Barriers to Entry and Success Facing International Candidates to the US CPA Exam: Profile, Performance and Choice of Licensing Jurisdiction

Abstract

In June 2 2022, the National Association of State Boards of Accountancy (NASBA) announced that international candidates will no longer be restricted, by their international residency, from writing the US-CPA Exam in International Testing Centers. This move may increase the number of international candidates, and the frequency with which they write the US-CPA exam. Considering the significance of this change, it is surprising to learn the lack of peer-reviewed studies exploring the profile of international candidates for the US-CPA Exam in terms of performance and choice of licensing jurisdiction. Our study analyses data from NASBA comparing results of domestic and international candidates that wrote the US-CPA Exam and provides a profile of international candidates, examine their relative performance, tests predictors of choice of licensing jurisdiction, and -with it- provide valuable information for national and international stakeholders for whom the US-CPA Exam results are delivered and evaluated, and where the information is used in planning and decision-making. We find that removing access barriers in a jurisdiction is significantly and consistently associated with decreasing the performance gap between international and domestic candidates and with improvements in the performance of international candidates. Additionally, we find that while the number of big accounting firms within a jurisdiction does not directly impact the performance of international candidates, it is associated with increasing the performance gap between international versus domestic candidates, benefiting the latter.

Presenters

Jose Cao Alvira
Professor, Department of Finance, Information Systems and Economics, City University of New York, New York, United States

Details

Presentation Type

Paper Presentation in a Themed Session

Theme

Networks of Economy and Trade

KEYWORDS

US CPA Exam, International Candidates, Predictors, Machine Learning