Gender Knowledges, Cultures of Equality, and Structural Inequality: Interpreting Female Employment Patterns in Manufacturing Through Interpretable Machine Learning

Bediha Sahin ()
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Bediha Sahin: Faculty of Education, Hacettepe University, Beytepe, Ankara 06800, Turkiye

Social Sciences, 2025, vol. 14, issue 9, 1-18

Abstract: Persistent gender inequality in industrial employment continues to challenge inclusive labor systems worldwide. While education and labor market reforms have expanded opportunities for women, structural barriers remain deeply embedded in manufacturing sectors. This study adopts a systems-based perspective to investigate the institutional, demographic, and health-related factors shaping female employment in manufacturing across ten countries from 2013 to 2022. By integrating feminist political economy with interpretable machine learning techniques—including Random Forest, Gradient Boosting, and Extra Trees regressors—the study models non-linear and interactive relationships among thirteen structural indicators drawn from the World Bank’s World Development Indicators. The findings reveal that general female labor force participation is the strongest and most consistent predictor of women’s inclusion in manufacturing. Health-related variables, such as maternal mortality and fertility rates, exhibit strong negative effects, underscoring the continued influence of caregiving burdens and inadequate health systems. Education indicators show more variable impacts, suggesting that institutional context mediates their effectiveness. The use of SHAP and Partial Dependence Plots enhances the transparency of the models and supports a more nuanced understanding of how structural forces shape gendered labor outcomes. In addition to modeling structural inequalities, this study highlights how gender knowledges and cultures of equality are contextually produced and negotiated within the manufacturing sector. The findings underscore the importance of understanding both global systems and local cultural frameworks in shaping gendered employment outcomes. By linking interpretable machine learning with systems thinking, this research provides a holistic and data-driven account of industrial gender inequality. The results offer policy-relevant insights for designing more inclusive labor strategies that address not only economic incentives but also the social and institutional systems in which employment patterns are embedded.

Keywords: gender inequality; female employment; manufacturing sector; structural indicators; interpretable machine learning; systems analysis; cross-national panel data; gender knowledges; cultures of equality; intersectionality (search for similar items in EconPapers)
JEL-codes: A B N P Y80 Z00 (search for similar items in EconPapers)
Date: 2025
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