arXiv 27 Jan 2025 · Econometrics · 1 citations (OpenAlex)
arXiv:2501.16120 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Lemley, M. A (2009) Our bizarre system for proving copyright infringement | 0.811 | 4 | 2 | 100% |
| 2 | –-, –- and –- (1999) Voluntary export restraints on automobiles: Evaluating a trade policy | 0.737 | 3 | 3 | 67% |
| 3 | Balganesh, S., Manta, I. D. and Wilkinson-Ryan, T (2014) Judging similarity | 0.737 | 3 | 2 | 100% |
| 4 | Eizenberg, A (2014) Upstream innovation and product variety in the us home pc market | 0.737 | 3 | 2 | 100% |
| 5 | Fan, Y (2013) Ownership consolidation and product characteristics: A study of the us daily newspaper market | 0.737 | 3 | 2 | 100% |
| 6 | Han, S., Schulman, E. H., Grauman, K. and Ramakrishnan, S (2021) Shapes as product differentiation: Neural network embedding in the analysis of markets for fonts self | 0.737 | 3 | 2 | 100% |
| 7 | Mankiw, N. G. and Whinston, M. D (1986) Free entry and social inefficiency | 0.737 | 3 | 2 | 100% |
| 8 | Waldfogel, J (2012) Copyright research in the digital age: Moving from piracy to the supply of new products | 0.737 | 3 | 2 | 100% |
| 9 | Berry, S. T (1992) Estimation of a model of entry in the airline industry | 0.644 | 2 | 2 | 100% |
| 10 | Berry, S., Eizenberg, A. and Waldfogel, J (2016) Optimal product variety in radio markets | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 91 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | From Unstructured Data to Demand Counterfactuals: Theory and Practice | 0.644 | 2 | 2 |
| 2 | Econometrics with Pre-Trained Embeddings for Unstructured Data | 0.405 | 1 | 1 |