Predicting GDP Growth Based on Educational Pass Rates: An Analytical Approach Using Historical Data

Overview

This project explores the correlation between educational outcomes, specifically the official matric pass rates, and economic performance in South Africa. By analyzing historical data from 1995 to 2023, I aim to develop a predictive model to estimate the future GDP based on variations in the pass rates. The project culminates in predicting the GDP for the year 2030, assuming a 10% increase in the matric pass rate from 2022.

Table of Contents

1. Introduction
2. Data Collection
3. Data Exploration
4. Data Preparation
5. Model Training
6. Prediction
7. Results
8. Conclusion
9. Acknowledgments

Introduction

Despite significant investments in education, understanding the direct relationship between educational outcomes and economic growth remains complex. This project investigates the correlation between historical pass rates and GDP growth to predict future GDP changes based on educational improvements. By employing machine learning techniques, this analysis aims to provide insights into how educational advancements can drive economic growth, aiding policymakers in informed decision-making.

Data Collection

The data for this project was collected from a publicly available sources.
Official matric pass rate was obtained from Mybroadband and South African government. GDP figures for South Africa from 1995 to 2023 was collected from Macrotrends. The dataset is stored in a CSV, SA_matricratepass_gdp.

Data Exploration

I started by examining the dataset to understand its structure and identify any potential outliers. A box and whisker plot was used to visualize the distribution of both the official matric pass rate and GDP values. The observations indicated a generally high pass rate and significant economic growth over the years, suggesting a potential positive correlation between the two variables.

Data Preparation

The dataset was cleaned by removing any outliers, specifically the data for the year 2023, which was identified as an outlier. We then split the data into features (year and official matric pass rate) and the target variable (GDP). The data was further divided into training and testing sets to train the machine learning model.

Model Training

A RandomForestRegressor model was used for this project due to its robustness and ability to handle complex relationships between variables. The model was trained using the training data, with the year and official matric pass rate as features and the GDP as the target variable. The training process involved splitting the data into training and testing sets, fitting the model, and ensuring that the feature names were consistent throughout.

Prediction

To predict the GDP for 2030, I created a new input data point with the year set to 2030 and the official matric pass rate increased by 10% from the 2022 value. This input data was fed into the trained model to obtain the GDP prediction for 2030.

Results

The model predicted that the GDP for South Africa in 2030, assuming a 10% increase in the official matric pass rate from 2022, would be approximately $394.42 billion.
Observed trends

My analysis reveals a clear correlation between South Africa’s GDP and the official matric pass rate over the years. When the pass rate increases, the GDP also tends to rise, and when the pass rate drops, the GDP tends to fall. For instance, from around 1998 to 2003, there was a significant increase in the pass rate, accompanied by a rise in GDP. However, from 2003 to 2010, the pass rate declined sharply, which slowed the GDP growth. Conversely, from 2009 to 2014, we observed a sharp increase in the pass rate, and the GDP grew as well. This pattern continues, with the pass rate drop in 2020 and 2021 due to COVID-19 causing a slight decrease in GDP. These trends suggest a strong link between educational outcomes and economic performance in South Africa.

Conclusion

This project successfully demonstrated a positive correlation between educational outcomes and economic performance in South Africa. By using historical data and machine learning techniques, we were able to predict the future GDP based on an increase in the matric pass rate. These insights can help policymakers understand the potential economic benefits of investing in education.

Acknowledgments

I would like to thank the sources of the data and the contributors to the various libraries used in this project, including pandas, numpy, matplotlib, seaborn, scikit-learn, and mlflow. Their tools and resources made this project possible.

Contact

For any questions or further information, please contact me via jonasgiven18@gmail.com.

Thank you for exploring this project! I hope it provides valuable insights into the relationship between education and economic growth in South Africa.