Why statistics must lead the narrative of the Indian economy’s emergence

India’s statistical system has evolved significantly. Surveys are larger and more sophisticated. Data is processed faster. Digital tools have improved collection and dissemination. Official statistics today occupy a central place in our policy discourse in a way they did not even a decade ago. This institutional shift deserves recognition.

Yet, greater visibility has not translated into greater trust. Many citizens listen to official numbers and quietly ask themselves: Does this reflect what I see around me? That is the ‘moment of truth’ for data credibility. If they feel distant from experience, even robust statistics may struggle to persuade.

Take employment. Our unemployment rate is measured with methodological care. But when people discuss jobs, they often mean something broader: stability, income security and prospects for advancement.

In India, employment is fluid. A worker may be self-employed one season, engaged in casual labour the next and assisting in a family enterprise after that. A single unemployment rate cannot capture that volatility or the anxiety that accompanies it. When these nuances are not explained clearly, the data can appear incomplete even when it is technically accurate.

The poverty debate illustrates the point even more sharply. We hear that nearly 250 million people have moved out of poverty in recent years, an important achievement. Yet, over 800 million receive free foodgrains. To many observers, this appears contradictory. If poverty has declined so substantially, why does such a large segment of our population still depend on food support?

The answer lies in understanding what each measure is intended to capture. Poverty is assessed against a consumption benchmark at a particular moment in time. Food assistance programmes serve a different purpose. They are designed to reduce vulnerability in an economy where incomes fluctuate and employment remains largely informal. Households that have crossed the poverty threshold may still be economically fragile. Without explaining this distinction, our progress can seem inconsistent or overstated.

Inflation tells a similar story. Headline consumer price inflation may remain moderate. Yet, households frequently feel that prices, especially of such essentials as food, remain high. Aggregate indices conceal variation across categories and income groups. Food has a greater weight in lower-income budgets and tends to be more volatile.

When communication focuses only on the overall figure without clarifying what is driving it, the data can feel detached from a family’s daily reality.

For policymakers, the issue is somewhat different. They are rarely short of statistics. What they often require is timely interpretation. Data that gets released after budgets are finalized or programmes are redesigned rarely influences decisions, regardless of data quality. Statistics are most effective when they arrive at the moment policy choices are being shaped, accompanied by concise explanations of their implications. Statistical evidence has greater impact when it is aligned with decision cycles than published in isolation.

Trust, too, depends on how institutions respond to scrutiny. In a polarized environment, statistical findings can quickly get politicized. Responding solely with technical language may create more distance than clarity. Being transparent about assumptions, revisions and margins of error does not weaken institutions. On the contrary, openness strengthens confidence. Citizens understand that measurement is complex. What they expect is candour.

There is also a broader question of narrative control. In areas such as wealth distribution, hunger and university performance, global indices increasingly shape perceptions about India. Whether one agrees with their methodology or not, they influence how the country is assessed. If domestic systems do not regularly produce credible measures in these fields, we risk responding to external assessments instead of framing progress through our own evidence.

Statistical sovereignty does not mean rejecting global comparisons. It means ensuring that India has consistent, transparent and contextually grounded statistics of its own. Authority emerges from continuity, clarity and confidence.

Looking ahead, capacity building must widen its scope. Technical rigour will always remain foundational. But interpretation, communication and sustained thematic reporting are equally important.

Every major statistical release should answer three straightforward questions: What does this show; why does it matter; and what are its limits? Institutionalizing regular reporting in areas central to the national and global discourse, such as inequality, nutrition, innovation and regional disparities, would further strengthen credibility.

India has the technical capability to produce statistics of global standard. Our next step should be to ensure that those statistics shape public understanding and inform policy when it matters most. For a country aspiring to a Viksit Bharat (or developed-country status) by 2047, the accuracy of our counts is essential. But sustaining trust, continuity and narrative leadership is just as important.

In the end, statistics do more than measure progress. They help a nation understand itself. And if India does not tell its own statistical story with clarity and confidence, someone else will tell it for us.

The author is managing director and chief executive officer of People Research on India’s Consumer Economy.