Innovation, Growth and Industrial Policies
Paper Session
Saturday, Jan. 3, 2026 8:00 AM - 10:00 AM (EST)
- Chair: Yong Wang, Peking University
Privacy Concerns, Firm Innovation, and Optimal Taxation in the Data Economy
Abstract
This paper introduces consumer data as an input for R&D in a Schumpeterian growth model where consumers balance the economic benefits of selling data against privacy concerns. In a decentralized market equilibrium, a higher degree of privacy concerns reduces the supply of consumer data, hindering innovation and leading to a lower growth rate. Compared to a social planner's allocation, the decentralized equilibrium features socially excessive (insufficient) innovation, if the degree of privacy concerns is larger (smaller) than a threshold. This threshold value depends on three factors, i.e., the step of innovation, the mark-up on product markets, and the data intensity in R&D. We analyze the optimal taxation that restores socially efficient allocation and discuss the role of digital infrastructure in shaping the innovation and growth in this model.Turing Growth Model
Abstract
Inspired by the pioneering Turing Machine model introduced by Turing in 1936, this research endeavors to develop a unified growth model for the digital economy, integrating data with computing power, storage, and algorithms. In this framework, algorithms serve as the production functions of data and computing power. Data can be generated alongside consumption activities, albeit at the cost of privacy. Once stored, data become non-rival among firms and sectors. In contrast, computing power, is a rival factor allocated to different firms. Consumer privacy concerns cap the long-term growth of data volumes at the rate of population growth, rendering data-related parameters ineffective in influencing the growth rate. This constraint on data expansion reduces the demand for labor in storage production, thereby facilitating a gradual reallocation of labor toward computing power production. Consequently, improvements in computing power, such as those described by Moore's Law, emerge as a crucial driver of economic growth. We examine how frictions, including monopoly power, dilution costs of computing power, and distorted innovation incentives in a decentralized economy, impact the allocation of data and computing power. Additionally, we analyze the market power held by data intermediaries and computing power providers. By comparing welfare outcomes under different allocations and conducting comparative statics (which reflect variations in production and innovation algorithms), we highlight that eliminating the monopoly markup of data intermediaries is generally advantageous. However, such a measure may have unintended negative consequences for computing power providers.Judicial Protection of Intellectual Property Rights and Innovation, Evidence from China
Abstract
This paper empirically investigates how China’s judicial protection of IPR affects corporate innovation both in the short run and long run. We show that IPR protection immediately enhances innovation mainly by increasing corporate transparency and social trust. We further explore the development patterns of IPR protection using continuous exposure methods, and find that sustained and long-term judicial protection of IPR is essential for better promoting corporate innovation.Discussant(s)
Zikun Liu
,
Yale University
Danxia Xie
,
Tsinghua University
Haiping Zhang
,
University of Auckland
Wan Xu
,
Peking University
JEL Classifications
- O3 - Innovation; Research and Development; Technological Change; Intellectual Property Rights
- E2 - Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy