Covid-19 DiD Analysis
R
Causal Inference
Regression
Econometrics
Identifying the influences of Early-Policies to the COVID-19 transmission and Deaths
Project Overview
This project estimates the early policy effects on COVID-19 transmission and damages through four dependent variables: daily new cases, cumulative cases, daily deaths, and cumulative deaths. The data is provided by the World Health Organization and Our World in Data.
Two different Difference-in-Difference models were implemented to estimate the effects of Mandatory policies, Optional policies, and no-control policies. The analysis found that mandatory policies had the most significant impact on limiting and decreasing COVID-19 transmission and damages, followed by optional policies, while no-control policies were the least effective.
Key Concepts Applied
- Difference-in-Difference (DiD): Utilized DiD models to compare the impact of different policy stringency levels on infection and death rates over time.
- Policy Categorization: Countries were categorized into three groups based on early policy stringency:
- Mandatory Countries (e.g., China): Strict travel restrictions and enforced preventive measures.
- Optional Countries (e.g., Canada, Japan, UK): Advised but not strictly enforced measures.
- No-control Countries (e.g., USA, India): Limited restrictions and guidance.
- Statistical Analysis: Analyzed the effects on new cases, cumulative cases, new deaths, and cumulative deaths using empiricial statistical methods.