Cross-Country Outliers

41 Pages Posted: 9 Aug 2019

See all articles by John C. Adams

John C. Adams

University of Texas at Arlington

Sattar Mansi

Virginia Polytechnic Institute & State University

David M. Reeb

National University of Singapore

John K. Wald

University of Texas at San Antonio

Date Written: August 6, 2019

Abstract

We investigate whether outliers in cross-country samples and the common methods we use to address them affect the trustworthiness of our empirical results. Our analysis begins by documenting recent international business (IB) research practices in the identification and treatment of outliers. We then explore the bias and error from using sample-wide univariate outlier detection and treatment methods in studies that use cross-country datasets. Additional analysis, using cross-country data on profitability, executive compensation, and R&D, demonstrates the magnitude of the bias and error from common outlier approaches in IB research. We propose outlier mitigation strategies at both the sample and country levels.

Keywords: Outliers in International Data, Winsorizing, Trimming, Multivariate Identification

JEL Classification: C15, C18, F3, G30

Suggested Citation

Adams, John C. and Mansi, Sattar and Reeb, David M. and Wald, John K., Cross-Country Outliers (August 6, 2019). Available at SSRN: https://ssrn.com/abstract=3433186 or http://dx.doi.org/10.2139/ssrn.3433186

John C. Adams

University of Texas at Arlington ( email )

Box 19449 UTA
Arlington, TX 76019
United States
904-476-2946 (Phone)

Sattar Mansi (Contact Author)

Virginia Polytechnic Institute & State University ( email )

David M. Reeb

National University of Singapore ( email )

Mochtar Riady Building
15 Kent Ridge Drive
Singapore, 119245
Singapore

HOME PAGE: http://www.davidreeb.net

John K. Wald

University of Texas at San Antonio ( email )

1 UTSA Circle
San Antonio, TX 78249
United States
210-458-6324 (Phone)

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