A Mathematical Framework to Characterise Disparity in the Female-to-Male LFPR Ratio across States of India
DOI:
https://doi.org/10.12724/ajss.77.3Keywords:
Sustainable Development Goal (SDG), Gender Equality, Labour Force Participation Rate (LFPR), Female LFPR, Male LFPR, ClusteringAbstract
We focus on the Ratio of Female-to-Male labour force participation rate (LFPR), a Sustainable Development Goal (SDG) indicator and a key indicator defined by Niti Aayog in the SDG India Index 2023–24 for measuring India’s progress towards the goal of gender equality. We use mathematical techniques to show that this indicator should be considered separately for urban and rural areas. We develop a framework to statistically establish that the value of this indicator in urban areas is a proxy for the socio-cultural factors adversely impacting female LFPR across states of India. We categorise the states of India into three groups by applying the machine learning technique of clustering over this indicator for urban areas. We establish the validity of this clustering or grouping of states by noticing that the common gender discrimination factors, such as female literacy rate and crime rates against women, show noticeably different behaviours across the three categories of states. We conclude by demonstrating that if we normalise female-to-male LFPR in rural areas for socio-cultural factors, we can actually characterise the decrease in female LFPR with an increase in income levels, a phenomenon currently explained in the literature as higher LFPR in economic distress.
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