INTRODUCTION:

The explicit recent steady and consistent economic growth visualised in India while the constant decline in female labour force participation rate is being a puzzling phenomenon. The report of the employment and unemployment survey in the period 2004-05 to 2009-10 shows that the rate of female labour force participation had decline to 26.5 per cent from 33.3 per cent in rural areas and from 17.8 per cent to 14.6 per cent in urban areas (NSSO 2011). The people those who are very concerned and interested in well being of women must make use of available women’s participation more effectively and efficiently for economic activities.

Women’s employment is a critical factor in their progression towards economic independence and is also considered as an indicator of their overall status in society (Mammen and Paxson 2008). The prevailing gender gap in employment has been considered as a macroeconomic implication as well. Based on data from 2000-2004, the United Nations Economic and Social Commission for Asia and Pacific (ESCAP) estimates that if India’s female labour force participation reached parity with that of United States (86 per cent), its gross domestic product (GDP) would increase by 4.2 per cent a year and growth rate by 1.08 per cent representing an annual gain of $19 billion. A 10 per cent permanent increase in female labour force participation would lead to increase in growth rates by 0.3 per cent (UNESCAP 2007). Surprisingly, there is rather limited and mixed evidence on the impact of economic growth on women’s employment. The present paper is intended to analysis the state-level panel data for the period 1983 – 2010 to estimate the relationship between FLFP and economic growth. Initially, the study examines relationship between net domestic product and the role of women’s economic activity of the states to testify the Ushaped formation. It refers that the increasing economic growth of the state initially decreases with female labour force participation but it eventually increases the economic development and undergoes a structural transformation in the country. Finally we can also explore that the disaggregate economic growth composition role in explaining women’s economic activity. The decline in employment is temporary and simply reflects the development process which would be corrected itself in the course of period when the U-shaped hypothesis is confirmed. Otherwise, if the hypothesis is not supported, then other underlying causes of the decline need to be explored and policies designed to deal with them. The knowledge about the nature of growth (sectors) which promotes women’s economic activity will help policy makers to both target growth in particular sectors and identify roadblocks for women in other sectors. To the best of our knowledge, such a comprehensive exercise to test the relationship between economic growth and women’s labour force participation has not been undertaken for India.

Female Labour Force Participation of the Sampled Countries:

Though there was a high growth rate found during the economic reform period, the study of economists of World Bank has found that the ability of the women to the access of job opportunities in the new economy has been precarious. The rate of FLFP in India has remained low which has been considered as one of the lowest rate of female labour force participation in the world (121 out of 131 countries) by the international labour Organisation in 2013. Hence, India’s status has been observed as the lowest in South Asia with the exception of Pakistan. At the international level only few of the Arab countries having lower rate of FLFP than India. As per the publication of the World Bank in April 2017 in the article “the precarious drop reassessing patterns of FLFP in India” had found that the FLFP dropped by 19.6 million women from 2004-05 to 2011-12.

Table – 1: The Rate of Female Labour Force Participation of the Sampled Countries

S. No	Countries	Rate of FLFP (in %)
1	Nepal	79.9
2	China	63.9
3	United States	56.3
4	Bangladesh	57.4
5	European Union	50.8
6	Sri Lank	35.1
7	India	27.0
8	Pakistan	24.6
9	Arab World	23.3

Source: International Labour Organisation (2013).

The female labour force participation had declined by 11.4 per cent to 31.2 per cent from 42.6 per cent from 1993-94 to 2011 – 12. The study also found that approximately 53 per cent of this drop of the FLFP occurred among the age category 15 – 24 years in rural India. As a result of improvement in educational enrolment among the young cohort, attainment of socio-economic status and household composition had largely contributed to the drop of FLFP in India. However, some of the reasons found for the decreasing rate suggest that it was not entirely a negative trend. For instance, the stable income of the family which has been indicated by the increasing share of the regular income earners and reducing share of the casual labour in the composition of the family labour supply had led the women family members to choose dropping out of the labour force participation.

The recent expansion of the secondary education and rapidly changing social norms in India made more working women between the age group 15 – 24 years to opt and to continue their level of education rather than joining in the labour market activities. This is the classical example for the recent reduction of female labour force participation in India. Hence the rate of female labour force participation had declined more drastically during 2004-05 to 2011 – 12 than during 1993-94 to 2004-05. During the period of 1993-94 and 2004-05, there were 31 million female labour force added in the labour market in India. On the other hand, during the period of 2004-05 to 2011-12 in seven years there was a significant reduction in female labour force participation by 19.2 million in India.

Trends of Female Labour Force Participation (FLFP):

The women labour force participation is being as an indicator and driving force for the rapid socio-economic growth of a country. But the development outcome of a country and the impact role of women’s participation in the labour market are very complex. Hence, the female labour force participation is significantly varies between developing and developed economies. Even though the rate of female labour force participation in East Asia and Sub-Saharan Africa has reached around 2/3 proportion, the rate of < 1/3 of the women only has an opportunity to take part in the labour market activities in South Asia, Middle East and North Africa. Many socio-economic reasons such as economic growth, increasing educational attainment, falling fertility rate and social norms were identified for those driven force.

The disparity and gender gaps in the labour market is being highest in South Asian countries. The rate of men labour force in South Asia was 80.7 per cent in 2013, the rate of female labour force participation were only 30.5 per cent. The historical stereo type gender role was observed to affect the outcome of considerable diversity in female labour participation. Due to the growth of the textile garment sector in the urban sector and increased spread of micro-credit facilities in rural region had reflected in increasing longer term FLFP trends in Bangladesh. The women labour force participation in Nepal has reached 79.4 per cent in 2010-11, whereas, it was estimated as 54 per cent at Maldives in 2009-10. There was some improvement observed in Pakistan particularly in the urban areas, while it has been remained stable in Sri Lanka even though it witnessed robust economic growth and strong improvements in social indicators in recent years.

The trend of longer term female labour force participation in India have been puzzling, while occurred despite strong economic growth and rising wages and incomes, the rate of female engagement in the India labour force market is being in the declining trend. The FLFP in India was 34.1 per cent in 1999-00 which has declined to 27.2 per cent in 2011-2012. The FLFP rate of rural women had decreased to 25.3 per cent in 2011-12 from 26.5 per cent in 2001-10. But at the same time, the rate of FLFP in urban area had increased from 14.6 per cent to 15.5 per cent over the same period.

As per the evidence of the 68^th round, there was no overall reversal rate found in the FLFP. It was estimated to be 22.5 per cent for all age categories which was a slight slump from the 23.3 per cent recorded in 2009-10. In this regard, the female labour force participation rate in rural areas is showing a continuous declining trend, while it reported an increase in the urban areas. The data from 2011-12 also reveals that fewer women in rural areas are working; however, if they are working, they are more likely to be in subsidiary or more marginal employment in comparison to 2009-10. The ability and decision about the women participation into the market force depends upon various socio-economic variables at the micro and macro levels. Based on global evidence, some of the most important drivers include educational attainment, fertility rates and the age of marriage, economic growth/cyclical effects, and urbanization. In addition to these issues, social norms determining the role of women in the public domain continue to affect outcomes. In India, much of the discussion on the falling trends has focused on four key factors: 1) rising educational enrolment of young women; 2) lack of employment opportunities; 3) effect of household income on participation; and 4) measurement (Chaudhary and Verick, forthcoming; Kapsos et al., 2014; Mazumdar and Neetha, 2011).

Over the period of time, India has made a considerable progress in increasing access to education for women. However, the economic growth of the country has not meant to create a large number of job absorbs especially the women in rural regions. Despite inadequate job creation, household incomes did raise, which potentially reduced women’s participation, especially in subsidiary activities (“income effect”) due to change in preferences. Finally, though most women in India work and contribute to the economy in one form or another, much of their work is not documented or accounted for in official statistics, and thus women’s work tends to be under-reported. In India, a substantially high proportion of females report their activity status as attending to domestic duties.

The rate of all rural and urban female attending to domestic work was estimated as 35.3 per cent and 46.1 per cent respectively in 2011-12. It was being at the rate of 29 per cent and 42 per cent in 1993-94 respectively. The quite interesting things were also observed that a significant proportion of women engaged in domestic works extended their willingness to accept the other income earning works if it is available at their household premises. Out of the total women engaged in domestic works, there are 34 per cent in rural areas and about 28 per cent in urban areas reported their willingness to accept other works like tailoring in both rural and urban areas. Among the women who were willing to accept work at their household premises, about 95 per cent in both rural and urban areas preferred to work on regular basis. About 74 per cent in rural areas and about 70 per cent in urban areas preferred ‘part-time’ work on a regular basis while 21 per cent in rural areas and 25 per cent in urban areas wanted regular ‘full-time’ work (Chaudhary and Verick, forthcoming).

The inclusive and sustainable development process of the society have felt the necessity and importance of female’s labour force participation and access to decent work. However, in the society the women continue to face lot of barriers to enter into labour market activities and to access decent works and disproportionately face a range of multiple challenges relating to access to employment, choice of work, working conditions, employment security, wage parity, discrimination, and balancing the competing burdens of work and family responsibilities. Hence, normally women are heavily represented in the informal economy where their exposure to risk of exploitation is usually greatest and they have the least formal protection. On the basis of these insights, policy makers in India and throughout the region should take a comprehensive approach to improving labour market outcomes for women through improving access to and relevance of education and training programs, skills development, access to child care, maternity protection, and provision of safe and accessible transport, along with the promotion of a pattern of growth that creates job opportunities. Beyond standard labour force participation rates, policy-makers should be more concerned about whether women are able to access better jobs or start up a business, and take advantage of new labour market opportunities as a country grows. The encouraging and enabling policy framework for women’s participation should be constructed with active awareness of the “gender-specific” constraints that face most women. Gender responsive policies need to be contextually developed. Ultimately, the goal is not merely to increase female labour force participation, but to provide opportunities for decent work that will, in turn, contribute to the economic empowerment of women.

The Women’s Labour Market Contribution in Indian Context:

The individual data from NSS survey rounds mainly on the basis of education, income, employment opportunities or cultural factors have been discussed as a driving force for FLFP in this section. The causal mechanisms which affect women’s economic activity are not really well taken and no explanations were applicable across the context. The factors affecting female’s labour market opportunities have also intervene among themselves in making it a tricky to separate their effect. For instance, the impact of education depends on both available economic opportunities and cultural perceptions that administer the female labour force participation. To some extent this will also be mediated by the economic status of the households. In the olden days and in the traditional practice where the men plays the role of providing for the family, the role of women is relative nil in the labour market activities which could well reflected in both their and the households preferences. But a working woman could make a signal economic hardship issues for the household and thus, with increasing income of the household, there is a tendency for women to move out of the labour market. This situation would play out effectively when men’s economic opportunities are expanded and the rise in their wage rates thus making it feasible for women to concentrate her energies in the reproductive sphere (Rangaraja & Kaul, 2011). The NSS data for the period 1999-2000 were analyzed using logistical regression models, Olsen and Mehta (2006) found a U-Shaped curve for employment by level of women education. The illiterate and lower level educated women and those with university degrees more likely to work more than middle level educated women.

The economically weaker section women use to face more burdens of domestic work and outside employment, the increasing household income and cultural norms makes women to opt out of employment. The highly educated women can afford to employ domestic help and thus are able to participate in the labour market. The NSS data reviewed by Kannan & Raveendran (2012) using bivariate analysis has not supported the income effect hypothesis. The study mainly observes that the reduction of labour force participation is from rural areas and is largely from poorer households. The declining lab

our force participation rates among women with rising household economic status is also consistent with women’s labour supply acting as a insurance mechanism for households. Attanasio et.al. (2005), presents the conceptual framework where heightened uncertainty over future earnings increases women’s LFP, particularly when the household does not have savings or access to credit. Female labour force participation in rural areas also tends to increase during periods of distress (droughts or decline in growth rates of agricultural output, depressed wages and so on), and recede again when the economy improves (Himanshu 2011; Bhalotra and Umaña-Aponte 2012). In fact, the increasing employment growth during 1999-00 to 2004-05 can be partially attributed to the crisis in the agricultural sector which forces the non-working population to enter the labour market to supplement household income (Abraham 2009).

The increased accessibility of educational institutions and facilities had a positive effect in the ratio of school enrolment; as a result the rate of women participation has declined in the age group 15 – 23 years. The study has found a considerable Net State Domestic Product (NSDP) as a proxy for employment opportunities while the states have witnessed rapid economic growth during the period of 2004-05 to 2009-10, most of them have experienced a declining rate of female labour force participation. This was the result of India’s poor employment generation inspite of strong economic growth. An important factor that could impact women’s labour force participation is the National Rural Employment Guarantee Act (NREGA) enacted in 2005. It guarantees 100 days of employment per household annually and has provisions to ensure that men and women are paid equally along with child care facilities on work sites. Due to this it has been found to have a positive impact on women’s economic activity (Azam 2012). Using difference-in-difference framework, the author finds that NREGA has a positive impact on female labour force participation rate wherein the NREGA districts experienced a smaller decline in female labour force participation between 2004-05 and 2007-08 than non NREGA districts in the country.

Regional Variations in Employment and Economic Growth:

The NSSO’s survey taken between 1983-84 to 2009-10 about the employment and unemployment data has been drawn for the women’s economic activity. The sample has been restricted the women aged category between 25 – 59 years to isolate the trend in employment from an increase in education among the younger cohorts. The net state domestic product per capita and the sector wise contribution is obtained from CSO 2011 (Central Statistical Organization). Table -1 describes that the changing trend of all region’s female labour force participation aged category 25-59 years from 1983-84 to 2009-10 of India. The NSSO round 2009-10, rate of employment and changes of the last 5 years from there were also presented. The overall review of the data reflects that the rate of women for both paid and unpaid labour force participation for the period has declined at the national level. During the period of 1983-84 to 2009-10 the rate of women labour participation has declined by 22.8 per cent and 24.3 per cent for unpaid and paid works respectively. However, lot of differences has been found in the trends of female labour force participation within regions in India.

Table 2: Trends of Region wise Female Labour Force Participation Rates (FLFPR) by (%)

Regions	Participation rates, 2009-10			Change in participation rates, 2004-05 to 2009-10			Change in participation rates, 1983-84 to 2009-10
Regions	Paid	Unpaid	LFPR	Paid	Unpaid	LFPR	Paid	Unpaid	LFPR
North	23.2	12.2	36.0	-14.4	-26.9	-19.3	-16.2	-14.1	-14.3
Centre	22.6	18.4	41.1	-0.4	-38.0	-21.9	-14.1	-30.3	-22.2
North-East	17.3	13.0	31.6	-0.6	-40.4	-21.2	-19.2	120.3	14.9
East	15.3	7.1	22.6	-25.7	-49.6	-36.7	-45.6	-34.9	-42.5
West	30.1	15.1	45.7	-16.4	-32.9	-22.3	-25.1	-19.3	-22.7
South	38.5	11.4	51.0	-7.0	-35.2	-16.0	-15.8	-19.1	-15.3
India	26.0	13.1	39.6	-10.0	-37.9	-22.0	-22.8	-24.3	-22.7

Source: Authors’ calculations from several rounds of NSSO unit level data.

Notes: All participation rates used in this paper are based on usual principal and subsidiary activity status (UPSS). LFPR stands for total labour force participation rate (LFPR) and is the sum of paid, unpaid participation rates and the unemployment rate.

Table 3: Trends in sector wise composition of women by sector and region (%)

Regions	Sector wise composition of women in the work force, 2009-10					Change in sector wise composition of women e in the work force, 1983-84 and 2009-10
	Agri.	Manu.	Cons.	Services	Mining	Agri.	Manu.	Cons.	Services	Mining
North	70.1	6.4	2.4	20.9	0.2	-15.5	38.8	637.3	73.9	119.5
Centre	76.9	5.5	8.8	8.7	0.2	-11.4	-7.1	806.5	45.2	-61.4
North-east	67.9	4.0	9.6	18.3	0.2	-9.9	-55.0	2686.4	21.4	-37.0
East	59.4	18.7	4.0	17.4	0.6	-23.4	79.4	691.1	57.7	-2.9
West	72.3	5.9	1.8	19.9	0.2	-12.9	3.1	14.5	107.0	32.2
South	61.4	14	5.6	18.5	0.6	-18.6	33.6	428.8	45.8	37.3
India	68.4	9.8	5.6	15.8	0.4	-15.3	23.2	477.1	58.8	-8.1

Source: Authors’ calculations from several rounds of NSSO unit level data.

Notes: Definition of Region: North – Jammu & Kashmir, Himachal Pradesh, Punjab, Haryana, Delhi and Chandigarh; Centre – Uttar Pradesh, Rajasthan and Madhya Pradesh; East - Bihar, Orissa and West Bengal; West - Gujarat, Maharashtra and Goa; South – Andhra Pradesh, Karnataka, Tamil Nadu, Puducherry and Kerala; North-East – Sikkim, Assam, Arunachal Pradesh, Nagaland, Mizoram, Manipur and Tripura. Data for states created in 2000 (Jharkhand, Chhattisgarh and Uttarakhand) were merged with the original states to maintain comparability over time periods.

Table 4: Trends of Regional Workforce and type of employment among women (%)

Regions	Type of employment among women in the work force, 2009-10						Change in type of employment among women in the work force, 1983-84 to 2009-10
	Self-employment		Contributing family workers		Casual workers		Self-employment		Contributing family workers		Wage		Casual workers
	Ag.	Non-ag.	Ag.	Non-ag.	Ag.	Non-ag.	Ag.	Non-ag	Ag.	Non-ag.	Ag.	Non-ag.	Ag.	Non-ag.
North	33.1	6.1	32.1	2.4	17.0	4.9	4.4	-20.1	49.8	-0.5	53.8	67.1	-43.0	132.9
Centre	13.7	4.7	41.6	3.2	5.5	21.4	9.8	-36.0	29.9	-9.7	-19.0	45.8	16.0	272.7
North-east	12.1	7.3	39.6	3.2	14.2	12.0	11.6	-59.5	-32.8	95.5	125.9	-27.0	-8.6	133.4
East	8.5	13.9	24.4	7.3	10.3	26.1	9.5	-53.9	86.5	6.3	47.0	85.3	-25.7	73.0
West	7.7	7.3	30.3	3.1	12.7	34.2	4.7	-52.7	82.2	4.7	9.7	101.6	-7.8	4.1
South	5.7	9.6	16.5	6.3	12.5	39	10.5	-66.4	35.4	-12.1	30.6	71.1	0.2	67.4
India	10.7	8.0	29.0	4.6	10.5	28.4	8.8	-46.5	44.4	-2.9	14.5	70.3	-5.0	95.0

Source: Authors’ calculations from several rounds of NSSO unit level data.

Note: Ag.: Agricultural sector, Non-ag.: Non-agricultural sector

Table -5: Level and Change in Sectoral Composition of NSDP, (%)

Regions	Sectoral composition of NSDP, 2009-10					Change in composition of NSDP, 1983-84 to 2009-10
Regions	Agri- culture	Mining	Manufac-turing	Constru-ction	Services	Agri- culture	Mining	Manufac-turing	Constru-ction	Services
North	10.5	0.1	9.3	11.4	68.7	-71.2	-52.7	-30.3	108.9	54.3
Centre	22.2	3.0	13.4	10.7	50.7	-58.2	46.5	10.2	163.8	76.3
North-east	22.4	2.0	3.5	16.2	55.9	-47.7	41.3	-25.5	55.2	37.7
East	20.5	3.2	9.3	7.7	59.3	-54.1	63.7	-35.0	33.0	78.5
West	7.8	2.5	23.6	8.9	57.1	-71.0	17.3	-14.1	86.2	47.5
South	11.2	0.7	19.0	10.1	58.9	-63.2	99.6	5.8	-37.2	68.4
India	13.7	1.4	13.0	11.4	60.5	-63.0	24.4	-11.1	35.2	56.5

Source: Authors’ calculation based on state level Central Statistical Organisation data.

Economic Growth and Women’s Economic Activity:

The female labour force participation rate for the age group 25-59 years at the state level on the log of net state domestic product (NSDP) per capita at constant prices of 2004-05 using the base-line model is given below:

FLFPR_it= α+ β1 LNNSDP_it + β2LNNSDP² +β3 Ed_it +μ_it ------------------- [1]

Where

- i denote state and

- t denotes time and

- Ed is the percentage of women 25-59 years of age who have completed secondary school.

The U- shaped model of the women association with economic growth of the country is derived from one of the key pathways is education. The increasing level of education of the women equips them to be eligible for service sector jobs which the economy generates. This variable made a significant increase in educational levels of young girls in India. If the U hypothesis holds, labour force participation will decrease initially with increase in per-capita net state domestic product (1 < 0) and start increasing after attaining a certain level of development (2 > 0). It is more appropriate to use a fixed-effect estimator for region and time-specific fixed effects, as given in equation 2 which bases identification over-time variation in the states while allowing for time trends.

FLFPR_it= α+ β1 LNNSDP_it + β2LNNSDP² + β3 Ed + Ω_i + δ_t + μ_{it --------------------} [2]

Where

- Ω are region dummies8 and

- δ are time dummies.

The impact of cultural, social and other unobservable on women’s economic activity are captured by the region’s fixed effects. For example, impact of fertility rates, women’s overall status, and extent of patriarchy on women’s labour force participation would be captured by the region variables. The significant impact on women’s economic activity has been resulted from these factors vary substantially across the regions. In central and eastern states found a high fertility rates than other parts of the country, and in southern states, the women are more mobile and empowered as compared to other regions of India.

The NSSO surveys do not provide complete wage information for all workers. It is collected only for the wage and casual workers leaving out a significant proportion of women who are self-employed or contributing workers. The unemployment data does not capture the high under-employment rate in the population and is not a good proxy of discouraged-worker effect. While equation (2) accounts for fixed effects, it does not deal with the persistence of labour force participation rate over time and the possible endogeneity of Net State Domestic Product and education variables. To address these the study turns to dynamic panel data estimation methods developed by Arellano and Bond (1991) and used for similar applications (Gaddis and Klasen 2012; Tam 2011; Luci 2009):

FLFPR_it= α+ FLFPRγit -1 + β1 LNNSDP_it + β2LNNSDP² + β3 Ed_it + δ_t + μ_it --------- [3]

The first lag of female labour force participation rate is included to account for dynamics of the process wherein current participation rates are impacted by its own past values. This model cannot be estimated using Ordinary Least Squares (OLS) methods as the lag term would be co-related with error term and there is a problem of endogeneity with the possibility that NSDP might be determined simultaneously with labour force participation rate. This paper draws on Gaddis and Klasen (2012), and uses the difference Generalised Methods of Moments (GMM) estimator for the analysis. The difference GMM is designed to handle small number of time periods relative to observations, along with issues of autocorrelation, fixed effects and endogenous independent variables (Roodman 2006; Arellano and Bond 1991; Blundell and Bond 1998). In difference GMM, regressors are transformed by differencing or taking forward orthogonal deviations, and lagged levels are used as instruments.

Table 6: Impact of Female Labour Force Participation Rate (25-59 years) in Economic Development, (OLS)

Variables	Labour force participation rate				Paid work participation rate	Unpaid work participation rate
Variables	[1]	[2]	[3]	[4]	[5]	[6]
Log per capita NSDP (constant 2004-05 prices)	232.95***	145.741***	155.230***	47.537	2.861	38.658
	(50.064)	(49.356)	(59.479)	(59.944)	(43.012)	(32.244)
Square log per capita NSDP (constant 2004-05 prices)	-11.82***	-6.814***	-8.452***	-2.476	0.069	-2.235
	(2.456)	(2.475)	(2.839)	(2.929)	(2.127)	(1.598)
% women completing secondary school		-0.779***		-0.784***	-0.398***	-0.430***
		0.174		0.177	0.141	0.124
Time fixed effects	No	No	Yes	Yes	Yes	Yes
Region fixed effects	No	No	Yes	Yes	Yes	Yes
Constant	-1097.19***	-714.75***	-662.70**	-171.264	-0.484	-141.072
	(254.244)	(246.907)	(310.724)	(308.388)	(219.151)	(164.219)
Adjusted R-Squared	0.104	0.17	0.207	0.269	0.262	0.358
Model-P-value	0.000	0.000	0.000	0.000	0.000	0.000
Number of observations	160	160	160	160	160	160
Number of states	28	28	28	28	28	28

Notes: Significance Levels - * p<0.10, ** p<0.05, *** p<0.01. Dependent variables are the participation rates. Robust standard errors are reported.

Table 5 shows the results of the OLS regression models with varying controls (equations 1 and 2) before moving to the panel models. The results do not support the U-shaped relationship between economic development and women’s economic activity. In fact, columns 1 to 3 indicate that the presence of an inverted-U shaped relationship: the coefficient on log per capita NSDP is positive and significant and the coefficient on the squared term is negative and significant. This implies women’s labour force participation (FLFP) increases with development, plateaus at a certain stage and then starts declining. But the inverted U-shaped relationship loses its significance once the variable control for time and region fixed effects (column 4). There are a substantial proportion of women involved in unpaid work and the characteristics of women doing paid and unpaid work are likely to be different. The analysis is repeated separately for paid and unpaid participation rates (columns 5 and 6, respectively) with similar results to the overall model.

Women’s education proxied by percentage of women in the state, who have completed secondary education, has a consistent and significant negative impact on labour force participation across the various models. Alternate specifications of education (literacy rates, completion rates of primary and middle school) were attempted to check for robustness of the results. All estimations resulted in qualitatively similar findings. This finding is consistent with the literature on the relationship between education and women’s economic activity. Analysing NSS data for 1993-94, Das and Desai (2003) find that educated women in India are less likely to be employed, which is attributed to a lack of employment opportunities rather than social norms restricting their movement. Several researchers have found that a U-shaped relationship exists between educational status and women’s labour force participation at any given point in time. Klasen and Pieters (2012) argue that women with low-levels of education are forced to work to contribute towards household income; while women with very high levels of education are attracted towards the labour market due to high wages. Women between the two groups face social stigmas associated with female employment without the economic need for their income.

CONCLUSION:

India has undergone a rapid economic growth with minimal ups and down, structural changes, increased rate of urbanization, higher level of educational attainment and its impact in decreasing the fertility rates among other things. However, during the same study period there was a steady and gradual fall in women’s contribution in economic activities in India. 23 per cent of the FLFP has declined during this period between 25-59 years old women. In this paper we investigate the relationship between economic development, composition of economic growth and women’s employment. In some cross-country case studies, researchers have found a U-shaped relationship between economic development and women’s employment. The results of the study indicate that the decline in female labour force participation in India is not part of the “normal” development process which will reverse itself with more growth, as has been experienced by some other countries.

Economic growth in India has not been employment intensive. This likely affects women’s options more than it affects men. Agriculture and manufacturing sectors are typically labour intensive but have not led the overall economic growth in India. The service sector has been the key driver of growth but requires high skills that a majority of women do not possess. These results clearly point to the fact that growth by itself is not sufficient for increasing women’s economic activity. The process of growth is also an important consideration. The challenge of trying to understand women’s economic activity is that it is influenced by both, market (outside forces) as well as by household and family context (inside forces) to a greater degree than men’s economic activity. A different set of policies will be needed to encourage women to overcome social and cultural constraints to their joining the labour force. Due to data limitations, the current research is unable to unpack the impact of such variables. This remains an agenda for future research.

REFERENCES:

1. Abraham, Vinoj. 2009. “Employment Growth in Rural India: Distress Driven?” Economic & Political Weekly xliv (16): 97–104.

2. Anker, Richard. 1997. “Theories of Occupational Segregation by Sex: An Overview.” International Labour Review 136 (315).

3. Arellano, Manuel, and Stephen Bond. 1991. “Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations.” The Review of Economic Studies 58 (2) (April 1): 277. doi:10.2307/2297968. http://restud.oxfordjournals.org/content/58/2/277.abstract.

4. Attanasio, Orazio. 2005. “Female Labor Supply as Insurance Against Idiosyncratic Risk.” Journal of the European Economic Association 3 (2-3): 755–764. http://onlinelibrary.wiley.com/doi/10.1162/jeea.2005.3.2-3.755/abstract.

5. Baliamoune-lutz, Mina. 2007. “Gender Inequality and Growth in Sub-Saharan Africa and Arab Countries.”

6. Bhalotra, SR, and M Umaña-Aponte. 2012. “The Dynamics of Women’s Labour Supply in Developing Countries.” Institute for the Study of Labor (IZA) Working Paper. Institute for the Study of Labor (IZA) Working Paper. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1591706.

7. Blundell, Richard, and Stephen Bond. 1998. “Initial Conditions and Moment Restrictions in Dynamic Panel Data Models.” Journal of Econometrics 87 (1) (November): 115–143. doi:10.1016/S0304-4076(98)00009-8. http://linkinghub.elsevier.com/retrieve/pii/S0304407698000098.

8. Bruno, Michael, Martin Ravallion, and Lyn Squire. 1998. “Equity and Growth in Developing Countries: Old and New Perspectives on the Policy Issues.” World Bank Policy Research Working Papers No 1563 (January).26

9. CSO. 2011. “Selected Socio-Economic Statistics India, 2011”. Central Statistics Office, Ministry of Statistics and Programme Implementation, Government of India, New Delhi.

10. Das, M, and Sonalde Desai. 2003. “Why Are Educated Women Less Likely to Be Employed in India?: Testing Competing Hypotheses”. Social Protection Discussion Paper Series. http://www.sonaldedesai.org/das_and_desai_2003_on_women.pdf.

11. Dutta, Mousumi, and S. Satyabhama. 2010. “Regional Variations in Occupational Segregation in India: Some Evidence.” In Agriculture Industry Interactions: An Approach to Developing Economies, ed. Debnarayan Sarker, 143. Gyan Publishing House.

12. Fatima, Ambreen, and Humera Sultana. 2009. “Tracing Out the U-shape Relationship Between Female Labor Force Participation Rate and Economic Development for Pakistan.” International Journal of Social Economics 36 (1/2): 182–198. doi:10.1108/03068290910921253. http://www.emeraldinsight.com/10.1108/03068290910921253.

13. Gaddis, Isis, and Stephan Klasen. 2012. “Economic Development, Structural Change and Women’ s Labor Force Participation: A Re ‐ Examination of the Feminization U Hypothesis”. Univerisity of Gottingen, Germany. http://www.uni-goettingen.de/en/82144.html.

14. Himanshu. 2011. “Employment Trends in India: A Re-examination.” Economic & Political Weekly xlvI (37): 43–59.

15. Kannan, K P, and G Raveendran. 2012. “Counting and Profiling the Missing Labour Force.” Economic and Political Weekly VOL XLVII (No 6).

16. Klasen, Stephan, and Francesca Lamanna. 2009. “The Impact of Gender Inequality in Education and Employment on Economic Growth: New Evidence for a Panel of Countries.” Feminist Economics 15 (3): 91–132.27

17. Kottis, AP. 1990. “Shifts over Time and Regional Variation in Women’s Labor Force Participation Rates in a Developing Economy: The Case of Greece.” Journal of Development Economics 33: 117–132. http://www.sciencedirect.com/science/article/pii/030438789090009Z.

18. Kotwal, Ashok, Bharat Ramaswami, and Wilima Wadhwa. 2011. “Economic Liberalization and Indian Economic Growth: What’s the Evidence?” Journal of Economic Literature 49 (4) (December): 1152–1199. doi:10.1257/jel.49.4.1152. http://pubs.aeaweb.org/doi/abs/10.1257/jel.49.4.1152.

19. Luci, Angela. 2009. “Female Labour Market Participation and Economic Growth.” International Journal of Innovation and Sustainable Development 4 (23/2009): 97–108.

20. Mammen, Kristin, and Christina Paxson. 2008. “Women ’ s Work and Economic Development.” The Journal of Economic Perspectives 14 (4): 141–164.

21. Mehrotra, Santosh, Ankita Gandhi, Partha Saha, and Bimal Kishore Sahoo. 2012. “Joblessness and Informalization : Challenges to Inclusive Growth in India

22. Neff, Daniel, Kunal Sen, and Veronika Kling. 2012. “The Puzzling Decline in Rural Women ’ s Labor Force Participation in India : A Reexaminatio Area Studies. http://www.giga-hamburg.de/dl/download.php?d=/content/publikationen/pdf/wp196_neff-sen-kling.pdf.

23. NSSO. 2011. Employment and Unemployment Situation in India. Vol. 537. Ministry of Statistics and Program Implementation, Goverment of India.

24. Olsen, Wendy, and Smita Mehta. 2006. “A Pluralist Account of Labour Participation in India.” Working Paper Global Poverty Research Group. http://economics.ouls.ox.ac.uk/14041/.

25. Rangarajan, C, and Padma Iyer Kaul. 2011. “Where Is the Missing Labour Force ?” Economic and Political Weekly XLVI NO 39: 68–72.

26. Ravallion, M., and G. Datt. 1996. “How Important to India’s Poor Is the Sectoral Composition of Economic Growth?” The World Bank Economic Review 10 (1) (January 1): 1–25. doi:10.1093/wber/10.1.1. http://wber.oxfordjournals.org/content/10/1/1.short.

27. Registrar General of India. 2011. Sample Registration System Bulletin. http://censusindia.gov.in/Vital_Statistics/SRS_Bulletins/Final-MMR Bulletin-2007-09_070711.pdf.

Received on 08.12.2018 Modified on 16.01.2019

Res. J. Humanities and Social Sciences. 2019; 10(2): 270-278.

DOI: 10.5958/2321-5828.2019.00049.4