The invisible churn in India’s labour markets


One of the most basic concepts in economics is stock versus flow. Stock is a variable measured at a point in time. An individual’s net worth is a stock variable. Flow, on the other hand, is measured over a period of time. GDP, measured in a quarter or a year, is a flow variable.

It makes little sense to compare stock and flow.

In certain cases, neither stock nor flow may be to represent a certain concept. Labour markets are a good example. A country’s unemployment rate can increase or decrease over time, and people can move from one form of employment to another. But headline numbers on unemployment rate and kind of employment do not tell us about the degree of churn in the labour market.

For instance, consider 100 workers and 50 unemployed people in an economy in a given period. Suppose this number changes to 105 and 45 in the next period. This could be a result of five unemployed persons joining the ranks of the employed or 40 employed persons joining the rank of the unemployed and 45 of those unemployed in the first period finding employment. As is obvious, the extent of churn in the labour market will be far greater in the second case than the first.

Researchers, across disciplines, use something called the panel-data technique to track subjects within the population of interest – in this case not just the macro composition of the labour force but also the individual attributes of each member – across time. Study of labour markets, as is to be expected, uses panel-data technique extensively, provided it is available.

India did not collect panel data for its labour markets until the Periodic Labour Force Survey (PLFS) was started in 2017-18. PLFS, in urban areas, changed this for good, as it started following a household for four consecutive quarters for studying their status in the week before the survey. However, there has not been any panel-data analysis of the Indian labour market thus far. The reason? Codes assigned to households – survey data identifies households by codes instead of, say, name of the head so that their identity is unique, among other reasons such as privacy of respondent – in PLFS data made it nearly impossible for researchers to track households.

A working paper published this month, Indian urban workers’ labour market transitions, by Jyotirmoy Bhattacharya, who teaches economics at Ambedkar University in Delhi has managed to solve the coding barrier in panel-data analysis of PLFS numbers for the first time. Researchers often spend long periods trying to clean data sets. A lot of PhD projects over many years basically do exactly this. Bhattacharya’s work, therefore, is not an insignificant contribution.

The findings are worth taking note of, because they show that headline numbers in PLFS grossly underestimate the amount of churn in Indian labour markets and some of the pre-conceived notions based on these numbers are worth revisiting.

Social stigma might be driving India’s low reported unemployment rates

Quality employment is India’s biggest challenge. There is a widespread consensus that official unemployment rates underreport the job-crisis in India. Bhattacharya’s paper offers an explanation for the low unemployment rates in India. The unemployed pretend that they are not even looking for a job. This, technically speaking, takes them out of the unemployment rate calculation, which is the share of unemployed persons in the total labour force (people working or looking for a job).

The way in which the paper calls out the pretence of the unemployed (of not being unemployed) is by looking at the share of non-participants in the labour force who took up work in the next quarter. It is only slightly less than the share of unemployed taking up work, especially for women. This is only one of the entrenched gender disparities in India’s labour markets.

Marriage and childbirth have opposite effect on the fortunes of men and women in the job market

There are enough anecdotal accounts of women being forced to quit their jobs after getting married or giving birth to a child. While a stock analysis of married/unmarried women can give an idea of whether they work or not, panel data is more revealing as it can tell whether marital status leads to job loss and gain. Since PLFS still does not follow an individual for longer than four quarters, it is not possible to analyse a sufficient sample of women under-going marriage or childbirth.

However, even keeping other factors unchanged, the paper’s findings confirm the anecdotal belief. Married women and those with children aged five years or below are more likely to lose their jobs. For men, the reverse seems to be true. To be sure, women are at a disadvantage even without being married or having children.

A huge gig economy of the poor

With the advent of technology platforms and start-ups there is a lot of talk about the gig economy, where workers can work for multiple employers. What many people do not realise is that the Indian labour market is already a gig economy, where a large number of workers keep shifting jobs. Once again, the headline numbers, which capture the net flow within different types of jobs, do not capture this movement. However, a calculation of gross flows in the paper gives more clarity on this question.

Of the average 34.37% men who were salaried or earning regular wage in a quarter, 32.12% remained regular in the consecutive quarter, and 2.25% changed their status from salaried to other working or non-working categories. This is to say that 6.55% (2.25/34.47) of salaried men left such work in the consecutive quarter. A similar calculation is possible for in-flows. Of the 34.45% regular workers in the successive quarter, 32.12% were those who were regular in the earlier quarter, and the rest 2.33% arrived from other working and non-working categories. That is to say, 6.79% (2.33/34.45) of salaried men in the successive quarter were in-flows.

These gross inflows and outflows from the status of regular workers, the best paid, is the lowest among the three kinds of workers, for both men and women. These flows are the highest for casual workers, the worst paid workers in India. This means that it is the poorer worker who changes jobs more.

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