Home / Politics / Policy /  How not to measure job numbers in India

New Delhi: Across the world, jobs are perhaps the most common electoral promise. The general election next year will inevitably raise questions about the record of the Bharatiya Janata Party (BJP)-led government in creating jobs. This, in turn, raises questions about India’s employment data measurement.

Historically, India has captured employment data through surveys which come with significant time lags. To address this, the government established a task force in 2017 to revamp India’s employment data architecture. The task force recommended using data from sources such as the Employees’ Provident Fund Organisation (EPFO), Employees’ State Insurance Corporation and the Pension Fund Regulatory and Development Authority.

Yet, as Radhicka Kapoor of the Indian Council for Research on International Economic Relations (ICRIER) highlights in a research paper in the latest issue of Economic And Political Weekly, even these new measures have significant problems.

One key limitation in these databases is the duplication across the various schemes. Another limitation is partial coverage. Policy changes also influence the numbers in these databases. For instance, the government is now actively trying to increase enrolment on the EPFO database. The EPFO has formed special squads to carry out inspections on firms to prevent under-reporting of employees. Consequently, increases in EPFO numbers may simply be because of better enrolment of existing employees rather than creation of new jobs, Kapoor points out. More generally, Kapoor notes that these employment data reforms draw on practices in the US and UK, two developed countries where the nature of the employment challenge and employment data requirements are significantly different. In the US and UK, almost all jobs are in the organized sector and the main challenge is open unemployment; in India, where employment is dominated by jobs in the unorganized sector, the major challenge is disguised unemployment. In this context, spending significant resources on high-frequency employment data which says little about the quality of employment may serve little purpose.

ALSO READ: Rethinking India’s Employment Data Architecture

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