|
on Knowledge Management and Knowledge Economy |
|
Issue of 2026–04–13
three papers chosen by Laura Nicola-Gavrila, Centrul European de Studii Manageriale în Administrarea Afacerilor |
| By: | Hanming Fang; Xian Gu; Hanyin Yan; Wu Zhu |
| Abstract: | We develop a high-precision classifier to measure artificial intelligence (AI) patents by fine-tuning PatentSBERTa on manually labeled data from the USPTO’s AI Patent Dataset. Our classifier substantially improves the existing USPTO approach, achieving 97.0% precision, 91.3% recall, and a 94.0% F1 score, and it generalizes well to Chinese patents based on citation and lexical validation. Applying it to granted U.S. patents (1976–2023) and Chinese patents (2010–2023), we document rapid growth in AI patenting in both countries and broad convergence in AI patenting intensity and subfield composition, even as China surpasses the United States in recent annual patent counts. The organization of AI innovation nevertheless differs sharply: U.S. AI patenting is concentrated among large private incumbents and established hubs, whereas Chinese AI patenting is more geographically diffuse and institutionally diverse, with larger roles for universities and state-owned enterprises. For listed firms, AI patents command a robust market-value premium in both countries. Cross-border citations show continued technological interdependence rather than decoupling, with Chinese AI inventors relying more heavily on U.S. frontier knowledge than vice versa. |
| JEL: | C55 G14 O31 O33 O34 O57 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35022 |
| By: | Claudia Collodoro (Dipartimento di Politica Economica, DISCE, Università Cattolica del Sacro Cuore, Milano, Italy); Lucrezia Fanti (Dipartimento di Politica Economica, DISCE, Università Cattolica del Sacro Cuore, Milano, Italy - Instituto di Economia, Scuola Superiore Sant’Anna, Pisa, Italy); Jacopo Staccioli (Dipartimento di Politica Economica, DISCE, Università Cattolica del Sacro Cuore, Milano, Italy - Instituto di Economia, Scuola Superiore Sant’Anna, Pisa, Italy); Maria Enrica Virgillito (Dipartimento di Politica Economica, DISCE, Università Cattolica del Sacro Cuore, Milano, Italy - Instituto di Economia, Scuola Superiore Sant’Anna, Pisa, Italy) |
| Abstract: | This work provides a comprehensive large-scale analysis of artificial intelligence-based worker management (AIWM) systems from an industry-wide exposure perspective focusing on traditional industries. We begin by examining the knowledge production underlying these workforce management tools and leverage technology patent-classification to identify their dynamics and specific features. For this purpose, we use patent data retrieved from Orbis Intellectual Property covering the years 1975 to 2022, considering patents filed with both the EPO and the USPTO. Furthermore, to identify patents related to AIWM heuristics, we retrieve their full text from Google Patents and conduct a textual analysis using a dependency parsing algorithm. Finally, using the dictionary of human tasks provided by O*NET, we construct a measure of exposure to AIWM systems for individual human tasks and occupations. Linking the technological and labour market domains, we find that the professions most exposed to AIWM systems are those at the top of organisational hierarchies. |
| Keywords: | Artificial Intelligence Worker Management, Sector-level Analysis, Patenting Activity, Techno-organisational Change |
| JEL: | O14 O33 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:ctc:serie5:dipe0056 |
| By: | Daniel Goller; Enzo Brox; Stefan C. Wolter |
| Abstract: | Why do young people sort into poorly fitting occupations? This paper shows that imperfect self-knowledge about skills is an important source of skill mismatch at labor market entry. We use unique data from standardized professional aptitude tests linked to administrative records on educational trajectories and early labor market outcomes in Switzerland. The data allow us to observe objective skills and subjective skill beliefs for many productivity-relevant skills in a high-stakes setting. We document large differences among individuals in how well their beliefs align with their skills. Imperfect self-knowledge predicts misaligned occupational aspirations, higher realized skill mismatch, and a higher probability of dropout. Guided by a Roy-style model of occupational choice with imperfect self-knowledge, we interpret these findings as evidence that distorted self-assessments at the school-to-work transition contribute to the misallocation of talent. |
| Keywords: | Information frictions, Occupational choice, Skill mismatch, Self-knowledge |
| JEL: | D83 J24 J41 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:iso:educat:0253 |