Never Done, Poorly Paid, and Vanishing: Female Employment and Labour Force Participation in India
Why are women unable to find access to gainful employment?
One of the most significant features of the Indian labour market has been falling labour force participation rates (LFPRs) for women. The female labour force participation rate—the ratio of women in the total population who either have a job or are actively looking for one—has been steadily falling since the 43rd round of the National Sample Survey Office (NSSO) survey, only registering a rise during the period 1999–2000 to 2004–05, before falling again in 2011–12. The Labour Bureau Employment and Unemployment Survey records that female labour force participation has fallen to about 27.4 per hundred in 2015–16, one of the lowest rates in the world.
Much work has been done on the factors driving falling labour force participation rates: some writers such as Bhalla (2019) believe that the drop in female LFPR can entirely be accounted for by the increased enrolment in education. He argues that if female LFPRs in 2011–12 were of the same level as they were in 1983, then there would be an extra 33 million women in the labour force. The difference in educational enrolment for women between the two periods is around 35 million. Thus, educational attainment alone accounts for the fall in female LFPRs.
Such an argument, however, is flawed, for it does not address why a woman enrolled in education in 1983 has been unable to find gainful employment in 2011: this drop in female LFPRs is seen across all age levels in both rural and urban sectors in every round of NSSO surveys[1] (data for which has been collected till 2011–12) and not just for the 15–24 age group—the ages at which people decide to enrol themselves for higher education.
Figure 1: Age-specific LFPRs for Urban Women
Source: Various NSSO reports
Figure 2: Age-specific LFPRs for Rural Women
Source: Various NSSO reports
If Indian working women were not burdened enough, they have been hit by a further blow in recent years—that of large-scale job loss. Not only are women exiting the labour force in significant numbers, women who remain in the labour force are unable to secure gainful employment. Data collected by the Centre for Monitoring Indian Economy (CMIE) reveals that the unemployment rate for women in 2018 was 15.7 per hundred, as compared to 5.4 for men. According to Vyas (2019), out of a total of 11 million jobs that were lost in 2018, women lost 8.8 million jobs.
What Has Driven Changes in the Unemployment Rate?
To examine the linkages between female LFPR and unemployment rates, data from CMIE surveys on unemployment (CMIE 2016, 2018) is studied, covering the periods September–December 2016 and September–December 2018. The data recorded here are not comparable with NSSO surveys;[2] the point of this analysis is to look at the experience of women’s work outcomes across Indian states from the twin lenses of the decision to seek and the ability to find gainful employment.
Female unemployment rates and LFPRs were collected for 2016 and 2018 for 25 states.[3] At the national level, the CMIE surveys outline a dire predicament for the working woman: in 2016, the LFPR for women was 14.5 per hundred women, with urban women’s LFPR at 14.2 and rural women’s LFPR at 14.7. By 2018, the aggregate LFPR fell to 11.1 per hundred women, and 10.4 and 11.4 per hundred for urban and rural women respectively. Over the same period, however, unemployment rates for women actually fell, from 18.6 per hundred in 2016 to 15.7 per hundred in 2018. This is a puzzling outcome, since the total number of employed women actually fell over the time period in question, from 52.9 million in 2016 to roughly 44.2 million in 2018. How can the unemployment rate fall when the number of employed women reduces over the same time period?
While CMIE data also includes data on the unemployed who wish to work but are not actively seeking it, we confine ourselves to the strong case,[4] adopting the standard definition of unemployment.
The unemployment rate (UER) is defined as the proportion of the labour force that is unemployed. The UER, therefore, can be defined as 1 minus the employment rate, or:
UER = 1 – (employed ÷ labour force)
Dividing the numerator and denominator of the employment rate by the total population, we get:
UER = 1 – ((employed/population)÷(labour force/population))
Which implies that UER = 1 – (employed-to-population-ratio/labour force participation rate)………………(1)
Unsurprisingly, equation 1 tells us that if the proportion of the employed in the total population increases, the unemployment rate will fall. However, if the employed-to-population ratio (hereafter EPR) does not change, the unemployment rate will still fall if the labour force participation rate decreases. Even if the EPR falls, that is if the amount of employment reduces, the unemployment rate will still reduce if the labour force participation rate falls by a larger proportion.
The female employment-to-population ratio fell from 11.79 in 2016 to 9.36 in 2018, a total fall of around 20.66% over the two years. The labour force participation rate fell from 14.49 to 11.11, a total fall of 23.3%. Thus, the fall in the unemployment rate over the two years is not so much due to more jobs being created—the number of employed women actually registered a fall in absolute numbers—but rather due to a significant fall in the number of women participating in the labour market. If the female LFPR in 2018 was the same as that in 2016, the female unemployment rate would have almost doubled from 18.6% in 2016 to nearly 35% in 2018.
Unemployment and Labour Force Participation Across States
Figures 3 and 4 outline the relationship between female LFPRs and UERs for 25 states in 2016 as well as 2018. In 2016, we see a weakly negative relationship between labour force participation and unemployment: states with low LFPRs have high UERs, though the strength of this relationship is not very significant. However, this relationship is strengthened by 2018, with the slope of the trendline drawn through the data points becoming much steeper. Clearly, significant changes have occurred over this entire period.
Figure 3: Female LFPR and Unemployment Rate, 2016
Source: CMIE (2016)
Figure 4: Female LFPR and Unemployment Rate, 2018
Source: CMIE (2018)
However, the above graphs do not pinpoint what exactly drives this changed relationship between LFPRs and unemployment rates. Is it due to the fact that unemployment rates are rising in states where LFPRs do not change much, such as in Haryana, where the LFPR rose marginally from 7.74 to 8.22, while the UER doubled from 27.54 to 54? Or, is it due to the fact that LFPRs are reducing in certain states, such as Bihar, where the UER decreased marginally from nearly 50 in 2016 to 46.64, while the LFPR fell from 4.19 to 1.96?
Figure 5 charts out the change in the unemployment rate over time for all states in the sample. The y-axis shows the unemployment rate in 2018, while the x-axis shows unemployment rate in 2016. The 45-degree line indicates all those points on the graph where the y-axis value equals the value on the x-axis. Therefore, a point on the 45-degree line would indicate that the unemployment rate for that state stayed constant during the period; points above the line would mean that the unemployment rate in 2018 was higher than that in 2016 for a particular state, while points below the line would mean that the unemployment rate reduced over the period.
The vertical line drawn indicates the female unemployment rate at the all-India level in 2016, while the horizontal line indicates the rate for 2018. The intersection of the two lines, therefore, shows the situation for India, which experienced a fall in the unemployment rate over the period. But there is substantial variation when it comes to the performance of states.
Figure 5: Change in Female Unemployment Rates, 2016-2018
Source: CMIE (2016, 2018)
States that lie to the left of the vertical line are those that had unemployment rates less than the all-India average in 2016; out of these states, only three—Chhattisgarh, Chandigarh and Himachal Pradesh—showed a significant increase in unemployment by 2018. The rest of the states in the study all experienced unemployment lesser than the 2018 all-India average, even though states like Gujarat, West Bengal and Maharashtra showed an increase in unemployment.
The six states that had a UER higher than the 2016 average suffered an increase in unemployment by 2018. Moreover, only 2 states that had higher than average unemployment in 2016, Goa and Karnataka, were able to reduce unemployment below the 2018 average. Equation 1 allows us to break down the changes in the unemployment rate to whether states were able to increase employment, or suffered female withdrawal from the labour market. Figure 6 shows the change in the LFPRs for states over the period of 2016 to 2018. Only six states out of 25 registered an increase in labour force participation rates. It is also concerning that the southern states—Andhra Pradesh, Telangana, Karnataka, Kerala and Puducherry experienced significant falls in LFPRs from already high levels. Kerala and Karnataka, who had LFPRs higher than the national average in 2016, fell below it by 2018.
Figure 6: Change in LFPR, 2016-2018
Source: Same as Figure 5
Figure 7: Change in Employment-Population Ratios, 2016-2018
Source: Same as Figure 5
Apart from West Bengal, Himachal Pradesh, Assam and Gujarat—and to a minor extent, Odisha—every state recorded a reduction in the employment–population ratio (Figure 7). This is an extremely dismal situation that highlights not just the crisis in employment generation, but also the heavy burdens faced by women in India’s labour market.
The following tables show the statewise percentage change in the EPR and the LFPR over the period 2016–2018 for two sets of states, those whose UERs increased (Table 1) and those whose UERs reduced (Table 2). Twelve states experienced an increase in unemployment rates, while 13 states registered a reduction. These two tables highlight the stark difference in employment outcomes for women.
Table 1: States with Increased UERs, 2016-2018
| EPR change (%) | LFPR change (%) | |
| Chandigarh | -36.6 | 0.7 |
| Chhattisgarh | -48.6 | -26.5 |
| Delhi | -20.0 | -7.3 |
| Gujarat | 10.7 | 16.7 |
| Haryana | -32.7 | 6.2 |
| Himachal Pradesh | 12.3 | 130.8 |
| Jharkhand | -57.8 | -45.1 |
| Madhya Pradesh | -65.9 | -62.9 |
| Punjab | -33.9 | 2.4 |
| Rajasthan | -53.4 | -31.4 |
| Uttarakhand | -60.2 | -58.9 |
| West Bengal | 39.0 | 42.9 |
Table 2: States with Reduced UERs, 2016-2018
| EPR Change (%) | LFPR Change (%) | |
| Andhra Pradesh | -35.8 | -40.9 |
| Assam | 17.3 | -2.9 |
| Bihar | -50.3 | -53.2 |
| Goa | -29.0 | -42.2 |
| Jammu and Kashmir | -32.2 | -48.2 |
| Karnataka | -47.8 | -56.0 |
| Kerala | -15.0 | -45.9 |
| Maharashtra | -10.6 | -13.2 |
| Odisha | 3.6 | 1.0 |
| Puducherry | -42.8 | -47.3 |
| Tamil Nadu | -24.7 | -34.9 |
| Telangana | -23.9 | -27.6 |
| Uttar Pradesh | -20.2 | -23.7 |
The severity of the situation is highlighted in Table 2: out of the 13 states with reduced UERs, only two states were able to generate positive net employment: Assam and Odisha. Yet, even their performance is not unequivocally positive. Assam saw a reduction in female LFPRs, while Odisha saw very marginal gains in female EPR and LFPR; these indicators grew by 3.6% and 1% over the entire two-year period. It is only because women exited the labour force in significant amounts in the other states that the reduction in employment for women did not lead to a rise in the unemployment rate. The data shows that the past two years have seen an absolute collapse in the ability of the Indian economy to generate suitable livelihood opportunities for Indian women.
The Role for Policy
One possible criticism of this study lies in the fact that the data source used is not compatible with earlier estimates of employment and unemployment in India. But in the absence of any sort of official statistics on employment—be it the thick rounds of the NSSO, the Periodic Labour Force Surveys, the Parliamentary Report on the effects of demonetisation, or the report on job creation by the Micro Units Development and Refinance Agency (MUDRA) loans scheme—the surveys released by the CMIE are a valuable tool that cannot be discarded.
Whichever government comes into power following the elections will inherit an economy that is collapsing on the employment front. Indian working women are beset by a range of problems, both structural and long-term as well as short-term. The reduction in labour force participation by women may be due to social pressures keeping them out of the labour market or because the economy has been unable to generate enough jobs commensurate with rapid economic growth. However, the past few years have seen not just an inability to generate new jobs, but also a net reduction in the number of jobs. The only reason that unemployment rates for women are not rising is because women are withdrawing themselves from seeking work. Unemployment has never been a part of mainstream discourse in India. The fact is that it is now the enduring legacy of the Modi government.
Combating the long-run decline in female LFPRs requires concerted policy effort. The most obvious policy measures are by making the workspace more inclusive for women, through introducing measures to increase the period of paid maternity leave, strengthening sexual harassment laws, providing for crèches at the workplace, safe transport, etc. Effective legislation must be backed by suitable financial intervention to ensure that employers do not turn women away on the grounds that the cost of hiring women are more prohibitive—a justification which reveals more about the private sector’s resistance to bringing about progressive change than the supposed dangers in interfering with free market signals.
These measures, however, are still inadequate: they target the private corporate sector and it is difficult to ensure that these measures are adopted in the informal sector, which is where a majority of working women currently operate. Even if the government is able to ensure that small-scale operating enterprises adhere to these laws, little can be done about self-employed women. Furthermore, one of the significant factors that keeps women out of the workforce is not found operating within the workspace, but rather from within the home. As Afridi et al (2016) show, much of the decline in female LFPR has been concentrated amongst rural married women. The demands made on married women play a major role in them withdrawing themselves from the labour force, and as such, policy cannot be confined to urban workspaces; it must also directly combat regressive social mores.
Combating the short-run questions of job loss and labour force withdrawal of discouraged workers requires a different approach. The tragedy is that India already has mechanisms to deal with these issues. An expansion in the Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) scheme and the strengthening of safety nets to provide subsidised food and welfare measures is required, the framework for which already exists. Yet current policy has led to a weakening of these safety nets. Fiscal orthodoxy has meant limited space to expand welfare schemes at a time when they are sorely needed, while the adoption of technological measures has often resulted in a denial of welfare rather than easier access to them (Aggarwal 2017; Drèze et al 2017).
A refusal to expand public expenditure—or even cut it—in the midst of rising unemployment is textbook austerity, a policy stance that has been discredited in the wake of the European experience. It is disheartening to note that the Modi government seems insistent on maintaining the fiscal deficit target. What is needed is a significant public investment push to raise incomes and demand in the economy. This would lead to a rise in demand for labour—as businesses and enterprises see improved prospects for profits—and perhaps an increase in female employment in sectors which earlier terminated the services of female workers. Since MGNREGA is a self-targeted scheme, women looking for work would automatically look to enrol themselves in it; a further boost to female labour force participation could be provided by increasing wages for women in the scheme.
Policy needs to be geared not only towards raising employment, but also focusing on the problems of Indian women in the labour market, for they have been—and continue to be—disadvantaged along multiple fronts. If women’s work was never done and poorly paid before (Ghosh 2009), it is now in danger of completely vanishing.
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