Post-Labor Economics Guide

Overview

Post-labor economics studies the economic consequences of advanced automation -- the possibility that AI and robotics will displace human labor at a scale and speed that overwhelms traditional adjustment mechanisms. While technological unemployment is an old concern (dating to the Luddites and Keynes's "Economic Possibilities for Our Grandchildren"), the current wave of AI capabilities has made the question urgent: what happens to labor markets, income distribution, and economic growth when machines can perform most cognitive and physical tasks?

This is not science fiction. The academic literature on task displacement, skill-biased technological change, and automation risk has produced substantial empirical findings and theoretical frameworks. Researchers from economics, political science, sociology, and computer science are converging on these questions.

This guide covers the key theoretical models, empirical evidence, policy proposals (UBI, robot taxes, stakeholder funds), and methodological approaches for studying the economics of automation. It is designed for researchers entering this rapidly growing field and for those in adjacent disciplines who need to engage with the economic arguments.

Theoretical Frameworks

The Task-Based Model of Automation

The canonical model (Acemoglu & Restrepo, 2018, 2019) decomposes production into tasks rather than jobs:

Production = f(Tasks performed by Labor, Tasks performed by Capital)