A Gendered Landscape: A Quantitative and Qualitative Analysis of Women's Employment Across Professional and Non-Traditional Sectors in Contemporary India

  • Unique Paper ID: 191204
  • Volume: 12
  • Issue: 8
  • PageNo: 5741-5747
  • Abstract:
  • This research offers a state-wise analysis of women's employment across over 50 diverse occupations in India, from highly skilled professions (Doctors, Engineers) to informal sectors (Drivers, Electricians). Utilizing a mixed-methods approach with primary data from the Periodic Labour Force Survey (PLFS) 2022-23 and other national datasets, the study maps the gendered distribution and identifies factors driving labor market segregation. The findings reveal a stark dichotomy: women are well-represented, even nearing parity, in traditional "pink-collar" sectors like Medicine, Teaching, and Banking. Conversely, their participation is critically low in core STEM fields (Engineering), strategic government roles (IAS), and most blue-collar, field-based, and informal occupations. Southern states (Kerala, Karnataka) consistently show higher female workforce participation than northern states (Bihar, UP). These disparities are attributed to educational gaps, persistent patriarchal social norms defining "suitable" work, wage discrimination, and the heavy burden of unpaid care work. The paper concludes that India's labor market is deeply segmented by gender. It advocates for a comprehensive strategy, including targeted skill development, stronger anti-discrimination laws, and improved public childcare infrastructure, to achieve a more equitable and inclusive workforce.

Copyright & License

Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

BibTeX

@article{191204,
        author = {Jayanta Majumder and Dr. Parimal Sarkar},
        title = {A Gendered Landscape: A Quantitative and Qualitative Analysis of Women's Employment Across Professional and Non-Traditional Sectors in Contemporary India},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {5741-5747},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=191204},
        abstract = {This research offers a state-wise analysis of women's employment across over 50 diverse occupations in India, from highly skilled professions (Doctors, Engineers) to informal sectors (Drivers, Electricians). Utilizing a mixed-methods approach with primary data from the Periodic Labour Force Survey (PLFS) 2022-23 and other national datasets, the study maps the gendered distribution and identifies factors driving labor market segregation.
The findings reveal a stark dichotomy: women are well-represented, even nearing parity, in traditional "pink-collar" sectors like Medicine, Teaching, and Banking. Conversely, their participation is critically low in core STEM fields (Engineering), strategic government roles (IAS), and most blue-collar, field-based, and informal occupations. Southern states (Kerala, Karnataka) consistently show higher female workforce participation than northern states (Bihar, UP).
These disparities are attributed to educational gaps, persistent patriarchal social norms defining "suitable" work, wage discrimination, and the heavy burden of unpaid care work. The paper concludes that India's labor market is deeply segmented by gender. It advocates for a comprehensive strategy, including targeted skill development, stronger anti-discrimination laws, and improved public childcare infrastructure, to achieve a more equitable and inclusive workforce.},
        keywords = {Women's Employment, Female Labor Force Participation Rate (FLFPR), Occupational Segregation, Gender Parity, Informal Sector, Skilled Professions, India, State-wise Analysis, Periodic Labour Force Survey (PLFS).},
        month = {January},
        }

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