This is the real problem with India’s employment data

Faced in February 2019 with a leaked report that showed record high unemployment, Prime Minister Narendra Modi in a speech to Parliament declared that the problem was that jobs in the new economy were not being captured by the data. 

It is true that some employment is not adequately understood yet by the National Sample Surveys, but the problem isn’t exactly that the government is missing people in the gig economy. A bigger issue is that official classifications can make low-paying, insecure jobs seem like robust employment. Among the codes in the National Classification of Occupations that India follows is this one—Code 121: Directors and Chief Executives. By official data, this is the most common occupation for urban men and the third most common occupation for urban women (after domestic cleaners and garment workers). Sounds high-skilled and well-paying? Not so much, labour economists find; it might just be a fancy-sounding way of describing people who run their own small one-person enterprise.

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Of the women workers described as directors and chief executives, 99 per cent were actually self-employed, of which around one-third worked as unpaid family workers. These women were mainly engaged within self-help groups and co-operatives as ‘partners’, and had thus been recorded as directors or working proprietors, even as their activities for the most part remained confined to food processing and textile and garment manufacturing. A large proportion of self-employed women workers were also engaged in outsourced manufacturing work, typically characterised by low earnings, long hours and lack of any form of social protection.

The gulf between the jobs that people want for themselves and for their children and the jobs that are actually available is enormous, and widening. Across age, location, caste and class—in fact, even more so for upper castes and the rich—the first preference of Indians in terms of employment is a government job. 

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In addition to some stability, these jobs also offer better pay. At all levels, but particularly at the lowest education and skill levels, private-sector salaries are below public-sector salaries. Due to a guaranteed minimum salary in government service, a cleaning worker in a government office is likely to earn far more than a domestic servant doing the same work in a private home or business, the IHDS shows. In 2012, a rural agricultural wage labourer could expect to earn about ₹17,500 per year, while the urban non-agricultural labourer could expect to earn about ₹60,000. But an illiterate male working in a salaried government job could expect to make ₹144,000 per year.

Government or public-sector employment also serves as a moderating influence on other forms of social inequalities that market forces exacerbate. While women earn lower salaries in both public and private sector, the ratio of female to male salaries is considerably higher in the public sector than in the private sector. Similarly, salary inequalities among various social groups are larger in the private sector than in the public sector. Regardless of the sector, forward castes have higher salaries than OBCs, Dalits, Adivasis and Muslims. But the differences in government salaries by social group are lesser in the public sector at both lower and higher skill levels.

For the most prestigious category of white-collar jobs, caste hierarchies have remained largely static over the past five decades. The share of men who are in professional or salaried jobs is already by far the highest among Brahmin, and then non-Brahmin forward-caste men, even with reservation in place for those from backward castes and no reservation for upper castes, on account of the disproportionate access historically high levels of education and income give to the upper castes.

Much of this can be explained by the difference in educational attainment. But this is not a full explanation. In a classic experiment, the economists Sukhadeo Thorat and Paul Attewell found that Dalit respondents to job advertisements were less likely to be called than upper-caste respondents with the same qualifications.

Since 2011-12, the government has not published a full Employment and Unemployment survey, as the NSSO reports are called, but has moved from 2017 to the PLFS. This aims to provide higher-frequency (quarterly) employment and unemployment data, but statistical aims are at the mercy of political forces. The first PLFS annual report was expected in December 2018, but the government delayed its release until after the 2019 Lok Sabha elections, presumably because the numbers were bad.

The NSSO could do a better job of collecting data from the informal sector (just as it should do a better job of obtaining data on the professions of the superrich). The NSSO has itself constituted numerous committees which have come out with dense reports on ways to improve its informal-sector data. The PLFS, which the government had largely ignored thanks to its unflattering numbers, was supposed to be one step in this direction. Once the 2019 elections had come and gone, the government went back to quietly releasing the PLFS reports regularly and without much fanfare or controversy.

All of this was pre-pandemic. Although the PLFS was meant to be quarterly, there was no labour data available right through 2020 up until August 2021, as the pandemic threw not just lives and jobs, but also administrative systems out of gear. As a result, many economists have turned to the CMIE, a large sample panel survey that is private, paid and closed. Modi and his administration, on the other hand, have chosen to point to administrative data like payroll statistics.

Modi is right—there is a problem with jobs data. But it isn’t the one he claims, that the data doesn’t capture jobs. The problem is that the government is neglecting its own data mechanisms, which can capture all the data about real Indian jobs, and suppressing them when they prove inconvenient. India’s jobs crisis is two-fold—not enough jobs and suppressed data.

Excerpted with permission from Whole Numbers and Half Truths: What Data Can and Cannot Tell Us About Modern India by Rukmini S., published by Context, an imprint of Westland Publications.

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