In April 2026, the Ministry of Statistics and Programme Implementation (MoSPI) released draft guidelines to standardise District Domestic Product (DDP) estimates, finalising them in June. That month, the National Statistics Office (NSO) also released employment and informal-enterprise data for 46 million-plus cities. MoSPI describes these data as inputs to city-level GDP estimation, rather than estimates of city GDP themselves.
These are steps in the right direction, but they expose a crucial gap in how India measures urban economic activity. Where one city dominates its district, DDP may provide a reasonable approximation of its economic output. Elsewhere, it combines the output of several towns and rural areas, obscuring important differences between them. Some city economies also extend across district boundaries. Without city-level estimates, policymakers cannot reliably separate a city’s economic output from that of the wider district.
India’s City-Level Data Gap
The lack of a comprehensive city-level measure of economic output reflects two deeper problems: coverage and comparability. The 2019 City GDP framework, prepared by the Ministry of Housing and Urban Affairs (MoHUA), identified these problems in its review of district estimates across five States. The estimates used different methodologies, price series and reporting periodicities, limiting their use as a common building block for city estimates.
MoSPI’s new guidelines establish a common methodology for compiling DDP, while changes to the Periodic Labour Force Survey (PLFS) and the Annual Survey of Unincorporated Sector Enterprises (ASUSE) strengthen the basis for district-level estimation. These reforms improve district statistics, but they do not resolve the mismatch between district boundaries and city economies. Comparable district estimates are still not comparable city estimates.
This gap also shows up in urban research. Studies using district-level income as a proxy for city output caution that doing so introduces substantial measurement error. The consequences matter for major public investment: the Smart Cities Mission alone has involved nearly ₹1.64 lakh crore across about 8,000 projects in 100 cities.
Consistent city-level output estimates would help policymakers assess whether economic activity is growing alongside major investments and whether it is keeping pace with population growth. Combined with compatible employment measures, they would also help track changes in productivity. Spatial data would serve a different purpose: identifying where activity is concentrated and how investment connects to those locations.
City-Level Measurement Matters for Policy
The June NSO reports cover 46 cities with populations of at least one million in the 2011 Census. These cities account for 13 percent of establishments, 16 percent of workers and 21 percent of gross value added in India’s unincorporated non-agricultural sector. They nevertheless represent only part of India’s urban economic landscape. Comparable city-level data remain limited for the many smaller towns beyond this group. A statistical system confined to million-plus cities would miss much of the variation in India’s urban economy.
Urban mobility illustrates why this variation matters. A study of 154 Indian cities found wide differences in travel speeds, driven more by differences in uncongested mobility than by congestion. Slow movement therefore does not always indicate the same underlying problem. Measures aimed at reducing congestion may have limited effects where road networks and other constraints keep speeds low even outside peak hours.
City-level economic estimates would help establish the scale and trajectory of these different urban economies. Spatial indicators, considered alongside transport and land-use data, would help policymakers identify economic clusters and the connections they need.
Building a City-Level Economic Measurement System
The 2026 LEAP Cities Index combines 43 official, administrative and satellite indicators for 233 non-metro cities. It shows that fragmented data can be assembled into a comparable city-level picture. Its composite scores, however, are not estimates of economic output, and some indicators retain the limitations of the district or State data from which they are drawn.
India should build two complementary layers.
The first should strengthen the official statistical chain from State to district to city. Building on MoSPI’s 2026 guidelines, PLFS and ASUSE data could contribute, alongside sector-specific sources, to allocating district output across cities, as envisaged in the 2019 framework. Where sufficient data exist, city output could be estimated more directly. Elsewhere, allocation-based estimates should be clearly labelled, with their methods and uncertainty published.
For practical purposes, this work could begin with administrative city boundaries, while recognising that they do not always coincide with functional urban economies. Consistent boundary definitions would be essential for comparing cities and tracking change over time.
The second layer should provide an annual spatial indicator of economic activity, offering signals between fuller output estimates and extending observation to cities with limited survey coverage. Night-time lights can contribute because they provide consistent spatial coverage and are available annually or even monthly. Indian evidence suggests a significant relationship between night-time lights and economic activity, and the Economic Survey 2025–26 identifies them as a useful indicator of urbanisation and economic activity. They should nevertheless complement official accounts rather than substitute for them.
This spatial layer could combine night-time lights with administrative indicators such as bank deposits and credit, vehicle registrations and freight movement. Calibration against DDP could provide a district-level benchmark, but would not by itself establish accuracy within the district. Validation against independent city-level data would be needed wherever available.
The aim should be a system that makes cities comparable while making the limits of each estimate visible. City output estimates would establish economic scale and change; spatial indicators would help locate activity and track emerging patterns. Together, they would give policymakers a firmer basis for assessing urban investment without treating every available proxy as city GDP.