---
id: "lib-2026-03-00-ai-output-and-employment-cesifo-working-paper"
type: "library"
title: "AI, Output, and Employment (CESifo Working Paper 12579)"
lang: "EN"
org: "CESifo"
source_type: "Paper"
date: "2026-03-01"
week: "2026-W09"
url: "https://nosov.ai/library/lib-2026-03-00-ai-output-and-employment-cesifo-working-paper/"
original_url: "https://www.ifo.de/DocDL/cesifo1_wp12579.pdf"
tier: "A"
tier_name: "Strong evidence"
tier_reason: "Johnston (UT Austin, NBER) and Makridis (ASU, Stanford) estimate AI's sector-level effects with near-universal US administrative data, a difference-in-differences design, placebo tests and robustness checks; method shown, results checkable."
summary_basis: "full-text"
original_minutes: "60"
tags: ["ai adoption", "output", "employment"]
---

# AI, Output, and Employment (CESifo Working Paper 12579)

Tier A (Strong evidence): Johnston (UT Austin, NBER) and Makridis (ASU, Stanford) estimate AI's sector-level effects with near-universal US administrative data, a difference-in-differences design, placebo tests and robustness checks; method shown, results checkable.

Summary based on the full text.

## In short

CESifo Working Paper 12579 (25 March 2026), previously circulated as "The Labor Market Effects of Generative AI". Andrew C. Johnston and Christos A. Makridis find that US industry-state sectors with more AI exposure saw output rise about 7% per standard deviation from 2021, when enterprise AI tools such as the GPT-3 API and GitHub Copilot entered workflows. Employment also rose, but only where AI augments workers. Because output grew faster than pay, labour's share of income fell.

## Key numbers

- 7%: rise in sector output per one standard deviation of AI exposure, emerging from 2021
- 4%: rise in employment where AI exposure is augmentative (needs human collaboration); no significant effect where it automates
- about 29%: share of AI-induced output gains that accrue to labour
- roughly 5 percentage points: fall in labour share of income per standard deviation of AI exposure
- over 95%: share of US wage and salary jobs covered by the QCEW employment data used

## What it reports

- One standard deviation more AI exposure raises sector output by 7%, employment by about 4% and wage bills by 5% by the end of the sample.
- Splitting exposure: where AI needs human collaboration, employment rises 4%; where AI can do tasks alone, effects are small and insignificant.
- Only about 29% of the output gain goes to labour. The labour share falls roughly 5 percentage points per standard deviation.
- Wage gains concentrate among younger and more educated people, groups with higher AI adoption.
- Effects start in 2021, earlier than ChatGPT, which the authors argue is the right treatment date for enterprise AI.
- Event-study coefficients for 2017-2019 are near zero (no pre-trends). A placebo using an unrelated occupational trait predicts nothing.

## How they got it

State by 3-digit NAICS panel, 2017-2024. Exposure is built from Eloundou et al. (2024) occupation scores weighted by pre-2020 occupation shares. Output from BEA state GDP by industry, employment from the QCEW (over 95% of US wage and salary jobs, including firm entry and exit), wages from the American Community Survey. Identification uses the same industry's different occupation mix across states, with state-by-year and industry-by-year fixed effects and controls for teleworkability and interest-rate exposure.

## Limits

- Exposure is a fixed technical measure, not observed AI use.
- Working paper, not yet peer reviewed. Sector-level estimates cannot show which firms or staff gained or lost.
- The 2021 timing differs from studies dated to ChatGPT's November 2022 launch, so results are not directly comparable.

## Why it matters for companies

The paper gives large-sample support for designing AI around augmentation: in exposed sectors that used AI with people, output and jobs both grew, while automation-type exposure did not add jobs. The labour-share result is the warning: most gains went to capital, which will shape pay negotiations, works council discussions and how firms explain AI benefits to staff.

Original: https://www.ifo.de/DocDL/cesifo1_wp12579.pdf