Action Needed: Address Corruption and Income Inequality to Improve Education in Small Island Nations

At an associate event of the United Nations ESCAP Asia Pacific Sustainable Development Forum 2025, we discussed how several Pacific island countries have invested a high percentage of their GDP into education but have seen poor results. Out of curiosity, I decided to investigate further, leading to the following findings.

Small island nations face unique challenges that significantly impact their education systems. Among these challenges, corruption and income inequality (measured by the Gini Index) stand out as critical factors influencing education outcomes. This article explores how these two parameters affect the quality of education in small island countries and highlights the importance of addressing these issues to improve educational attainment.

Corruption and Education Outcomes

Corruption in small island nations can take various forms, including bribery, embezzlement, and nepotism. These practices undermine the effectiveness of educational policies and the equitable distribution of resources. When funds meant for education are siphoned off through corrupt practices, schools are left underfunded, teachers are underpaid, and students lack essential learning materials.

A high level of corruption erodes public trust in the education system, leading to lower enrollment rates and higher dropout rates. Parents may be reluctant to invest in their children's education if they believe that the system is corrupt and that their children will not receive a quality education.

Income Inequality and Education Outcomes

Income inequality, as measured by the Gini Index, reflects the disparity in income distribution within a country. High income inequality often correlates with unequal access to educational opportunities. In small island nations, children from low-income families may struggle to afford school fees, uniforms, and other educational expenses.

Moreover, income inequality can lead to disparities in the quality of education provided. Schools in wealthier areas may have better facilities, more qualified teachers, and access to advanced learning resources, while schools in poorer areas lag behind. This creates a cycle of poverty where children from low-income families are unable to break free from their socio-economic constraints due to limited educational opportunities.

Regression Analysis Findings

Our regression analysis of small island nations' education outcomes revealed that both the Gini Index and Corruption Index significantly impact total PISA scores. The analysis showed that:

• Gini Index: A higher Gini Index, indicating greater income inequality, is associated with lower total PISA scores. This suggests that income inequality negatively affects educational attainment.
• Corruption Index: Higher levels of perceived corruption are also linked to lower total PISA scores, highlighting the detrimental effect of corruption on education quality.

These findings underscore the importance of addressing both corruption and income inequality to improve education outcomes in small island nations.

Conclusion

To enhance education outcomes in small island nations, it is crucial to implement policies that reduce corruption and income inequality. Strengthening anti-corruption measures, ensuring equitable distribution of educational resources, and providing financial support to low-income families can help create a more inclusive and effective education system.

Appendix

Data Tables

Small Island Nations Education Outcomes

Countries used for regression analysis: Maldives, Fiji, Barbados, Jamaica, Mauritius, Seychelles, Bahamas, Trinidad and Tobago, Cyprus, Sri Lanka, Antigua and Barbuda, Dominica, Grenada, Saint Kitts and Nevis, Saint Lucia, Saint Vincent and the Grenadines, Kiribati, Marshall Islands, Micronesia, Nauru, Palau, Samoa, Solomon Islands, Tonga, Tuvalu, Vanuatu, Cape Verde, Comoros, Guinea-Bissau, S?o Tomé and Príncipe, Timor-Leste.

Example row:

| Country           | Total PISA Score |
|-------------------|------------------|
| Cyprus            | 1350             |
        

Small Island Nations Input Parameters

Countries used for regression analysis: Maldives, Fiji, Barbados, Jamaica, Mauritius, Seychelles, Bahamas, Trinidad and Tobago, Cyprus, Sri Lanka, Antigua and Barbuda, Dominica, Grenada, Saint Kitts and Nevis, Saint Lucia, Saint Vincent and the Grenadines, Kiribati, Marshall Islands, Micronesia, Nauru, Palau, Samoa, Solomon Islands, Tonga, Tuvalu, Vanuatu, Cape Verde, Comoros, Guinea-Bissau, S?o Tomé and Príncipe, Timor-Leste.

Example row:

| Country           | Gini Index | Corruption Index |
|-------------------|------------|------------------|
| Maldives          | 31.3       | 40               |
        

Data Sources

• PISA Scores: Programme for International Student Assessment (PISA) 2022 results.
• Gini Index: World Bank data on income inequality.
• Corruption Index: Transparency International's Corruption Perceptions Index 2024.

Regression Model

The regression model used to analyze the impact of the Gini Index and Corruption Index on total PISA scores is as follows:

| Parameter        | Coefficient | Std. Error | t-Statistic | P-value |
|------------------|-------------|------------|-------------|---------|
| Constant         | 1084.9388   | 178.457    | 6.079       | 0.000   |
| Gini Index       | -2.1634     | 3.048      | -0.710      | 0.484   |
| Corruption Index | -0.9923     | 1.949      | -0.509      | 0.615   |
        

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