There have been few studies that quantitatively investigate the opportunities and risk factors driven by digital transformation. In addition, there has been a lack of quantitative research that estimates the impacts of policy interventions to resolve potential problems in the future digital transformation era. Against this background, this study attempts to explore the intrinsic characteristics of digital transformation-led technological changes. In addition, this study explores the long-run impacts of digital transformation on the socioeconomic system in terms of economic growth, employment, and distribution, using a computable general equilibrium (CGE) model.
The results show that digital transformation has the potential to accelerate routine-biased and capital-biased technological changes. In this regard, we have found that economic growth driven by digital transformation disproportionately increases relative demand for capital and non-routinized cognitive tasks over routinized tasks. This shift in the value-added composition is found to have the potential to deepen income inequality, as higher income groups benefit from greater tasks premiums and capital earnings. Furthermore, the quantitative findings suggest that the promotion of the dynamic interaction between digital transformation-led technological change and lifelong learning may alleviate the potential risks induced by technological changes. Based on these findings, this study attempts to redefine the role of innovation policy in making a successful transition to the digital transformation era.