Is there Gender Inequality in the workplace?
＊此為R語言學期報告，沒有厲害的社會學分析，主要在練習程式語言畫圖和選出最適模型、並解釋模型。＊
Introduction
Gender issues have always been an essential topic in recent years. According to the Women’s Global Development and Prosperity (W-GDP) report in 2020, it states that if male and female have equal opportunities to participate fully in economic activities and social issues, the country will be safer and more secure. The engagement of female’s human force will also be a great benefit to the development of the economy. However, gender inequality is everywhere. According to the World Economic Forum, it could take 170 years to eradicate the disparity in pay and employment opportunities for male and female. The society needs urgent action to close the gender equality gap. (Treanor (2016)) There are many studies that point out female are facing more challenges than male in their life, which includes the level of education (Jacobs (1996)), the pay gap in work places (Zakharchuk (2020)), and the oriented gender of a particular job position (Zou (2015)). The study is going to analyze the income data of 1000 respondents released by private company “Glassdoor”, in an attempt to understand the relationship between gender-stereotyped and job, education and the factors that may influence their annual pay.
Data
“Glassdoor” is a website that allows users to participate in reviewing companies. Current and former employees of any company can comment on employers anonymously on the site to ensure the complete information is discolored. It also allows users to submit and view salary anonymously and apply for jobs on its platform.
The data set has been taken from Glassdoor and focuses on income for various job titles based on gender. The original primary purpose of the data is to identify the depth of the gender-based pay gap, and this study will use the various variables provided by the database to further analyze the impact of gender factors on payment, education and job choices.
The following charts can give a rough view of the original data of the variables in this dataset:
Methods
There are 468 female respondents and 532 male respondents in the dataset.
This study selected seven variables from the database: the respondents’ job title (JobTitle), gender (Gender), age (Age), the educational level (Degree), the years of education (Education), Number of the year worked (Seniority), and their annual pay (TotalPay).
Among them, the study converted “Education” from the nominal variables of “High School”, “College”, “Masters”, and “PhD” to “years of education”, which are 12, 16, 18, and 23 years respectively; “TotalPay” is the sum of the two variables “BasePay” and “Bonus”, which is the actual annual pay of the respondents.
The following is an example of the adjusted dataset:
Analysis
Next, in the analysis part, the study will be divided into four sub-topics to discuss the relationship between gender and various factors that may affect respondents’ pay. The four sub-topics are: Gender and Job Title; Gender, Job Title and Annual Pay; Gender and Education level; Gender, Education level and Annual Pay.
Before starting, it is helpful to observe the correlation diagram to grasp the relationship between the variables. According to the correlation diagram, the variables that affect the “Total Pay” the most are the respondent’s “Seniority” and “Age” (0.5). Although “Gender” and “Education” level are also positively correlated with total salary, the impact is relatively scanty (0.2).
As shown in the latter figure, the gender ratio of most job titles in the dataset is 1:1. As an exception, there are more male than female in occupations of Software Engineer and Manager, and female are more than male in Marketing Associate.
Calculating the annual pay of different genders, the average pay of male is 104,918.68 USD, and female is 96416.83 USD. male’s pay for a year is about 8501.85 USD more than female’s, which is about 9% of female’s pay. If we look at the average pay of different job titles, we can see that the positions with higher average salaries for male are Driver, IT, Marketing Associate, Sales Associate and Software Engineer; for female are Data Scientist, Financial Analyst, Graphic Designer, Manager and Warehouse Associate. The average pay that male are higher than female the most is Software Engineer, and for female higher than male is Data Scientist.
It can be seen from both the annual Total Pay models of Data Scientist and Software Engineer that the most significant factors affecting respondents’ pay are Education, Age and Seniority.
Data Scientist:
Data Scientist’s best model:
Software Engineer:
Software Engineer’s best model:
The average Age and Seniority of Data Scientist by genders are:
Moreover, for Software Engineer are:
The next step is to calculate the average education level of different genders. The chart below shows that the average years of education for male and female are 17.3 years and 16.9 years. male are slightly received more extended education than female. Besides, the number of people with a bachelor or below degrees are similar between genders, but there are more males than females with graduated school degrees.
It can be seen from the chart that the higher the education level, the higher the average annual pay. Among the same academic qualifications, male’s salaries are all higher than female’s. The most significant gap is located at a college degree. The gender pay gap is 11755.01 USD.
According to the bachelor’s Total Pay model, the most significant factors affecting the respondents’ annual pay are Gender, Age and Seniority.
College degree:
College degree’s best model:
In the previous models created by job titles, the pay increases with the higher Age and Seniority in the data. However, in the model, the pay of male is higher than female, but the average Age and Seniority are:
Discussion
The gender ratio of most job titles in this database is 1:1, and there are only a few occupations that are exceptions. It does not seem to fit the real social situation. Previous studies have shown that female labor participation has made vital progress in the past half-century, but employment segregation by gender and gender pay gap still exists. Although these differences are shrinking with social progress, there is still more room for improvements. (World Bank (2011))
However, several trends can still be seen from this data. Calculating the average annual pay of all respondents according to gender shows that the average pay of male is slightly higher than that of female. Further analysis can indeed reveal that the average pay of male and female is higher and lower between different job titles. Still, the enormous pay gap between male and female is 11886.43 USD by Software Engineer, and male are paid higher than female.
In the two Total Pay models, the factors that significantly affect the annual pay are Education, Age and Seniority. The study further analyzes the average years of education by gender. It can be found that male received longer education than female. Judging from the number of people in different education levels, male and female are almost the same before college, but more male had master’s and doctoral degrees.
Looking closely at the gender pay gap based on education levels, it can find that female’s annual pay in every section is lower than male. There is the most significant difference in bachelor respondents, which even reaches 11755.01 USD.
Generally speaking, although “Gender Inequality” is not obvious in this dataset, it is still visible in the analysis. It is satisfying to know that many things have improved with the progress of society over the past few decades, but inequality is still hidden in many aspects and needs to be resolved with the promotion of regulations and awareness.
Code Appendix
＊＊＊Please visit the following link to access to the R code＊＊＊
https://docs.google.com/document/d/1xxwtVBLIfvle7HEijmwDe66nJPxAmlbpFuL4f294dJI/edit?usp=sharing
References
World Bank. 2011. “Gender Differences in Employment and Why They Matter.” World Development Report 2012: Gender Equality and Development, no. 5.
Jacobs, Jerry A. 1996. “Gender Inequality and Higher Education.” Annual Review of Sociology 22 (1): 153–85.
Treanor, Jill. 2016. “Gender Pay Gap Could Take 170 Years to Close, Says World Economic Forum.” The Guardian.
Zakharchuk, Olha. 2020. “The Gender Pay Gap: Main Causes and Features in Different Countries of the World.”
Zou, Min. 2015. “Gender, Work Orientations and Job Satisfaction.” Work, Employment and Society 29 (1): 3–22.
