Article

Gender Wage Discrimination Bias? A Meta-Regression Analysis

Hendrix College, Minneapolis, Minnesota, United States
The Journal of Human Resources (Impact Factor: 2.37). 09/1998; 33(4):947-973. DOI: 10.2307/146404
Source: RePEc

ABSTRACT

This study provides a quantitative review of the empirical literature on gender wage discrimination. Although there is considerable agreement that gender wage discrimination exists, estimates of its magnitude vary widely. Our meta-regression analysis (MRA) reveals that the estimated gender gap has been steadily declining and the wage rate calculation to be crucial. Large biases are likely when researchers omit experience or fail to correct for selection bias. Finally, there appears to be significant gender bias in gender research. However, it is a virtuous variety where researchers tend to compensate for potential bias implicit in their gender membership.

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    • "Previous research has demonstrated negative workplace outcomes due to biases related to weight (Roehling, Pichler, and Bruce 2013; Rudolph et al. 2009) and gender (Brownell and Walsh 2008; Eagly, Makhijani, and Klonsky 1992; Joshi, Son, and Roh 2015; Stanley and Jarrell 1998). A great deal of research suggests that being heavy is related to higher incidence of discrimination and other negative employment outcomes (Roehling, Roehling, and Pichler 2007). "
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    ABSTRACT: The average weight of employees in the United States workforce is increasing. Importantly, relatively heavier employees are often subject to stereotypes, prejudice, and discrimination based solely on their weight. These biases may be further influenced by factors such as employee gender and the specific nature of the job. Thus, we employ the stereotype content model (SCM) to examine the multiplicative effects of weight and gender and argue that perceptions of employee warmth are more salient than perceptions of employee competence in customer service contexts. In support of our hypotheses, we found that weight and gender interacted to influence warmth, such that heavy women were perceived to be higher in warmth relative to less heavy women (with no effect for men). Furthermore, perceptions of warmth predicted service satisfaction, whereas perceptions of competence did not. Finally, perceptions of warmth (but not competence) explained the relations between weight and gender and service satisfaction for female (but not male) customer service agents. We end with a discussion of the theoretical implications related to the SCM along with practical implications for service industry organizations and employees.
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    • "The perceived lack of fit (Heilman, 1983, 2012) or incongruity (Eagly & Karau, 2002) between women's assumed capabilities and the demands of leadership positions evokes the impression that women are not equipped to handle such male-typed tasks. This fact has various negative consequences for women aspiring to these positions (see Eagly & Karau, 2002; Heilman, 2012; for overviews): it fosters a male bias in hiring decisions (Schein, 2001), wage decisions (Eagly & Karau, 2002; Stanley & Jarrell, 1998), and employmentrelated recommendations (Heilman & Okimoto, 2008; Heilman, Wallen, Fuchs, & Tamkins, 2004). "

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    • "The Blinder-Oaxaca decomposition originated and has been widely used in the study of labor market discrimination (Blinder 1973; Oaxaca 1973). Economists and sociologists have, for instance , used it to decompose wage and earnings differences based on gender (e.g., Stanley and Jarrell 1998; Weichselbaumer and Winter-Ebmer 2005) and race (e.g., Darity, Guilkey, and Winfrey 1996; Kim 2010). Although Blinder-Oaxaca decompositions have been a mainstay of empirical research on discrimination, they can be, in principle, applied to explain differences in any continuous outcome across any two groups. "
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    ABSTRACT: This article introduces the R package oaxaca to perform the Blinder-Oaxaca decom-position, a statistical method that decomposes the gap in mean outcomes across two groups into a portion that is due to differences in group characteristics and a portion that cannot be explained by such differences. Although this method has been most widely used to study gender-and race-based discrimination in the labor market, Blinder-Oaxaca decompositions can be applied to explain differences in any continuous outcome across any two groups. The oaxaca package implements all the most commonly used variants of the Blinder-Oaxaca decomposition for linear regression models, calculates bootstrapped standard errors for its estimates, and allows users to visualize the decomposition results.
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