Copyright and Competition: Estimating Supply and Demand with Unstructured Data

TL;DR

This paper investigates how copyright affects competition and welfare in creative industries with unstructured, high-dimensional attributes, using neural network embeddings and a structural model based on font marketplace data.

Contribution

It introduces a novel method to quantify unstructured product attributes with neural embeddings and develops a structural model to analyze copyright's impact on supply, demand, and welfare.

Findings

Copyright can increase consumer welfare by promoting product relocation.

Competition is localized in the visual attribute space.

The optimal copyright policy depends on the interaction with technological advances.

Abstract

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.