Introduction

Data Processing/Comparison of South Korea’s income data to analyze the income disparity before/after COVID situation. This project was made up of two groupmate including me.

Hands-on Role

• Kyungseob Shim: Data Validation check, Code Cleansing/Neating, Visualizing

• Seongdeok Oh: Ideation, Variable selection (based on sociology theorem), Result Translation, Initial Code Writing(His profile attached below:)

Datasets

Korean Welfare Panel Study Data (2016 - 2021)

KOWEPS - Korea Welfare Panel Study

Libraries used

• Numpy, Pandas, Matplotlib, Seaborn

Purpose

• This project focuses on the income disparity changes in South Korean Society. Specifically, comparison before and after the COVID-19 pandemic of individual’s income(monthly salary) and gender, age groups, education level, occupation, and region in South Korea through Python programming.
• Not only in the world but also South Korean society, as the economy becomes difficult due to the COVID-19 pandemic, the income disparity widens, which can deepen the income inequality problem.

Initial Questions asked to myself

• Is there significant income disparity based on result of Comparative Analysis of data?
• If so, is that correlated significantly to variables, such as gender, age groups, education level, occupation and region?

Comparative Analysis