Updated: March 17, 2020 10:48:58 am
India is one of the few countries in the world where women’s work participation rates have fallen sharply — from 29 per cent in 2004-5 to 22 per cent in 2011-12 and to 17 per cent in 2017-18. Both the NDA and UPA governments have found themselves in a hot seat trying to defend economic policies that may have pushed women out of the workforce. Trying to explain whether women are choosing to focus on domestic responsibilities or whether they are pushed out of the workforce has become a minor industry among economists.
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Strangely, the one explanation we have not looked at is whether the declining quality of economic statistics may account for this trend. Our pride in the statistical system built by PC Mahalanobis is so great that we find it unimaginable that it could fail to provide us with reliable employment data. However, as challenges to economic statistics have begun to emerge in such diverse areas as GDP data and consumption expenditure, perhaps it is time to consider the unimaginable. Is the decline in women’s labour force participation real or is it a function of the way in which employment data are collected?
The anatomy of the decline in women’s work participation rates shows that it is driven by rural women. In the prime working age group (25-59), urban women’s worker to population ratios (WPR) fell from 28 per cent to 25 per cent between 2004-5 and 2011-12, stagnating at 24 per cent in 2017-18. However, compared to these modest changes, rural women’s WPR declined sharply from 58 per cent to 48 per cent and to 32 per cent over the same period. Among rural women, the largest decline seems to have taken place in women categorised as unpaid family helpers — from 28 per cent in 2004-5 to 12 per cent in 2017-18. This alone accounts for more than half of the decline in women’s WPR. The remaining is largely due to a drop of about 9 percentage points in casual labour. In contrast, women counted as focusing solely on domestic duties increased from 21 per cent to 45 per cent.
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How do we explain this massive change? Rather than assuming a sudden transformation that has turned Indian women into housewives or an economic catastrophe that has pushed women out of the labour force, let us consider the unthinkable — it is the change in our statistical systems that drives these results. The questionnaires through which the National Statistical Office (NSO) collects employment data have not changed, but the statistical workforce has, and the surveys that performed reasonably well in the hands of seasoned interviewers are too complex for poorly trained contract data collectors.
The National Sample Surveys (NSS) do not have a script that the interviewer reads out. They have schedules that must be completed. The interviewer is trained in concepts to be investigated and then left to fill the schedules to the best of his or her ability. Picture questioning a rural woman, busy juggling chapatis and a baby, “what was your primary activity over the last year? Is there another activity that you did for at least 30 days?” She thinks for a moment and says, “well, I looked after this baby and I cooked and had to take care of my mother-in-law when she was sick for a month”. Had the interviewer bothered to probe, she might have said and I also took care of a cow and sent my son to sell the milk and worked in my neighbour’s field. An experienced, well-trained investigator may know how to probe for this. However, with shortage of funds and trained personnel, the NSS increasingly relies on contract investigators hired for short periods, who lack these skills.
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Do we need to return to the days of permanent employees or can we design our surveys to overcome errors committed by relatively inexperienced interviewers? A survey design experiment led by Neerad Deshmukh at the NCAER-National Data Innovation Centre provides an intriguing solution. In this experimental survey, interviewers first asked about the primary and secondary activity status of each household member, mimicking the NSS structure. They then asked a series of simple questions that included ones like, “do you cultivate any land?” If yes, “who in your household works on the farm?” Similar questions were asked about livestock ownership and about people caring for the livestock, ownership of petty business and individuals working in these enterprises. The results show that the standard NSS-type questions resulted in a WPR of 28 per cent for rural women in the age group 21-59, whereas the detailed activity listing found a WPR of 42 per cent — for the same women. This is an easily implementable module that does not require specialised knowledge on the part of the interviewer.
In our concern with ostensibly declining women’s work participation, we have missed out on identifying sectors from which women are excluded and more importantly, in which women are included. For rural men, ages 25-59, between 2004-5 and 2017-18, casual labour declined by about 6 percentage points. However, this decline is counter balanced by regular salaried work which increased by 4 percentage points. Thus, it seems likely that men are exchanging precarious employment with higher quality jobs. In contrast, women’s casual work has declined by 9 percentage points while their regular salaried work increased by a mere 1 percentage point. Moreover, the usual route to success, gaining formal education, has little impact on women’s ability to obtain paid work. Rural men with a secondary level of education have options like working as a postman, driver or mechanic — few such opportunities are open to women. It is not surprising that women with secondary education have only half the work participation rate compared to their uneducated sisters. Thus, the focus on employment for women needs to be on creating high quality employment rather than getting preoccupied with declining employment rates.
It may be time for us to return to the recommendations of ‘Shramshakti: Report of National Commission on Self Employed Women and Women in the Informal Sector’ and develop our data collection processes from the lived experiences of women and count women’s work rather than women workers. Without this, we run the risks of developing misguided policy responses.
This article first appreared in the print edition on March 17, 2020 under the title “Count work, not workers.” The writer is professor of sociology at University of Maryland and professor and centre director, NCAER-National Data Innovation Centre. Views are personal.
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