Abstract

As National Statistical Offices (NSOs) face the challenges of the digital transformation, the accuracy of traditional labor surveys is increasingly compromised by "Response Bias" and the limitations of qualitative self-assessment. This research proposes a strategic shift in measuring human capital within "Employment and Wage Surveys" by transitioning from subjective scales (e.g., Poor/Good) to an objective "Task-Based Frequency Matrix."
The study addresses the critical phenomenon of "Intra-occupational Skill Heterogeneity," demonstrating that occupational titles—such as physicians or engineers—often mask significant variations in actual skill intensity. By implementing a standardized "AI and Technical Skill Module," the proposed methodology captures granular data on the frequency and complexity of daily tasks. This approach effectively identifies "Underutilized Skills" (Skill Wastage), particularly in cases where highly specialized professionals perform routine roles that diverge from their educational attainment.
The methodology includes a proposed pilot study designed to validate these quantitative metrics against the ISCO-08 classification. The findings aim to provide a scalable model for NSOs to bridge the gap between "Possessed" and "Exercised" skills. Ultimately, this framework supports the UN Integrated Demographic and Social Statistics Framework, providing a robust evidence base for national planners to optimize human capital allocation and align wage structures with measurable productivity in an AI-driven market.