Analyzing Gender Wage Gaps

Group Members: Sarah "Sally" Schafer, Vianne Bui Nguyen, Jessica Robinson

Inspiration

Through this project we aim to:

• Uncover information about the gender wage gap
• Help policymakers, industries, and the general public see where the gender wage gap is highest
• Motivate people to act to reduce the gender wage gap

What it does

We used multiple data sources and created visualizations to analyze the gender wage gap across countries, education levels, and occupations.

How we built it

We used the following data sets to create visualizations using the matplotlib and plotly libraries.

• Glassdoor Data from Kaggle
• OECD Data
• Our World in Data Project, from which we used the dataset on the proportion of women in senior and middle management positions.

Challenges we ran into

Our original plan was to complete the project in ed workspaces, but we eventually ran into data storage issues. We then transitioned our project to working with github, which our group had little to no experience with. However, this gave us the opportunity to practice using github.

Accomplishments that we're proud of

• Completing our first full, original data analysis project with python
• Learning a new visualization library, plotly
• Managing and merging multiple data sets
• Adapting to technical difficulties
• Gaining experience with github
• Learning to use Visual Studio Code

What we learned

• The Gender Wage Gap is extremely present and deserves our attention and concern.
• The Wage Gap is influenced by a number of factors, including education, occupation, country, and more.

We also learned how to use plotly, Visual Studio Code, and github, and practiced using our python skills.

What's next for Analyzing Gender Wage Gaps

• Analyzing how the wage gap affects all genders, including non-binary people
• Analyzing how the wage gap varies between different races and ethnicities
• Analyzing how the wage gap varies with other demographics, such as religion, sexual orientation, and disability
• All of the above analysis requires collection of data, which requires sufficient privacy measures.
• Data collection should also ensure members of the data set self-identify their gender.
• Advocating for and implementing policy action to reduce the gender pay gap

Built With

• python