INSUBCONTINENT EXCLUSIVE:
It is heartening to note that the government has finally accepted the recommendations and released the monthly payroll data from Sep-17 to
For the sake of constructive debate, we believe it is pertinent to point out some of the serious fallacies that are being put forward in
public domain, as otherwise the debate could soon turn into an unwanted slugfest.
To begin with, the EPFO and PFRDA data are quite robust
Specifically, the EPFO data summarises the bucker-wise payroll created for the 6-month period ended February 2018
The good thing about the data is that all (a) 3.1 million records are Aadhar linked; (b) the records are post June 2017, when the Amnesty
scheme ended (that had enrolled about 10 million records); and (c) for each age-wise band, the estimates are net of the members enrolled and
ceased during the month as per records of the EPFO.
Surprisingly, in their eagerness to dismiss these numbers as a flash in the pan, there
have been analysis suggesting that such growth is primarily because of Amnesty scheme, even when we are all aware that it ended three months
While it is true there the number of subscribers have jumped in earlier years partly aided by an extremely successful Amnesty scheme
(nothing wrong in that, though), the growth of 3.1 million has nothing to do with that, at least
In other words, the frantic pace to EPFO database seems to have kept pace in FY18 too.
Secondly, it is true that such 3.1 million does not
entirely reflect new jobs, as there is indeed some formalisation
However, there is a counter argument too
earlier such people were not getting their social security benefits, which now they are formally entitled to
Second, while all payroll are not new jobs, it is entirely feasible that a large percentage of jobs being created in the 18-25 age-bucket
(that cluster towards 21 as the median) may be new ones
Remember this number is as much as 1.86 million, 60% of 3.1 million!
Now, the question of such data being net of members ceased paying
contribution and Aadhar linked
These ensures there is no duplicity of records, as like PFRDA but that could be a problem with ESIC, where Aadhar linkage is not mandatory
Here is, however, a catch
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
We believe the data about retirees during the month whose account has been settled should 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 US does.
There is another valid criticism regarding the EPFO data
It is possible that during any year, companies having existing employees less than 19 would come into EPF newly and the entire set of
These does not represent new payrolls as observed rightly
However, this is a normal event every year and we believe that this figure is not material on net basis.
For example, even if we take only
the number of joinees from published EPFO records that are in 18-25 age-group at 1.86 million and exclude all first payments from joinees
who are 25+ years (1.2 million in number), but some of whom definitely have also joined the payroll for the first time.
Under NPS, even if
The others like corporates may have only Tier-II account and hence considering them will again lead to double counting
The data indicate that on an average of 60,000 subscribers are joining every month, again a fairly reasonable estimate.
Now, a food for
There are also a critic of the payroll estimates that suggest how can one reconcile the payroll estimate with a declining consumption growth
For the record, consumption growth, yes it has slowed down, but the average consumption growth still has been 6.7% in the last 2
years.
Interestingly, even if for the sake of argument we believe that 6.7% is a lowly number, then we must remember that the EPFO jobs are
mostly low paying below 15,000 per month and hence unlikely to lift consumption dramatically.
So what next The government may now endeavour
to collect monthly details on a national basis of total government employees, both central and State outside the NPS, parastatals and all
government-controlled organisations outside the EPF to provide full data about employment.
The skill ministry should be requested to use the
monthly data to assess skilling requirement by geography based on jobs category
We should also extensively use machine learning and deep learning methods to predict payroll count for every quarter at an industry wise
This will help in a big way to skilling and re-skilling
Let us make a new beginning.
(Authors are group chief economic advior, SBI; and professor, IIM Bangalore, respectively
