Data-Driven Analysis of the Global Fiscal Response to the Pandemic
Python ● Data Cleaning, Analysis and Visualization ● Natural Language Processing ● Text Analysis As the pandemic swept across nations, I couldn't help but wonder: Which fiscal policies truly made a difference? That's when this personal project was born, driven by a desire to uncover the standout successes and areas for improvement based on GDP percentage change.
My mission? It's clear—to extend a helping hand to the nations hit hardest. Drawing inspiration from the triumphs of others, I meticulously examined income levels, policy authorities, and the nuances of implemented strategies. This led to the identification of both the top 10 and bottom 10 countries in terms of GDP fluctuation. These insights serve as the guiding star in formulating policy recommendations for a more robust recovery.
From dissecting GDP shifts pre and post-pandemic to analyzing the strategies that yielded the most promising outcomes, I invite you to join me in this deep dive into the economic landscape. Together, we'll unravel how nations weathered the storm and unearth lessons for a stronger, more resilient future. Eager to explore further? Dive right in!
Questions to Answer
How did the GDP change around the world before, during and after the pandemic?
Which countries were hit the worst?
What steps were taken by the best-performing countries to improve their economy?
Policy implementation suggestions to worst-performing countries based on all previous analyses and observations
Data Processing
Data Extraction
1. Load two datasets consisting of Policy Measures Data Implemented due to the pandemic and data consisting of the GDP of every country for analysis.
Source:
- "COVID-19 Finance Starter EDA" hosted by Bojan Tunguz
- Annual GDP for every country in the world by the World Bank with 271 records from 1960 to 2021
2. Only data of pre, during, and post covid required so the years 2018 - 2021 were filtered out from GDP dataset
Date Merging
Merged the GDP dataset and COVID dataset using the Country ISO3 code as the joining parameter for the same.
Data Preparation
Filtered column names of the merged dataset using the columns required for further analysis Reordered columns as per requirement.
Analysis of the Policies
Common policy implementation and termination timeline:
Most policies were implemented at the start of the pandemic, march 2020. Some countries took preventive measures by implementing policies early in February 2020. Some countries took extreme short-term policies. It can be to: curb the risk of high inflation and rollback failed policies to prevent further issues
2. Common policy measures across best and worst performing countries
70% of best-performing countries aggressively promoted investments and 100% took measures to protect their banks and other financial institutions. Only 20% of the worst-performing countries took steps to promote investments and took measures to protect their banks and other financial institutions.
The majority of best-performing countries took measures to increase liquidity in their markets. Only half of the worst-performing countries took measures to increase liquidity in the market.
3. Detailed policy measures across best and worst performing countries:
70% of best-performing countries actively promoted digital payments to prevent the spread of infection. Only 30% of the worst-performing countries took measures to promote digital payments.
Most best-performing countries took aggressive market movement measures to control the prices of goods in the short term. Only 30% of the worst-performing countries took any market movement measures.
Recommendations
Implement policies to support borrowers but terminate them on time to curb further
Inflation promote digital payment methods and invest more in digital economy
Implement policies to promote investments and support banks and financial institutions