In recent years, there has been a dramatic growth in financial remittances into India. According to official Reserve Bank of India (RBI) data, remittances have grown enormously: from $2.1 billion (Rs 10,394 crore) in 1990-91 to $53.9 billion (Rs 2.8 lakh crore) in 2009-10. The share of private transfer receipts in India’s GDP rose from 0.7 per cent in 1990-91 to 3.6 per cent in 2009-10. These numbers, however, have been taken at face value and there has been little attempt to look closely at how valid they might really be.
The macro-numbers from the RBI, based on balance of payments (BoP) data, put the figure for remittances for 2007-08 at Rs 1.6 lakh crore. The National Sample Survey (NSS) 64th Round, conducted between July 2007 and June 2008, focused on “Migration in India”. It estimated that total remittances from those whose present place of residence was “another country” was Rs 16,706 crore — an order of magnitude different from the RBI’s BoP data. In principle, the aggregation of the micro-household data at the all-India level should be equal to the macro-BoP data — or at least close to it. It is certainly the case that in India the difference between NSS aggregates and macro numbers is not unique to remittances. There has been growing concern, for instance, at the widening discrepancies between NSS consumption figures and national income accounts, with the former declining to just 43 per cent, according to the 2009-10 survey data. But an order-of-magnitude difference is a very different cup of tea. What might explain this massive discrepancy, and what are its implications?
While the RBI’s data is from April 2007 to March 2008, the NSS survey period is from July 2007 to June 2008. If remittances have been growing steadily, then the fact that the RBI’s BoP data lag the NSS survey period should have led the aggregate RBI numbers to be less (not more) than the NSS data. There is a possibility that the NSS question on remittances – “whether sent remittances during the last 365 days” – could imply that the aggregation should be done over 2006-07 instead of 2007-08; but even then the RBI data for remittances in 2006-07 is Rs 1.3 lakh crore — eight times the NSS aggregate.
Researchers have found consistent under-reporting in household surveys relative to BoP figures in other countries as well, but none have been as large as in the case of India. In recent work with Randall Akee, we estimate that, at least in the case of NRI account-holders (nearly half of India’s remittances inflows come through this route), survey respondents appear to underestimate actual remittance receipts by nearly half — but not an order of magnitude (Randall Akee and Devesh Kapur, “Remittances and Rashomon”, Centre for Global Development Working Paper 285, January 2012).
A different way to cross-check data would be to compare data on remittance outflows from a country to India provided by an official source from that country with the data for remittance inflows into India from that country estimated by Indian authorities. Earlier RBI estimates of the geographical break-up of remittance inflows put the share of North America between 30 and 35 per cent in one report and 44 per cent in another. The table compares the estimates of remittance flows from the United States to India from a recent report from the Congressional Budget Office, with corresponding figures from the RBI for remittance inflows from North America. The discrepancy is almost a factor of five.
If we grant that the RBI data is for North America and not just for the US, the difference would still be at least a factor of four since the Indian-born population in the US is at least four times that in Canada, and an important factor underlying the large increase in remittances to India in the last decade appears to have been the influx of Indian IT workers to the US through the H1-B visa programme.
In both cases, the numbers are estimates and there is no a priori reason to believe that the CBO estimates are more accurate than the RBI’s estimates. But when the differences are of the order of $10 billion, one might think that curious minds might want to investigate this further — or a risk-averse bureaucracy might want to strategically ignore it.
These huge differences between macro-BoP data and micro-survey data matter for several reasons. Estimates of income mobility, inequality and growth will all be affected by this large mis-measurement. At the microeconomic level, misreporting at the household level could significantly affect attempts to estimate the effect of remittances on various types of household investment and consumption expenditures.
So what is going on? The money is surely coming in, as the RBI’s BoP data indicates. But is it remittances? While the NSS data are very likely underestimates, it is equally likely that there is considerable misclassification going on in the RBI’s BoP data — a considerable fraction of the remittances numbers is probably disguised capital flows with some of it return capital flight/money laundering. If so, this could mean that India’s current account deficit is higher by perhaps around one per cent of GDP, which takes the country’s BoP into more worrisome territory.
Furthermore, while remittance inflows are regarded as a stable source of foreign exchange inflows, if in reality some of this is actually capital account flows they may be more volatile and India may well be in for a nasty surprise if there is a sudden crisis. While those who send money to their families will continue to do so, those who are actually using it for investment purposes will stop, resulting in a substantial decline of what is being officially recorded as remittances. Something like this happened in 1990-91 — sharply amplifying the BoP crisis at the time.
In principle these questions could be addressed by a more careful analysis of data, especially that fraction of remittances coming through non-resident Indian, or NRI, accounts. For instance, how many NRI accounts that get more than $50,000 a year are likely to be for “family maintenance” (which is what remittances are defined as)? Unfortunately, while there are growing concerns about the quality of NSS data, at least they are put out in the public domain for researchers to use, scrutinise and criticise. The RBI’s data is, however, treated as sacrosanct with little outside scrutiny. If transparency is the best disinfectant, it is amazing just how little India is willing to carefully look at the sources of the $50 billion inflows that the RBI calls “remittances”.
The writer is director of the Centre for the Advanced Study of India at the University of Pennsylvania