Sex differences in educational achievement have been discussed for years in the literature, with various suggested causes such as differences in prosperity and equality of opportunity (e.g., Giofre et al., 2020; Eriksson et al., 2020). Understanding these differences is crucial for developing effective educational policies and practices that promote gender equality. International large-scale assessments (ILSAs) have been one of the data types used for these analyses, often in a cross-sectional manner (see e.g. the review by Archer, 2019). Oberleiter et al. (2023) conducted such a cross-sectional analysis using data from several rounds of TIMSS and PIRLS, combined with country-level data on indicators of prosperity, economic equality, gender gaps, and educational spending. They concluded that the influence of these indicators on sex differences in achievement is subject-specific, and that sex differences in mathematics and science are larger in more egalitarian and prosperous countries. The present presentation will replicate the study by Oberleiter et al. (2023) using a pseudo-panel approach (Kristensen, 2024; Jæger, 2013), which involves creating synthetic cohorts from repeated cross-sectional data. This method allows for tracking changes over time and controlling for unobserved heterogeneity, accounting for fixed effects in estimating the influence of macro-level indicators on sex differences. This approach provides a sounder basis for avoiding issues such as omitted variable bias, allowing for more robust claims about causal effects than the cross-sectional method used by Oberleiter et al. (2023). The presentation leverages that ILSAs are representative samples of students in a country, and hence also representative samples of boys and girls in these countries. By splitting data from participating countries in all cycles of TIMSS and PIRLS where country-level indicators used in Oberleiter et al. (2023) are available, the presentation replicates the study as a pseudo-panel study across available datapoints, dividing each country’s sample into boys and girls. Preliminary analyses on TIMSS data from grade 4 across the cycles 2007 to 2019 (Nielsen, 2024) indicate that an increase in gender equality decreases the sex differences in mathematics, while economic indicators do not seem to influence the gap between sexes. These preliminary findings contrast with those of Oberleiter et al. (2023), suggesting that the relationship between gender equality and educational achievement may benefit from a higher degree of gender equality. The presentation will extend these preliminary analyses and discuss the implications of using cross-sectional versus more causality-oriented methods for examining sex differences and their possible causes.

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