Execution and Evaluation: A New Occupational Measure and Long-Run Employment Gradients
创建于 更新于
摘要
This paper introduces an occupation-level execution share by scoring 19,265 O*NET task statements under fixed execution and AI-capability rubrics, enabling a direct comparison with AI capability and linking to employment trends. The execution share is shown to be reproducible across model coders and O*NET vintages, distinct from AI-capability and routine-task intensity, and adds only modest incremental power beyond existing evaluation measures. In harmonized panels, employment growth is lower in execution-heavy white-collar occupations across multiple periods, with a secular, between-family gradient rather than a pure AI-era shock; the vintage-valid capability gradient steepens after 2022, though the change is dated and not causally attributed to AI. The results establish a workable measure and a chronology for potential labor-market effects, emphasizing measurement framework and future causal-testing needs rather than asserting a causal AI-driven decline.
