Electricity consumers: when the numbers don’t add up

The regulator has approved the government’s motion for changes in electricity tariffs, following the base tariff revision notified in January 2026. The February revision, however, is not incremental tinkering. It materially reshapes the residential tariff structure through the introduction and revision of fixed charges, alongside a downward adjustment in industrial tariffs.

The inflationary consequences of this redesign are significant and likely underestimated.

Residential consumers who were previously outside the fixed-charge regime have now been brought into its ambit. Those already paying fixed charges face upward revisions in most cases. More importantly, the structure of fixed charges has shifted fundamentally: from a flat monthly charge to a per-month, per-kW of sanctioned load basis. This is not a cosmetic change. It alters the effective tariff incidence, particularly for low-usage households with relatively higher sanctioned loads. Even protected consumers are no longer exempt. In fact, they stand to be among the most affected categories, an issue that warrants a deeper, separate examination.

For now, however, there is a more immediate and troubling problem.

The consumer numbers do not add up.

Less than a week apart, two official documents issued by the same ministry and the same division present electricity consumer data that are not merely inconsistent, but irreconcilable. One is the government’s motion submitted to the regulator. The other is a tariff-category table presented by the Ministry of Energy’s Power Division as part of the determination process. The discrepancies between the two are massive.

This is not a matter of marginal adjustments or definitional nuances. The motion places total domestic consumers at 30.4 million. The decision document puts the number at 34.4 million. A difference of four million consumers is not statistical noise. It is a structural gap.

Within categories, the divergence is even more stark. In the 101–200 unit protected slab, the motion reports 6.1 million consumers. The decision shows more than 12.5 million, more than double. In the first unprotected slab, the motion records 5.6 million consumers, while the decision reduces this to just 0.9 million. Similar mismatches appear across other slabs.

These are not trivial classification errors. They fundamentally alter how revenue recovery is understood.

From a research perspective, this makes reconciliation of tariff revenue almost impossible. Revenue projections hinge on consumer counts by slab, their respective consumption patterns, and the incidence of fixed charges. When the base population itself is unstable, any downstream calculation becomes unreliable.

The implications for inflation measurement are even more serious.

Electricity tariffs feed directly into the CPI basket. Fixed charges, in particular, affect the effective price paid by households regardless of consumption. When entire consumer categories are newly subjected to fixed charges, or face higher fixed costs tied to sanctioned load, the inflation pass-through is neither small nor linear. Estimating this impact requires accurate slab-wise consumer numbers. With two official datasets telling entirely different stories, one is forced to ask: which numbers will the Pakistan Bureau of Statistics rely on for CPI computation?

This ambiguity risks understating inflation at a time when credibility in price statistics is already under strain.

Had these numbers come from different departments, or been separated by a long time lag, one could attempt to rationalise the divergence. But this is the same division, issuing conflicting figures within days. That makes the situation deeply uncomfortable and difficult to defend.

The Ministry of Energy owes the market, policymakers, and researchers a clear explanation. Either the motion relied on outdated or incorrect data, or the figures presented during the determination process were revised without disclosure. Both scenarios are problematic.

Electricity tariffs are not just an accounting exercise. They affect household budgets, inflation expectations, and macroeconomic policy responses. When the foundational data are this inconsistent, the entire analytical edifice becomes shaky.

This is too large a discrepancy to be glossed over. A clarification is not optional. It is essential.