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Gender-Pay-Gap

R code used in the articles:

(1) Gender Pay Gap And People Analytics: A Practice With Open Data by Littal Shemer Haim, January 2019

(Quoted from the article)
The gender pay gap analysis in this article is straightforward. HR managers with a B.A. education can handle it, with a little help from a data scientist. I encourage HR practitioners who start their journey in People Analytics to practice it. The data is available, and the insights may be vital.

(2) Finding Hidden Patterns In Gender Pay Gap Data by Littal Shemer Haim, April 2022

(Quoted from the article)
Exploring gender pay averages across tenure ranges reveals that while both genders start at a similar earning level and are promoted while gaining tenure, men are promoted at higher rates, as the different slope indicates. In addition, ANOVA reveals that the interaction between gender and tenure variables is significant.

(3) Gender Pay Gap: More Hidden Patterns by Littal Shemer Haim, August 2022

(Quoted from the article)
gender is not the only predictor interacting with tenure. It is also interacting with assignments, full-time and part-time. As they gain tenure, the accumulative gap between men and women throughout their careers may stem from their full- and part-time positions. An intervention to close the pay gap should take into account these insights.

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R code used in the article "Gender Pay Gap And People Analytics: A Practice With Open Data"

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