Updated: December 7, 2021 8:12:39 am
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Over the past week or two, India has thrown up several unedifying pictures of thousands of unemployed youth being at the receiving end of some kind of police action. Be it the viral videos from Banaskantha in Gujarat  or from Murshidabad in West Bengal or indeed the statewide protests in Uttar Pradesh. In all cases, the key underlying issue was massive discontent due to unemployment. These are, of course, not the only examples but they are indicative of a larger worry: Widespread and high levels of unemployment across the country.
Earlier in the year, ExplainSpeaking had written why rising unemployment — and not boosting GDP growth — will be the biggest challenge before India.
— ANI (@ANI) December 4, 2021
As the year winds down, the question is: Why are so many policymakers and experts failing to grasp the scale of joblessness in India?
The answer lies in the incorrect metric being used by them, and the general public, to assess the level of unemployment.
Let’s understand this by using the example of one state — Uttar Pradesh. I am choosing UP for two reasons. One, it is the most populous state in the country. Two, because, on the face of it, the unemployment rate in UP is not as high as it is in some other states, and as such, it enables us to understand why several Indian politicians are underestimating the severity of this crisis.
Let’s pick up the story from a few months ago.
On September 17, Uttar Pradesh Chief Minister Yogi Adityanath stated that the unemployment rate in the state was more than 17 per cent in the year 2016 and had now come down to four to five per cent.
To be sure, the CM was factually correct. According to data accessed from the Centre for Monitoring Indian Economy (CMIE), UP’s unemployment rate was 16.82% during the period May-August 2016. It is also true that UP’s latest unemployment rate — for the period May-August 2021 — is 5.41%.
So, in exactly 5 years, UP’s unemployment rate has fallen from nearly 17% to 5.41%.
However, as comforting as these numbers are, they provide a partial picture.
To begin with, it is noteworthy that the UP CM chose to compare the current unemployment rate to the data from 2016. The fact is, the BJP government under the current CM came to power in March 2017. But the same CMIE data shows that UP’s unemployment in March 2017 was as low as 3.75%. In other words, if one looks at just the unemployment rate metric, UP today is worse off from when the current government took over.
What explains UP’s unemployment rate declining so sharply just between 2016 and 2017? How did the previous government under Samajwadi Party (SP) manage to bring down the unemployment rate from 17% to under 4% within a year? Equally puzzling is how the SP ended up losing the Assembly election despite such a massive improvement in the unemployment rate?
The key lies in understanding the Labour Force Participation Rate (or LFPR).
The Labour Force consists of persons who are of 15 years of age or more and are either of the following two categories:
1. are employed
2. are unemployed and are willing to work and are actively looking for a job
In other words, the LFPR essentially provides the percentage of the working-age (15 years or more) population that is asking for a job; it represents the “demand” for jobs in an economy. It includes those who are employed and those who are unemployed. The Unemployment Rate (UER) is nothing but the number of unemployed as a proportion of the labour force.
So to understand the sharp fall in the unemployment rate in UP during 2016 and 2017, one has to look at the LFPR during that period. As both the chart and table below show, UP’s LFPR ( represented by the green line) fell from 46.32% to 38.4% during the period over which its UER (represented by the blue line) fell from 17% to just 3.75%.
Fall in UP’s Unemployment Rate is essentially because of a fall in the Labour Force Participation Rate; the secular decline in Employment rate captures the combined effect
What does this mean?
What essentially happened is that between May 2016 and April 2017, the demand for jobs in UP fell sharply — by almost 8 percentage points. In absolute numbers, this meant that more than 1.1 million exited the labour force in a matter of just 11 months. Not surprisingly, most of these people — who stopped looking for work — were being counted as the unemployed until they quit the labour force. As they exited the labour force the number of unemployed people fell by an almost similar amount — 1 million. In essence, this brought down the unemployment rate in UP between 2016 and 2017.
Mahesh Vyas, the CEO of CMIE, nods in agreement. “The UER has come down because the LFPR has come down. Every time LFPR goes up, the UER also goes up,” he states as he explains the concept.
This also clears up why the SP government might have suffered at the hustings. In other words, the sharp fall in UP’s unemployment rate did not happen as a result of an increased supply of new jobs but due to a sharp decline in the demand for jobs as dismayed workers stopped looking for work.
It is important to point out that this period in question essentially maps the aftermath of the Modi government’s decision to demonetise high-value currency notes on November 8, 2016. The move hit the informal sector and farm sector employment by sucking all liquidity out of the system.
Between Sep-Dec 2016 to the end of April 2017, the LFPR fell from 46.3% to 38.4% — and in the process, resulted in the UER falling from 16.8% to just 3.75%.
In fact, little has changed since. If one looks at UP’s data, the UER mirrors the movement in the LFPR.
Another interesting case in point is the Covid disruption in 2020. Data shows how between September 2019 and April 2020, the LFPR had risen and also resulted in a rise in the UER. But once the Covid lockdown took effect, the UER fell sharply — unsurprisingly due to the fall in LFPR.
In fact, UP’s unemployment rate has fallen sharply in the one year since the start of the Covid pandemic — from almost 12% in Jan-Apr 2020 to less than 5% by April 2021. But again, the reason was the fall in LFPR.
To sum up, UP has been achieving lower unemployment rates by dismaying workers from even joining the labour force — thus reducing the demand for jobs — instead of creating a supply of millions of new jobs.
“The Employment Rate (ER) is a combination of the UER and the LFPR,” he states as he shares the data. The ER refers to the number of employed people as a percentage of the working-age population.
The chart and table above provide a clear picture of what has happened to the Employment Rate (represented by the red line) in UP since 2016. The proportion of employed people (as a percentage of the working-age population) has fallen from 38.4% to 33%.
In absolute terms, this means that while the working-age population in UP grew from 146.9 million at the start of 2016 to 169.2 million in August 2021 — that is a growth of over 15% — the total number of people with jobs contracted from 56.4 million to 55.8 million — a decline of 1%.
If we look at the data from April 2017, when the BJP government took charge, to now, the working-age population grew from 150.8 million to 169.2 million — a growth of over 12% — while the total number of people with jobs grew from 55.7 million to 55.8 million — a growth of just 0.2%.
That, in a nutshell, captures the true picture of unemployment in UP as it goes into another election.
What about India?
As the chart and table below show, the story repeats itself at the national level as well. Lower employment rates do not signal lots of new jobs, rather fewer people demanding them. The crucial metric — ER — captures this by showing a secular decline since the start of 2016 — from 42.84% to 36.77%.
India’s Employment Rate has plummeted since 2016
But more than the percentages, it is the absolute numbers that bring out the level of distress more clearly. What the ER data shows is that in the five years between May-August 2016 and May-August 2021, while India’s working-age population went up by 13% from 951 million to 1,071 million, the total number of employed people in India fell by 3.5% from 408 million to 394 million.
Thanks to the fact that India has one of the lowest labour force participation rates in the world and the impact that fluctuations in LFPR have on how we calculate joblessness, it is misleading to look at unemployment rates. To truly capture the distress due to joblessness, policymakers should look at the employment rate.
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