Studies on female labour force participation (FLFP) and economic development gave contrasting results, either supporting the existence of a U-shaped relationship or against such relationship. Interpretations of results of such studies need caution due to the associated problem areas and selection of indicators which are non-exhaustive or strongly correlated.  Avoiding problems of logarithmic transformations, scaling/normalization and issues related to multiple regressions, the paper gives two countries specific FLPR indices at t-th year 

considering all relevant indicators with different score-ranges, distributions and inter-correlations. The index 

by multiplicative aggregation (Method-1) and cosine similarity (Method-2) are linearly related and satisfy desirable properties like monotonically increasing continuous scores, time-reversal test, formation of chain-indices, facilitate identification of critical dimensions or indicators, measurement of progress across time, etc.  Considering theoretical advantages and applications,

 by Method-1 is recommended for easy comprehension. Future studies on empirical investigation of the properties of the proposed measures of 

suggested.

Classification Codes: O10; O52; J21

Female labour force participation (FLFP) is taken as number of female labor participants of age 15–64 divided by the total female population in the same age group, where labor force participation includes those employed plus unemployed (actively seeking work). FLFP has 

two major implications:(i) women’s empowerment to promote equal economic rights, access to employment, and economic activities, and control over economic resources across gender and (ii) gender inequality to achieve the targets of Goal-5 of Sustainable Development Goals (SDG-5) by 2030 which includes among others recognition of contributions of unpaid and domestic work, equal opportunities, participation in education and employment [11]. 

However, gender gaps exist in health, education, policy areas, and economic participations as per Global Gender Gap Report 2020 [32].  Globally, FLFP is little over 50%, against 80% for men and gender gap in participation is highly significant in South Asia, Middle East and North Africa where the participation rate of men exceeds three times the rate among females [16]. Non-utilization of women to reach their full potential signifies tremendous loss of human capital and even a roadblock to economic advancement [24]. FLFP is an important dimension of Gender Inequality Index (GII) by (UNDP, 2022) [29], Gender Gap Index (GGI) [32].

The U-shaped female labour force function (FLFF) curve based on income effect (shift from traditional farm activities to works in secondary/tertiary sector) and substitution effect (rise in educational level) results in increased value of women’s time in the agriculture dominated economy and encourages women to move back into the paid labour force with rise in service or tertiary sector [14]. FLFF curve is negatively slopped initially with industry dominated economic development, followed by plateau and increasing trend with increase in level of education of women resulting in increased value of women’s time in the market giving the U-shape. However, the relationship is influenced by a host of factors like availability of job opportunities, socio-geo-economic pattern of living which prevents the females to move to other places in search of jobs; different allocations of time and efforts by gender in paid and unpaid works, policies and legislation of national governments, etc. 

Empirical investigations on U-shaped FLFF curve have given contrasting results. In the context of India, (Olsen and Mehta, 2006) [20], found U-curve between employment and female educational status. Women of poor families work both at home and out of home. But when their income levels improve, they leave their outside works and concentrate on their household activities. Well educated women of higher income groups employ domestic helps and concentrate more on their economic activities out of their homes. Inverted U-shaped curve was observed between FLFPR and income with inflexion point at extremely high-income levels [23], between literacy rate and FLFPR at Uttarakhand state of India [1], and the Goldin hypothesis did not hold true for rural areas.  Dispute exists regarding verification of the U-shaped feminizing theory [3]. Need is felt for consideration of issues and methods of finding relationship between estimates of FLFPR and its correlates. Methodological issues in empirical relationships of FLFPR with its correlates by multiple linear regressions involving number of countries, transformations of the chosen variables, etc. might have resulted in divergent results.

The paper gives two methods of finding multidimensional index of FLPR of a country at t-th year 

by aggregating all chosen correlates of FLPR facilitating better comparisons, plotting its fluctuations across time and statistical test of significance.

Estimation of FLPR depends heavily on the data, methods of estimation and may not support the U-shaped hypothesis for non-OECD countries [13]. In India, FLFPR declined from 34.1% in 1999-00 to 27.2 % in 2011-12, despite strong economic growth associated with rising wages and incomes, unlike urban women for whom FLFPR increased from 14.6% to 15.5%. Decreasing trend of FLFPR was also observed for rural women. For example, FLFPR in Bihar declined between 2004–05 and 2018–19, with a modest increase after 2018–19, despite continuous economic growth rates [21]. However, women working in home were counted as unpaid workers and not counted in in FLFPR in 2011-12. As per ILO estimates, FLFPR in India was 23.5% in 2019 which has improved to 37.0% as per the Periodic Labour Force Survey Report 2022-23, released by the Ministry of Statistics and Programme Implementation, Govt. of India on 9th October, 2023. The increased FLFPR using the usual definition of labour force (employed for at least 30 days in a year) signifies a considerable improvement towards women's empowerment and their active involvement in India's socio-economic and political development. The upturn in FLFPR could be attributable to Government policies and legislations including substantial initiatives targeting girls' education, skill development, entrepreneurship facilitation, safety in the workplace etc. and have played important roles in advancing the agenda of 'women-led development'. Growth of service sector contributing 53% India’s GDP (2021-22) and generating large-scale employment of educated women has also contributed to improve FLFPR in India. As per the Economic Survey 2022-23, Ministry of Finance & Corporate Affairs, GoI, 2023, growth of the service sector was 8.4% (YoY) in FY 2022 and likely growth of 9.1% in FY2023 for the Gross Value Added (GVA) in the services sector.

Problem areas - Multiple Linear Regressions:

Data: 

Measurements of factors influencing FLFPR are not uniform across countries and time. Women working in home where as unpaid workers were not counted in India’s FLFPR in 2011-12. Usual definition of labour force considering those employed for at least 30 days in a year was adopted subsequently. National Sample Survey Organization (NSSO) used well defined activity status codes to each household member reflecting types of activities undertaken. For example, activity code 93 is assigned to domestic duties including free collection of goods (vegetable, firewood, cattle feed, etc.), tailoring, etc. for household use. 

Use of a single self-reporting question in survey to measure labour force status of an individual, especially for rural population is prone to errors.  This is against ILO recommendations of additional ‘recovery questions’ in the questionnaire.

Model: 

Empirical investigations on FLPR often involve fitting regression equation. For example, [25] considered equation of the form

The equation implies