Selection bias and the Big Four premium: new evidence using Heckman and matching models

Clatworthy, Mark Anthony, Makepeace, Gerald Henry and Peel, Michael John ORCID: https://orcid.org/0000-0002-7444-390X 2009. Selection bias and the Big Four premium: new evidence using Heckman and matching models. Accounting and Business Research 39 (2) , pp. 139-166. 10.1080/00014788.2009.9663354

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Abstract

Many prior studies have found that large auditors charge significantly higher fees for statutory audit services, potentially resulting from higher audit quality and/or a lack of competition in the audit market. However, recent research using a Heckman two‐step procedure attributes the large auditor premium to auditor selection bias. In this paper we examine the limitations of the Heckman model and estimate the large auditor (Big Four) premium using decomposition and matching methods on a large sample of UK private companies. Our analysis suggests that Heckman two‐step estimates are highly sensitive to changes in sample and model specification, particularly the presence of a valid identifying variable. In contrast, the propensity score and portfolio matching methods we employ point to a persistent large auditor premium, consistent with the majority of previous studies. Conclusions of the premium vanishing when selection bias is controlled for therefore appear premature. Since the Heckman model is increasingly used in auditing and other areas of accounting research, our discussion and findings are likely to be of more general interest.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Business (Including Economics)
Subjects:	H Social Sciences > H Social Sciences (General) H Social Sciences > HD Industries. Land use. Labor H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management H Social Sciences > HF Commerce H Social Sciences > HF Commerce > HF5601 Accounting H Social Sciences > HG Finance
Uncontrolled Keywords:	Audit fees ; Big Four premium ; Matching estimators ; Propensity score matching ; Selection bias ; Twostep Models
Publisher:	Taylor & Francis
ISSN:	0001-4788
Last Modified:	19 Oct 2022 09:07
URI:	https://orca.cardiff.ac.uk/id/eprint/20053

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