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The pandemic has created a definite scar in labour markets. There have been persistent reports based purely on employment surveys of permanent job losses, a significant spike in unemployment, particularly among youth and a compression of India’s labour force from 495 million in 2019 to 472million in 2020, as per latest World Bank data.

Some of the concerns, specifically the squeeze in employee expenses are genuine concerns. Employee expenses data for 284 listed companies for FY21 reveal the largest decline was in the smallest firms indicating that employees of smaller firms were impacted most due to Covid-19 pandemic.

However, it is important to understand the dynamics of India’s labor market, also through the use of EPFO payroll data rather than joining in the cacophony of labor market disruptions during the pandemic as it typically deflects us from actual problem confronting India today of creating more quality jobs.

In fact, in one of the classic papers by Diamond, Mortensen and Pissarides (2010 Nobel winners) it has been shown that unemployment remains high even when jobs are available (so relevant in the context of India’s shift in unemployment age).

What are the observations from India’s payroll data for FY21?

First, as per EPFO and NPS, India created 100.4 lakhs payroll ( sum of 95.4 lakhs through EPFO and 5.82 lakhs through NPS in FY21) nearly unchanged from 102.3 lakhs in FY20, indicating Indian labour market though faced with massive disruptions in FY21, did not do that badly in FY21. However, it should be mentioned these are low-quality jobs.

Second, when we break up 95.4 lakhs jobs created by EPFO payroll, 41.2 lakhs were through second jobs, 44 lakhs through first jobs and 9.3 lakhs were through formalization.

The important point to note is that there was a decline in first-time jobs in FY21 by 16.9 lakhs, though the no of second-time jobs / no of members who rejoined the payroll rose by 17.9 lakhs. This clearly indicates that people were coming back to the labour market in the later part of FY21, as the situation had improved at that time till the second wave came. The rate of formalization however declined by 1.2 lakhs, reflecting the disruptions in the MSME space.

Third, NPS data indicates that there is a decline of 1.74 lakh new subscribers in 2020-21, of which State Govt. payrolls lost 1.06 lakh, followed by Non-Govt of 36,416 and 31,420 in central Government.

Fourth, the ratio of women enrolment to total enrolment in EPFO data has remained at 23% in FY20 and has not changed significantly in FY21.

Fifthly, we must use payroll data and not employment surveys simultaneously to have a more meaningful debate of India’s unemployment, lest it could be significantly biased and unidirectional as is now. There are advantage of employment surveys as done in India by NSSO and CMIE is that they enable researchers to measure and analyze changes over time in socio-demographic and economic situations, as well as the attitudes, opinions and behaviors of individuals or aggregates of individuals.

Household panels enable researchers to study household change and the changing dynamics of the individuals within it. However, there are limitations of such datasets. On the flip side, limitations of such datasets include, but are not limited to, problems in the design, collection, and management of date for panel surveys.

These include the problems of coverage (incomplete account of the population of interest), nonresponse (due to lack of cooperation of the respondent or because of interviewer’s error), recall (respondent not remembering correctly), frequency of interviewing, interview spacing, reference period, the use of bounding to prevent the shifting of events from outside the recall period into the recall period, time-in-sample bias etc.

Additionally, the data suffers from the intrinsic deficiency of reactivity. For example, if we ask people questions about the status of women at two or more points in time, the questioning process itself might produce opinion shifts. Perhaps the act of asking people about the status of women makes them more sensitive to women’s issues.

This increased sensitivity might mean they are more likely to favor or oppose changes in the status of women during later surveys. This is called reactivity, because the respondents are reacting to the initial questioning. Another issue is the issue of attrition (i.e., respondents dropping out of the study).

We are not sure whether the surveys done in India live up to all such challenges. A case in point is that the University of Michigan, in their Consumer Surveys, always asks consumers about their anticipation of unemployment rate changes, and that is subsequently validated. We are not sure whether the employment survey in India which is modelled along the lines of the University of Michigan Consumer Survey, addresses such an issue!

Lastly, there are also problems with EPFO data. The classification of the EPFO job data leaves a lot be desired. For example, Expert Services that constitute the largest segment of payroll creation do not tell us anything at granular level. Another problem is regarding retiring employees.

Given that retirees are also netted out, this may imply a downward bias to net EPFO numbers as retirees mean a new vacancy and hence a new hire. We believe the data about retirees during the month whose account has been settled must also be disclosed by EPFO as a separate line item. This can be revised in later disclosures, but this is a suggestion that EPFO might well consider, apart from giving out the payroll creation across all industry groups as the US does. EPFO should start releasing non-farm productivity (as in the US) estimates at least for those sectors for which we have output data from CSO’s GVA database. This will fill a huge lacuna in productivity estimates in India.

Clearly, the employment debate in India is perhaps haplessly one-sided and only talks about problems and not the solutions! The time has now come to have a better depiction of employment numbers by using all available data sources, including payroll.
(This article is written by Dr. Soumya Kanti Ghosh, Group Chief Economic Adviser, State Bank of India)

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