Data-driven innovation and growth

Li, Hao and Wang, Gaowang and Yang, Liyang (2024): Data-driven innovation and growth.

Abstract

We develop an endogenous growth model where data drives innovation. In this model, big data fosters quality improvements by influencing the likelihood and magnitude of successful quality-enhancing innovations. It also promotes variety innovation through the efficient allocation of labor as a fixed cost, ultimately driving long-run economic growth. The social planner reduces the welfare costs associated with monopoly production and internalizes the externalities present in decentralized economies. As a result, the optimal growth rate exceeds the equilibrium growth rates under two data property rights regimes. Data property rights play a crucial role in determining long-run growth and steady-state welfare, which depend largely on two key model parameters: the weight for privacy and the frequency of creative destruction. This model also explores the interactions between quality innovation and variety innovation.

• Item Type: MPRA Paper
• Language: English
• Keywords: data as innovation; endogenous growth; data property rights; interactions between quality innovation and variety innovation
• Subjects: E - Macroeconomics and Monetary Economics > E1 - General Aggregative Models; O - Economic Development, Innovation, Technological Change, and Growth > O3 - Innovation ; Research and Development ; Technological Change ; Intellectual Property Rights; O - Economic Development, Innovation, Technological Change, and Growth > O4 - Economic Growth and Aggregate Productivity > O41 - One, Two, and Multisector Growth Models

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