Abstract

 


 



Binary Controls for the Corporate Marginal Tax Rate


George A. Plesko


University of Connecticut School of Business

December 2010

2011 American Accounting Association Annual Meeting - Tax Concurrent Sessions

Abstract:     
This paper examines the properties of simulated corporate marginal tax rates to assess their incremental benefit over other measures of corporate tax status. While binary variables have generally been replaced by simulated rates when marginal tax rate controls are needed, the results of this paper suggest a variety of advantages to using binary variables (or combinations of binary variables) in empirical specifications. In both cross-sectional and panel data settings, the results demonstrate that simple and easily constructed binary variables provide statistically equivalent results. Further analysis shows that binary variables are also statistically superior to alternatives to simulated rates that have been suggested in their absence. Additional tests show that using binary variables to control for firms’ tax incentives in empirical studies has several advantages over other available tax rate measures, including dramatically larger sample sizes, the ability to capture greater cross-sectional variation in tax incentives, and coefficient estimates that represent readily observable and discrete tax states.

Current version not for citation.

Number of Pages in PDF File: 29

Keywords: corporate tax rate, marginal tax rate, taxes, measurement error

JEL Classification: G3, H25, M4, C200

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Date posted: August 5, 2011  

Suggested Citation

Plesko, George A., Binary Controls for the Corporate Marginal Tax Rate (December 2010). 2011 American Accounting Association Annual Meeting - Tax Concurrent Sessions . Available at SSRN: http://ssrn.com/abstract=1904925 or http://dx.doi.org/10.2139/ssrn.1904925

Contact Information

George A. Plesko (Contact Author)
University of Connecticut School of Business ( email )
School of Business
Storrs, CT 06269-2041
United States
860-486-6421 (Phone)
Feedback to SSRN (Beta)


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