Research & Publications

The United States labor market exhibits a persistent coexistence of high job vacancy rates and prolonged unemployment duration, a pattern that challenges standard labor market theory. This paper argues that a portion of contemporary frictional unemployment is artificially induced by automated recruitment systems that rely on deterministic keyword-based screening. By analyzing system architecture and evaluating semantic matching through experimental simulations, the study quantifies how automated screening design contributes to candidate exclusion and labor market inefficiency.

Automated resume screening systems are now central to large-scale hiring, yet growing evidence suggests that rigid screening logic can exclude qualified candidates before human review. This paper introduces a measurement-based framework for quantifying algorithmic friction in automated recruitment systems. Through controlled simulations comparing keyword-based screening with semantic matching approaches, the study evaluates excess false-negative rejection attributable to semantic mismatch and system design. The results highlight efficiency losses induced by deterministic screening pipelines and provide empirical grounding for assessing hiring system performance.