Endogenous growth under multiple uses of data

Lin Cong, Wenshi Wei, Danxia Xie and Longtian Zhang

Journal of Economic Dynamics and Control, 2022, vol. 141, issue C

Abstract: We model a dynamic data economy with fully endogenous growth where agents generate data from consumption and share them with innovation and production firms. Different from other productive factors such as labor or capital, data are nonrival not only among firms but also in their uses across sectors, which affect both the level and growth of economic outputs. Despite the vertical nonrivalry, the innovation sector dominates the production sector in data usage and contribution to growth because (i) innovations are cumulative and benefit from data that are durable and dynamically nonrival; and (ii) innovations “desensitize” raw data into knowledge when entering production, which allays consumers’ privacy concerns. Data uses in both sectors interact in generating allocative distortions and an apparent substitutability due to labor’s rival usage across sectors and complementarity with data. Consequently, growth rates diverge under a social planner and a decentralized equilibrium, which is novel in the literature and has policy implications. Specifically, consumers’ failure to fully internalize knowledge spillover while bearing privacy costs, combined with firms’ market power, leads to an underpricing of data and inefficient data supply, causing underemployment in the innovation sector and suboptimal long-run growth. Improving data usage efficiency is ineffective in mitigating the underutilization of data, but interventions in the data market and direct subsidies hold promises.

Keywords: Big data; Endogenous growth; Innovation; Nonrivalry; Privacy (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (10)

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Working Paper: Endogenous Growth Under Multiple Uses of Data (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:dyncon:v:141:y:2022:i:c:s0165188922001014

DOI: 10.1016/j.jedc.2022.104395

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