Volatility of the unemployment rate

by Mahesh Vyas

The unemployment rate fell to 5.6 per cent during the week ended October 29. This was a significant fall from the 6.9 per cent rate during the preceding week. But, the 6.9 per cent rate was a significant rise from the 5.7 per cent in its preceding week.

What do we make of such volatility in the unemployment rate when it rises by 1.1 percentage points over a week only to drop by 1.2 percentage points in the next week? Given that the unemployed are of the order of 25 million, this implies that a few million people move in and out of being employed or unemployed.

Are such changes for real? Do people move in and out of employment in such large numbers over a week? Or are these changes a reflection of sample changes from one week to the next?

Vivek Moorthy, Economics and Social Sciences Professor at Indian Institute of Management, Bangalore raised this question to me in a recent conversation. He pointed out that volatility of the unemployment rate in most developed countries is quite low. A small change in the rate indicates a change economic conditions. In fact small changes can move markets. In comparison, the month-to-month volatility in India is much higher than in the US - too high for comfort.

Prof Moorthy’s observation is bang on but, his concern may not be. No wonder the Indian financial markets don’t give a penny. They have been on a nearly perpetual roll. No one quite knows why they keep rising relentlessly but nobody cares because its making many rich and who would like to spoil such a profitable party.

Back to Professor Moorthy’s question on volatility of the unemployment rate.

It is true that unemployment rate in India is a lot more volatile than in developed countries. We study the period January 2016 through August 2017 since this is the period for which we have data for India, thanks to the BSE-CMIE partnership in producing such statistics. We compare the volatility of monthly series of unemployment rate over this 20-month period. Interestingly, this period can be divided equally into a pre-demonetisation and a post-demonetisation period as November 2016 sits exactly at the center.

Over this period, volatility of the monthly unemployment rate in India is much higher than in OECD countries. The coefficient of variation was 8.3 times higher. But this period contains multiple shocks of demonetisation and GST. It could be useful to see the volatility before these shocks became effective.

During the 10 months of January through October 2016, volatility of the monthly unemployment rate was 7.7 times that in OECD countries. So, the high relative volatility cannot be explained by the shocks. The post-demonetisation period did see a substantial increase in the volatility of monthly unemployment rate. The coefficient of variation increased from 8.8 per cent (pre) to 23.5 per cent (post). Since there is a huge difference between the pre and post-demonetisation period, the overall coefficient of variation is even higher at 32.5 per cent.

During recent times no country has faced economic shocks such as by India and therefore it is not a good idea to compare its data for the entire period with the others. But, it should be perfectly fine to make such a comparison for the period before demonetisation. The coefficient of variation for India during this period is 8.8. The closest among OECD countries is Hungary at 7.2 per cent. Next comes Turkey at 6.2 per cent and then Estonia and Israel at 5.8 and 5.2 per cent.

It is apparent that high volatility is not uncommon. It is the nature of employment that plays an important role in determining volatility of the unemployment rate. In India, a large proportion of the labour force does not have a regular job. People are mostly employed as daily wage workers, agricultural labourers, small farmers and self-employed traders. These include the assortment of plumbers, masons, cargo loaders, badli workers, etc. These move in and out of "jobs" fairly rapidly.

It is the high proportion of these workers in India that makes the unemployment time-series volatile. What is useful is to see is in the monthly unemployment rate series is that there is a trend. It is not random. It therefore does carry useful information. But, I’d like to not waste Prof Moorthy’s observation. It would be useful to add a new series to the unemployment rate - one that is based on only the organised sector. We should expect low volatility in this.


First Published in Business Standard Link

CMIE STATISTICS
Unemployment Rate
Per cent
5.6 -0.0
Consumer Sentiments Index
Base September-December 2015
95.8 +0.4
Consumer Expectations Index
Base September-December 2015
93.9 +0.6
Current Economic Conditions Index
Base September-December 2015
98.7 0.0
Quarterly CapeEx Aggregates
(Rs.trillion) Dec 16 Mar 17 Jun 17 Sep 17
New projects 2.33 3.79 2.06 0.99
Completed projects 1.01 1.94 1.14 0.99
Stalled projects 1.13 0.70 2.66 0.64
Revived projects 0.18 0.67 0.30 0.22
Implementation stalled projects 0.82 0.33 0.67 0.61
Updated on: 19 Nov 2017 4:20PM
Quarterly Financials of Listed Companies
(% change) Dec 16 Mar 17 Jun 17 Sep 17
All listed Companies
 Income 6.2 10.2 9.9 8.6
 Expenses 6.3 11.9 10.1 9.9
 Net profit 40.3 15.7 -19.1 -17.9
 PAT margin (%) 6.1 6.0 5.4 6.0
 Count of Cos. 4,507 4,438 4,288 2,716
Non-financial Companies
 Income 5.9 11.8 10.6 8.6
 Expenses 7.2 15.6 10.8 8.6
 Net profit 24.5 -2.5 -24.4 -5.0
 PAT margin (%) 6.2 6.2 5.3 6.9
 Net fixed assets 6.9 10.0
 Current assets 2.6 2.1
 Current liabilities 8.8 9.9
 Borrowings 4.8 4.1
 Reserves & surplus 6.2 8.1
 Count of Cos. 3,488 3,439 3,340 1,982
Numbers are net of P&E
Updated on: 19 Nov 2017 4:29PM
Annual Financials of All Companies
(% change) FY14 FY15 FY16 FY17
All Companies
 Income 10.0 5.2 1.1 6.6
 Expenses 9.9 5.2 1.2 6.8
 Net profit -2.2 1.6 -12.9 20.2
 PAT margin (%) 3.2 3.2 2.9 6.5
 Assets 12.3 9.4 9.6 8.8
 Net worth 9.6 8.7 10.3 8.5
 RONW (%) 6.2 6.1 5.1 9.6
 Count of Cos. 23,895 23,579 20,184 3,629
Non-financial Companies
 Income 9.7 4.3 0.1 6.5
 Expenses 9.3 4.5 -0.6 7.4
 Net profit -2.7 -5.5 11.5 14.3
 PAT margin (%) 2.2 2.1 2.6 6.5
 Net fixed assets 11.6 13.4 15.1 7.3
 Net worth 8.2 7.1 10.6 7.2
 RONW (%) 5.1 4.9 5.4 11.0
 Debt / Equity (times) 1.1 1.1 1.1 0.6
 Interest cover (times) 2.0 1.9 2.0 3.7
 Net working capital cycle (days) 69 67 66 50
 Count of Cos. 19,154 19,053 16,707 2,782
Numbers are net of P&E
Updated on: 15 Nov 2017 2:29PM

Data added for HPI at Assessment prices and HPI at Market prices