Over two lakh crore in CSR since 2014.
The poorest districts got the least.
An investigative mapping of India's Corporate Social Responsibility landscape reveals a staggering geographical divide. While capital centers flourish, the aspirational districts remain shadowed by industrial neglect.
CSR clusters in Tier-1 corridors while aspirational districts see minimal funding.
Our methodology cross-references Ministry of Corporate Affairs data with NITI Aayog MPI headcount ratios to reveal the widening gap in social capital allocation across 651 districts.
Bihar's poverty rate is more than 4× Maharashtra's, yet it receives roughly 20× less CSR per person.
From data to decision.
Four steps to surface your highest-opportunity district. No dashboards, no noise. Just evidence, ranked.
Choose Your Sector
Pick a development sector (education, health, sanitation, or any of ten focus areas). When a sector is selected, only CSR spending in that sector is used to compute the funding gap (G). Districts with no activity in the chosen sector will show a maximum gap. Need (N) and Persistence (U) remain unchanged.
Set Your Priorities
Three weight sliders. Drag them to reflect what matters most to your foundation. Poverty severity, funding gap, persistence. Weight each to match your theory of change, or pick a preset.
See Ranked Districts
A live-ranked ledger of every scored district, ordered by philanthropic opportunity. Click any state on the map to filter down to that geography.
Download a Brief
Click any district to generate a research brief with key data, context, and gaps worth studying. Export as PDF.
Opportunity Simulator.
Adjust weights, select a sector, and click any state to surface your highest-opportunity districts.
POS spans 0–81.2 at All Sectors (default weights). Picking a sector rescales the funding-gap component: districts with zero CSR in that sector saturate Ĝ at 1.0, so scores can reach ~85.5.
Source · NITI Aayog MPI 2023 · MCA CSR via Dataful.in · Census 2011 · Briefs AI-assisted and require independent verification
Scoring Controls
Calibrate the Index
Weight given to MPI headcount ratio
Weight given to CSR under-funding vs tier median
Weight given to districts where poverty has not improved
Quick Presets
India · Territorial Atlas
Click a state to filter rankings
District Ledger
0 districts
What the data reveals.
Three signals from the ledger. Each points to a different kind of gap, and a different lever for philanthropic capital.
The same methodology works anywhere there is public data.
Education infrastructure gaps, health access disparities, and energy poverty are next in the investigation pipeline.
Future research will apply this methodology to education, health, and energy infrastructure data.