Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.14850

Displacement Is Endogenous: AI Workers in a Search-and-Matching Labor Market

Ibrahim Denis Fofanah
View original source ↗Read source document ↗Export metadata ↓

Abstract

We build a labor market in which AI systems enter as workers: they apply for jobs, pass screenings, get hired, and get dismissed, alongside humans. Firms post vacancies, workers search with friction, and product demand slopes downward. Across 30 seeded runs per configuration, AI entry at 70% of the human wage raises unemployment by 4.1 percentage points (95% CI 3.5 to 4.6) and leaves 12.2 fewer humans employed (CI 10.5 to 13.9). Layoffs dominate when AI is much cheaper than humans: the layoff margin accounts for 7.2 of the lost jobs and reduced hiring for 5.0, though most dismissed workers find work again. A wider price gap reduces displacement, because cheap AI makes firms expand: at price parity, with zero dismissals, the unemployment effect is largest at 9.2 points (CI 8.6 to 9.9), all of it through hiring. That parity result survives cutting AI applications from five per period to one. Demand elasticity decides how many workers AI displaces. Screening changes who gets hired and leaves the displacement total unchanged.

Explore related subjects

econ.GNcs.CYq-fin.EC

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ibrahim Denis Fofanah. 2026-10-10. Displacement Is Endogenous: AI Workers in a Search-and-Matching Labor Market. https://arxiv.org/abs/2610.14850

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Which Text Matters: Handling Model Uncertainty in Text-as-Data Regressions, with an Application to Central Bank Communication

Social scientists routinely convert text into covariates like topic shares, ideal points or sentiment scores, and insert them into statistical models of interest. This two-step design is convenient, but it is rarely guided by theory: little tells the analyst which text-based covariates to construct or include. Each approach embodies contestable measurement choices, and competing operationalizations of the same construct can support different conclusions, so a single research question gives rise to a menu of competing regression models. Which text constructed covariates belong in a model, and in what functional form, is thus a problem of model uncertainty. We propose spike-and-slab generalized additive models (SSGAMs), which jointly select covariates and determine whether their effects are linear or nonlinear. We illustrate the approach with the communication of a powerful unelected institution, asking which of 45 competing text indicators from 224 monthly European Central Bank press conferences, spanning late 2002 to mid-2025, track professional inflation expectations. A sparse set is selected; widely used alternatives, and every tested nonlinear effect, carry no incremental information.

econ.GN↗
arXiv2026

Extracurricular Exercise and Academic Achievement: Evidence from the China Education Panel Survey

Does physical exercise enhance adolescent academic achievement, and is there an optimal daily duration? Linking two waves of the China Education Panel Survey ($N=8,247$), we examine this association using restricted cubic splines and classroom fixed effects. Prospective estimates without prior-score controls place non-exercising students 0.162 mean subject-z units below peers exercising one hour daily. Flexible baseline-achievement controls shrink this shortfall by 85.1 percent to 0.024. The exercise block is not jointly significant ($p=0.301$), and curvature tests fail to reject linearity ($p=0.659$). Subgroup estimates yield similar gender profiles, with stronger evidence of location variation in English. These findings establish the importance of academic history in observed exercise gradients and challenge prescriptive exercise targets derived from fitted observational turning points.

econ.GN↗
arXiv2026

Whom Do AI Agents Work For? Role Assignment Induces Sponsorship Bias in LLM Recommenders

Large language models (LLMs) now serve as conversational shopping assistants on platforms that also sell advertising. These AI agents face a conflict of duty. They advise consumers who rely on their judgment, yet are deployed by platforms that benefit when sponsored listings are chosen. Sponsorship disclosures, designed to allow consumers to penalize paid placements, now reach the AI agent rather than the consumer, and the agent's evaluation of them is hidden from the consumer. Drawing on the fiduciary concept of conflict of duty, we argue that an agent's evaluation of a sponsored listing should not depend on which party deployed it. In controlled choice experiments, we manipulate assigned roles in the system prompt to name either a traveler or a booking platform as the agent's principal. Platform delegation significantly attenuates the penalty that agents apply to sponsored listings and weakens the skepticism that disclosure triggers in their reasoning traces. We replicate out findings across LLMs and reasoning depths. A second study decomposes the disclosure label and shows that the divergence between the two delegates widens significantly when the paid placement is attributed to the platform. Stricter terminology ("Sponsored" instead of "Promoted") lowers choice of paid listings but does not close this gap when the platform is named. The findings show that disclosure mandates designed for human consumers cannot by themselves protect consumers in AI-mediated commerce.

econ.GN↗