nep-ipr 2026-06-29 papers

on Intellectual Property Rights

Issue of 2026–06–29
two papers chosen by
Giovanni Battista Ramello, Università di Turino

Patents, firm rents, and worker compensation: Causal evidence from quasi-random patent allocation By Alam, Afroza; Diegmann, André
Market Design for AI: Beyond the Copyright Binary By Yan Dai; Maryam Farboodi; Negin Golrezaei; Sepehr Shahshahani

Patents, firm rents, and worker compensation: Causal evidence from quasi-random patent allocation

By:	Alam, Afroza; Diegmann, André
Abstract:	This paper provides new causal evidence on how patent allowances affect firms and their employees based on quasi-random assignment of patent applications to examiners. Exploiting employer-employee records with newly linked German firm data and web-scraped patent documents, we show that patent-induced shocks reduce firm exit, improve productivity, and increase wages, with rent-sharing elasticities between 0.10 and 0.21. Wage gains are broadly observed across occupational tasks, with high heterogeneity: managers benefit disproportionately in publicly traded firms, whereas broader wage increases accrue to workers in non-traded firms. Our findings highlight the role of institutional features and firm organization in shaping how rents are shared.
Keywords:	firm performance, innovation, rent sharing, worker compensation
JEL:	D22 J31 O31 O34
Date:	2026
URL:	https://d.repec.org/n?u=RePEc:zbw:iwhdps:341391

Market Design for AI: Beyond the Copyright Binary

By:	Yan Dai; Maryam Farboodi; Negin Golrezaei; Sepehr Shahshahani
Abstract:	How can we design a market of human-generated content for use in training AI models that both enables technological progress and preserves individual incentives for high-quality content creation? Existing approaches take polar positions: a "free-for-all" model based on fair use and a "strong intellectual property rights" model. We show that both fail: Free-for-all does not compensate creators, and -- by modeling as a static Stackelberg game -- strong intellectual property rights also underpower creative incentives. We find this especially true for more innovative creators, a phenomenon we term the "originality penalty." Extending this insight to a dynamic model, we find another market failure undermining AI model performance, even for an initially good model: Such a model induces greater reliance by humans on AI-assisted creation, resulting in homogenized content feeding back into training, which degrades the model performance -- a "curse of precision." We further propose a market design with a data intermediary internalizing cross-creator externalities and subsidizing innovative contributions, thereby restoring efficiency.
Date:	2026–06
URL:	https://d.repec.org/n?u=RePEc:arx:papers:2606.12260

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