Why Indian States Need To Govern In Real Time

We do not see the world as it is. We see it as we are. This is as true of economics as it is of psychology. The statistics through which states understand their economies are not neutral mirrors. They are shaped by habit and the limits of what could once be measured, and as the world changes, these habits become blinders. For India, where growth, welfare delivery, climate risk and urbanisation are increasingly driven by state and city governments, this is central to governance effectiveness.

A revealing analogy comes from medicine. In 1990, when General Electric corrected a flaw in its MRI software to produce more accurate images of fatty tissue, many radiologists resisted the update. They had grown accustomed to the older, compressed scans and worried that unfamiliar images might lead to misdiagnosis. GE was eventually forced to include a “classic” view, allowing doctors to continue seeing the distorted images they had learned to interpret. The problem was not the technology, but the comfort of expertise built around a distorted signal.

Economic measurement today faces a similar risk. GDP, inflation and unemployment are not the economy. They are images or compressed representations shaped by a “small data” world. These measures are wrong. But they have become overly comfortable, classic views of a world that no longer exists.

The Broken Window of Growth

The most glaring flaw in the classic view is its inability to distinguish between value creation and distress. Consider the air quality crisis in Delhi. When pollution spikes to hazardous levels, the sale of air purifiers, nebulisers and cough syrups surges. Hospitals see more admissions. Pharmaceutical companies sell more drugs. In the cold logic of GDP, this registers as economic growth. The economy has grown because money changed hands. But has the country prospered?

A measurement system that counts a parent buying asthma medicine for a choking child as a positive economic transaction, indistinguishable from buying a meal, is fundamentally incomplete. It confuses frantic spending for survival with the genuine creation of welfare.

The Digital Transformation of Visibility

Fortunately, the digitalisation of society has offered us a way out of this blindness. A quarter century ago, most economic activity left little trace. Today, payments, mobility, logistics and energy use, generate continuous data streams. India’s Goods and Services Tax Network (GSTN) illustrates this shift vividly. Processing billions of invoices, it offers a near real-time pulse of commercial activity far more granular than quarterly GDP estimates. During the pandemic, such data revealed the uneven geography of economic contraction weeks before official statistics caught up.

This transformation also changes the unit of measurement. Traditional statistics are territorial. They measure district income, state GDP, national GDP, etc. But modern economies operate as networks of flows. Consider the Jawaharlal Nehru Port near Mumbai. Standard district GDP accounting attributes its entire economic value to Raigad district, where it is physically located. Yet the port’s true footprint extends across multiple states including freight corridors, financial services (mostly in Mumbai City), and even logistics in Pune, Jalna and Wardha districts. We are attempting to measure a fluid network with a static ruler.

From Warehouses to Reflexes: The Speed of Governance

Static measurement also misses the speed of our response to external stimuli. It is increasingly this speed that determines outcomes. For decades, we have judged India’s performance by how much money we allocated, how many hospitals we built and how many tons of grain we stored. This is the warehouse view of governance. It assumes that capacity equals capability. Yet in a volatile world of climate shocks and pandemics, the true measure of a state is not its size, but its reflexes.

Maharashtra offers a vivid example of what happens when the signal arrives faster than the fiscal architecture can absorb it. The state budget for 2020-21 was framed in early March 2020, just before the pandemic shut down economic activity. Within weeks, GST collections had deteriorated sharply. Total receipts, excluding borrowings, fell short of the budget estimate by about ₹58,000 crore, a 17 percent gap. The budget had assumed the state economy would grow about 12 percent. It instead contracted roughly 6 percent (in the revised estimates). The problem was not that the state made a forecasting error or lacked a signal. It was that the signal moved through administrative systems faster than it could enter formal budget architecture.

Odisha’s disaster management experience also illustrates this perfectly. After the devastating 1999 super cyclone, the state didn't just build more shelters. It built a better nervous system. By the time Cyclone Fani struck on 3 May 2019, the state evacuated 1.2 million people in about 24 hours, with a speed and coordination that drew praise from the United Nations as a global benchmark. The real difference was the rate of response and not just resources.

For most of the modern era, the state was the most informed actor in its own territory. Tax returns, surveys and administrative reports ultimately flowed back to public institutions. That hierarchy is weakening. Today, large private platforms often observe labour demand, mobility, payments and consumer stress before the state does, and at far finer resolution. Payroll processors can see job losses before labour departments. Payment networks can detect regional demand stress before national accounts. Logistics firms can identify bottlenecks before commerce ministries. When private systems know more about economic conditions than public institutions do, the state is no longer the most informed actor.

This has direct consequences for federal governance. Centre-state asymmetries are no longer only fiscal. They are informational. The Centre allocates through formulas, budget cycles and legacy datasets. States experience shocks through live systems including tax receipts, crop stress, labour demand, electricity load and disease burden. When those two clocks run at different speeds, misallocation becomes inevitable.

The Political Economy of Truth

If real-time data is so effective, why do classic views persist? The barrier is institutional inertia. Data shapes incentives. When statistics arrive with long lags, decision-making adjusts to slower feedback cycles. Real-time data exposes failures instantly. A power outage, a spike in rural distress or a drop in tax compliance becomes visible immediately.

Furthermore, the data fabric required for this vision faces the same resistance. Indian departments act as silos, guarding data to preserve turf. Integrating land records with satellite imagery and bank credit flows is technically tractable but institutionally radical. It requires a shift from data as proprietary departmental asset to data as public digital infrastructure.

This same methodology also carries a warning. The tools that infer distress can easily slide into surveillance. The closer measurement moves toward ground truth, the stronger is the case for transparency about what is collected and why. It requires independent oversight and methods that allow insight without exposure.

The Path Forward

The radiologists eventually adapted to better MRI images. Economists and policymakers must do the same. For Indian states, the agenda is not to replace GDP, but to surround it intelligently. It is to build systems that measure flows rather than just stock, and reflexes rather than just levels. AI allows us to separate distress from progress, and illness from well-being. Those states which adapt to this reality will define the frontier of how modern states see, learn and act.

Disclaimer: The views expressed in this article are those of the author and do not necessarily reflect the views of the publication