Terms of Trade | India’s payment revolution is a statistical gold mine. Tap it

From average size of business enterprises to migration patterns and financial flows, tech revolution can help draw an intricate picture of the Indian economy

A week from now, at the end of August, the National Statistical Office will publish India’s GDP data for the quarter ending June 2022.

There is every reason to believe that using traditional data sources with the money trail generated by the payment revolution will make things better.  (Shutterstock)

While the GDP numbers will offer much-awaited insights into the nature and extent of the economic recovery of the Indian economy, there is a widespread consensus that GDP figures do not tell us everything about the Indian economy.

What GDP numbers don’t tell us

Some of these limitations are systemic. GDP numbers are an exercise in sampling-based estimation and the quarterly GDP numbers often involve an additional layer of estimation even in this estimation exercise. That fact that a revision of a percentage point in GDP growth rates between the first estimates and final figures has become a norm now only corroborates this point.

Then there is the question of the GDP estimation process being constrained by the lack of availability of other important statistical indicators. India does not have a consumption expenditure survey (CES) after 2011-12. Unless a new CES is conducted and the results published — the 2017-18 CES findings were scrapped by the government — the Consumer Price Index (CPI) and GDP series cannot be updated. This means the data we are getting is obsolete, if not completely contaminated.

Similarly, the absence of a fresh Economic Census and survey of unorganised sector enterprises has meant that the estimation of output generated in the informal sector has likely become more and more off the mark.

The solution to these kinds of problems is as much technocratic and institutional as it is political. The former cannot undertake reforms until the latter has given a green light.

These constraints notwithstanding, is there something else which can be done to provide better statistical insights about the Indian economy? A case can be built that India is sitting on an untapped gold mine of data which has been produced by the technological revolution in the payment system space. This is best explained with a few examples.

Average turnover of a business enterprise

Anybody familiar with elementary data on firm size in India will know that things such as tax returns or databases such as the Centre for Monitoring Indian Economy’s (CMIE) Prowess (they continue to be a valuable source of information) do not give us the true picture on this count.

The sixth Economic Census – which was conducted in 2013-14 – shows that the average number of workers in an economic establishment in India was 2.24 and more than half of them were operating from within households or without any fixed premises. The ubiquitous chai shop, retail store or the local tailor are some such examples. While it is unlikely that these firms would have changed a lot in terms of the number of employees or even the kind of premises they work from, it is eminently possible that most of them have shifted to e-payment platforms such as UPI and e-wallets.

Because these payment accounts have to be classified as commercial, there is a good way of getting data on the turnover of such enterprises. This data, believe it or not, is just a few clicks and codes away, provided the custodians of data approve its publication. In fact, it gets better. Because this data is generated on a 24X7 basis, one can easily pinpoint whether and to what extent business became lean and how much as it recovered after the pandemic.

Blue-collar migrant remittances

The 2016-17 Economic Survey published alternative estimates of migration in India based on passenger data from the Indian railways. The map that the Survey came up with was fascinating, as it pinpoints exact districts which are among the largest senders and recipients of migrants in India.

Even ten years ago, it was common practice among blue-collar migrant workers to send money through informal channels in India given the difficulty of using formal channels. The proliferation of Aadhar and mobile internet has changed the game and online transfer of money is pretty common now.

What if someone were to geotag such money transfers — of course, they will have to be anonymised — to build a heat map of money flows from migrant-receiving centres to migrant-sending villages in India? Imagine a situation when we know that 45% of the money received in a village in a district in Assam or Bihar comes from another interior village in Kerala or a textile mill cluster in Surat.

This kind of information, when read with the value of crop production in a state or even the country can tell us a lot about the importance of migrant remittances in India’s blue-collar economy. Of course, researchers have tried to look at such questions, but they have never had access to data which is so comprehensive in scope.

Have traditional data sources become redundant?

Not at all. We would still need the CES and the Periodic Labour Force Surveys (PLFS). Data from electronic transactions, while it will provide useful and high-frequency insights, cannot tell us about various important aspects of the economy.

Whether people are eating less protein and more calories or whether they are working as salaried workers or running their own enterprises or whether caste and gender are important drivers of economic performance and rewards are some such examples.

In fact, there is every reason to believe that using traditional data sources with the money trail generated by the payment revolution will make things better.

One of the biggest problems facing the National Sample Surveys has been the growing discrepancy between them and the National Account Statistics. Can using the payment system data help bridge this gap? There is a leap of faith involved in this claim. But given the rapid proliferation of electronic payments in India, there is merit in taking this leap of faith with an open mind and without a pre-decided agenda.

Every Friday, HT’s data and political economy editor, Roshan Kishore, combines his commitment to data and passion for qualitative analysis in a column for HT Premium, Terms of Trade. With a focus on one big number and one big issue, he will go behind the headlines to ask a question and address political economy issues and social puzzles facing contemporary India

The views expressed are personal