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Introduction -- Education contexts -- State of research -- Theoretical explanations for gender inequality: the macro-meso-micro model -- Data and analytical strategy -- Multilevel results -- Discussion -- Limitations and further research possibilities -- Conclusion.

Contents of the dataset on country/macro level includes nine variables considering school and education system characteristics as well as country characteristics:
number of school types/tracks for 9th grade/15-year-olds; age at first selection; preschool obligation; compulsory school years/education years (with pre-primary school); government expenditure on education, total (% of GDP); mean years of schooling; Human Development Index (HDI); Gender Inequality Index (GII); women's share of seats in parliament (in %)

The data set contains information on 82 countries and regions that participated in the PISA study. Most of the data are for the school year period of 2017/18, however older and newer data is used as well if other sources were not available. The documents used to create the dataset (including European Commisssion, OECD, education ministries) can be found in the reference list in the excel file and can be requested from the author.

Code/syntax is provided here for a thesis expected to be published in late 2023. The previous citation can be found in the field 'Publications' / 'Publikationen'.

Abstract of the thesis:
To date, gender inequalities persist and vary between countries, e.g., the gender gap in mathematics and reading competencies, indicating that school and education system characteristics affect gender inequalities. Previous research demonstrates that schools and education systems do vary between countries and indicates that gender-specific educational inequalities are less well researched. Educational inequalities lead to inequalities in living standards and expectations, e.g., health, income, and social participation. Therefore, my aim is to explain how education system and school characteristics affect the gender gap in mathematics and reading competencies.
To explain the effects of school and education system characteristics on gender inequalities, I adapt the macro-meso-micro model. At the micro level, I use theories, e.g., gender-specific socialization, highlighting how gender-specific expectations and stereotypes cause gendered interest and skills and therefore gender differences in mathematics and reading. Deriving a macro-meso-micro link, I explain how education system characteristics such as competition, differentiation, and standardization, and school characteristics, e.g., school time, retention rate, could increase or decrease the gender-specific socialization effects, leading to larger or smaller gender gaps in mathematics and reading competencies.
On this basis, I perform a cross-national comparison of 78 countries participating in the Programme for International Student Assessment (PISA) 2018, combined with further researched macro data (https://doi.org/10.7802/2605) with three-level mixed-effects models. The results show that boys have an advantage in mathematics, girls have an advantage in reading, the gender effects are slightly higher for reading, and the gender effects at the school level are more pronounced than those at the country level.
Furthermore, the considered school and education system characteristics differently affect the gender gaps in mathematics and reading competencies. The school characteristics of school time and retention rates and the education system characteristics of competition and compulsory schooling (partly) explain the gender gaps. Above-average school time affects the gender gaps in favor of girls, decreasing the gender gap in mathematics and increasing it in reading, while above-average competition and compulsory schooling affects the gender gaps in favor of boys, increasing the gender gap in mathematics and decreasing it in reading. Furthermore, schools with above-average retention rates decrease the gender gap in reading.