Self-selection bias in human stress research: a systematic review

2021 ◽  
Vol 131 ◽  
pp. 105514
Author(s):  
S. Alarie ◽  
S.J. Lupien
Keyword(s):  
Systematic Review ◽  
Selection Bias ◽  
Stress Research ◽  
Self Selection ◽  
Human Stress

Women on the board of directors and corporate tax aggressiveness in Australia

2016 ◽  
Vol 29 (3) ◽  
pp. 313-331 ◽  
Author(s):  
Grant Richardson ◽  
Grantley Taylor ◽  
Roman Lanis
Keyword(s):  
Board Of Directors ◽  
Selection Bias ◽  
Tax Avoidance ◽  
Multivariate Regression Analysis ◽  
Corporate Tax ◽  
Content Type ◽  
Tax Aggressiveness ◽  
Self Selection ◽  
The Board Of Directors ◽  
The Impact

Purpose This paper aims to investigate the impact of women on the board of directors on corporate tax avoidance in Australia. Design/methodology/approach The authors use multivariate regression analysis to test the association between the presence of female directors on the board and tax aggressiveness. They also test for self-selection bias in the regression model by using the two-stage Heckman procedure. Findings This paper finds that relative to there being one female board member, high (i.e. greater than one member) female presence on the board of directors reduces the likelihood of tax aggressiveness. The results are robust after controlling for self-selection bias and using several alternative measures of tax aggressiveness. Research limitations/implications This study extends the extant literature on corporate governance and tax aggressiveness. This study is subject to several caveats. First, the sample is restricted to publicly listed Australian firms. Second, this study only examines the issue of women on the board of directors and tax aggressiveness in the context of Australia. Practical implications This research is timely, as there has been increased pressure by government bodies in Australia and globally to develop policies to increase female representation on the board of directors. Originality/value This study is the first to provide empirical evidence concerning the association between the presence of women on the board of directors and tax aggressiveness.

Reviews and Self-Selection Bias with Operational Implications

2021 ◽  
Author(s):  
Ningyuan Chen ◽  
Anran Li ◽  
Kalyan Talluri
Keyword(s):  
Selection Bias ◽  
Operations Management ◽  
Choice Model ◽  
Theoretical Models ◽  
Empirical Literature ◽  
Service Firms ◽  
Performance Review ◽  
Self Selection ◽  
Ex Post ◽  
Biased Ratings

Reviews for products and services written by previous consumers have become an influential input to the purchase decision of customers. Many service businesses monitor the reviews closely for feedback as well as detecting service flaws, and they have become part of the performance review for service managers with rewards tied to improvement in the aggregate rating. Many empirical papers have documented a bias in the aggregate ratings, arising because of customers’ inherent self-selection in their choices and bounded rationality in evaluating previous reviews. Although there is a vast empirical literature analyzing reviews, theoretical models that try to isolate and explain the bias in ratings are relatively few. Assuming consumers simply substitute the average rating that they see as a proxy for quality, we give a precise characterization of the self-selection bias on ratings of an assortment of products when consumers confound ex ante innate preferences for a product or service with ex post experience and service quality and do not separate the two. We develop a parsimonious choice model for consumer purchase decisions and show that the mechanism leads to an upward bias, which is more pronounced for niche products. Based on our theoretical characterization, we study the effect on pricing and assortment decisions of the firm when potential customers purchase based on the biased ratings. Our results give insights into how quality, prices, and customer feedback are intricately tied together for service firms. This paper was accepted by David Simchi-Levi, operations management.

scholarly journals The Use of Official Statistics in Self-Selection Bias Modeling

2016 ◽  
Vol 32 (4) ◽  
pp. 887-905 ◽  
Author(s):  
Luciana Dalla Valle
Keyword(s):  
Selection Bias ◽  
Small Businesses ◽  
Real Data ◽  
Sources Of Information ◽  
Quality Of Data ◽  
Official Statistics ◽  
Step Method ◽  
Development Education ◽  
Self Selection ◽  
Available Information

Abstract Official statistics are a fundamental source of publicly available information that periodically provides a great amount of data on all major areas of citizens’ lives, such as economics, social development, education, and the environment. However, these extraordinary sources of information are often neglected, especially by business and industrial statisticians. In particular, data collected from small businesses, like small and medium-sized enterprizes (SMEs), are rarely integrated with official statistics data. In official statistics data integration, the quality of data is essential to guarantee reliable results. Considering the analysis of surveys on SMEs, one of the most common issues related to data quality is the high proportion of nonresponses that leads to self-selection bias. This work illustrates a flexible methodology to deal with self-selection bias, based on the generalization of Heckman’s two-step method with the introduction of copulas. This approach allows us to assume different distributions for the marginals and to express various dependence structures. The methodology is illustrated through a real data application, where the parameters are estimated according to the Bayesian approach and official statistics data are incorporated into the model via informative priors.

Information asymmetry and self-selection bias in bank loan announcement studies

2011 ◽  
Vol 101 (3) ◽  
pp. 684-694 ◽  
Author(s):  
Pankaj K. Maskara ◽  
Donald J. Mullineaux
Keyword(s):  
Information Asymmetry ◽  
Selection Bias ◽  
Bank Loan ◽  
Self Selection

Selection Bias and Self-Selection

2010 ◽  
pp. 242-266 ◽  
Author(s):  
James J. Heckman
Keyword(s):  
Selection Bias ◽  
Self Selection
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