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The Employment Conundrum


Over the last three months, I have had the opportunity of engaging with our clients across various forums and cities. What provided a platform for this interaction was my briefing on four critical initiatives that we believe will, if properly implemented, serve as game changers with a palpable impact on economic output. The question that consistently came up almost everywhere was on the perception of jobless growth and consequently, rising unemployment within India. This has possibly been based on recent press reports and television debates that consistently cite certain headline statistics. These suggest a fall in employment levels between 2011-12 and 2015-16 compared to vigorous growth in earlier years, since 2004-05. Even on the surface, this conclusion does not gel fittingly with other statistics. For instance, indirect tax collections and consumption expenditure, which are both proxies of aggregate spending and wellbeing, do not corroborate falling employment. Tax collections have been rising by 15.5% pa more recently vis-à-vis 14% in previous years. Moreover, between 2012 and 2016 growth in private consumption expenditure actually outpaced that in overall GDP, which was not the case earlier. Both observations would seem to contradict the inference that employment growth has abruptly turned from positive to negative. We examined this ‘conundrum’ in some detail and found that the popular notion is indeed a simplistic one and the real picture is perhaps more positive. This paper will present our analysis.

To begin with, it is relevant to point out that the term ‘employment’ has generous interpretations in India’s workforce statistics. In addition to those gainfully employed on a full time basis, it also includes unpaid workers, part timers, apprentices, etc. Oddly, it includes almost anyone who may have done even a few days of work during the year. It therefore becomes necessary to draw distinctions when unravelling statistics and not rely solely on the headline figure.

Methodology and definitions
India has no hard data on employment. Very few people, in any event, pay taxes and there is no system to collect payroll data, which is the norm in most other countries. India therefore has to rely on survey-based statistics. Primarily there are two sources for this. First, the National Sample Survey Organisation (NSSO), which undertakes a comprehensive survey but only every five years; second, the Labour Bureau, which publishes a more frequent survey, annually. Strangely, despite similar definitions and with a sizeable sample, exceeding interviews with 100,000 households, figures from the two sources do not always match. Worse, there are internal conflicts such as state totals not adding up to the national count. The only way to enable any sort of comparison is through recalibrations and adjustments, which we therefore had to do. One would logically assume that the most comprehensive survey would be the national census. However, this is undertaken only once in a decade. Its findings are released 5-6 years later and are therefore practically useless. Consequently, our research used NSSO data for 2004-05 and 2011-12 along with Labour Bureau data for 2011-12 and 2015-16.

The workforce for the purposes of this exercise is defined as adults above the age of 14 years, seeking work. This would include those currently employed, either with a full time job or part time, as well as the unemployed. Everybody else is defined as being out of labour force (OLF). This includes students, home-makers, pensioners, invalids and really anybody not looking for a job. Therefore, the workforce plus OLF constitutes the adult population of the country.

Gross employment hides more than it reveals
The way the statistics are commonly presented makes no distinction between full and part time employees. Therefore, even those that may have worked for a few weeks in a year are counted as those with a job. Based on these definitions which at the risk of reinforcement are obvious over-estimates, employment fell from 467 million in 2011-12 to 462 million in 2015-16. During the same period, the figure for the unemployed rose from 10 million to 18 million. The comparative sums for the period between 2004-05 and 2011-12 are 451 million employed and 11 million unemployed, leading to the deduction that things have worsened in the more recent time period. However, a more complete break down of the population into the following categories – full time, part time, unemployed and OLF – led to different conclusions. Those with proper, full-time jobs actually rose from 409 million to 444 million in the four year period i.e. 35 million new full-time jobs in four years, or 8.6 million a year. Against this, 26 million jobs were created in the previous 7-year period, from 383 million to 409 million, at an annual accretion of a mere 3.7 million jobs a year. The comparison is turned on its head.

What has really changed is part time employment. In 2004-05, there were an astonishing 68 million people who were part time workers. They were effectively bloating the employment count as many of them may have worked for no more than a few weeks in the year. By 2011-12 that number dropped only slightly to 58 million. The real change happened thereafter with part timers falling to 19 million by 2015-16. This prompts the question, where did these 39 million people end up? Since unemployment increased by only 8 million during this period, it would imply that the remaining 31 million people either shifted to full time jobs or, more plausibly, chose to stop working, a conclusion substantiated by the swell in the OLF population from 377 million to 446 million. The fact is, many part timers were really students who should not have been working in the first place or home makers doing extra jobs possibly to make ends meet. Their exit from the workforce would suggest that the earlier compulsions no longer apply, presumably because incomes have risen and non-working options have become feasible or preferable. One could conversely argue that they exited because those jobs were no longer available. This may well be true in some cases but is unlikely to explain the majority of the shift. A scenario in which full time jobs are being created at more than twice the earlier rate while proxy indicators are robust, cannot logically be reconciled with a large-scale evaporation of part time jobs.

In terms of sectors, agriculture remains the largest employer in the country. However, aggregates have dropped over the ten year period FY05 to FY16 from 253 million to 211 million. There has been a commensurate rise in sectors such as construction, trade and other services. This should also be construed as good news since agriculture is the least productive sector of the economy and services, the most. Over the ten year period, the share of employment in services has risen from 32% to 44%.

What about unemployment?
Official unemployment, as previously mentioned, increased from 11 million to 18 million between FY05 and FY16. Here again, the headline figure does not tell the full story. The fact is, there were 107 million unpaid workers in 2004-05, which incorrectly are counted as ‘employed’. These are basically family members working in household enterprises, ‘kirana stores’ or farms, for no salary or wages. Their inclusion within the ranks of the employed is frankly an artificial suppression of the unemployment scores. Many analysts have in the past called out this figure for what it really is – disguised unemployment – yet the statistics continue to be compiled in the same way. Over the ten year period from 2004-05 to 2015-16, this population has fallen drastically to 62 million. Therefore, effective joblessness, including both unpaid workers and the officially unemployed, is down from 118 million to 80 million. This from any benchmark should be construed as a positive development and quite the opposite of what the superficial figures tell us.

What further substantiates the unemployment conundrum is an analysis of National Rural Employment Guarantee Scheme (NREGS[1]) figures. If unemployment were truly rising, NREGS numbers should have responded commensurately. Instead, in the four year period between FY12 and FY16, NREGS enrolments declined from 75 million to 68 million. Even more remarkably, only 10% of those enrolled actually completed the 100 days of work that the scheme offers. One explanation for this might be that since NREGS is now on the Aadhar platform, duplicate and ghost accounts may have been removed. Still, this cannot explain the sheer magnitude of the shift. A more plausible explanation would seem to be that real unemployment is actually falling, as alluded to above; therefore, the number of people requiring NREGS support has also dropped.

Ghosh & Ghosh
A recent study by two economists, Professor Pulak Ghose of the Indian Institute of Management, Bangalore and Dr Soumya Kanti Ghosh, Chief Economist of the State Bank of India, based on social security databases concluded that in 2018, approximately 7 million formal sector jobs have been created. In fact, the study received such publicity having gone counter to the grain of previous thinking that Prime Minister Narendra Modi himself referred to it during his recent engagements with industry. Basically, the authors used data from the following sources: the Employees Provident Fund Organisation (EPFO) which comprises of 55 million subscribers from companies with over 20 employees; the Employees State Insurance corporation with 12 million subscribers from companies with over 10 employees; the Government Provident Fund consisting of 20 million subscribers; and finally, the New Pension Scheme (NPS[2]) with 0.5 million subscribers which mostly replaces GPF and applies to Government employees that entered service after January 2004.

Messrs Ghosh & Ghosh concluded that the formal sector payroll stock as of March 2017 was 90.2 million. New job creations were 5.8 million in 2017 and about 7 million in 2018, on a gross basis (i.e. without netting off retirements). The methodology they adopted was conservative and rigorous to say the least. The analysis, based on assumptions that contained a strong downward bias, ensured that the study could not be accused of even the slightest over-estimation. Some of these were as follows. First, only those in the age band of 18-25 making continuous contributions were counted as additions to the workforce, minimising duplication due to shifts in employment from the unorganised to the organised sector. Moreover, only employees making continuous contributions and whose information was complete in every sense, without a single data point/field missing, were counted as being employed. Approximately 42 million records that did not satisfy these conditions were precluded. Third, since the EPFO covers companies with 20+ employees, incremental data from ESIC was taken only for those with under 20 employees with a view to avoiding duplication in job creation. Finally, approximately 30 million formal sector workers were excluded from the study because they are not covered by social security databases. These include professionals such as chartered accountants, lawyers, doctors, architects and other consultants; police forces; teachers and school staff. It would be logical to assume that these numbers would also be rising and should add to both the national stock of jobs as well as incremental employment.

From a statistical perspective the methodology adopted by the Ghosh study appears conservative from every benchmark. Most importantly, it is based on payroll data and therefore free of estimation errors. It would be logical to assume that the robust trend demonstrating rising employment within the formal sector would lead to a consequential increase amongst the ranks of the self employed or indeed those in more informal engagements.

Mudra
A constant gripe by small businesses is their inability to gain access to credit. Whilst this applies to small and medium enterprises, its impact is even more profound in the context of cottage industries and micro enterprises. In order to fill the funding gap the Government launched Mudra, a scheme to ensure higher flows of credit to small and micro enterprises. Under the programme, lending is carried out banks, micro-finance institutions and non banking finance companies. An individual with a sensible business plan may avail of a Mudra loan up to Rs 10 lacs. Whilst the average loan size is around Rs 50,000, total disbursements since launch in April 2015, have exceeded Rs 3.2 trillion with approximately 75 million borrowers. Of these, 22.4 million were first timers. More importantly, 45% of disbursements have been in favour of women. The intent of this exercise was presumably to generate economic activity and churn amongst a segment of the business community and self employed individuals who were previously unable to access formal credit markets. Quite obviously, the scheme has been successful and should therefore have a consequential impact on employment generation, none of which has so far reflected in official statistics. It is hard to estimate what this is but some analysts believe new job creation could be as high as 20-30 million.

In conclusion, it would be reasonable to assume that contrary to popular perception, employment in India has actually been rising and at a pace much quicker than in previous years. Empirical evidence, not only through a deeper scrutiny of survey findings but equally from the outcome of Mudra, a rise in consumption together with indirect taxes, supports this view.

Be that as it may, India still needs to create jobs perhaps at a rate faster than what it is currently doing. Communal agitations, for instance those in Haryana, Gujarat and Maharashtra, are testimony to this. Current estimates suggest that 6-8 million qualified individuals enter the workforce every year. Having obtained some level of formal education they are no longer satisfied with traditional forms of work such as farming and wage labour. If their aspirations are to be fulfilled the industrial economy needs to grow at rates exceeding 10% per annum. This clearly requires massive investments in manufacturing and a lesser reliance on imports. But that is clearly a different subject, worthy of another research-based analysis.



[1] NREGS is a Government-funded programme that guarantees 100 days of employment to one adult member of every rural household.
[2] The New Pension Scheme is based on defined contributions, replacing GPF, which works on defined benefits.

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