Execution and Evaluation: A New Occupational Measure and Long-Run Employment Gradients

2026-07-22

AI Summary

This study addresses a key limitation in existing occupational exposure metrics, which fail to distinguish between human roles in task execution versus evaluation, thereby obscuring the nuanced impact of artificial intelligence (AI) on employment. Leveraging 19,265 task descriptions from O*NET, the authors propose a reproducible “execution share” measure that explicitly differentiates AI capabilities, routine task intensity, and human execution roles, enabling the construction of an occupation-level AI capability exposure index. Panel data regression analyses reveal persistently subdued employment growth since 2012 in white-collar occupations intensive in execution tasks. Moreover, a pronounced gradient effect of AI capability emerged after 2022, though causal inference remains tentative. This work offers a novel measurement framework and empirical evidence to better understand the heterogeneous labor market effects of AI.

Abstract

Artificial intelligence automates execution more readily than evaluation: producing output is cheap, judging whether it is correct is not. Exposure measures rank tasks by whether AI can perform them, not by which function the human supplies. I score all $19{,}265$ O*NET task statements under fixed rubrics to build occupation-level execution and AI-capability shares. The execution share is reproducible across model coders and O*NET vintages and distinct from AI capability and routine-task intensity; it is a model-based measure, not human-validated ground truth, and adds only modest power beyond O*NET's evaluation activities. In a harmonized panel, employment growth is lower in execution-heavy white-collar occupations in every window since 2012, and equality of slopes cannot be rejected: the gradient is a secular trend rather than an AI-era event, largely between occupational families. The vintage-valid capability gradient steepens after 2022, a change that is dated but not causally attributable. The evidence establishes a measure and a chronology, not an AI-caused effect.

Problem

• artificial intelligence
• execution vs evaluation
• occupational exposure
• employment gradients
• task automation

Innovation

• execution vs. evaluation
• occupational task measurement
• AI exposure
• employment gradient
• O*NET task scoring