Sanjeev Ahluwalia | Looking Through Data Fog: Decode Truth, Lies in India

What distinguishes truth from lies? Statistics, of course. Nothing illustrates the criticality of timely, accurate, consistent data for sound economic decision-making than US Federal Reserve chairperson Jerome Powell’s lament about contemporary monetary policy being like “driving slowly through a foggy night”. He was referring to the consequences of reducing the US Federal Reserve rate below four per cent, where it has stood since September 2025, after two sequential cuts of 0.25 per cent each since December 2024, amidst heightened economic uncertainty from President Donald Trump’s tantrums.

Christopher Waller, a contender for succeeding Mr Powell and a proponent of lower reserve rates to support growth and risk inflation, counters that “driving slowly does not mean coming to a halt”, pointing to the cost of excessive caution in foregone growth, by making decisions overly data driven. The long US government shutdown (which recently ended), over the two-month-old deadlock over budget appropriations, has reduced the reliability and timeliness of unemployment and inflation data, as statistical staff were progressively laid off pending approval of new appropriations.

There is a close relationship between statistical capacity and development, amongst other variables — think energy consumption, education or health services. According to the World Bank’s Statistical Performance Index, most OECD countries — a club of 38 rich countries sharing a commitment to democracy and the market economy — rank in the top quintile. Next comes most of Southeast Asia, Latin America, Russia and Central Asia, Turkey, Saudi Arabia, Egypt, South Africa and Sri Lanka in the fourth quintile.

India is in the third quintile, along with select countries in South Asia, East Africa and West Africa. A low positioning for a country, where modern, official statistics started in 1881 — more than six decades before it became independent in 1947 and which till, the 1970s, was a regional leader in statistical practices.

Nor has World Bank support in 2010 to improve statistical capacity in state governments helped. The only consolation is that other countries like Rwanda and Vietnam, which also availed of World Bank financing for the same purpose, are similarly ranked, though Brazil, another borrower, is better off in the fourth quintile.

One plausible, albeit not the primary reason, for Brazil’s better performance, could be that constitutional guarantees protect the autonomy of the statistical commission in Brazil. In India, statistical agencies work under the executive, leading to the potential loss of credibility with the public, when government becomes the umpire of its own performance.

There are, of course, external umpires of a government’s functioning. Consider for instance, the global bond markets which track developments closely and punish government indiscretions in real time. Bond markets reacted vigorously in 2024 to the disastrous Budget tabled by the short-lived Liz Truss government in the UK. The government proposed unfunded tax cuts — a favourite right-wing stimulus — contrary to the advice from the Office for Budget Responsibility, a watchdog reporting directly to Parliament. In response, the government’s bond yields increased by about four per cent and the pound sterling weakened. Sadly, in developing economies, including India, about 30 per cent of the government’s borrowing is funded by the compulsory purchase of government bonds by banks and exposure to the global debt market is limited, which dulls the market response to government indiscretions. Having said that, India has never defaulted on its debt obligations. More significantly, global debt markets have a near term perspective and take national statistical data at face value, so it is no good relying on external pressure to improve statistical quality.

The impact of dodgy public statistics is primarily on the efficiency of the domestic economy. Taxpayers end up paying more that they need to for the same services and the needy and voiceless get deprived of essential services. The Indian government is acutely aware of the political fallouts of such downsides. This is why the BJP — a “report card” government — which prides itself on extending the largest ever web of social services, has given up on targeting only the deserving. Consider the extended food support and the cash support programme paid to all farmers, which extends to about 55 per cent of the population of 1.45 billion. State governments separately finance free electricity or free bus rides for women and other innovative service support programmes. Targeting only the deserving requires good data — to avoid inclusion and exclusion errors — or risks raising public dissatisfaction over unfair allocations. The fiscal cost of improper targeting is about two per cent of GDP. Improving the efficiency with which governments target subsidies is the only sustainable future course to bridge the impossible duality between enhancing the Union government’s capital expenditure (4.3 per cent of GDP this year) for better infrastructure and defence preparedness and continued reduction of the fiscal deficit (4.4 per cent of GDP) this year to more sustainable levels. Better targeting is both a data adequacy and a political economy issue. As the government ignores the fiscal cost of about two per cent of GDP on undeserved subsidies, it recently took a very “strict” stand on the revision of registered voters for the just-ended elections in Bihar, using some of the digital identification markers now available. This led to the inclusion of new voters, amounting to 2.7 per cent, but also the deletion of 8.2 per cent of the 78.9 million voters. Of the delisted votes, 34 per cent had died, another 11 per cent were duplicate votes, but as many as 55 per cent were presumed migrated or untraceable. Expectedly, a political storm followed, alleging motivated decision making in a state where the BJP is part of the government.

Institutional changes to the statistical system were last suggested by the Rangarajan Commission in 2001 but only partly implemented. Digitisation of the economy is incomplete without digitised public data generation, because it is cost effective, albeit with appropriate safeguards and inclusive provisions for laggard digitisers and the aged. India’s last decennial Census was held in 2011. It is now delayed by four years — making poverty estimates, unemployment levels and whether we are the largest country by population, into guesstimates. The Union home ministry, which is authorised by the Census Act 1948 for this purpose, is mulling digitisation instead of the traditional, decentralised process of state governments using schoolteachers as enumerators — over which the Centre has limited control. It is not just the US Federal Reserve which has stopped rather than go wrong under pressure in the data fog. But at what cost?

The writer is Distinguished Fellow, Chintan Research Foundation, and was earlier with the IAS and the World Bank