Skill dependencies uncover nested human capital

TL;DR

This paper uncovers a nested structure in skill dependencies within US labor data, showing that advanced skills build on fundamental ones and influence wages, automation risk, and mobility.

Contribution

It reveals a directional, nested structure of skills affecting career paths, wages, and automation, with evidence of increasing barriers over time.

Findings

Skills more aligned with the nested structure command higher wages.

Advanced skills require longer education and are less automatable.

The nested skill structure has become more pronounced over two decades.

Abstract

Modern economies require increasingly diverse and specialized skills, many of which depend on the acquisition of other skills first. Here we analyse US survey data to reveal a nested structure within skill portfolios, where the direction of dependency is inferred from asymmetrical conditional probabilities-occupations require one skill conditional on another. This directional nature suggests that advanced, specific skills and knowledge are often built upon broader, fundamental ones. We examine 70 million job transitions to show that human capital development and career progression follow this structured pathway in which skills more aligned with the nested structure command higher wage premiums, require longer education and are less likely to be automated. These disparities are evident across genders and racial-ethnic groups, explaining long-term wage penalties. Finally, we find that this nested structure has become even more pronounced over the past two decades, indicating increased barriers to upward job mobility.