 Data deepening and nonbalanced economic growthRichard Freeman, Buyuan Yang and Baitao Zhang Journal of Macroeconomics, 2023, vol. 75, issue C Abstract: As a newly emerging factor, data can promote economic growth by driving technological progress, and nonbalanced growth between digital industries and nondigital industries has been notable in recent years. This paper provides a novel growth model with two sectors that differ in the degree of data deepening and the factor structure of the production function. In the model, data in one sector is the by-product of economic activities not only in its sector, but also in the other sector. More importantly, data utilization within and across sectors can spur new ideas and promote technological innovation. The model indicates that increases in the stock of data in the two sectors have opposite effects on the allocation of skilled labor between the two sectors. The skill premium (the wage of skilled labor relative to the wage of unskilled labor) decreases with an increase in the fraction of skilled labor employed in the data-extensive sector. With credible parameter values, model calibration shows that faster growth of output occurs in the more data-intensive sector and the high skill premium persists in the long run. Keywords: Data deepening; Nonbalanced growth; Skill premium (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:jmacro:v:75:y:2023:i:c:s0164070423000034 DOI: 10.1016/j.jmacro.2023.103503 Access Statistics for this article Journal of Macroeconomics is currently edited by Douglas McMillin and Theodore Palivos More articles in Journal of Macroeconomics from Elsevier |
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