Graph-Based Analysis of AI-Driven Labor Market Transitions: Evidence from 10,000 Egyptian Jobs and Policy Implications

TL;DR

This study uses a detailed knowledge graph to analyze labor market transitions in Egypt, revealing limited viable pathways for displaced workers and emphasizing the need for targeted reskilling strategies.

Contribution

It introduces a validated knowledge graph linking Egyptian jobs and skills, providing a novel framework to assess labor mobility and transition feasibility.

Findings

Only 24.4% of at-risk workers have viable transition pathways.

Process-oriented skills are key leverage points in transitions.

Majority of displaced workers face structural barriers requiring reskilling.

Abstract

How many workers displaced by automation can realistically transition to safer jobs? We answer this using a validated knowledge graph of 9,978 Egyptian job postings, 19,766 skill activities, and 84,346 job-skill relationships (0.74% error rate). While 20.9% of jobs face high automation risk, we find that only 24.4% of at-risk workers have viable transition pathways--defined by $\geq$3 shared skills and $\geq$50% skill transfer. The remaining 75.6% face a structural mobility barrier requiring comprehensive reskilling, not incremental upskilling. Among 4,534 feasible transitions, process-oriented skills emerge as the highest-leverage intervention, appearing in 15.6% of pathways. These findings challenge optimistic narratives of seamless workforce adaptation and demonstrate that emerging economies require active pathway creation, not passive skill matching.