Chile's RSH algorithm classifies 8.99 million households (85.6 % of the population) into socioeconomic tiers that gate access to all means-tested state benefits. Biweekly updates since 2023; 779,000 households newly eligible after November 2023 recalibration.
8,994,018
Households covered by RSH (2023)
17,136,753
People covered by RSH (2023)
85.6 %
Share of national population covered (2023)
1,686,507
Households reclassified after November 2023 recalibration
779,000
Households newly eligible for benefits (40%/60% most vulnerable tiers) (November 2023)
Details
Maturity
Established
Promoter
Ministerio de Desarrollo Social y Familia
Period
2016–present
Keywords
social protection, proxy means test, benefit targeting, administrative data integration, poverty measurement
Context
Introduced in 2016 to replace the universally criticised CAS in-person survey card, the RSH is Chile's centralised algorithmic socioeconomic classification system, fusing self-reported household data with seven administrative registries (tax authority SII, social security AFP/IPS, vehicle registry, property register, health fund FONASA/Isapre, education spending and child-support records) to produce a welfare-band score via a proxy means test.
Activities
The RSH score determines eligibility for every means-tested programme in Chile — housing subsidies, education grants, healthcare co-payments and emergency cash transfers. Since May 2023, scores are recalculated biweekly (previously monthly). The system is listed on Chile's official public AI Algorithm Repository (algoritmospublicos.cl) with documented inputs, methodology and update schedule; households may request supervised corrections, and unpaid alimony is no longer counted as income following a 2022 rule change.
Results
As of 2023, the RSH covers 8,994,018 households (17,136,753 people, 85.6% of Chile's population). The November 2023 recalibration, based on the Casen 2022 household survey, reclassified 1,686,507 households, with 779,000 households newly entering the 40% and 60% most-vulnerable tiers and becoming eligible for benefits previously denied to them.
Conclusions
Known limitation: informal-economy earnings are structurally under-captured, creating a known inclusion-error bias for precarious workers.
Implementation
Implementation detail (cost, timeline, staffing, conditions for success) is not yet available for this practice.
Data sources
Where this practice's information was retrieved from, and when.
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