Gender Inequality is negatively associated with academic achievement for both boys and girls

Academic achievement is a crucial determinant of future success, impacting employment, socioeconomic status, and quality of life. While numerous studies have explored the microsystem level (family, school) influences on academic achievement, macro-level factors like socioeconomic contexts remain less investigated. Existing research indicates a positive association between economic development and academic achievement. However, economic inequality, measured by the Gini coefficient, has been linked to negative social outcomes, including lower academic achievement. This study focuses on the impact of gender inequality, measured by the Gender Inequality Index (GII), on academic achievement, considering its potential interaction with economic inequality. Previous research has shown a correlation between gender inequality and academic achievement at the country level, but this study aims to confirm this relationship at the student level and compare the relative effects of gender and economic inequality. The hypotheses are: 1) a negative relationship exists between gender inequality and academic achievement for both genders; and 2) gender inequality is more strongly associated with academic achievement than economic inequality.

Existing research demonstrates a strong correlation between economic inequality (measured by the Gini coefficient) and negative social outcomes, including lower academic achievement. Studies using PISA data have shown that more egalitarian countries (lower Gini coefficients) tend to have higher average achievement scores, a higher proportion of high-achieving students, and fewer low-achieving students. Neo-materialist theory and social psychology theories provide potential explanations for this negative association. Concerning gender inequality, research has focused mainly on gender gaps in math achievement, with mixed findings. However, recent studies show an association between gender inequality and academic achievement for both boys and girls at the country level. These studies are limited by their country-level analysis, neglecting the hierarchical structure of the data and the potential for ecological fallacy, and by a lack of consideration of the role of economic inequality. This study addresses these limitations by combining student- and country-level analyses using multilevel modeling and machine learning techniques to compare the roles of gender and economic inequality.

This study utilizes data from three cycles of the Programme for International Student Assessment (PISA) – 2012, 2015, and 2018. The dataset includes information on students' math, reading, and science performance, along with country-level data on economic inequality (Gini coefficient) and gender inequality (Gender Inequality Index, GII). The GII is a composite index based on three dimensions: female reproductive health (maternal mortality ratio and adolescent birth rate), gender empowerment (parliamentary seats held by women and secondary education), and gender labor market status (labor force participation rate). The analysis comprises two levels: country-level and student-level. At the country level, Pearson correlations and stepwise regression analyses were conducted to examine the relationships between GII, Gini coefficient, and academic achievement (mean scores across math, reading, and science). Robustness checks included separate analyses for boys and girls, mediation analyses to assess the mediating role of GII between the Gini coefficient and academic achievement, and a fixed-effects model using panel data to examine causality. At the student level, multilevel mixed-effects models were used to analyze the effects of GII, Gini coefficient, and other covariates (gender, socioeconomic status, school-level variables, GDP per capita) on academic achievement. Three machine learning techniques (LASSO, random forest, XGBoost) were used for model selection to further compare the explanatory power of GII, GDP per capita, and Gini coefficient. Finally, multilevel models were used to examine the contribution of each dimension of the GII to the association with academic achievement. Sampling weights were used in the student-level analyses to account for the PISA sampling design.

Country-level analyses revealed significant negative correlations between both GII and Gini coefficient with academic achievement across all three years (2012, 2015, 2018) and for both boys and girls. However, stepwise regression analyses demonstrated that GII was a stronger predictor of academic achievement than the Gini coefficient. After including GII in the model, the association between the Gini coefficient and academic achievement became non-significant. Robustness checks supported these findings: stepwise regressions for boys and girls separately showed GII as the only significant predictor; mediation analysis showed that GII mediated the relationship between the Gini coefficient and academic achievement; and a fixed-effects panel regression indicated that only GII, not the Gini coefficient, significantly predicted changes in academic achievement over time. Individual-level analyses using multilevel mixed-effects models consistently showed a significant negative association between GII and academic achievement across all three years. The Gini coefficient was not significant in these models. Machine learning methods consistently ranked GII as the strongest predictor of academic achievement among the country-level variables. Finally, analyses examining the dimensions of the GII indicated that gender inequality in reproductive health was most strongly associated with academic achievement in 2015 and 2018.

The findings strongly support the hypothesis that gender inequality, more so than economic inequality, is negatively associated with academic achievement for both boys and girls. The stronger effect of gender inequality may be due to its more proximal influence on students' learning environment and experiences, mediated by gender stereotypes. Negative stereotypes can hinder performance among disadvantaged students, while positive stereotypes can lead to 'choking under pressure' among advantaged students. The consistent negative association across genders points to a societal-level impact of gender inequality, affecting both boys and girls through pervasive gendered practices in families, schools, and broader society. This contrasts with the usual finding that economic factors are primary drivers in education, suggesting that gender inequality's influence is more direct and pervasive. The significant contribution of gender inequality in reproductive health further highlights the importance of addressing women's health and opportunities as a key strategy for improving overall educational outcomes.

This study provides robust evidence that gender inequality is a major impediment to academic achievement, exceeding the influence of economic inequality. The negative impact extends to both boys and girls, suggesting a pervasive societal effect. The strong association of reproductive health with academic outcomes underscores the importance of addressing this dimension of gender inequality. Future research should explore within-country educational inequality and causal mechanisms more deeply, going beyond the cross-sectional nature of PISA data.

The study's conclusions are limited to the use of PISA scores and GII as indicators of academic achievement and gender inequality, respectively. The country-level analysis may mask within-country inequalities. The cross-sectional nature of the PISA data and the non-experimental study design limit causal inferences. Future studies should consider using alternative measures and methodologies to enhance the understanding of these complex relationships.