Making the EPFO data useful

by Mahesh Vyas

Data releases from the EPFO tell us that registrations for formal jobs have been growing very well. As an aside, the new de facto definition of a formal job seems to be an EPFO enrollment. We will let that be.

The most recent release of the EPFO data showed that 685,841 new registrations took place in April 2018. Such data has been released only since September 2017. The cumulative EPFO registrations since then are 4.1 million. This works out to about half a million registrations on an average, during a month.

This is an impressive record. Half a million formal jobs every month will make a big difference to the overall well-being of households. At this rate we are producing about 6 million formal jobs a year.

Formal jobs are better paying and more secure. EPFO registrations by definition suggest that these jobs ensure that workers save a part of their earnings for their retirement days.

In many ways, the economy has been yanked into formalisation. Introduction of GST was perhaps the biggest source of formalisation. Demonetisation did help in formalisation as well. The government also took direct action in driving enterprises to register their employees under EPFO between January and June 2017. Registrations jumped up between March 2017 and July 2017 by 10 million because of this.

The collateral damage of the above steps apart, collectively they did achieve the good of getting a much larger chunk of the work force under the EPFO ambit.

Further, it is good that the government has started releasing the enrollment numbers from the EPFO every month. These releases can be improved in several ways. I list the three most easily implementable improvements.

First, we must release the gender break-up of these enrollments. This should be very easy. Gender classification is easier than age-classification which is currently provided in the data releases. Such data will help us understand the problem of female participation in the formal sector much better than our very little understanding today.

Low female labour participation rate is well-documented by household surveys by NSSO, Labour Bureau and CMIE. The subject has been well researched as well, largely using NSSO data which is much richer in dealing with the subject compared to other sources.

Gender-wise break-up of the EPFO data can provide early clues on the impact of government measures such as extended maternity benefits and reduced deduction under EPF. There has been some concern that the former could adversely affect employment of women in the formal sector.

Second, the age-wise distribution should be more granular. The current age-distribution of the data is not the norm and, it can be misleading. The norm is to present such data in equal 5-year age-intervals. The first interval can be 15-19 years, the second 20-24 years, the third 25-29 years and, so on. It is important the the intervals are equal. The age intervals used by EPFO in their public presentations are of uneven intervals - the first is an open interval of less than 18 years followed by four-year intervals - 18-21 years and 22-25 years and then there is one with a three year interval, 26-28 years followed by a 7-year interval, 29-35 years and then an open-ended interval.

As a result, what looks like a drop in employment in the age-group 26-28 years is in fact partly the result of a smaller age-interval used. Similarly, the increase in enrollments in the next interval is partly because the age-interval is much larger.

Third, EPFO data should be provided with an industry-wise break-up where the industry classification conforms to some conventional industry classification system. It has often been argued that since we now how have EPFO data there is no need for an enterprise survey. This is not a persuasive argument and therefore it is not a good idea that the Labour Bureau’s Quarterly Employment Survey be discontinued. But, if the EPFO data has to fill-in for an enterprise survey then it must provide industry-wise distribution of the data.

EPFO data is not of much use to help us understand our employment challenges. Its use in understanding changes in employment in the formal sector is also doubtful because of the confounding of the formalisation process.

But, it can be useful to understand the nature of jobs in the formal sector - their changing composition and the impact of policies such as promoting women’s welfare on their employment.


First Published in Business Standard Link

CMIE STATISTICS
Unemployment Rate
Per cent
5.5 +0.1
Consumer Sentiments Index
Base September-December 2015
96.1 +0.6
Consumer Expectations Index
Base September-December 2015
96.5 +0.6
Current Economic Conditions Index
Base September-December 2015
95.5 +0.5
Quarterly CapeEx Aggregates
(Rs.trillion) Sep 17 Dec 17 Mar 18 Jun 18
New projects 1.26 1.49 3.43 2.27
Completed projects 1.25 1.16 1.43 0.82
Stalled projects 0.69 0.88 3.41 0.30
Revived projects 0.34 0.24 0.26 0.22
Implementation stalled projects 0.78 0.71 1.92 0.03
Updated on: 22 Jul 2018 8:20PM
Quarterly Financials of Listed Companies
(% change) Sep 17 Dec 17 Mar 18 Jun 18
All listed Companies
 Income 7.9 12.0 10.0 18.9
 Expenses 9.0 13.0 16.8 20.7
 Net profit -18.0 -14.3 -82.1 11.9
 PAT margin (%) 5.5 4.8 1.2 14.9
 Count of Cos. 4,502 4,493 4,288 134
Non-financial Companies
 Income 8.2 13.3 11.5 18.6
 Expenses 8.1 12.3 12.4 21.4
 Net profit -6.1 13.2 -2.6 7.5
 PAT margin (%) 6.2 6.4 6.5 14.4
 Net fixed assets 9.2 11.9
 Current assets 2.9 8.0
 Current liabilities 11.0 10.3
 Borrowings 3.4 1.8
 Reserves & surplus 7.9 7.8
 Count of Cos. 3,461 3,464 3,319 99
Numbers are net of P&E
Updated on: 22 Jul 2018 8:20PM
Annual Financials of All Companies
(% change) FY15 FY16 FY17 FY18
All Companies
 Income 5.6 1.8 5.9 11.6
 Expenses 5.7 1.9 5.9 16.1
 Net profit 0.1 -9.7 25.3 -43.1
 PAT margin (%) 3.0 2.8 3.4 4.3
 Assets 9.5 10.2 7.4 14.3
 Net worth 8.5 11.3 7.6 13.7
 RONW (%) 5.8 4.9 5.8 5.3
 Count of Cos. 26,129 24,412 21,971 368
Non-financial Companies
 Income 4.9 1.0 5.7 9.9
 Expenses 5.0 0.3 6.1 9.2
 Net profit -8.6 19.9 20.2 8.9
 PAT margin (%) 2.0 2.4 3.0 12.1
 Net fixed assets 13.3 17.8 6.6 55.5
 Net worth 7.0 12.1 6.5 10.3
 RONW (%) 4.6 5.1 5.9 15.0
 Debt / Equity (times) 1.1 1.1 1.0 0.3
 Interest cover (times) 1.9 1.9 2.1 8.8
 Net working capital cycle (days) 66 65 62 7
 Count of Cos. 21,306 20,431 18,292 255
Numbers are net of P&E
Updated on: 20 Jul 2018 4:05PM