EPFO: Data shows the Covid pandemic hurt younger workforce more

31 May 2021 | Covid-19, Economy, Employment, EPFO

Ankur Bhardwaj

Ankur Bhardwaj

Editor, Centre for Economic Data and Analysis

Kanika Mahajan

Kanika Mahajan

Associate Professor of Economics, Ashoka University


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According to the International Labour Organization’s data, India had an unemployment rate of 7.11 percent in 2020, the highest it has been since 1991. Earlier, CEDA-CMIE analysis showed that manufacturing employment in India had halved between 2016-17 and 2020-21. In a country like India, hoping to benefit from its demographic dividend, real time employment data can help shape policy responses. More so, for formal employment, which many youths aspire for in the country. Formal jobs provide higher wages, skill upgradation, job security, and social security benefits. Formalization also facilitates gains in aggregate productivity. This makes tracking formal jobs in the economy important.

One such source for tracking formal jobs in India is the data released by the government on new subscribers or members who enroll in social security schemes like the Employees’ Provident Fund (EPF), Employees’ State Insurance (ESI) and the National Pension Scheme (NPS). Given the large informal sector in the country, these data are likely to reflect the employment trends only for 20% of India’s workforce. [1] They are also beset with the usual caveat that any payroll reporting based employment trends would carry i.e., the changes could simply reflect transitions from informal to formal sector employment. 

In the Indian context, there is another notable concern. The law requires an organization with 20 or more employees to register with the EPFO. If an organization with 19 employees hires another employee, then it will be required to register all 20 under EPFO. This will show up as an additional 20 employees in the EPFO data. As a measure of formalization of 20 jobs, this would be useful. But it cannot be read as the creation of 20 new jobs, as it only involves formalization of 19 of the already existing informal jobs. Despite these caveats, these data provide a source for tracking real time formal sector employment trends, even if the numbers remain debatable, as highlighted in some earlier writings as well. [2]

EPFO has been releasing monthly net-payroll data since April 2018, including aggregated data from September 2017 -March 2018. These provide a month-by-month snapshot of changes in the organized sector employment by providing age-wise exits and entries in the payroll data in a month, besides state-wise, and industry-wise payroll data. 

In this data narrative, we have focused on monthly data between April 2019 and March 2021. [3] We have used the EPFO data to understand the changes brought about in the organized sector employment by the Covid-19 pandemic.

Figure 1 tracks the number of establishments which remit their first Electronic Challan cum Receipt (ECR) in a month for 2019-20 and 2020-21. We observe a sharp dip (78 percent) in the number of establishments which remit their first ECR in April 2020 compared to April 2019. This is an expected outcome as April was the first full month of the nationwide lockdown in India in 2020-21. This decline was arrested in subsequent months, and we see a 46 percent increase in October 2020 over October 2019. With the first wave of the Covid-19 pandemic over by December 2020, we observe an increase in this number in January, February, and March 2021. The count in March 2021 was 26 percent more than March 2020 although it was a 22 percent decline on a sequential basis (March 2021 vs February 2021).


Figure 1

In Figure 1, the primary Y-axis on the left represents the number of establishments remitting their first ECR in the month. The secondary Y-axis on the right represents the percentage change in number of establishments remitting ECRs in that month in 2020-21 over 2019-20

Tracking changes in net payroll: By age-group

Net payroll is defined as the sum of new EPF subscribers and number of EPF members who exited and resubscribed minus the number of EPF subscribers who exited.

Net payroll = (New EPF subscribers + EPF subscribers who exited and rejoined) – EPF subscribers who exited

EPFO provides data across six age groups, less than 18 years, 18 to 21 years, 22 to 25 years, 26 to 28 years, 29 to 35 years, and more than 35-year-olds.

Table 1 shows us the net payroll numbers for each age group in 2019-20 and 2020-21. It also shows us the absolute change in numbers on a year-on-year basis. We find that the youth suffered more because of the pandemic in 2020-21. Less than 18-year-olds saw a 19 percent dip in net payroll additions while 18 to 21-year-olds saw a 15.6 percent decline.

Table 1

In April 2020 and May 2020 (first two full months of the pandemic), the net payroll across most age groups was negative (Figure 2). These two months saw net 2.85 lakh and 2.89 lakh EPF subscribers exiting. The impact of the pandemic was so severe that in April 2020, the net payroll count was 154 percent less than April 2019, that is the number had slipped into negative territory, or that there was a net reduction in total EPF subscriber count in April 2020. 

The months of May, June, and July 2020 also saw a net reduction in subscriber count. The reduction was most severe in the age group 35 years or older which saw 80,502 net exits in April 2020 and 1,06,726 net exits in May 2020. The large reduction in this group could also be on account of retirements with no commensurate additions. Figure 2 illustrates these trends. 

Figure 2

In Figure 2, the primary Y-axis on the left shows the age group-wise net payroll addition in 2019-20 and 2020-21. Negative numbers in a month represent more exits than additions in that month. The secondary Y-axis on the right shows the percentage change in net payroll additions for that age-group in a month in 2020-21 over the same month in 2019-20. To select an age group or to look at the figure for all age groups together, click on the tab in the bottom-right corner.

We also observe a strong impact of the pandemic on those in the less than 18-year-old, 18 to 21-year-old, and 22 to 25-year-old categories. Together these three categories saw net payroll additions of 10,55,633 between April to June 2019. Struck by the pandemic in 2020-21, instead of net additions, these three saw net exits (3,817) between April and June 2020. Thus, the pandemic had an outsized impact for those on the verge of joining the workforce. The numbers for growth in March 2021 are high due to the base effect in March 2020 but otherwise the growth has hovered around zero throughout 2020-21 except for the month of October 2020.  We can see this in Figure 2.

State-wise changes

Overall, 2020-21 saw a 4.8 percent decline in net payroll additions compared to 2019-20. We now examine the changes across the States of India.

Figure 3 looks at the change in net payroll addition across top 20 states in 2019-20 and 2020-21. These are 20 states with the highest net payroll additions in 2020-21. States are ranked from left to right according to the increase in payroll additions over previous year. Karnataka which saw the highest dip of 23 percent is ranked on the left while Odisha which saw the highest increase (41 percent) is seen on the right corner. Visibly, states that accounted for large payroll additions in 2019-20 have seen the largest dip in the net payroll numbers during the pandemic, thus reflecting a stalling of the formalization process. The big urban centers in these states, marred by the higher pandemic caseload and lockdowns to control its spread, are likely to be the drivers behind both additions in the previous years and greater slowdown in 2020-21.


Figure 3

In Figure 3, primary Y-axis on the left shows the absolute number of net payroll additions in a state in the years 2019-20 and 2020-21, while the secondary Y-axis on the right shows the percentage change in net payroll additions in a state in 2020-21 over 2019-20

Conclusion: The pandemic prevented the entry of young workers

The above analyses have thrown up three key takeaways. One, the biggest setback of the Covid pandemic was to those age groups that are likely to be composed of first-time labor market entrants or the employed youth. The youth, despite their already high unemployment rates, as reported in the PLFS surveys, have perhaps suffered even further with a decline in formal sector jobs. The gap between their aspirations and what is available in the market only seems to have swelled up in the past year. It has set India’s younger workforce back by preventing or delaying their entry into organized employment. Second, we also see a reduction in subscriber count for those older than 35. Those above 35 years of age, may have limited scope for acquiring new skills, thereby limiting their mobility. This makes them uniquely vulnerable, and their exit can be a worrying long-term concern. 

Finally, the urban centers which were the major formal sector employment generating engines have clearly been the worst hit. While the GDP showed growth at 0.4% in Q3 of 2020-21, the lacklustre growth in formal sector employment, despite a large dip early in the pandemic shows that the road to recovery in quality employment is not easy.

Authors Note: The 35+ age group net payroll data for September 2020 for the state of Bihar has been arrived at by taking an average of August and October 2020 numbers as the original numbers seemed extraordinarily high and perhaps a data entry error. This also revises the national net payroll for the month of September 2020 (and for 2020-21). Please see table below:

[1] According to a study by IIM, Bangalore, and State Bank of India, around 80 percent of India’s workforce is in the unorganized sector (NSSO 2012 estimates). P23, Towards a Payroll Reporting in India, Pulak Ghosh and Soumya Kanti Ghosh

[2] In a recent study, Chetty et al. (2020) use data from payroll firms in the U.S. to track real time non-farm employment trends during COVID-19.

[3] Data for March 2020 have been derived using monthly figures from April 2019 to February 2020 and total numbers for 2019-20


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