Abstract
*University of Southern California; tUniversity of Colorado at Boulder. We would like to thank Gilbert Bloom of KPMG Peat Marwick, Bob Rosen of Ernst & Young, Wayne Gazur, Robert Jamison, Sally Jones, Stewart Karlinsky, and David Mason for their assistance in validating the instruments; Eugene Willis and the AICPA for allowing us to collect data at the National Tax Education Program; Stephen Conrad of Arthur Andersen, John Lanning of KPMG Peat Marwick, Jerry Marrs of Ernst & Young, and Randy Stein of Coopers & Lybrand for allowing us to collect data at their respective firms; Minou Bohlin, Linda Levy, David Mason, and Paul Walker for their research assistance; and Vairum Arunachalam for his assistance in collecting data. The authors also gratefully acknowledge the helpful comments of three anonymous referees, Alison Ashton, Robert Ashton, C. Brian Cloyd, David Frederick, Joan Luft, Robert Libby, Laureen Maines, Mark Nelson, Michael Roberts, Frank Selto, D. Shores, Ira Solomon, Rick Tubbs, S. Mark Young, and workshop participants at Arizona State University, Cornell University, Duke University, Indiana University, the University of Illinois Tax Symposium, the Journal of Accounting Research Conference, University of Texas at Arlington, University of Utah, and University of Wisconsin. Finally, the financial support of the KPMG Peat Marwick Foundation and the University of Colorado is gratefully acknowledged. 1 We infer expertise in this study from the level of performance in a specific task, here issue identification in tax planning. This inference is consistent with much of the literature on expertise in accounting and other disciplines (e.g., Bonner and Lewis [1990],
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