---
id: "evidence-world-bank-labor-demand"
type: "evidence"
stance: "corroborating"
org: "World Bank"
canonical_reference: "World Bank working paper, \\\\\\\"Labor Demand in the Age of Generative AI.\\\\\\\""
tags: ["occupation-exposure", "replication", "corroborating"]
related: ["claim-post-chatgpt-demand-shift", "concept-ai-automation-displacement", "framework-task-categorization-scoring"]
sources: ["reskilling"]
sourceVaultSlug: "hbr-seg-reskilling"
originDay: 10
articleStem: "hbr-edu-35-ai-changing-labor-market"
sourceUrl: "https://hbr.org/2026/03/research-how-ai-is-changing-the-labor-market"
sourceTitle: "Research: How AI Is Changing the Labor Market"
---
# World Bank — Labor Demand in the Age of Generative AI

**Source:** World Bank working paper, *"Labor Demand in the Age of Generative AI."* (enrichment ref [8])

**What it finds:** Occupation-level analysis of generative AI's impact on labor demand **before and after ChatGPT**, using treatment variation similar to the source paper. It also finds **declines for automation-prone jobs and increases for augmentation-prone jobs** — the same bifurcation pattern, though with **different exact magnitudes**.

**How it bears on this vault (strongest direct corroboration):**
- *Independently replicates the direction* of [[claim-post-chatgpt-demand-shift]] and the automation/augmentation split ([[concept-ai-automation-displacement]] vs. [[concept-ai-augmentation-complementarity]]).
- *Validates the methodological family* of the [[framework-task-categorization-scoring|task-based exposure approach]].
- Its divergence on exact numbers reinforces treating the source's −13% / +20% / −7% as article-specific estimates rather than universal parameters.