AI summaryⓘ
The authors propose a new way to measure a country's productive capacity called the Effective Cognitive Population (ECP), which adjusts population size by skills and how well those skills are used, specifically focusing on AI readiness. They combine human capital measures with AI preparedness factors to better predict a country's economic output, improving accuracy over previous methods. Their results show significant shifts in country rankings when using ECP instead of just counting people, but the interaction between AI readiness and skills is treated as a planning guideline rather than proven cause-and-effect. The ECP helps planners but doesn't predict population changes or directly measure AI's impact on productivity.
Effective Cognitive PopulationHuman Capital Index PlusAI Preparedness IndexDemographic AccountingProductive CapacityPopulation Skills AdjustmentEconomic Output PredictionDigital InfrastructureRegulation and EthicsMultiverse Analysis
Abstract
National planning counts population, human capital, and artificial-intelligence preparedness in separate ledgers. Demographic accounting has advanced from headcount to skills-adjusted stocks and still debates how much age structure retains once skills are modeled, yet no existing unit carries the conditions under which preparedness becomes productive capacity. This study introduces the Effective Cognitive Population (ECP), a decomposable unit that weights population by capability and by the conditions under which capability is deployed, anchored to the World Bank Human Capital Index Plus (HCI+) and the non-overlapping dimensions of the IMF AI Preparedness Index. The architecture is portable in principle; the case tested here is artificial intelligence, which has a published preparedness index. For 144 countries, HCI+ becomes a productivity level, AI opportunity uses digital infrastructure and innovation integration, conversion governance uses regulation and ethics, and the benchmark is ECP = N H(1 + AC). Against 2024 total output on identical population bases, ECP raises criterion R-squared from 0.849 for the HCI+-adjusted stock to 0.882 and lowers leave-one-country-out RMSE from 0.723 to 0.641, with the working-age comparison identical and bootstrap intervals excluding zero. Eighty-nine of 144 countries move at least ten rank positions from headcount, mostly through the human-capital adjustment itself. Results are stable across denominators, vintages, aggregation forms, and a 27-rule multiverse. The direct A by C interaction is not statistically supported, so the conjunction is a planning rule rather than causal complementarity. ECP is a diagnostic ledger whose scope excludes forecasts of population decline and estimates of AI's causal productivity effect.