Detecting Gender Salary Inequity in the Presence of within Gender Inequities

Persistent Link:
http://hdl.handle.net/10150/194208
Title:
Detecting Gender Salary Inequity in the Presence of within Gender Inequities
Author:
Nzeukou, Marcel
Issue Date:
2005
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
In this dissertation, I explore the systematic failure of the current state of the art statistical techniques to detect gender salary inequity in a special case to propose a more appropriate quantitative method for analyzing gender salary discrimination. This research contributes in three key areas for the development of the quantitative analysis of salary inequity detection. I uncovered salary inequities within gender groups that can mask the salary discrimination between these groups. I then proposed the Two-stage Classification Regression as an appropriate novel statistical method. Finally, the additional propositions made can enhance future salary inequity research.Regardless of the outcome of any gender salary inequity study, we can often find a subgroup of females that is discriminated against when compared to the rest of females. Likewise, a subgroup of males may also be victim of salary inequity when compared to other males. In this context, the first main discovery is that the existence of salary inequities within gender groups can prevent regular statistical techniques from detecting salary inequity between males and females. Detecting this form of salary inequity will increase the sensitivity of the statistical test and hedge its potentially higher risk to the institution.Facing such a statistical problem, the second main contribution was devising a novel statistical approach that can not only succeed where other techniques systematically fail, but also provide a new framework for a more informative statistical analysis. In addition, a more comprehensive definition of salary inequity that goes beyond the simple measure of gender salary gap was derived.The third significant contribution is a set of propositions aiming at framing the agenda for future research on salary inequity studies. A statistical test was proposed to determine when the outcomes of these the linear regression and reverse regression techniques can be expected to be the same. Also, the probability model which is not estimable, but the most robust model was shown to be equivalent to the logistic regression model which is easily estimable, but somewhat difficult to interpret. The goal is to create theoretical supports for better statistical and econometric analyses.
Type:
text; Electronic Dissertation
Keywords:
Salary inequity; discrimination; compensation gap; fairness
Degree Name:
PhD
Degree Level:
doctoral
Degree Program:
Mathematics; Graduate College
Degree Grantor:
University of Arizona
Advisor:
Myers, Donald E.
Committee Chair:
Myers, Donald E.

Full metadata record

DC FieldValue Language
dc.language.isoENen_US
dc.titleDetecting Gender Salary Inequity in the Presence of within Gender Inequitiesen_US
dc.creatorNzeukou, Marcelen_US
dc.contributor.authorNzeukou, Marcelen_US
dc.date.issued2005en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractIn this dissertation, I explore the systematic failure of the current state of the art statistical techniques to detect gender salary inequity in a special case to propose a more appropriate quantitative method for analyzing gender salary discrimination. This research contributes in three key areas for the development of the quantitative analysis of salary inequity detection. I uncovered salary inequities within gender groups that can mask the salary discrimination between these groups. I then proposed the Two-stage Classification Regression as an appropriate novel statistical method. Finally, the additional propositions made can enhance future salary inequity research.Regardless of the outcome of any gender salary inequity study, we can often find a subgroup of females that is discriminated against when compared to the rest of females. Likewise, a subgroup of males may also be victim of salary inequity when compared to other males. In this context, the first main discovery is that the existence of salary inequities within gender groups can prevent regular statistical techniques from detecting salary inequity between males and females. Detecting this form of salary inequity will increase the sensitivity of the statistical test and hedge its potentially higher risk to the institution.Facing such a statistical problem, the second main contribution was devising a novel statistical approach that can not only succeed where other techniques systematically fail, but also provide a new framework for a more informative statistical analysis. In addition, a more comprehensive definition of salary inequity that goes beyond the simple measure of gender salary gap was derived.The third significant contribution is a set of propositions aiming at framing the agenda for future research on salary inequity studies. A statistical test was proposed to determine when the outcomes of these the linear regression and reverse regression techniques can be expected to be the same. Also, the probability model which is not estimable, but the most robust model was shown to be equivalent to the logistic regression model which is easily estimable, but somewhat difficult to interpret. The goal is to create theoretical supports for better statistical and econometric analyses.en_US
dc.typetexten_US
dc.typeElectronic Dissertationen_US
dc.subjectSalary inequityen_US
dc.subjectdiscriminationen_US
dc.subjectcompensation gapen_US
dc.subjectfairnessen_US
thesis.degree.namePhDen_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineMathematicsen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorMyers, Donald E.en_US
dc.contributor.chairMyers, Donald E.en_US
dc.contributor.committeememberShaked, Mosheen_US
dc.contributor.committeememberWatkins, Josephen_US
dc.identifier.proquest1583en_US
dc.identifier.oclc137355999en_US
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