Publication Details
Abstract
Information asymmetry and frictional unemployment in the labour markets of emerging economies generate a structurally suboptimal allocation of human capital and produce a persistent deviation of GDP from its potential level. In Uzbekistan, approximately 8–10 percent of the annual cohort of 600,000 new labour-market entrants fail to find employment commensurate with their skill profile in the formal economy, while a parallel structural shortage manifests itself in the unmet demand on international digital platforms. The objective of this study is to convert the classical “Triple Helix” model — encompassing the State, the Education system and the Private sector (including international platforms) — into a unified AI-augmented macroeconomic ecosystem and to empirically assess its impact on frictional unemployment and digital services exports. Methodologically, the study deploys a modified Cobb–Douglas-type matching function, M = A(SI) · U^α · V^β, in which A(SI) denotes the technological efficiency coefficient that grows endogenously through artificial-intelligence-mediated matching. Calibrated parameters (α = 0.52; β = 0.48; A(SI) baseline 1.00 → 1.38 under full deployment) yield a simulation outcome in which average matching time falls from 38 days to 11 days, frictional unemployment declines by 1.9 percentage points, and digital services exports rise by an additional USD 3.2–3.8 billion by 2030. The annual macroeconomic effect of the ecosystem is estimated at 1.8–2.4 percent of GDP. The study advances a replicable institutional-technological template for the digital transformation of labour markets in resource-constrained economies.