A Statistical Analysis and Strategic Recommendations on Global Educational Investment and Poverty Reduction

Abstract

Purpose: This study examines the relationship between educational funding and poverty alleviation worldwide. By analyzing data from 1960 to 2023, encompassing 71 countries, it aims to understand how increasing educational investment impacts poverty rates. Methodology: The analysis utilized data from the World Bank’s World Development Indicators. Data cleaning was performed using Excel, while statistical analyses were conducted using Python’s sci-kit-learn, SciPy, NumPy, Matplotlib, and IBM’s SPSS. The methodologies included a normal model setup, Gaussian Process Regression (GPR), linear regression, hypothesis testing, and confidence interval computation to establish correlations and predict outcomes. Findings: The study discovered a negative correlation between education funding and poverty rates. Specifically, a 1% increase in educational spending as a percentage of GDP correlates with a 3.09% reduction in poverty rates. The 95% confidence interval of [-4.979, -1.201] and the hypothesis test with a p value of 0.002 on the slope of the regression line further reinforce the observed negative trend. GPR predictions indicate that the decrease in poverty rate changes from about 5% to 10% of population as educational funding rises from 0% to 1.5% of GDP. The likelihood of annual poverty rate increase stands at 40.46%, with a potential 0.4052% rise in such cases. Unique contribution to theory, practice, and policy: This study recommends progressive educational funding reforms, targeted tax credits for educational investments, and strategic educational programs aligned with labor market needs. Policy implications suggest a multilateral approach involving governments, corporations, and citizens to foster substantial improvements in education and poverty reduction efforts. These findings advocate for data-driven policy reforms to optimize the socio-economic benefits of educational funding globally.