Political Corruption and Auditor Behavior: Evidence from US Firms

Abstract

This paper investigates the impact of political corruption on auditor behavior in the United States. We find that US firms headquartered in more corrupt regions pay higher audit fees, have longer audit report lags, and are more likely to receive a going concern audit opinion. Political corruption is a manifestation of a weak institutional environment and, as such, weakens the rule of law. In addition, political corruption erodes the public’s belief in a political system and reduces interpersonal trust. Our results suggest that auditors assess the risk and trustworthiness of their clients based on where firms are headquartered. The results are robust to using a 2SLS regression analysis and a propensity-score-matched sample. This study extends the prior research on political corruption and the client risk management strategies used by external auditors. Moreover, the current study will be helpful to regulators considering the more explicit role of external auditors in corruption risk assessment.

Keywords:

• Political corruption
• Audit fees
• Audit report lag
• Going concern audit opinion

Acknowledgements

We thank Chris Hogan (Associate Editor) and two anonymous reviewers for their helpful comments and suggestions.

Notes

1 ‘Political corruption’ is a narrower concept than ‘corruption.’ ‘Corruption’ may include corruption convictions in both the public and the private sector. However, in this study, we focus on corruption convictions of governmental officials (i.e. public corruptions), so throughout the paper we use the term ‘political corruption’ when discussing the focus and results of our studies.

2 Those studies examine the effect of corruption at the firm level, whereas a limited number of studies address (1) whether political corruption is one of the risk factors in engaging with an audit client and (2) how external auditors react to the increase in business risk relating to political corruption.

3 See https://www.export.gov/article?id=China-State-Owned-Enterprises.

4 COR_PER_CAPIT can be calculated as follows: In 2008, Cook, Illinois had a population of 5,294,664 and 43 corruption cases, the value of COR_PER_CAPIT is 0.8121.

5 The terms ‘federal judicial district’ and ‘district court district’ are used interchangeably.

6 The corruption data are collected from https://www.justice.gov/criminal/pin.

7 We estimate a Cox hazard model using a bankruptcy dummy that is equal to one if the firm has filed bankruptcy and zero otherwise. The independent covariates are the same as those used in Campbell et al. (2008). We thank Sudheer Chava for providing the bankruptcy data.

8 The increase in audit fees is calculated using the COR_PER_CAPIT coefficient presented in Table 4. Specifically, we estimate difference between the audit fees paid by a company in the median corruption region (COR_PER_CAPIT of 0.26) and the audit fees paid in a more corrupted region (an increase in one standard deviation of corruption per capita or an increase in COR_PER_CAPIT by 0.28).

9 The increase in audit report lags in response to the increase of political corruption is calculated as the difference between the audit report lags for a company located in the median corruption region and the audit report lag in the region where corruption is higher than the median level.

10 The results are available on request.

11 For brevity, we only report the results of the variables of interest in Tables 7 –9.

12 They include all control variables used in the previous equations.