India’s unemployment challenge can’t be tackled by doles as usual

Youth unemployment has increased significantly between 2011-12 and 2021-22—nearly doubling.
Youth unemployment has increased significantly between 2011-12 and 2021-22—nearly doubling.


Structural changes in our economy have resulted in improved education levels and provided a modicum of social security

The National Statistics Office introduced a regular survey on employment through the Periodic Labour Force Survey (PLFS) in 2017, and has since been releasing regular annual and quarterly reports. Its data shows that since 2017, there has been a steady improvement in the employment situation, with a rise in both labour-force and work-force participation between 2017-18 and 2021-22, as shown in the accompanying table.

If these numbers are combined with the estimated increase in population, then they show a remarkable improvement in the availability of employment opportunities. If we compare the data with the last quinquennial survey of employment/unemployment (i.e., the EUS) in 2011-12, then the picture by 2021-22 is distinctly better.

Graphic: Mint
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Graphic: Mint

This improvement in employment opportunities is accompanied by a clear downside: Indian unemployment rates are higher than those in 2011-12. The gap was particularly large in 2017-18, and has since reduced. However, even comparing figures in 2021-22 to 2011-12 shows that unemployment is higher in all categories, and for rural males has doubled since 2011-12. The increased unemployment rate when combined with the increase in population clearly shows that a far larger number of people are looking for work than in the past.

What could be the cause of this seeming paradox? A conventional explanation has been that India’s demonetization of high-value currency notes in November 2016 followed by GST implementation in mid-2017 delivered a shock to the economy, particularly its informal sector. This shock is said to still persist, causing the high unemployment rate; improved GST collections are then attributed merely to improved performance of the organized formal sector.

It has also been argued that demonetization and covid both caused substantial reverse migration from urban to rural areas, and resulted in increased dependency on agriculture.

However, neither of these explanations is borne out by the data.

The problem with the first argument is that the number of people identifying as being ‘self- employed’ is higher now than in the past.

Students of economics know that there are three categories of self-employed workers: own account workers (i.e., those who work for themselves); employers; and workers in household enterprises. Own-account workers and employers can be considered a proxy for informal sector enterprises.

In 2011-12, 52.2% of workers described themselves as being self-employed. In 2021-22, this figure had increased to 55.8%. Moreover, this characteristic remains broadly the same across the two periods even if we restrict ourselves to those identifying as own-account workers or employers. If we take into account the increase in population between 2011-12 and 2021-22, the data suggests that there would have been a significant rise in the number of informal establishments.

Further, while comparable data is not available from the EUS, the PLFS does have data on the average monthly earnings of self-employed workers. These figures show an increase between 2017 and 2022, and are comparable to earnings received by casual labour. Thus, it is unlikely that the contribution of these informal enterprises to value-addition will be significantly lower than previously estimated. This suggests that the sharp adverse impact of demonetization and GST on employment is unlikely to have been a reason for a rise in India’s unemployment rate.

The argument of reverse migration also does not stand up to scrutiny.

In 2021, a special exercise was done as part of the PLFS to assess the impact on migration. Its results showed that the migration rate in 2020-21 was nearly the same as that of the National Sample Survey 64th round in 2007-08. Further, the proportion of workers in ‘usual status’ describing themselves as being primarily in agriculture had also reduced between 2011-12 and 2021-22.

So, if there is neither a substantial fall in the informal sector, nor a return to agriculture, what explains the country’s increased unemployment?

First, let’s look at the age profile of the unemployed. Youth unemployment has increased significantly between 2011-12 and 2021-22—nearly doubling. The unemployment rate is also higher for those with higher levels of education. One reason for the rise in unemployment therefore is increased education levels among the young. As education levels improve, this will continue to be a major source of unemployment, as more educated workers would be unwilling to settle for unskilled work. It is important to note that since 2017-18, the data has shown a marked improvement in employment among the youth in virtually all education categories, again indicating that job availability has been improving.

The second part of the explanation comes from an increase in social security measures. The government has increased the distribution—in both quantity and coverage—of cheap food under the National Food Security Act. This reduces the pressure on an individual to take up work, allowing them to spend more time “looking for work", and contributing to a rise in the unemployment rate as compared to 2011-12, when no such social security was available. In addition, direct money transfers to farmer households and certain categories of vulnerable populations would have also acted as a support mechanism.

The rise in unemployment can thus be attributed to structural changes in the economy which have resulted in improvements in education levels and provided a modicum of social security. Neither of these amounts to a deterioration in the quality of life. While the presence of high unemployment— particularly among the educated young—is indeed a policy challenge, its answer will not be found in traditional solutions such as MNREGA.

T.C.A. Anant is a former chief statistician of India

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