this term captures any choice-specific (here, audit firm-specific) utility component that is common across all potential buyers (here, client firms). This might be, but does need not to be, tied explicitly to the brand itself.

  15. Examples include GAO (2008) and Dunn et al. (2011), and Dunn et al. (2013).

  16. While concentration measures (like the Herfindahl-Hirschman index) are sometimes used for measuring the extent of competition, high concentration can occur in both highly competitive and highly uncompetitive industries, as discussed by Sutton (1991) and others.

  17. We find non-Andersen clients exhibit even less elastic demand for the audit firm that they had hired in the prior year, on the order of −0.3 (Panel C of Table 7). However, these are short-run elasticities that audit firms are unlikely to use as pricing guides.

  18. The industrial organization and marketing literatures typically divide product differentiation into vertical and horizontal dimensions. Under vertical differentiation, all market participants share the same rankings of products’ quality levels. Under horizontal differentiation, competing products differ in their characteristics and consumers differ in their evaluation of the product characteristics. Differentiation of either type can confer market power to a seller.

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Author information

Authors and Affiliations

  1. Tuck School of Business at Dartmouth College, 100 Tuck Hall, Hanover, NH, 03755, USA

    Joseph Gerakos

  2. University of Chicago Booth School of Business and NBER, Chicago, IL, USA

    Chad Syverson

Authors
  1. Joseph Gerakos
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  2. Chad Syverson
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Corresponding author

Correspondence to Joseph Gerakos.

Additional information

We thank Peter Easton and W. Robert Knechel for their comments.

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Gerakos, J., Syverson, C. Audit firms face downward-sloping demand curves and the audit market is far from perfectly competitive. Rev Account Stud 22, 1582–1594 (2017). https://doi.org/10.1007/s11142-017-9418-y

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  • DOI: https://doi.org/10.1007/s11142-017-9418-y

Keywords

  • Auditing
  • Demand estimation
  • Competititon

JEL Classification

  • M42
  • L84