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http://ssrn.com/abstract=2166039
 
 

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A Measurement Approach to Binary Classifications and Thresholds


Pingyang Gao


University of Chicago - Booth School of Business

October 23, 2012

Chicago Booth Research Paper No. 12-51

Abstract:     
Classifications and thresholds are common in accounting rules. This paper explains one benefit of a binary classification and studies the properties of an optimal threshold in a model with managers’ opportunistic influence on raw evidence. A threshold that partitions raw evidence to a binary classification affects not only the ex post use of information by stakeholders but also the ex ante earnings management by managers. These dual functions differ qualitatively. As a result, the ex ante optimal threshold is not ex post efficient, creating a time inconsistency problem. By suppressing ex post information, a binary classification serves a commitment device to implement the ex ante optimal threshold and makes the accounting report more informative overall in equilibrium than the disclosure of raw evidence.

Number of Pages in PDF File: 30

Keywords: Binary Classifications, Thresholds, Earnings Management, Accounting Standard Setting

JEL Classification: M41, M49, G28, G38

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Date posted: October 24, 2012 ; Last revised: November 28, 2012

Suggested Citation

Gao, Pingyang, A Measurement Approach to Binary Classifications and Thresholds (October 23, 2012). Chicago Booth Research Paper No. 12-51. Available at SSRN: http://ssrn.com/abstract=2166039 or http://dx.doi.org/10.2139/ssrn.2166039

Contact Information

Pingyang Gao (Contact Author)
University of Chicago - Booth School of Business ( email )
5807 S. Woodlawn Avenue
Chicago, IL 60637
United States
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