How these numbers are made
Everything here is computed from the raw government files. Nothing is copied from another village directory.
Sources
Linking the two
Mission Antyodaya's VILLAGE CODE is the Census 2011 village code,
so the two link directly on code — no name matching, which would be unreliable
given that the same village is transliterated differently in each file. Across
all of India 6,57,870 villages are covered and the code join succeeds for
97.3% of Mission Antyodaya villages.
Where a village spans more than one gram panchayat, Mission Antyodaya surveys it once per panchayat and each return repeats the whole village's population. Adding those rows would double or triple the village. We therefore take the largest single return, never the sum.
When we do not show a change figure
Of 5,86,306 villages present in both sources, we publish a population change for 4,56,712 (77.9%). The rest are held back for these reasons:
We also suppress the 2020 sex ratio unless the male and female counts add up to the stated total within 2%. Without that check the national average appears to collapse by about 130 points; almost all of that is arithmetic that does not reconcile, not a real change.
Where the data is strong, and where it isn't
The share of villages with a usable change figure varies a lot by state. This is a property of how the 2020 survey was conducted, not of the villages.
| State | Villages compared | Change measurable | 2020 figure copied from 2011 |
|---|---|---|---|
| Mizoram | 611 | 93.9% | 0.8% |
| Meghalaya | 5,873 | 93.3% | 1.3% |
| West Bengal | 36,925 | 92.5% | 3.3% |
| Haryana | 6,339 | 91.6% | 2.3% |
| Madhya Pradesh | 51,413 | 89.6% | 5.5% |
| Bihar | 39,000 | 89.2% | 2.7% |
| Uttar Pradesh | 95,952 | 89.1% | 5.3% |
| Himachal Pradesh | 17,571 | 86.7% | 4.6% |
| Manipur | 2,273 | 85.3% | 2.7% |
| Sikkim | 414 | 83.6% | 0.2% |
| Punjab | 12,029 | 83.6% | 11.0% |
| Nagaland | 1,073 | 83.3% | 3.4% |
| Assam | 24,327 | 81.3% | 4.1% |
| Chhattisgarh | 19,477 | 80.2% | 17.0% |
| Jharkhand | 28,993 | 79.7% | 11.9% |
| Jammu And Kashmir | 5,691 | 77.2% | 10.8% |
| Rajasthan | 43,136 | 76.0% | 16.5% |
| Tripura | 799 | 75.8% | 0.1% |
| Uttarakhand | 15,017 | 74.2% | 17.7% |
| Andhra Pradesh | 16,250 | 74.1% | 15.9% |
| Tamil Nadu | 14,984 | 71.6% | 16.3% |
| Karnataka | 26,847 | 69.9% | 21.8% |
| Arunachal Pradesh | 5,022 | 67.1% | 15.6% |
| Odisha | 46,833 | 57.6% | 36.7% |
| Maharashtra | 40,540 | 56.9% | 38.5% |
| Goa | 318 | 56.3% | 39.6% |
| Gujarat | 17,628 | 50.7% | 47.2% |
| Telangana | 9,494 | 39.1% | 48.0% |
| Kerala | 985 | 27.4% | 67.6% |
Villages created after 2011
Some villages carry a code of 900000 or higher. Those are assigned by the Local Government Directory to villages created after the 2011 Census — usually a hamlet, thanda or dhani that became a revenue village in its own right. They have no 2011 baseline and are marked as such rather than shown as having grown from nothing.
Corrections
Two known problems in the source files are corrected here. In the Mission Antyodaya CSVs the column headers are offset by one against the data for a run of 18 columns, so a naive read attributes school, college, ration-card and clean-energy figures to the wrong fields. And several columns pack multiple answers into a single cell — the primary-school column alone contains eight separate fields, including student and teacher counts. Both are unpacked before anything is published.
Found something that looks wrong? The underlying figure is always attributed to its source year on each village page, so it can be checked against the original file.