The contribution of self-beliefs to the mathematics gender achievement gap and its link to gender equality

ABSTRACT

I brought together two strands of literature, one investigating the moderate but persistent underachievement of girls in mathematics in most countries, and the other examining the role of self-efficacy and other self-beliefs in predicting behaviour and achievement. I implemented detailed decompositions of the gender mathematics gap, both at the mean and for low and high performing students, for a large and diverse group of countries. I found considerable heterogeneity and different cross-country patterns in decomposition components and the contribution of self-beliefs. In OECD-Europe and more affluent East Asian countries, most or all of the gap is explained by gender differences in self-beliefs, especially self-efficacy; on the other hand, in Latin America and the Middle East, most of the gap remains unexplained. I also investigated the cross-country relationship between the gender mathematics gap and gender equity and found that a clearly negative association can be established after controlling for cross-country heterogeneity in gender differences in mathematics self-beliefs, which correlate with gender equality.

KEYWORDS:

• Gender mathematics gap
• self-beliefs
• decomposition methods
• gender equity

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No potential conflict of interest was reported by the author.

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Notes

1. Defined as: An individual’s capacity to formulate, employ and interpret mathematics in a variety of contexts (OECD, 2013).

2. Analysing one plausible value instead of five provides unbiased population estimates as well as unbiased sampling variances on these estimates (OECD, 2009). However, it is not possible to estimate the imputation variance.

3. All indices used meet the normality assumption.

4. Not reported, but available upon request.

5. Here I assume no general equilibrium effects.

6. The index reflects the economic, political, and educational opportunities of women.

7. Source: World Bank World Development Indicators, 2012.

8. It is reasonable to assume that R_max is less than 1, mainly because the dependent variable is measured with error, but also because there may be variation in Y which is not related to X.

9. The combined sample of 30 OECD countries consists of about 230,000 observations.

10. Oster (2017) parametrised R_max as R_max = Π*controlled R-squared, looking for an appropriate value of Π. Using replication files to examine a sample of published papers in top journals, some using randomised data and some observational studies which argued causality of the results, she found that less than 90% of results from randomised studies and only about half of the causality claims in non-randomised studies would survive bounding values of Π > 1.3. As R_max increases, even small changes in the coefficient tend to blow up.

11. Detailed results are available upon request.

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Chris Sakellariou

Chris Sakellariou is an Associate Professor of Economics at the department of Economics, School of Social Sciences, Nanyang Technological University in Singapore. He specialises in the Economics of Education, Educational Research, and research on Gender and Ethnicity.