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The Algorithmic Barrier: Quantifying Artificial Frictional Unemployment in Automated Recruitment Systems

Ibrahim Denis Fofanah

TL;DR

This paper addresses the paradox of high job vacancies with prolonged unemployment by arguing that deterministic keyword-based ATS screening creates artificial friction in labor-market matching. It formalizes artificial frictional unemployment, demonstrates through controlled simulations that semantic matching with high-dimensional vector embeddings substantially improves recall without sacrificing precision, and proposes JobOS as a candidate-side architectural layer that standardizes signals, verifies competencies, and securely governs data while interoperating with existing recruitment systems. The key contributions are (1) the formalization of artificial frictional unemployment, (2) empirical evidence that semantic matching reduces false negatives and improves screening efficiency, and (3) the JobOS architecture that augments current hiring pipelines with semantic translation, verification, and candidate-controlled data governance. The work suggests meaningful national-scale impacts, including shorter unemployment durations, better labor mobility, and increased productivity through improved matching efficiency, while grounding design choices in fairness, transparency, and privacy considerations.

Abstract

The United States labor market exhibits a persistent coexistence of high job vacancy rates and prolonged unemployment duration, a pattern that standard labor market theory struggles to explain. This paper argues that a non-trivial portion of contemporary frictional unemployment is artificially induced by automated recruitment systems that rely on deterministic keyword-based screening. Drawing on labor economics, information asymmetry theory, and prior work on algorithmic hiring, we formalize this phenomenon as artificial frictional unemployment arising from semantic misinterpretation of candidate competencies. We evaluate this claim using controlled simulations that compare legacy keyword-based screening with semantic matching based on high-dimensional vector representations of resumes and job descriptions. The results demonstrate substantial improvements in recall and overall matching efficiency without a corresponding loss in precision. Building on these findings, the paper proposes a candidate-side workforce operating architecture that standardizes, verifies, and semantically aligns human capital signals while remaining interoperable with existing recruitment infrastructure. The findings highlight the economic costs of outdated hiring systems and the potential gains from improving semantic alignment in labor market matching.

The Algorithmic Barrier: Quantifying Artificial Frictional Unemployment in Automated Recruitment Systems

TL;DR

This paper addresses the paradox of high job vacancies with prolonged unemployment by arguing that deterministic keyword-based ATS screening creates artificial friction in labor-market matching. It formalizes artificial frictional unemployment, demonstrates through controlled simulations that semantic matching with high-dimensional vector embeddings substantially improves recall without sacrificing precision, and proposes JobOS as a candidate-side architectural layer that standardizes signals, verifies competencies, and securely governs data while interoperating with existing recruitment systems. The key contributions are (1) the formalization of artificial frictional unemployment, (2) empirical evidence that semantic matching reduces false negatives and improves screening efficiency, and (3) the JobOS architecture that augments current hiring pipelines with semantic translation, verification, and candidate-controlled data governance. The work suggests meaningful national-scale impacts, including shorter unemployment durations, better labor mobility, and increased productivity through improved matching efficiency, while grounding design choices in fairness, transparency, and privacy considerations.

Abstract

The United States labor market exhibits a persistent coexistence of high job vacancy rates and prolonged unemployment duration, a pattern that standard labor market theory struggles to explain. This paper argues that a non-trivial portion of contemporary frictional unemployment is artificially induced by automated recruitment systems that rely on deterministic keyword-based screening. Drawing on labor economics, information asymmetry theory, and prior work on algorithmic hiring, we formalize this phenomenon as artificial frictional unemployment arising from semantic misinterpretation of candidate competencies. We evaluate this claim using controlled simulations that compare legacy keyword-based screening with semantic matching based on high-dimensional vector representations of resumes and job descriptions. The results demonstrate substantial improvements in recall and overall matching efficiency without a corresponding loss in precision. Building on these findings, the paper proposes a candidate-side workforce operating architecture that standardizes, verifies, and semantically aligns human capital signals while remaining interoperable with existing recruitment infrastructure. The findings highlight the economic costs of outdated hiring systems and the potential gains from improving semantic alignment in labor market matching.
Paper Structure (43 sections, 3 equations, 5 figures, 2 tables)

This paper contains 43 sections, 3 equations, 5 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Literature Review
  3. The "Hidden Worker" Phenomenon
  4. Information Asymmetry and Labor Market Signaling
  5. Algorithmic Bias and Deterministic Screening in ATS
  6. The Evolution of Semantic Matching Architectures
  7. Synthesis and Research Gap
  8. Methodology
  9. ATS as a Deterministic Classification System
  10. Semantic Representation via Vector Space Modeling
  11. Contextual Understanding with Transformer-Based Embeddings
  12. Experimental Design and Controlled Simulation
  13. Methodological Scope and Assumptions
  14. System Architecture
  15. Data Ingestion and Normalization Layer
  16. ...and 28 more sections

Figures (5)

  • Figure 1: High-level JobOS system architecture illustrating the modular pipeline from unstructured candidate data to ATS-compatible outputs.
  • Figure 2: Semantic matching pipeline illustrating embedding generation, vector indexing, and similarity-based retrieval.
  • Figure 3: JobOS data governance model illustrating candidate-controlled access, encryption boundaries, and audit logging.
  • Figure 4: F1-score comparison between keyword-based ATS screening and JobOS semantic matching.
  • Figure 5: Precision--recall trade-off across similarity thresholds for semantic matching.