Mind the New Numbers

Economic data are supposed to settle arguments. However, more often than not, they start them. Was growth strong or flattened by government spending? Was inflation benign or merely delayed in the supply chain? Did the currency weaken because of global risk aversion, or because investors lost confidence in the domestic macro story? These questions usually turn on the latest number. In India, they are increasingly turning on something more basic: what the number now measures.

India is entering a rare period of statistical renovation. A new Index of Services Production is scheduled for release on July 14, with 2024-25 as the base year. It will provide a monthly gauge of services activity, a long-overdue correction in an economy where services account for more than half of gross value added, but short-term momentum is still read through industrial output, core-sector data, purchasing-managers’ surveys and tax collections. The new index will cover nearly two-thirds of services GVA, but will initially exclude health, education, informal services and non-market services such as public administration and defence. It will rely heavily on GSTN and administrative datasets from sectors such as railways, aviation, banking and insurance.

This is progress, but also a warning.

A monthly services index will help the Reserve Bank of India, the finance ministry, bond traders and forecasters read the economy with less dependence on industrial proxies. But it may also give markets a cleaner view of the formal economy, than of the economy as a whole. A services index built substantially from GST and administrative data will capture organised, taxable, incorporated activity better than fragmented informal services. If it shows acceleration, the first question should not be whether India’s services economy is booming. It should be: which services economy does the data capture?

A stronger services print could lift GDP nowcasts, reduce expectations of RBI easing and steepen parts of the government-bond curve. It could also support the rupee by strengthening the case for foreign investment in Indian assets. But if the index is picking up formalisation as much as real acceleration, investors may mistake measurement gain for macro momentum. In a data transition, the surprise is not only the number. It is the bias embedded in the new instrument.

Prices are undergoing a similar change. India is moving toward a producer-price framework alongside a rebased Wholesale Price Index. The new architecture is meant to include output PPI, input PPI and services PPI, and will initially cover sectors such as banking, securities transactions, insurance, pension funds, railways, air passenger services and telecom. Officials have described a multi-year transition from WPI to PPI; economists expect the new data to improve analysis of inflation transmission before producer costs reach consumers.

This may prove more important for central banking than the services index itself. India’s monetary-policy framework is anchored to CPI. But markets price a broader nominal economy. Bond yields respond not only to household inflation, but to producer costs, margins, fiscal risks, administered prices, import shocks and the deflators used to convert nominal growth into real growth. A PPI that captures services and separates input from output costs can show whether companies are absorbing a shock, passing it on or cutting investment. That is exactly the zone where future CPI, corporate earnings and fiscal arithmetic meet.

The old shorthand—CPI for consumers, WPI for producers—will no longer be enough. The useful question for RBI will not be whether CPI is above or below 4% on a given day. It will be whether producer-price pressure is quietly migrating into services, wages, margins or administered prices. The useful question for bond markets will not be whether inflation is “contained”. It will be whether the contained number is still the relevant one.

Industrial data are changing too. May industrial output rose 5.1% year-on-year, above expectations, led by electricity and capital goods. But the reading was only the second under the revised 2022-23 base-year series, which also reflects a shift from wholesale prices to producer prices for calculating factory output. A 5% IIP print under the new series is not automatically the same macro signal as a 5% print under the old one. Analysts who splice them casually will build elegant charts on unstable foundations.

India’s CPI revision raises the same problem in sharper form. With food’s weight cut in the revised basket, a food shock will move headline inflation less than before, even if it still damages household budgets and political economy. That does not make the new CPI wrong; consumption baskets do change. But it does mean that monetary-policy comparisons across old and new series require backcasts, distributional inflation measures and more humility. A central bank can keep the same target and still be watching a different thermometer.

The United States offers a useful cautionary tale. Its statistical system is richer, older and more heavily traded. Yet, revisions still change the macro story. In the first quarter of 2026, US GDP growth was revised up to 2.1% annualised from 1.6%. On the surface that looked like stronger activity. But consumer spending was cut sharply to 0.5% from 1.4%, and final sales to private domestic purchasers were downgraded. The same release, therefore, supported two different narratives: a firmer headline economy, but a softer private-demand core.

For the Federal Reserve, that difference is not academic. Its officials have been emphasising data dependence while inflation remains uncomfortably high. Minutes of the June 16-17 meeting showed policymakers discussing scenarios in which persistent inflation could require higher rates, while softer inflation could justify holding or eventually easing. The June minutes put particular weight on incoming inflation data, with US CPI still elevated through May and services inflation a concern.  When the reaction function is so explicitly data-driven, revisions and definitions become part of monetary policy itself.

India is moving toward the same problem, but with a thinner market memory of data vintages. In the US, investors know that GDP, GDI, payrolls, inflation and productivity can be revised in ways that change the trade. They still get surprised. In India, the coming wave of new and rebased indicators could change estimates of potential growth, the output gap, real rates and inflation pass-through all at once. That is not a footnote for statisticians. It is a market event.

The implications for bonds are immediate. If the new services index shows stronger formal activity, traders may price less policy easing. If PPI reveals producer-cost pressure while CPI looks calm, the term premium may rise. If revised IIP and GDP data show stronger real activity because of new deflators rather than new production, the front end of the curve may overreact. In each case, the key distinction is between a real macro surprise and a measurement surprise.

The implications for foreign exchange are more subtle but no less important. The rupee is not moved every morning by statistical methodology. But foreign investors price countries partly by the credibility, continuity and interpretability of their data. A market that understands India’s new series will assign a lower uncertainty premium to Indian bonds and currency exposure. A market that sees discontinuity, selective comparison and inadequate backcasting will demand compensation.

That is why India should treat its data transition as part of financial-market infrastructure. The government should publish backcast series wherever possible, linking factors between old and new bases, full revision histories, plain-English methodology notes and vintage datasets that allow analysts to reconstruct what policymakers knew at the time. RBI should be explicit about how new indicators enter its reaction function. Does a new services index matter for its assessment of demand? Will services PPI affect its reading of future CPI? How should markets compare the old and new inflation baskets?

Better data will not eliminate uncertainty. It may initially increase it. That is the paradox of statistical reform. A poor indicator gives false comfort because everyone knows how to misuse it. A better indicator unsettles because it changes the map. India’s new macro data will eventually improve policy, forecasting and market pricing. But during the transition, investors should demand a new premium: data-vintage risk.

Macroeconomic credibility is not just about producing good numbers. It is about making clear what the numbers measure, how they differ from the old ones and how policymakers will use them. The next surprise in Indian bonds or the rupee may not come from inflation, growth or the Fed. It may come from the statistical machinery that tells markets what inflation and growth are.