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

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.

References

  • Anderson, S., De Palma, A., & Thisse, J. (1992). Discrete Choice Theory of Product Differentiation. Cambridge: The MIT Press.

    Google Scholar 

  • Angrist, J., & Krueger, A. (2001). Instrumental variables and the search for identification: From supply and demand to natural experiments. Journal of Economic Perspectives, 15(1), 69–85.

    Article  Google Scholar 

  • Brown, S., & Knechel, W. (2016). Auditor-client compatibility and audit firm selection. Journal of Accounting Research, 54(3), 725–775.

    Article  Google Scholar 

  • Copley, P., Gaver, J., & Gaver, K. (1995). Simultaneous estimation of supply and demand of differentiated audits: Evidence from the municipal audit market. Journal of Accounting Research, 33(1), 137– 155.

    Article  Google Scholar 

  • Deis, D., & Hill, R. (1998). An application of the bootstrap method to the simultaneous equations model of the demand and supply of audit services. Contemporary Accounting Research, 15(1), 83–99.

    Article  Google Scholar 

  • Dunn, K., Kohlbeck, M., & Mayhew, B. (2011). The impact of the Big 4 consolidation on audit market share equality. Auditing: A Journal of Practice and Theory, 30(1), 49–73.

    Article  Google Scholar 

  • Dunn, K., Kohlbeck, M., & Mayhew, B. (2013). The impact of market structure on audit price and quality, Unpublished working paper. Madison: University of Wisconsin.

    Google Scholar 

  • GAO (2008). Audits of public companies: Continued concentration in audit market for large public companies does not call for immediate action. Tech. Rep. GAO-08-163, United States Government Accountability Office.

  • Gaver, J., & Gaver, K. (1995). Simultaneous Estimation of the Demand and Supply of Differentiated Audits. Review of Quantitative Finance and Accounting, 5(1), 55–70.

    Article  Google Scholar 

  • Gerakos, J., & Syverson, C. (2015). Competition in the audit market: Policy implications. Journal of Accounting Research, 53(4), 725–775.

    Article  Google Scholar 

  • Guo, Q., Koch, C., & Zhu, A. (2017). Joint audit, audit market structure, and consumer surplus. Review of Accounting Studies, this issue.

  • Hay, R., Knechel, W., & Wong, N. (2006). Audit fees: A meta-analysis of the effect of supply and demand attributes. Contemporary Accounting Research, 23(1), 141–191.

    Article  Google Scholar 

  • Hsieh, C.H., & Moretti, E. (2003). Can free entry be inefficient? Fixed commisssions and social waste in the real estate industry. Journal of Political Economy, 111(5), 1076–1122.

    Article  Google Scholar 

  • Kanodia, C., & Mukherji, A. (1994). Audit pricing, lowballing and auditor turnover: A dynamic analysis. The Accounting Review, 69(4), 593–615.

    Google Scholar 

  • McFadden, D. (1999). Computing willingess to pay in random utility models. Trade, Theory, and Econometrics: Essays in honour of John S Chipman, 15, 253–274.

    Google Scholar 

  • Posner, R. (2001). Antitrust Law, 2nd edn. Chicago: University of Chicago Press.

    Google Scholar 

  • Rosen, S. (1974). Hedonic prices and implict markets: Product differentiation in pure competition. Journal of Political Economy, 82(1), 34–55.

    Article  Google Scholar 

  • Simunic, D. (1980). The pricing of audit services: Theory and evidence. Journal of Accounting Research, 18(1), 161–190.

    Article  Google Scholar 

  • Sutton, J. (1991). Sunk Costs and Market Structure: Price Competition, Advertising and the Evolution of Concentration. Cambridge: The MIT Press.

    Google Scholar 

  • Train, K. (2009). Discrete Choice Methods with Simulation. New York: Cambridge University Press.

    Book  Google Scholar 

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Correspondence to Joseph Gerakos.

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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