000			01857nam a22001577a 4500
999			_c519191 _d519191
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100			_aInamdar, Nirad _932084
245			_aUse of micro-segmentation to find determinants of women’s labour force participation and unemployment
260			_aThe Indian Journal of Economics
300			_a400(101), Jul, 2020: p.31-55
520			_aAs the COVID-19 pandemic has spread across the world, it has had an adverse impact on emerging economies like India. This effect is more severe for women and hence, there is a need to study women’s labour force participation (LFP) and unemployment separately. But in the Indian context, this topic had not seen enough research work until recently. Hence, this paper focuses exclusively on women and narrows the scope down to one state, viz. Maharashtra. Further, even within one state, there are disparities among different regions and this study provides a proxy to model them. We use data available from the India Human Development Survey 2nd Round for analysis. Drawing on similar research in this area, this paper offers two competing Probit models. The first model takes into account the joint impact of 7 variables. The second model divides the sample set into 108 microsegments, based on all possible combinations using the values of five variables – sector, marital status, social group, education level and rainfall. By using a generalised approach, our research is applicable to other states as well. Thus, our analysis identifies certain microsegments among women, which influence the likelihood of LFP and unemployment. – Reproduced
650			_aWomen’s labour force participation, Employment, Unemployment, India human development survey, Probit analysis. _929826
773			_aThe Indian Journal of Economics
906			_aLABOUR
942			_cAR