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Joint audit, audit market structure, and consumer surplus


We use a structural application of the discrete choice model to investigate how the introduction of a joint audit policy would affect audit market structure and consumer surplus. We perform this policy evaluation by identifying demand fundamentals in a joint audit regime and applying them to a single audit regime. We find that a joint audit requirement has the potential to change the audit market structure substantially but that the effects are sensitive to the specific policy design. For example, small audit firms gain market share in a joint audit regime but only if an equal sharing of the workload between the two joint auditors is not required. Our counterfactual analysis reveals that the introduction of a joint audit regime would be associated with a substantial loss of consumer surplus. The loss results from restricting clients from giving all of the audit work to their most preferred audit firm, but it is partly offset by gains in consumer surplus deriving from the opportunity to choose the best combination of auditor pairs.

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  1. The demand model imposes only a slight amount of horizontal differentiation via the error term included in the logit model.

  2. As a robustness check, we also estimate the model with the non-log predicted audit fees applying the correction procedure as in Wooldridge (2003, 207). The estimation results remain similar.

  3. The total number of possible combination of pairs of seven individual auditors is \(\left ({~}_{2}^{7}\right )=21\). In addition, it is possible to pick two small audit firms, resulting in 22 combinations overall.

  4. A classical example where alternatives are very similar to each other is the “red bus, blue bus” problem (Train 2009, 50)

  5. Please find the detailed derivation of the equation in the Appendix.

  6. We thank the referee for suggesting this robustness test.

  7. As a further validity check, we estimate a specification of the audit fee model that includes future realized values of MA_IND instead of prior year values. In untabulated regression analyzes, we find that the negative effect of MA_IND becomes gradually weaker for future realized values of MA_IND and insignificantly for values four years in the future. These findings suggest that merger activity leads price changes and not the reverse, providing support for the supply-shift effect of M &A.

  8. The demand estimation includes 24,662 observations because each of the 1,121 client firms is included for each of the 22 possible auditor-pair combinations.

  9. We also implement bootstrapping to correct the standard error for the two-step estimators (Petrin and Train 2010) and observe that it does not change the results of our estimation.

  10. We use the predicted auditor choices in the UK single audit regime as a benchmark for the predicted auditor choices in counterfactual UK joint audit regimes to control for potential prediction error.


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We thank Peter Easton (editor) and the anonymous reviewer for their constructive comments. We appreciate the helpful comments of John Christensen, Francesca Franco, Joachim Gassen, Ole-Kristian Hope, Kim Ittonen, Kathleen Nosal, Victor Reinhardt, Philipp Schmidt-Dengler, Dirk Simons, Naoki Wakamori, Jörg Werner and the seminar participants at the University of Mannheim, University of Mainz, University of Graz, University of Gothenburg, the EAA annual conference, the EIASM workshop on Accounting and Economics, the AAA Audit mid-year conference, the 8th EARNet Symposium and the Berlin Accounting Workshop. Aiyong Zhu gratefully acknowledge the support from the Chinese National Science Foundation (under grant 71502131). All authors contribute equally to this paper.

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Correspondence to Qiang Guo or Aiyong Zhu.



Conditional choice probability

The choice probability \(Pr_{(j,k)}^{it}\) of choosing a pair of audit firms is:

$$\begin{array}{@{}rcl@{}} Pr^{it}_{(j,k)}&\,=\,&Pr\left( u^{it}_{(j,k)}\!>\!u^{it}_{(n,m)},\forall n\neq i, m\neq k\right)\\ &\,=\,&Pr\left( \varepsilon^{i}_{jkt}\,-\,\varepsilon^{i}_{nmt}\!>\!V_{int}+V_{imt}+{\Gamma}_{it}(n,m)\right.\\ &&\left.-\left( V_{ikt}+V_{ijt}+{\Gamma}_{it}(j,k)\right),\forall n\neq i, m\neq k\right)\\ &\,=\,&{\int}_{A_{00}}\ldots{\int}_{A_{nm}}\ldots{\int}_{A_{56}}dF(\varepsilon^{i}_{jkt}-\varepsilon^{i}_{00t})\ldots dF\left( \varepsilon^{i}_{jkt}-\varepsilon^{i}_{nmt}\right){\ldots} dF\\&&\times\left( \varepsilon^{i}_{jkt}-\varepsilon^{i}_{56t}\right) \end{array} $$

Set A nm = V int + V imt it (n,m) −(V ikt + V ijt it (j,k)), ∀ni,mk. Given the type 1 extreme value distribution of \(\varepsilon _{lkt}^{i}\) for all l and k, the difference of \(\varepsilon _{jkt}^{i}-\varepsilon _{nmt}^{i}\) follows the logistic distribution, which yields to a closed form solution for the above integration as,

$${Pr}^{it}_{(j,k)}\,=\,\frac{\mathit{exp}[V_{ikt}\,+\,V_{ijt}\,+\,{\Gamma}_{it}(j,k)]}{\mathit{exp}[V_{i0t}\,+\,V_{i0t}\,+\,{\Gamma}_{it}(0,0)]\,+\,{\sum}^{6}_{l_{2}=l_{1}+1}{\sum}^{5}_{l_{1}=0} exp[V_{il_{1}t}\,+\,V_{il_{2}t}\,+\,{\Gamma}_{it}(l_{1},l_{2})]} $$
Table 9 Variable definitions

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Guo, Q., Koch, C. & Zhu, A. Joint audit, audit market structure, and consumer surplus. Rev Account Stud 22, 1595–1627 (2017).

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  • Joint audit
  • Demand estimation
  • Audit market structure
  • Consumer surplus

JEL Classification

  • M42
  • M48
  • L11
  • L84