A Mathematical Framework to characterize disparity in SDG 5 Indicator Female-to-Male LFPR Ratio across States of India

Abstract

We focus on the Ratio of Female-to-Male LFPR, a key indicator defined by Niti Aayog in SDG India index 2023-24 for measuring India’s progress towards SDG 5 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 mathematical 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 categorize 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 the framework by demonstrating that if we normalize female-to-male LFPR in rural areas for socio-cultural factors, we can actually characterize the decrease in female LFPR with increase in income levels, a phenomenon currently explained in the literature as higher LFPR in economic distress.