BISP eligibility isn't a simple income-based threshold — it uses a multi-dimensional poverty assessment called the Proxy Means Test (PMT) that weighs household composition, education levels, asset ownership, living conditions, and income from all sources into a single poverty score. Households scoring below a defined threshold (the "PMT cutoff") qualify for Kafalat and related programmes; households above it don't. Understanding the criteria helps families predict their likely eligibility, identify what circumstances might improve or worsen their score, and know what to expect before the NSER survey reaches their area.
What the PMT methodology actually measures
The PMT is a statistical model that estimates household consumption (and therefore poverty status) based on observable household characteristics, rather than relying on self-reported income alone. Self-reported income is notoriously unreliable in contexts like Pakistan where informal income, agricultural earnings, remittances, and irregular work make accurate income reporting difficult. The PMT works around this by looking at the visible markers of household economic status.
- Household composition — number of members, age distribution, dependency ratio (children and elderly versus working-age adults)
- Education levels — formal schooling completed by household members, particularly the head of household
- Asset ownership — agricultural land, livestock, vehicles (cars, motorcycles), household appliances (refrigerator, washing machine, TV)
- Living conditions — house construction type (pukka vs kacha), number of rooms, drinking water source, sanitation facility, electricity access
- Employment characteristics — occupation type of working members (salaried, self-employed, daily wage, agricultural)
- Geographic location — urban vs rural, district-level cost-of-living adjustments
- Income from formal sources — salaries, pensions, remittances from abroad, agricultural rental income
How household scores translate to eligibility
The PMT produces a continuous score, typically ranging from 0 to 100 (sometimes scaled differently in technical documentation). Lower scores indicate worse poverty conditions; higher scores indicate better economic status. BISP sets a cutoff threshold each programme cycle — currently around 32 on the standard scale, though the specific number adjusts based on programme budget and policy decisions.
Households with PMT scores at or below the cutoff qualify for Kafalat and its related programmes. Households above the cutoff don't qualify regardless of how close they are to the threshold — a household scoring 32.5 doesn't qualify if cutoff is 32, even though it's nearly identical to a household scoring 32. This sharp cutoff design is administratively practical but creates "near-poor" populations who genuinely struggle but technically don't qualify.
The cutoff isn't public information at the household-specific level — when you check eligibility, you see "eligible" or "not eligible" but not your specific PMT score or how far above/below cutoff you are. This is partly to discourage households from gaming their survey responses (knowing the specific factors they'd need to change), and partly because the score isn't meaningful as an absolute measure outside the BISP eligibility context.
The factors that most affect typical Pakistani households' scores
For most rural Pakistani households, the heavy-weight factors include: education of household head (uneducated head correlates with much higher poverty than educated head), house construction (kacha houses score much higher poverty than pukka houses), household appliance ownership (each appliance like refrigerator or washing machine reduces poverty score significantly), motor vehicle ownership (any car or motorcycle dramatically reduces poverty score), and agricultural land ownership above subsistence thresholds.
For urban Pakistani households, the heavy-weight factors shift somewhat: rented vs owned housing matters more in urban context, occupation type carries more weight (salaried employee in formal sector versus daily wage labor produces different scores), education levels of household head and spouse, and quality of utilities access (24-hour electricity, gas connection, treated water).
Family composition affects scores significantly. Households with many children relative to working adults (high dependency ratio) score worse poverty than households with balanced ratios. Single-parent households (typically widowed mothers) often score in poverty bands because losing a working spouse increases dependency burden. Multi-generational households with multiple working adults often score better than nuclear families with single working adult, all else being equal.
Why eligibility outcomes sometimes feel inconsistent
Families sometimes notice neighboring households or seemingly similar families having different eligibility outcomes. Several reasons explain this. The PMT model has many variables (typically 20-30 distinct factors); two superficially similar households can differ on subtle factors (one has a refrigerator the other doesn't, one has a slightly better-educated head, etc.) that shift their scores meaningfully. Geographic adjustments make identical asset profiles produce different scores in different districts.
Survey-data quality also varies. Surveyor experience, household members' truthfulness during the interview, time of day (some surveys conducted in haste with reduced thoroughness), and what items happened to be visible during the survey can affect the data captured. Two genuinely identical households can get different scores due to data capture variation alone.
Programme parameter changes between assessment cycles also create apparent inconsistency. A household scoring 31 (below cutoff of 32) gets eligible. Three years later, with the cutoff lowered to 28 due to budget tightening, the same household scoring 30 becomes ineligible. The household didn't change but the eligibility frame moved.
What's not part of PMT scoring
- 🚩 Political affiliation — never part of any legitimate BISP assessment; if asked, refuse and report fraudulent survey
- 🚩 Religious or sectarian identity — irrelevant to poverty assessment; never relevant to eligibility decisions
- 🚩 Caste or biraderi — Pakistan's social structure isn't a factor in PMT scoring
- 🚩 Family connections to government officials — eligibility is data-driven, not relationship-driven
- 🚩 Willingness to pay survey "facilitation" fees — surveys are free; any payment demand indicates fraud
- 🚩 Past programme participation — your historical eligibility doesn't determine current eligibility; each assessment is independent
How to think about whether your household likely qualifies
Rough self-assessment is possible without official tools. Households living in pukka houses with multiple rooms, owning a refrigerator, motorcycle or car, with adults working in formal employment, with children in private schools, are very unlikely to qualify regardless of stated income. The visible asset and lifestyle profile signals economic status that exceeds BISP's target population.
Households living in kacha or one-room construction, without major household appliances, where the head works as daily wage laborer or has no formal employment, where children attend public schools (or aren't attending), where utilities are intermittent or absent — these households are likely to score in BISP-eligible bands.
The grey zone is middle-income working families with mixed asset profiles. A formally-employed teacher or shop owner with modest assets, kids in modest schools, owning a basic motorcycle — these households sit near the threshold and outcomes depend on specific PMT factors and current cutoff. They might or might not qualify; the NSER survey is the definitive determination.
Frequently Asked Questions
Currently around 32 on the standard PMT scale, though the specific number isn't publicly emphasized and adjusts across programme cycles based on budget and policy decisions. The exact cutoff matters less for individual households than the broad question of whether their poverty profile is consistently in poverty bands. Households clearly in poverty (worse than typical cutoff) qualify reliably; households clearly out of poverty (better than typical cutoff) don't qualify regardless of cycle adjustments.
Limited room for appeal on scoring methodology. The PMT is statistically derived and applied uniformly; individual scores aren't adjusted based on appeals. The exception is appeals based on data accuracy — if the survey captured incorrect information about your household (wrong asset list, wrong household composition, wrong education levels), correcting these through formal complaint can result in score recalculation. But if your survey data is accurate and your score is genuinely just above cutoff, the eligibility outcome stands.
Remittances are factored into household income assessment but don't automatically disqualify. The amount, regularity, and household total income matter. A household receiving Rs. 50,000 monthly remittance from a child working overseas is unlikely to qualify because that income alone exceeds typical poverty thresholds. A household receiving Rs. 5,000 monthly remittance might still qualify if other household income is minimal and overall poverty profile remains in eligible bands.
Periodically, typically every 4-6 years based on new household survey data from Pakistan's national surveys. The variables included, their weights, and the scoring methodology get reviewed as Pakistan's economic structure evolves. The current PMT framework dates from the 2020-2022 revision period; the next significant update is likely in 2026-2028 based on new data collection. Beneficiaries should expect that eligibility cutoffs and methodology may shift over time.
Individual asset items contribute small amounts to the score, not large jumps. A basic refrigerator contributes some points (reflecting that owning one correlates with non-extreme poverty), but doesn't single-handedly disqualify otherwise eligible households. The PMT looks at the combination of assets — multiple appliances together signal stronger non-poverty than any single appliance. Strategic getting rid of refrigerators before surveys to qualify wouldn't produce big score shifts and the survey captures total household profile.
Several reasons. Their NSER survey may have captured inaccurate data (overstated household income or assets). Programme cutoffs may have tightened, moving previously eligible households into ineligible bands. Recent household changes (working family member returning, new income sources) may have shifted their actual poverty profile even if appearances remained similar. The PMT methodology occasionally misses genuinely poor households — particularly those in unusual circumstances that the model wasn't calibrated to capture. For genuine cases of apparent misfit between visible poverty and PMT-based ineligibility, the complaint portal can sometimes trigger reassessment.