According to the CMIE-CPHS, the female
workforce participation rate in 2017-18 was 10.5 per cent. For the same period, the PLFS estimate was 22 per cent. For men, in contrast, the estimates were similar, 71 per cent in PLFS, and 69 per cent as per the CMIE-CPHS. The differences are similar in other years. Therefore, the CMIE-CPHS consistently estimates the female WPR at 50 per cent of PLFS. Given the differences in the levels of WPR for women, cross-survey comparisons, at least in level terms, are not possible. But it is still useful to analyse the changes in the levels within each of these surveys to understand the extent and nature of the impact of the economic shock on women.
Assuming representative samples and appropriate weighting, the differences could be explained by variations in enumerator training, extent of probing, identity of the respondent, and type of questions asked. Several studies have investigated the issue
of measuring women’s paid work either through a critical examination of existing secondary data, or highlighting the need for sensitising enumerators, changing the kind of questions asked, or who they are asked to (Deshmukh et al. 2019; Deshpande and Kabeer 2019; Mondal et al. 2018; Sudarshan 2014). The type of work that women do may also be inherently more difficult to capture, for example by being part-time, irregular, interspersed with household work, or unpaid (in family enterprises).
The table compares the distribution of individuals in the working-age population in CMIE-CPHS (2017) against the PLFS (2017-18) by nature of activity. For men, the two distributions are similar. According to PLFS, about 37 per cent of the working-age population of men are in self-employment, compared to 33 per cent in CMIE-CPHS, a
difference of about 4 percentage points. The share of salaried workforce is also similar across the two surveys for men.
Own account worker Employer
Unpaid Worker (Total SE) Salaried
Casual wage worker Unemployed Student Domestic work Other
(Total out of labourforce) Males
29.7 1.7 5.5 (36.8)
16.6 17.2 4.8 14.9
0.9 8.8 (24.5)
Females 3.67
0.1 5.84 (9.6) 4.59 5.51 1.36 11.18 60.36
7.4 (78.9) Distribution
of working age
population across activity statuses, CMIE-CPHS and
PLFS
Sources and notes: CMIE-CPHS, PLFS 2017-18. CMIE-CPHS data corresponds with the PLFS survey period of July 2017 to June 2018. Refer to Abraham and Shrivastava (2019) for details. Salaried workers in CMIE-CPHS includes temporary and permanent salaried.
Males
33.0 14.8 21.5 3.7
27
Females
2.6 2.7 4.9 2.8
87.01
PLFS (2017-18) CMIE-CPHS (2017-18)
Self-employed Salaried
Casual wage worker Unemployed
Out of labourforce
However, for women, we see large divergences in the distribution. According to PLFS, self-employed workers (comprising unpaid workers, employers and own-account workers) account for nearly 10 per cent of working-age women. The CMIE-CPHS puts the number at 3 per cent. CMIE-CPHS does not distinguish between unpaid workers, own account workers and employers within the category of self-employed. But it is possible that failure to capture unpaid work in family enterprises (which is the predominant activity for self-employed women, as per PLFS) explains at least part of the underestimation. However this cannot explain the entire difference because the proportion of women in salaried work are also lower in the CMIE-CPHS data.
What are the implications of all this when we try to measure the impact of the pandemic on women workers? To the extent that women who are more affected by the economic shock are also those that the CMIE-CPHS does not capture as
being employed, it is possible that our numbers of job loss among women may be under-estimates.
The reverse may also apply, that is, if women that CMIE-CPHS does not capture are those who are least affected by job loss, then job loss numbers may be over-estimates. If recovery of work among women occurs into those activities that CMIE- CPHS does not capture, then recovery will be underestimated. Despite these caveats, however, we believe that estimating the impact of the shock on women workers identified in the data, and their employment trajectories remains a useful exercise.
One recent survey that tries to capture women’s paid work more accurately is the India Working Survey (IWS) conducted by researchers at Azim Premji University, Indian Institute of Management, Bangalore, and the University of Western Australia with support from the Initiative for What Works to Advance Women and Girls in the Economy (IWWAGE).
Comparing employment rate across surveys
3. Employment loss and recovery
This is a random survey carried out in Karnataka and Rajasthan. IWS uses a number of approaches to address standard oversights in measuring women’s paid work. Female respondents are interviewed by female enumerators. Men and women are asked detailed questions about their activity status in the week preceding the interview. These questions specifically ask whether an individual is engaged in each type of employment (own account work, unpaid work, salaried work, daily wage work) rather than leaving it to the person to list their activities.
It also allows individuals to list multiple activities that they engage in, for example, domestic chores alongside unpaid work in the family enterprise, or wage work.
For instance, the respondent is asked whether in the last week, they did “any kind of business, farming or other self-employed activity to generate income, even if only for one hour?”. Irrespective of their answer to this question, they are then asked, if in the last week, they “assisted without pay in a business/farm/livestock of a household or family member even if only for one hour”? In a similar vein, the respondent is asked whether they engaged in salaried work or casual wage work in the last week. The intention of this detailed step by step questioning of the kinds of work individuals engaged in over a week is to make sure that all kinds of employment activities are captured.
Although the three surveys (PLFS, CMIE-CPHS and IWS) do not use the same questions to arrive at employment status, we have tried to approximate
the employment definitions across surveys as closely as possible. See Appendix Section 4 for more details. Here we show estimates of the WPR based on a definition that considers an individual employed if they reported working for eight hours a day on average in market activities, i.e. a strict definition of being employed.
The estimates for the male WPR vary by 10-20 percentage points across the three surveys with PLFS reporting a rate 5-6 percentage points over CMIE-CPHS and IWS being 13-14 percentage points higher than PLFS. These differences are worth investigating further. However, much larger differences emerge when comparing female WPR across the three surveys. For Karnataka, the PLFS estimate is 20 percentage points larger than the CMIE-CPHS and the IWS estimate is 30 percentage points over PLFS. For Rajasthan the divergence between PLFS and IWS is less striking (7 percentage points) but that between CMIE-CPHS and PLFS is even larger than for Karnataka (25 percentage points). Thus the CMIE-CPHS and PLFS divergence that we saw at the national level manifests even more sharply within states.
The female WPR for Karnataka as reported in IWS is 57 per cent, a far cry from the numbers we are used to seeing for women’s participation in paid employment in India. The detailed questioning alongside self-reporting of statuses (rather than a proxy) may explain the higher levels of WPR for women and men compared to other surveys.
Endnotes
1 See Appendix Section 2 for details of this sample.
2 Google Mobility Data reports changes in
movement over time across six different categories of places - retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential. Changes for each day are reported vis à vis the corresponding day in the baseline week, where the baseline is the median value for the weeks from January 3rd 2020 to February 6th 2020. The Mobility Index is calculated as a simple average of the daily reported numbers for retail and recreation, groceries and pharmacies, parks, transit stations and workplaces for every day for every state for each month.
3 As compared to the corresponding non-pandemic period or what we refer to as the baseline period (i.e., between Sept-Dec, 2018 and Sept-Dec 2019) this movement in and out of the workforce is relatively higher. See Appendix Table 1 for baseline estimates.
4 See Chapter One and Appendix Section 4 for details of this survey and the sample.
5 An individual is identified as employed, if in the reference week of the survey, they worked for at least 20 hours in the week collectively across all kinds of activities.
6 The corresponding numbers in CMIE-CPHS, quoted earlier, were 46 per cent for women and 7.6 per cent for men. However, the two surveys are not comparable due to differences in sample size and questions asked.
7 See Appendix Section 2 for details on the creation of this panel.
8 As mentioned earlier, CMIE-CPHS estimates of women’s WPR is lower compared to PLFS estimates. Given this, it is possible that certain
likely, then that CMIE-CPHS underestimates the extent of employment loss among women.
On the other hand, if recovery from loss is into this kind of work, then, it is likely that CMIE-CPHS will not capture these women workers who have experienced an employment recovery, thereby overestimating the employment loss
among women.
9 CMIE-CPHS categorises an individual into
‘Unemployed, willing and looking for a job”
and ‘Unemployed, willing but not looking for a job”. The unemployment rate including both of these categories is referred to as the ‘greater unemployment rate’. The unemployment rates here include both categories unless otherwise specified.
10 We construct the same trajectories of
employment for the same period in the previous year. Appendix Table 2 shows the distribution of trajectories by each of the above dimensions- gender, age, caste, religion and region. The extent of job loss and no recovery for the same period in the last year is well below that seen now.
11 “Nearly 35% MSMEs close to winding up:
AIMO”, CMIE: Economic Outlook, 2 June, 2020, https://www.cmie.com/kommon/
bin/sr.php?kall=warticle&dt=2020-06-02%20 15:40:07&msec=706
12 This is a stratified, convenience sample
representing industries in manufacturing, services and trade. Bulk of the microenterprises in the sample are situated in tier-3 cities or rural areas.
13 https://dashboard.massentrepreneurship.org/
14 https://www.financialexpress.com/economy/
cpc-survey-paints-bleak-economic-outlook-for- maharashtra/1928464/
15 http://ficci.in/spdocument/23280/FICCI-IAN- Survey-Covid-19-Start-ups.pdf
4. Informalisation and earnings losses