Gender Pay Gap in the UK

Data analysis of gender pay gap public reports from UK companies. This data is public and available in the government's Gender pay gap service website.

Blog Post

The contextual background and non-technical analysis can be found here.

Dependencies

• Python 3
• Jupyter Notebook
• Pandas
• Numpy
• Matplotlib
• Seaborn

Files

• UK-Gender-Pay-Gap.ipynb Notebook containing the main analysis. It includes general descriptions, external references, the data transformation procedures, context narratives and the plotting scripts.
• sic_codes.py Module that handles SIC codes. It helps extending and transforming the original dataset with the companies' economic activities.
• data/UK-Gender-Pay-Gap.csv Dataset containing company pay gap reports of year 2018-2019.
• data/sic_codes.csv Datset with Standard Indutrial Classification codes for sectors and sections.

Analysis Results

1 - How balanced are pay quartiles by gender? In general, a big proportion of the highiest salaries in the workforce is assigned to men.

2 - Which economic activities have the largest pay gap? Several employers with the largest pay gaps are involved in education and economic activities such as construction, technical jobs, science and finances.

3 - Which economic activities have the largest pay gap? Lower and top pay quartiles of a company are key indicators to predict its pay gap. In contrast, the size of the company does not seem to influence it.

Acknowledgements

The "Data Science" nanodegree at Udacity. The "Applied Data Science" MSc unit at University of Bristol. My group for the final project; Thor, Shivangi, Sharath, Steve and Monica. Finally, Wes McKinney for writing "Python for Data Analysis".

Licence

MIT License | Copyright (c) 2020 D. Leandro Guardia V.