Can’t measure the unemployment rate based on CMIE data; loopholes in the survey lead to inconsistency in results.

In the quarter ending in December 2018, all occupation groups’ unemployment rate stood at 6.6 per cent.

Very recently, the Center for Monitoring Indian Economy (CMIE) — a private business information company — made headlines when it released data on India’s unemployment.

As per CMIE’s estimate, India lost 11 million jobs in 2018. Most of us would not disagree that numbers that make headlines in this 24X7 sensational breaking news era perhaps don’t guarantee to narrate the whole story.

Before going into details, the serious efforts that CMIE is making by putting all their data in the public domain for analysts to analyse and draw conclusions from needs appreciation. The purpose of this article is not to question the authenticity of the CMIE exercise but to present an analytical perspective in the wake of India’s realities.

Let’s start at a conceptual level. When does the unemployment rate in a country fall? When looking for jobs, people are finding them at a pace faster than the time of reference. So how has India performed on this critical parameter?

CMIE data on annual unemployment is available for only two financial years, 2016-17 and 2017-18. Between these two years, the unemployment rate decreased from 7.5 per cent to 4.7 per cent. Now that’s a noteworthy improvement and good news for job-seekers.

Further, CMIE data says that in the immediate period post demonetisation, often blamed as a dark phase for job creation (as economic activity slowed down), the unemployment rate showed a downward trend. Remember demonetisation was announced in November 2016, and CMIE data suggests that the unemployment rate for the quarter ending December 2016 stood at 6.5 per cent. This rate decreased to 4.8 per cent in March 2017 and further fell to 4 per cent in June 2017.

Cant measure unemployment rate based on CMIE data loopholes in survey has lead to inconsistency in results

Representational image. Reuters

The inference here is that employment levels significantly increased in India in the seven months that followed demonetisation. Counter-intuitive, isn’t it? Even the ardent supporters of demonetisation have argued that it may have hurt informal jobs and businesses with low working capital in the short term.

Experts have argued that post demonetisation, many women actually opted out of the labour force. If a significant number of working women lost jobs and decided to not look for work anymore, the unemployment rate should have actually gone up. But the opposite happened.

Also, think about it from a practical sense, if you are right out of your job due to an external shock and starved for cash, would you try harder and look for another job or sit back at home and lead a life of despair? To expect the aspirational Indian to give up employment without even a six-month fight appears unfair. The fact is that female labour force participation has been consistently declining in India (again as per CMIE), and the post-demonetisation period shows no blip. If such is the case, then why should unemployment rates be seen with a different lens?

Let’s move to employment among different occupation groups. In the quarter ending in December 2018, all occupation groups’ unemployment rate stood at 6.6 per cent. Out of the various groups, major ones such as “Businesses”, “Salaried employees”, and “Small traders & wage labourers” all saw a reduction in unemployment levels. Further, if we compare monthly unemployment rates for various occupation groups in the first and last month (January & December) of 2018, “Businesses”, “Salaried Employees”, and “Framers” all saw a dip in unemployment rates. Again, a happy story.

Another observation is that if you analyse quarterly CMIE data, almost all categories’ unemployment rate follows a typical range across time. Only for one occupation category unemployment levels for December ‘18 quarter broke the ceiling- “Retired/Aged”. For this group, the unemployment rate increased from 38 per cent to 93 per cent in two successive quarters. Thus, either a big chunk of retired Indians wanted to join the workforce between September-December 2018, or a lot of them were fired in this period.

Mind you. This effect was even more pronounced in rural areas, where the unemployment rate for “Retired/Aged” increased from 32 per cent to 95 per cent from one quarter to the next. It is also important to note that rural “Retired/aged” isn’t the same as “farmers”, which fall into a separate occupation group. These erratic variations fail the basic common-sense test and need to be consumed with a pinch of salt.

The only comparison to CMIE’s unemployment data can be made using Labor Bureau data and that for only one common year: 2016-17.

As per recent media reports, Labor Bureau estimated India’s unemployment rate at 3.9 per cent in 2016-17 (as against 7.5 per cent by CMIE).

The sample size and methodology used by both these agencies aren’t too different. However, such a stark difference in rates suggests only one thing- In a heterogeneous country such as India, such surveys will have considerable limitations and possibilities of differences.

This doesn’t mean that we should do away with employment surveys, but that we should go into the fine details and make a reasoned judgment that matches data with one’s qualitative experiences and sensibilities about India’s peculiarities. Broader conclusions from sensational headlines need to be taken with a pinch of salt.

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