Data Commons as a Brake-and-Boost for Creative Destruction: Toward Inclusive Schumpeterian Growth

Liao et al. (2023) show data are nonrival inputs that can simultaneously fuel horizontal and vertical innovation; Chen et al. (2019) show optimal capital taxes depend on the extent of creative destruction. We hypothesize that well-governed data commons (sectoral data trusts, interoperability standards) can reallocate the balance from pure displacement to “standing on shoulders”—increasing aggregate innovation while mitigating concentration and employment shocks. We’ll compare sectors with data-sharing consortia to matched controls, focusing on productivity growth, entry/exit dynamics, and inequality. Building on Kalvet (2016) and the governance/ethics concerns in digital platforms (Kangjun Li, 2024), we specify design features—access tiers, privacy-preserving analytics, reciprocity rules—that preserve competitive pressure while reducing duplicative R&D and the most wasteful forms of business-stealing. The novelty is to connect endogenous growth theory with concrete data governance institutions as policy instruments that reshape the micro-foundations of creative destruction. If the hypothesis holds, governments could substitute part of blunt capital taxation (Chen et al., 2019) with targeted data-sharing regimes that both accelerate innovation and cushion its social costs.

References:

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3. Optimal capital taxation in an economy with innovation-driven growth. Ping-ho Chen, A. Chu, H. Chu, Ching‐chong Lai (2019). Macroeconomic Dynamics.
4. The Role of Digital Innovation in Shaping Cultural Narratives: Analysing the Ethical Implications of Algorithm-Driven Media Platforms for Online Communities. Kangjun Li (2024). Critical Humanistic Social Theory.