#FromMyReadings, Issue 7, 2021
Urban Employment Data from and after the 2020 Lockdown
#FromMyReadings is a series of short summaries of books, chapters, papers, articles, and media I come across in my regular work as a researcher.
I try and put up an issue of #FromMyReadings at least once a fortnight, going up to once a week when there’s time. Mid-August to March is teaching season, so my posts reduce/stop during this time.
1. The Quarterly PLFS Bulletin: July-September 2020 (MOSPI, Government of India)
A few hours ago, the Ministry of Statistics and Programme Implementation (MOSPI) of the Government of India released urban employment data for the 2nd Quarter of Financial Year (FY) 2020, from July to September in a new Quarterly Bulletin. This is a particularly important quarter to study, given that it was immediately after the massive nation-wide lockdown which was imposed due to COVID-19 last year. My first impressions are recorded below.
Two things to note before anything else. First, the employment and labour force participation picture for India isn’t good generally. We know this already, but it’s also easy enough to see from the data in the Quarterly Bulletin. Labour Force Participation (LFP) for males above the age of 15 was below 75% even in 2019, well before the pandemic and the lockdown. For females, the LFP was far worse, hovering between 19-20%.
Second point: Large-scale national statistics like these need to be treated very conservatively. Unless there’s evidence for the opposite, it’s good to assume that the picture on the ground may be slightly different than what’s depicted on paper. Empirical data collected at large scales will also have flaws which are hard to detect/correct, there’s no getting around that. So what we have is a grim picture that may be worse than depicted.
It’s important to note that LFP is not employment — it simply indicates how many adults are either working OR looking for work. The Quarterly Bulletin suggests that about 25% of work-eligible adult men and over 80% of work-eligible adult women were (according to this data) not even looking for work before the pandemic hit.
Once the pandemic hit in March-April 2020, LFP rates didn’t change too much. For males above 15 years, LFP dropped from 74% to 72% while LFP for women above 15 years dropped from 22% to 20% (I’m rounding up here). Overall, a drop of about 2%, suggesting that many people were still either working or looking for work (more likely the latter, given the situation). Similar figures exist for younger workers between 15 and 29 years of age.
What’s really staggering to me is the change in the Worker Population Ratio (WPR). Between the Jan-March quarter and the April-June Quarter of 2020, the population of males above 15 years actually working dropped from 67% to 57%. This means that if we had (for example) 270 million males actually working in Jan-March 2020, about 40 million of them had stopped working in April-June. For context, imagine the entire population of Mumbai stopping work. Now imagine it a second time.
The drop for females is slightly better, from about 20% to 16%, but given that female participation in the labour force is so bad to begin with, the outcomes may be more extreme here, in ways that cannot be captured by tabular statistics. It may take a while for us to understand what impact COVID-19 and associated lockdowns had on female work and how much of it recovers over the long run.
An important point to note here is that I’m using “actually working” in a very loose sense. The Worker Population Ratio uses a criterion called ‘Current Weekly Status’ or CWS to determine if a person is working or not. CWS requires surveyors to ask workers if they have worked for a minimum of one hour in the week preceding the survey.
Now, this definition is deliberately designed to be loose, mostly to capture types of work different from the stereotypical 40-hour work week. However, it can also give us a fairly inflated picture of employment. One, or even 10 hours of work in a week may not mean much in wages, unless you’re in a really elite sector. This raises questions on how meaningful such employment actually is. Many workers during the April-June lockdown may not have earned enough for their families.
Was there any good news? A little bit. In the following Quarter (July-Sep 2020), WPR rose again a little bit. For male workers above 15 years, WPR rose from 57% to 65%, while for female workers above 15 years, it rose from 16% to 17%. This isn’t great news though. Note that WPRs for males and females in Jan-March was 67% and 20%. So many people who were working in Jan-March did not return to work in July-Sep.
If (to continue the earlier example) we lost 40 million male workers during the lockdown, we only got about 30 million of them back. In this example, about 10 million hadn’t returned by July-Sep. Imagine the entire population of Bengaluru not stepping out after the lockdown was lifted.
Again, the picture is worse for women workers above 15 years. If (for example), we had 80 million women workers in Jan-March, that number would have dropped down to 64 million during the lockdown, but only 68 million would have returned to work by July-Sep. In this example, over 12 million workers wouldn’t have returned to work once the lockdown was lifted.
Several things to keep in mind before ending:
- This data ends at July-September 2020. We’ll have to wait and see for employment data in future quarters to see what the full extent of the recovery is.
- I’ve only written about broad-brush pictures for male and female workers above 15 years of age. The Bulletin contains more data disaggregating workers by industry, employment type and so on. I’d encourage you to take closer looks at these before drawing deep conclusions (I know I’ll be doing the same).
- Please read Sections 3 and 4 of the Bulletin to understand the conditions under which this data was collected.
- A reminder that this is urban data. The data for rural workers may show a completely different picture.
Lastly, one thing I haven’t summarised here is the actual unemployment rate (how people were unemployed as opposed to employed).
I’m including a snapshot of the unemployment table below (Statement 5 in the Bulletin, Page 6). Following a long tradition established by annoying maths and statistics teachers, I’m leaving this one as an exercise for the reader.
PS — A plug. If you want another dive into India’s employment data, my colleague at IIHS, Viola Fatima Lewis, has just released an explainer on workers in India, over at IndiaSpend. It’s part of a larger series of explainers on urban data, so check it out.
