BentoUniAcc/Global_Economic_Indicators

Global Economic Indicators Analysis (2010–2023)

Author: Ben Topaz
Data Source: Kaggle - World Bank Global Economic Indicators

Note: The python notebook is attached in the files

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Explanation Video

Youtube link: [https://youtu.be/m7d4ZOhI5aU]

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Project Overview

This project explores global economic trends over a 14-year period. By cleaning and analyzing data from 217 countries and territories, the study tests established economic theories regarding the relationships between inflation, unemployment, and GDP growth.

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Technical Decisions & Data Cleaning

To ensure a reliable correlation analysis, the following data cleaning steps were implemented:

• Handling Missing Data:
  • Column Removal: Dropped indicators with >45% missing data (e.g., Public Debt, Tax Revenue, Government Revenue/Expense, Real Interest Rates).
  • Row Removal: Removed the years 2024 and 2025 entirely. Analysis showed that nearly 100% of the key metrics were missing for these years, making them statistically irrelevant.
• Country Name Spelling and Formatting:
  • Corrected country naming conventions to ensure compatibility with Plotly mapping tools (e.g. removing punctuation from names, changing "Russian Federation" to "Russia" and "Viet Nam" to "Vietnam").
• Outlier Management:
  • Identified and removed 79 countries/territories that acted as extreme outliers (e.g., Venezuela, Zimbabwe, and Sudan due to hyper-inflation; Libya due to war-impacted growth spikes). This was necessary to find global trends without them being skewed by localized instability.

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Questions

The analysis was driven by three primary questions:

1. Growth vs. Inflation: Is there correlation between inflation (CPI) and annual GDP growth?
2. Growth vs. Unemployment: Is there a relationship between unemployment and economic growth?
3. Inflation vs. Unemployment: Does the data support the economic theory of an inverse relationship between inflation and unemployment?

The main purpose of the analysis was to test a change in one of these factors leads to a change in the other, as the classical economic theory suggests. The correlation coefficient will show the extent to which the theory is correct.

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Visualizations & Analysis

Missing Values Grouped by Year

A histogram showing nulls (NaN) per year was used to indicate if certain years were unreliable. The findings from this histogram led to the removal of 2024-2025 from the dataframe.

Global Heatmap of Missing Values

A global heat map was used to find the amount of missing values per country, and identify regions completely absent from the dataframe.

Economic Indicator Distribution Box Plots

Box plots revealed a massive range of values even after initial cleaning. This led to the decision to filter countries based on outlier Mean, Max, and Min values across all four key metrics.

Before outlier cleaning

After outlier cleaning (after the mean min and max analysis shown below)

Mean, Max and Min Economic Indicator Histograms and Global Heat Maps

For each of the four indicators a global heatmap and a histogram highlighting the outliers was generated. These visualizations were the basis for the indicator distribution analysis provided following every indicator, and the outlier country removal following the analyses.

Note: More images can be found in the files and in python notebook (also attched in the files)

Correlation Scatter Plots

Regression lines were applied to scatter plots to visualize the strength of relationships between indicators.

Insights & Answers

• Inflation & GDP Growth (0.14): There is a weak positive correlation between these indicators. While they generally move in the same direction, inflation alone is not a strong or reliable predictor of economic growth. The correlation coefficient for both measurment types of inflation were very similar indicating that the data used was reliable.
• Unemployment & GDP Growth (-0.22): This represents the strongest relationship identified in the study. The inverse correlation suggests that economic growth is significantly more sensitive to unemployment than to inflation. The direction of the relationship proves the economic theory.
• Inflation & Unemployment (-0.075): The analysis found a very weak relationship between these variables raising questions regarding the economic theory that links high unemployment with disinflation and even deflation.

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Conclusion

The analysis concludes that while modern economic indicators generally follow the direction of classical economic theory, the correlations are significantly weaker than the theory suggests, and missing moderating factors likely account for the weak correlation coefficients.