Machinery industry as a digital catalyst

Evaluating R&D portfolios and trade-offs using a CGE-based analysis

Bibliographic Data

ID17600363
AuthorsYeongjun Yeo (0000-0001-9782-3924, Gachon University, corresponding author), Sungmoon Jung (0000-0003-1120-5987, Department of Economics, Dong-a University Bumin Campus , 225 Gudeok-ro, Seo-gu, Busan 49236)
Year2025
Publication date2025-12-11
Open AccessYes
TypeARTICLE
VenueScience and Public Policy (JOURNAL)
Journal identifiersISSN: 0302-3427 • E-ISSN: 1471-5430
PublisherOxford University Press (OUP) (PUBLISHER)
DOI10.1093/scipol/scaf096
OpenAlexW7141320344
LanguageEN
References cited54

This study analyzes the macro-structural effects of budget-neutral reallocations of government research and development (R&D) in Korea’s machinery and equipment industry using a recursive dynamic computable general equilibrium model named as Technology and Economy Modelling for Innovation Policy assessment (TEMIP) over 2019–2030. Three portfolios are evaluated: a machinery-centric R&D expansion, a dual R&D expansion combining machinery and software/information and communication technology (ICT), and a dual R&D expansion combining machinery and electrical/electronic equipment. Results show that the machinery–software mix generates the strongest aggregate gains, raising real gross domestic product (GDP) by 0.57 per cent and average sectoral total factor productivity (TFP) by 0.41 per cent through ICT-driven spillovers. The machinery–electronics mix achieves slightly smaller gains (GDP + 0.44 per cent, TFP +0 .36 per cent) but fosters the most diversified industrial structure. The machinery-only strategy yields the highest machinery output but the weakest economy-wide impact (GDP/TFP + 0.33 per cent). Labor decomposition indicates that the software-oriented portfolio increases the skill premium, while the hardware-oriented mix sustains more balanced labor contributions. Overall, the findings underscore the efficiency–inclusiveness trade-off and highlight the need for complementary diffusion and workforce policies in R&D allocation

Computable general equilibrium · Gross domestic product · Portfolio · Productivity · Technological change · Total factor productivity · Workforce · Innovation Diffusion and Forecasting · Innovation Policy and RD · Intellectual Property and Patents

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