---
id: "prereq-job-postings-as-demand-proxy"
type: "prereq"
source_timestamps: ["¶4", "¶5"]
tags: ["data-analysis", "labor-metrics"]
related: ["claim-post-chatgpt-demand-shift"]
reason: "Necessary to accept the validity of the 13% decrease and 20% increase statistics cited in the research."
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"
---
# Job Postings as a Proxy for Labor Demand

**Why you need this:** Necessary to accept the validity of the 13% decrease and 20% increase statistics cited in the research ([[claim-post-chatgpt-demand-shift]]).

The study relies heavily on analyzing **job postings (vacancies) from 2019 to March 2025**. The implicit assumption is that the *volume* and *content* (listed skills) of job postings accurately reflect real-time employer demand and shifting labor-market requirements.

**Enrichment note (important caveat):** This proxy has known limits. Some labor economists caution that postings capture **hiring intentions and composition** — but *not* wages, hours, internal moves, or informal hiring. Yale's Budget Lab ([[evidence-yale-budget-lab]]) emphasizes using *multiple* labor-market metrics (unemployment exposure, composition dissimilarity), showing postings-based signals may not fully map onto realized employment outcomes. A downstream agent should treat postings-derived magnitudes as directional demand signals, not settled employment facts.