Quantifying progress: The correlation between illiteracy rates and the human development index

Abstract

Background: The elimination of illiteracy represents a key objective of Sustainable Development Goals (SDGs) 4, essential for fostering quality education and lifelong learning opportunities. Previous research indicated that illiteracy posed severe negative consequences for community well-being and societal progress. This study aimed to investigate the precise quantitative relationship between the provincial illiteracy rate and the Human Development Index (HDI) across Indonesia, thereby emphasizing the critical importance of achieving SDG 4. Methods: Researchers employed a quantitative descriptive design with a correlational approach. The study utilized 2024 cross-sectional data on the illiteracy percentage and HDI for all 38 Indonesian provinces, which was obtained from the Statistics Indonesia/Badan Pusat Statistik (BPS). Following a test for normality, the non-parametric Spearman Rank correlation test was selected for the analysis, supplemented by visual examination via a scatter plot. Findings: The analysis revealed a strong, statistically significant inverse relationship between the provincial illiteracy rate and the HDI in Indonesia. The 2024 HDI data, spanning an interval of 1-100, showed an average of 73.5, with the maximum score (84.15) achieved by Special Capital Region of Jakarta and the minimum score (54.43) recorded by Highland Papua Province. Correspondingly, the illiteracy rates averaged 3.7%, with Highland Papua Province displaying the highest rate (29.63%) and Central Sulawesi province the lowest (0.25%). This inverse correlation confirmed the theory that low human capital, as measured by illiteracy, directly restricts developmental outcomes. Conclusion: The principal conclusion was that the eradication of illiteracy is crucial for significantly raising the HDI, directly supporting the successful realization of SDG 4, and enhancing overall public welfare. Novelty/Originality of this article: This research provided the most current (2024) and comprehensive correlation analysis using data from all 38 provinces in Indonesia, establishing a renewed empirical foundation that underscores the urgency of the SDG 4 target for regional development.