COVID-19's Impacts on Unemployment in China at the Provincial Level in 2020: A Regression and Time Series Analysis
Overview
This paper examines the differential impacts of COVID-19 on unemployment rates across Chinese provinces in 2020 using regression and time series models. The study finds that while COVID-19 increased unemployment nationwide, impacts varied significantly by province, with 22 provinces experiencing rising unemployment and 9 experiencing decreases. The research identifies geographical patterns in these impacts and acknowledges limitations related to data quality and granularity from Chinese government sources.
What this research paper covers
- Abstract and introduction establishing COVID-19's global and Chinese economic context
- Methodology describing regression and time series models applied to provincial-level data
- Results showing nationwide unemployment increase with geographic variation between West-Southern and East-Northern regions
- Discussion of limitations including potential data accuracy issues and lack of community-level granularity
Preview
The Study of COVID-19's Impacts On Unemployment In China On Provinces' Level In 2020 Using Regression and Time Series Model
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Abstract
Unemployment is a social problem that affects social stability and economic development. The emergence of COVID-19 epidemic led to major structural changes in the labor market worldwide. In the UK, COVID-19 resulted in widespread unemployment rate nationwide. However, China's political and social conditions are different from those in the UK, such as the fact that China has a much larger land area. COVID-19 caused the economy and geography to detriorate in many provinces in China in 2020. Because the crisis led to different policies in these provinces, the unemployment rates in these regions varied. The study findings are consistent with literature findings in the UK although different methods were used to measure and analyze the COVID-19 impact on China’s unemployment rate. This paper will run a regression model and time series model to analyze how COVID-19 affected the unemployment rate in China at the province level in 2020. Data of control variables and dependent variables were scraped from the Chinese government database. The data of COVID-19 was downloaded from Harvard University. After train-testing the model and visualizing the result, the study found that COVID-19 increased unemployment rates in the nationwide model. It caused unemployment in 22 provinces while nine provinces had a decreasing unemployment rate in 2020. COVID-19 impacts was also divided geographically into two parts: West-Southern and East-Northern.