The Special Intensive Revision (SIR) of electoral rolls in Uttar Pradesh has produced a political storm. Some commentators have gone further, asserting—confidently and precisely—that the SIR is responsible for a sharp fall in the proportion of women on the voter list, even circulating tidy-sounding figures about a “54-point” collapse in the gender ratio and “37 lakh missing women voters”.
There are two separate issues here. One is the scale of the UP revision, which is undeniably large and therefore demands exceptional transparency. The other is the claim of a quantified, women-specific collapse in UP, which—so far—rests on a basic problem: the underlying gender-disaggregated data required to prove it is not publicly available, from authorartative ECI sources.
When the data needed to make a claim has not been officially placed in the public domain, the right response is not to invent certainty. It is to insist on disclosure.
1) What we know for sure: the scale, timing, and categories of deletions
UP’s draft roll after SIR retains 12.55 crore electors out of a pre-revision roll of 15.44 crore—meaning 2.89 crore names are missing from the draft. That is 18.7% of the earlier electorate. Whatever one’s political preferences, this is an enormous administrative intervention in a democracy.
The publicly reported breakup of deletions is broadly as follows:
Shifted / migrated / absent (the largest bucket): about 2.17 crore
Deceased: about 46.23 lakh
Duplicate enrolment: about 25.5 lakh
In addition, among those retained in the draft roll, around 8.5% are reported as “unmapped”—expected to receive notices and be asked for documents before final inclusion.
The claims-and-objections window is short in real-world terms: January 6 to February 6, 2026, with a final publication scheduled for March 6, 2026.
These are the hard numbers that matter first—because they describe the size of the disruption, the administrative categories used, and the time available to correct errors.
2) What we do not know (yet): UP’s gender-wise pre- and post-SIR roll
Now to the core of the women-voter allegation.
To say that women’s enrolment has fallen after SIR in UP, one must be able to compare:
the number of male and female electors before SIR, and
the number of male and female electors after SIR, and ideally
the gender-wise deletions within each category (shifted, deceased, duplicate, etc.), plus age cohorts.
Without this, any UP-specific headline figure—whether “54 points” or “37 lakh”—is not a finding; it is a projection presented as fact.
This is not a minor quibble. In public argument, precision is a promise. If you claim “37 lakh”, you imply the arithmetic is anchored in official counts. If those counts are not available, precision becomes performance.
3) Why overconfident gender claims are especially risky in UP
Even if one accepts that SIR-type exercises can disadvantage women (more on that shortly), UP is a state where multiple factors can distort roll-cleaning outcomes in ways that are not automatically “anti-woman”, but are still harmful if badly executed:
Urban address churn and tenancy mobility: The highest deletion rates reported are in big, mobile districts—Lucknow (about 30%), Ghaziabad (about 28.8%), Kanpur Nagar (about 25.5%), Meerut (about 24.7%), Prayagraj (about 24.6%)—the very places where addresses change, forms go unreturned, and “not found” becomes a frequent administrative label.
Household form-filling and literacy gaps: UP’s gender gap in literacy remains significant. Any process that relies heavily on forms, deadlines, and documentation predictably increases error rates among those with less administrative access—often women, but not only women.
Category noise: “Shifted/absent” is not a biometric truth; it is a field classification. If that bucket is very large—as it is in UP—then small variations in how BLOs interpret “found/not found”, how neighbours respond, or how addresses are written can produce massive differences in outcomes.
These are precisely the conditions in which the public needs more disaggregated data, not louder claims.
4) A fair point the critics make—just not a licence for invented certainty
Let us be candid: the wider concern that revisions can hit women harder is not imaginary. In other states where gender-wise patterns have been reported and analysed, women have sometimes been removed at higher rates than men, and gender ratios on the rolls have fallen after similar exercises. That matters, because women’s political participation in India has been one of the most heartening democratic stories of the last two decades—rising turnout, greater visibility, and a stronger sense of electoral agency.
But here is the discipline we must keep: evidence from elsewhere is a warning, not a verdict on UP.
The correct way to argue, therefore, is this:
“Given what has been observed in some other SIR exercises, UP may also show a gender-skew.”
“Because UP’s deletions are unusually large (2.89 crore), even a modest skew could translate into very large absolute numbers.”
“Therefore the Election Commission must publish gender- and age-disaggregated deletion data urgently.”
That is a strong, responsible argument. It does not require turning an absence of data into a viral statistic.
5) The real democratic test: transparency, correction, and safeguards
If the SIR is genuinely aimed at improving roll integrity—removing the dead, the duplicated, the truly shifted—then transparency should be easy.
At minimum, the Commission should place in the public domain, for UP:
Gender-wise elector totals before SIR and in the draft roll.
Gender-wise deletions by category (shifted/absent, deceased, duplicate, other).
Age cohorts (especially 18–29 and 30–39), because error burdens often fall on the young and the newly mobile.
District-wise gender ratios before and after, so we can see whether the urban deletion wave has a gender signature.
An independent, sample-based audit of the “shifted/absent” bucket, because that is where the largest discretionary or mistaken exclusions typically hide.
And procedurally, the Commission must ensure that correction is realistic, not theoretical: longer help-desk hours, aggressive public communication, doorstep facilitation where feasible, and clear acceptance standards for documentation—especially for citizens whose names may be correct but “unmapped”.
6) A calmer conclusion: don’t deny the risk—deny the bluff
It is entirely legitimate to worry that a form-heavy, deadline-driven verification exercise could disproportionately harm women—particularly married women who have moved households, women with limited paperwork, and women whose enrolment has historically depended on household-level decision-making.
What is not legitimate is to push precise UP-specific gender-loss figures as if they were established fact when the public has not been shown the gender-wise roll data that would prove them.
Democracy is damaged in two ways: first, when eligible citizens are wrongly removed; and second, when public debate is driven by confident arithmetic that outruns evidence.
UP’s SIR is big enough to demand seriousness from everyone—the Election Commission, political parties, civil society, and commentators. The Commission must publish the missing disaggregated data. Until it does, the honest position is simple: the risk to women voters may be real, but the precise UP claims are not yet proven—and India should insist on proof, not propaganda.