The Labor Market Incidence of New Technologies

2025-04-05

AI Summary

This paper investigates the heterogeneous labor-market impacts of automation and artificial intelligence. Addressing a gap in the literature—namely, the neglect of how occupational skill proximity constrains worker mobility and amplifies wage inequality—the authors develop the Distance-Dependent Substitution Elasticity (DIDES) framework, which endogenizes the distributional effects of technological shocks as a function of skill-space distance between occupations. Using theoretical modeling, skill-space quantification, and dynamic general equilibrium estimation, they find that AI-driven disruptions concentrate heavily among skill-similar occupations, severely limiting inter-occupational mobility. Consequently, the primary effect is persistent wage divergence rather than large-scale employment reallocation. This mechanism resolves the apparent paradox of accelerating technological progress coexisting with sluggish labor-market adjustment and rising inequality alongside aggregate employment stability. The study thus introduces a novel paradigm for assessing technology-induced labor-market transformations.

Technology Category

• Philosophy and Ethics of AI: AI & Jobs/Labor
• Humans and AI: Other Foundations of Human Computation & AI
• Game Theory and Economic Paradigms: Mechanism Design

Application Category

• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

This paper develops a general framework for evaluating the incidence of labor market shocks, focusing particularly on automation and artificial intelligence. Unequal labor market shocks are shared among workers across occupations depending on their substitutability. Central to our theory is the concept of distance-dependent elasticity of substitution (DIDES), where substitutability between occupations declines with their distance in skill space. Our analysis reveals that automation and AI cluster in skill-adjacent occupations, generating limited employment shifts but significant wage disparities. Furthermore, the dynamic model demonstrates that limited mobility persists both during the transition and in the long run, limiting the labor market's capacity to absorb rapid technological progress.

Problem

• Analyzing labor market impacts of automation and AI technologies
• Developing distance-dependent substitution model for occupational mobility
• Quantifying wage and employment effects from technological clustering

Innovation

• Distance-dependent elasticity substitution framework
• Low-dimensional latent skill model mapping
• Technology clustering within skill-adjacent occupations