This paper reports the results of four experiments designed to examine the effects of different combinations of the forecast mechanisms and legal regimes different legal systems on auditor’s effort choice, independence behavior, and manager’s investment and reporting decisions. The experimental economics methodology is used to test a series of economic and behavioral hypotheses derived from a one-period game model in which (a) the manager is mandated to make earnings forecast before undertaking an investment, (b) the manager provides the quasi-rents and side payment to induce the auditor to compromise his independence, and (c) the auditor may commit either a technical audit failure or an independence audit failure. The experimental results document several important findings.
First, If the policy makers intend to enhance auditor independence and motivate more investments and truthful reporting, a system that consists of a mandatory forecast together with a ST regime is the most favorable one to achieve these goals. Second, the experimental results show the significant effects of
auditors’ ethical concerns and manager’s reputation concerns on their behavior. The existence of these effects suggests the necessity of considering human’s psychological factors in examining manager’s reporting, auditor’s legal liability, audit quality, and independence. Finally, the legal regime has a direct (indirect) effect on auditor’s (manager’s) effort and independence (investment and reporting) decisions while a mandatory forecast mechanism affects mainly on the manager’s investment and reporting decisions.
Several limitations and future research directions should be recognized. First, the experiments are designed and conducted based on a one-period model. Therefore, the effects of auditor’s and manager’s reputation cannot be examined. A direct extension of the study would be to extend the model and experiments into a multi-period setting. Second, the experiments have shown that manager’s investment decision and auditor’s effort and independence decision are affected by their reputation and ethical concerns. These results suggest that future game theoretical studies in auditor’s legal liability and independence should take these psychological factors into considerations in analyzing auditor’s and manager’s optimal strategies. Finally, prior studies in the area of psychology and behavioral accounting have shown that subjects’ tolerance for ambiguity (defined as the way subjects perceive and process information about ambiguous situations) may affect their decisions under uncertainty (Einhorn and Hogarth 1985; Ghosh and Ray 1992). Therefore, the experiments can be modified to investigate how tolerance for ambiguity may affect auditor’s and manager’s behavior.
FIGURE 1
aThe variables shown in this game tree are defined as follows: FCi denotes the manager’s financial forecast, where i ∈ {high, low}; Ii denotes the manager’s investment amount, where i ∈ {high, low}; H and L denote the high and low investment outcomes, respectively; ρ(Ii) denotes the probability that the outcome is H when the manager invests Ii amount; Rk denotes the manager’s financial report, where k ∈ {H, L}; q(eA) denotes the audit quality when the auditor’s effort level is eA; SH and SL denote the audit signals that the investment outcome is H and L, respectively;Hˆ and Lˆ denote the auditor’s high-outcome and low-outcome report, respectively; δ denotes the probability that the state of the economy is bad; AFind and AFtec denote auditor’s independence and technical audit failure, respectively; λ(eA) denotes the probability that the auditor will be held liable by the court when an AFind occurs; η(eA) denotes the probability that the auditor will be held liable by the court when an AFtec occurs; ST and NE denote the strict and negligence legal regimes, respectively. Letters A to J denote managers’ and auditor’s possible payoffs under different game outcomes and legal systems (see Appendix for detailed descriptions).
bThere is no audit failure under these two scenarios (no matter whether the state of economy is good or bad) because auditor’s report correctly informs the investors of the investment outcome and, thus, is not misleading.
cEven though an audit failure occurs under these two scenarios, the auditor is not held liable by the court because the state of economy is good and, therefore, the firm will not go bankrupt. In our model, only a violation of the going-concern will trigger a lawsuit against the auditor.
TABLE 1
Summary of Notations and Parameter Values
Variables Definitions Parameter Values*
(1) Investment Parameters:
e M Manager’s effort level for undertaking investment Ii i ∈ {high, low}
) AC Adjusting cost if manager’s reported earnings is
different from auditor’s report 5,000 EDs
Mr Manager’s compensation when audit report is r MHˆ = 9,500 EDs, MLˆ= 4,800 EDs SP Side payment paid by the manager to the auditor 4,500 EDs
SC Manager’s switching costs if auditor is dismissed 3,000 EDs (3) Auditor’s Parameters:
ER Present value of all future quasi rents 5,500 EDs (4) Legal Liability Parameters:
δ Probability that the state of economy is bad 0.6
) ( j
eA
λ Probability that the auditor will be held liable for AFind
NE regime: λ(ehighA )= 0.4, λ(elowA )=0.6 ST regime: λ(eAj)=1
) ( j
eA
η Probability that the auditor will be held liable for AFtec
NE regime: ( high) Dtec Total damage losses due to technical audit failures 7,500 EDs
Dind Total damage losses due to independence audit failures 11,200 EDs
TABLE 2
Comparisons of Equilibrium Predictions under Different Forecast and Legal Regime Combinationsa
Forecasts Mandatory Forecast No Forecast
Legal Regime Negligence Regime Strict Regime Negligence Regime Strict Regime
Equilibrium
aThe equilibria shown in this table are obtained by solving the game using the parameter values specified in Table 1 (see Appendix 2 for details).
bUnder the NE (ST) regime, there are two equilibria (a unique equilibrium) for each of the two damage apportionment rules.
cThe AFind rate is measured by the conditional probability that the auditor issues an Hˆ report when the audit signal is SL, i.e., AFind ≡p(Hˆ |SL).
dThe AFtec rate is measured by the conditional probability that the manager reports RH and auditor obtains an audit signal SH when the true investment outcome is L, i.e., AFtec ≡p(L|SH,RH)≡ p(RH |L)(1−ρ(Ii))(1−q(eA))/[ρ(Ii)+ p(RH |L)(1−ρ(Ii))(1−q(eA))].
TABLE 3
aThis study adopts a 2×2 factorial design with two between-subject variables: REGIME (manipulated at two levels: NE vs. ST) and FORECAST (manipulated at two levels: No vs. Mandatory). NE and ST denote negligence and strict legal regimes, respectively. Each experiment consists of 70 periods.
bUnder the NE regime, the probabilities that the auditor will be held liable by the court when there is an AFind and AFtec are λ(eA) and η(eA),
respectively. These two probabilities are both decreasing in auditor’s effort level. Conversely, under the ST regime the auditor will always be held liable if the investors sue the auditor, that is, these two probabilities are both equal to one. Therefore, in the experiments we manipulate λ(eA)and
) (eA
η at two levels (i.e., less than one vs. one) to reflect the fundamental difference between NE and ST regimes.
dThe subject pool consists of 80 senior Business School students, with 10 auditor-subjects and 10 manager-subjects randomly assigned to each experiment. All subjects draw to determine the role they will play in the experiments. At the beginning of each period, each manager-subject is endowed with 12,000 EDs and each auditor-subject is endowed with 10,000 EDs. Each subject plays the same role throughout all 70 periods.