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Constructing Women Empowerment Indices Based on Kernel PCA and Evaluating Its Determinants: Evidence from BDHS

Author

Listed:
  • Most. Suma Khatun

    (Hajee Mohammad Danesh Science and Technology University)

  • Rajib Dey

    (Hajee Mohammad Danesh Science and Technology University
    McGill University)

  • Md. Saifur Rahman

    (Hajee Mohammad Danesh Science and Technology University)

  • Mahmudul Hasan

    (Hajee Mohammad Danesh Science and Technology University)

Abstract

Women empowerment in developing countries is essential for driving sustainable development, reducing poverty, and fostering gender equality through increased access to education, economic opportunities, and political participation. Women in developing countries often face significant disempowerment, relying on male family members for support. This chapter examines the key dimensions and risk factors influencing women’s empowerment in Bangladesh a developing country, focusing on decision-making power in healthcare, major household purchases, and social visits. It also explores attitudes toward the justification of physical violence by husbands in scenarios such as the wife going out without informing him, arguing, neglecting children, refusing sex, or burning food. The study identifies a strong relationship between women’s decision-making power and their views on the justification of physical violence. This study employs Kernel Principal Component Analysis (KPCA), Kaiser–Mayer–Olkin test, Kolmogorov–Smirnov test, and ordinary logistic regression to find the objective of this study. Additionally, it finds significant associations between women’s empowerment and factors like type of residence, wealth index, and age group. These findings provide valuable insights for further research and highlight the need to track changes in women’s status of empowerment over time, considering various risk factors.

Suggested Citation

  • Most. Suma Khatun & Rajib Dey & Md. Saifur Rahman & Mahmudul Hasan, 2025. "Constructing Women Empowerment Indices Based on Kernel PCA and Evaluating Its Determinants: Evidence from BDHS," International Series in Operations Research & Management Science,, Springer.
  • Handle: RePEc:spr:isochp:978-3-031-95099-5_11
    DOI: 10.1007/978-3-031-95099-5_11
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