2025/01/27 by Sukjin Han, Kyungho Lee, Han, Sukjin +1 · 1 citation
Business, Management and Accounting · Economics, Econometrics and Finance · #Applications (stat.AP) #Econometrics (econ.EM) #FOS: Computer and information sciences #FOS: Economics and business #Innovation Policy and R&D #Intellectual Property and Patents #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Merger and Competition Analysis
paper · pdf · doi:10.48550/arxiv.2501.16120
openalex publication_date 2025/01/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We study the competitive and welfare effects of copyright in creative industries in the face of cost-reducing technologies such as generative artificial intelligence. Creative products often feature unstructured attributes (e.g., images and text) that are complex and high-dimensional. To address this challenge, we study a stylized design product -- fonts -- using data from the world's largest font marketplace. We construct neural network embeddings to quantify unstructured attributes and measure visual similarity in a manner consistent with human perception. Spatial regression and event-study analyses demonstrate that competition is local in the visual characteristics space. Building on this evidence, we develop a structural model of supply and demand that incorporates embeddings and captures product positioning under copyright-based similarity constraints. Our estimates reveal consumers' heterogeneous design preferences and producers' cost-effective mimicry advantages. Counterfactual analyses show that copyright protection can raise consumer welfare by encouraging product relocation, and that the optimal policy depends on the interaction between copyright and cost-reducing technologies.