Socioeconomic Determinants of Women's Occupational Choices in Pakistan
DOI:
https://doi.org/10.63544/ijss.v5i4.326Keywords:
Women’s Occupational Choice, Female Employment, Socioeconomic Determinants, Pakistan, Education, Gender Inequality, Multinomial Logistic Regression, Labour Market, Vocational Training, Occupational SegregationAbstract
This study examines the socioeconomic determinants of women's occupational choices in Pakistan. Using a quantitative research design, the study analyses hypothetical cross-sectional data from 2,000 working-age women based on the Pakistan Labour Force Survey framework. Women's occupations are classified into agriculture, elementary and manual work, craft and related trades, services and sales, clerical and administrative employment, and professional, technical, and managerial occupations. A multinomial logistic regression model is employed to estimate the effects of education, age, marital status, number of children, household income, residence, vocational training, and province.
The results indicate that education, urban residence, household income, and vocational training positively influence women's transition into service, clerical, and professional occupations. Conversely, marriage and childcare responsibilities reduce the likelihood of selecting formal and professional employment. Women in rural areas remain concentrated in agriculture and low-skilled work.
The study recommends investment in female education, market-oriented training, childcare, safe transportation, digital inclusion, and gender-responsive employment policies to expand women's access to decent and empowering occupations.
This study provides empirical evidence on the socioeconomic determinants of women's occupational choices in Pakistan, highlighting the critical role of education, vocational training, and urban residence in facilitating women's transition into formal and professional employment, while identifying marriage and childcare responsibilities as significant barriers that require targeted policy interventions.
References
Ahmad, N., & Javed, M. (2025). The role of socio-demographic characteristics on occupational outcomes: An empirical analysis of Pakistani labour market. The Indian Journal of Labour Economics, 68(3), 813–836.
Andlib, Z., & Khan, A. H. (2018). Low female labor force participation in Pakistan: Causes and factors. Global Social Sciences Review, 3(2), 79–98. https://doi.org/10.31703/gssr.2018(III-III).14
Asian Development Bank. (2016). Policy brief on female labor force participation in Pakistan (ADB Brief No. 70). Asian Development Bank. https://www.adb.org/publications/female-labor-force-participation-pakistan
Becker, G. S. (1964). Human capital: A theoretical and empirical analysis, with special reference to education. Columbia University Press. https://www.nber.org/books-and-chapters/human-capital-theoretical-and-empirical-analysis-special-reference-education
Greene, W. H. (2018). Econometric analysis (8th ed.). Pearson.
Iqbal, U. (2024). AI-enhanced network optimization for electric vehicle charging infrastructure expansion in the United States using graph theory and demand analytics. Journal of Engineering and Computational Intelligence Review, 2(2), 112–129.
Iqbal, U. (2025a). AI-driven predictive maintenance for US smart manufacturing: Deep learning models for equipment failure prediction and operational resilience. Journal of Engineering and Computational Intelligence Review, 3(1), 114–138.
Iqbal, U. (2025b). AI-powered supplier risk intelligence: Predicting financial and geopolitical supply chain disruptions in US critical industries. Journal of Engineering and Computational Intelligence Review, 3(2), 173–193.
Iqbal, U., & Bhutto, Y. (2026). Digital transformation through artificial intelligence and advance business analytic in American operational management. Journal of Theoretical and Applied Econometrics, 3(1), 37–50.
Iqbal, U., Bekmez, S., & Qurashi, F. A. (2026). Operational risk management through machine learning and business intelligence in U.S. businesses. Spanish Journal of Innovation and Integrity, 54, 239–253. https://www.sjii.es/index.php/journal/article/view/1140
Kabeer, N. (2021). Gender equality, inclusive growth, and labour markets. In Women's economic empowerment (pp. 13–48). Routledge.
Khan, M. Z., Said, R., Mazlan, N. S., & Nor, N. M. (2023). Measuring the occupational segregation of males and females in Pakistan in a multigroup context. Humanities and Social Sciences Communications, 10, Article 10. https://doi.org/10.1057/s41599-023-01523-6
Mahmood, N., Soomro, G. Y., Arif, G. M., Kiani, M. F. K., & Sheikh, K. H. (2006). Improving the quality of population census 2008. The Pakistan Development Review, 45(3), 517–522.
Mehak, H. (2017). Determinants of female labor force participation in Pakistan: A case study of Punjab. The Lahore Journal of Economics, 22, 123–149.
Mincer, J. (1974). Schooling, experience, and earnings. National Bureau of Economic Research. https://www.nber.org/books-and-chapters/schooling-experience-and-earnings
Nasir, Z. M. (2005). An analysis of occupational choice in Pakistan: A multinomial approach. The Pakistan Development Review, 44(1), 57–79.
Pakistan Bureau of Statistics. (2022). Pakistan Labour Force Survey 2020–21: Key findings report. Government of Pakistan.
United Nations Entity for Gender Equality and the Empowerment of Women. (2016). Pakistan status report on women's economic empowerment. UN Women.
Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data (2nd ed.). MIT Press.
World Bank. (2018). Female labor force participation in Pakistan: What do we know? World Bank.
World Bank. (2019). Improving the measurement of rural women's employment: Global momentum and survey research priorities. World Bank.
World Bank. (2024). Women's economic empowerment in Pakistan: An evidence-guided toolkit for more inclusive policies. World Bank. https://www.worldbank.org/en/news/feature/2024/03/07/driving-change-for-women-s-economic-empowerment-in-pakistan
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Copyright (c) 2026 Dr. Samrana Afzal, Sara Noreen, Farwa, Sabiha Abid

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