STATE CAPITALISM IN THE AI ERA: ASSESSING SOUTH KOREA’S DIGITAL AND GREEN NEW DEALS
DOI:
https://doi.org/10.62985/j.huit_ojs.vol26.no3E.509Keywords:
State capitalism, Korean New Deal, AI diffusion, total factor productivity, Bayesian inference, ESG impact assessment.Abstract
South Korea’s Digital New Deal (KRW 58.2 trillion), integrated with the Green New Deal, represents a major state-led strategy combining AI diffusion with sustainable growth. Whether such intervention is associated with a self-sustaining AI ecosystem or with subsidy dependency remains empirically underexplored. We evaluate the effectiveness of Korea’s dual New Deal using methodological triangulation: Interrupted Time Series (ITS) regression with Newey-West HAC standard errors, Bayesian inference with Bayes Factors and ROPE analysis, a most-different-systems comparative policy evaluation against the EU’s Horizon Europe, and a hybrid GRI-TCFD-SDG ESG impact assessment. Findings document an input-output asymmetry: R&D expenditure rose from 3.29% to 4.97% of GDP with very strong Bayesian evidence of structural change (BF₁₀ = 33.61), yet TFP growth shows only anecdotal evidence (BF₁₀ = 2.76). The ITS model (R² = 0.973) indicates that TFP variation is associated with R&D intensity (p < 0.01) and digital infrastructure (p < 0.001) rather than discrete structural breaks, though pre-existing trends cannot be ruled out. Because the design is non-experimental and the policy dummy is statistically insignificant (p = 0.20), all coefficients are interpreted as associations under stated assumptions rather than as causal effects. The ESG composite score of 64.5 (Grade C) indicates strong social progress but lagging environmental performance. Korea’s 4.5-fold higher GDP-share investment intensity relative to the EU is associated with stronger measurable outcomes, suggesting investment intensity rather than absolute budget size may be associated with policy effectiveness. These findings provide an associational, evidence-informed reference for emerging economies designing state-led AI industrial policy.
References
[1] I. Bremmer, The end of the free market: Who wins the war between states and corporations? New York: Portfolio/Penguin, 2010. ISBN: 9781591843506.
[2] H. Roberts, J. Cowls, J. Morley, M. Taddeo, V. Wang, and L. Floridi, “The Chinese approach to artificial intelligence: An analysis of policy, ethics, and regulation,” AI & Society, vol. 36, pp. 59–77, 2021, doi: https://doi.org/10.1007/s00146-020-00992-2
[3] Government of the Republic of Korea, “Korean new deal: National strategy for a great transformation,” Government of the Republic of Korea, Seoul, 2020. Available: https://english.moef.go.kr/pc/selectTbPressCenterDtl.do?boardCd=N0001&seq=4948
[4] Ministry of Science and ICT, “The Digital New Deal is to lead digital transition in the world after COVID-19,” Press release, Republic of Korea, Sejong, Jul. 15, 2020. Available: https://www.msit.go.kr/eng/bbs/view.do?sCode=eng&mId=4&mPid=2&bbsSeqNo=42&nttSeqNo=443
[5] Ministry of Science and ICT, “Digital New Deal harness the winds of change, bringing innovation!,” Press release, Republic of Korea, Sejong, Jul. 2021. Available: https://www.msit.go.kr/eng/bbs/view.do?sCode=eng&mId=4&mPid=2&bbsSeqNo=42&nttSeqNo=527
[6] Republic of Korea, Personal Information Protection Act, Act No. 16930 (partial amendment, Feb. 4, 2020). Korea Legislation Research Institute, Korea Law Translation Center. Available: https://elaw.klri.re.kr/kor_service/lawView.do?hseq=53044&lang=ENG
[7] Z. B. Khoury et al., “Enabling data-driven innovation: Learning from Korea's data policies and practices for harnessing AI,” World Bank Group Korea Office Innovation and Technology Note Series, no. 11, Washington, DC, Jan. 2024. Available: https://openknowledge.worldbank.org/bitstreams/37b6a9a8-56b5-46e2-805a-108afa524ec1/download
[8] R. M. Solow, “We’d better watch out,” New York Times Book Review, p. 36, Jul. 1987.
[9] P. A. David, “The dynamo and the computer: An historical perspective on the modern productivity paradox,” American Economic Review, vol. 80, no. 2, pp. 355–361, 1990, Available: https://www.jstor.org/stable/2006600
[10] P. A. Hall and D. Soskice, Varieties of capitalism: The institutional foundations of comparative advantage. Oxford: Oxford University Press, 2001. doi: https://doi.org/10.1093/0199247757.001.0001
[11] C. Johnson, MITI and the Japanese miracle: The growth of industrial policy, 1925-1975. Stanford, CA: Stanford University Press, 1982.
[12] A. H. Amsden, Asia's Next Giant: South Korea and Late Industrialization. New York, NY, USA: Oxford University Press, 1989.
[13] J. Kornai, “The soft budget constraint,” Kyklos, vol. 39, no. 1, pp. 3–30, 1986, doi: https://doi.org/10.1111/j.1467-6435.1986.tb01252.x
[14] A. Musacchio and S. G. Lazzarini, Reinventing state capitalism: Leviathan in business, Brazil and beyond. Cambridge, MA: Harvard University Press, 2014.
[15] E. M. Rogers, Diffusion of innovations, 5th ed. New York: Free Press, 2003.
[16] F. M. Bass, “A new product growth model for consumer durables,” Management Science, vol. 15, no. 5, pp. 215–227, 1969, doi: https://doi.org/10.1287/mnsc.15.5.215
[17] K. Yoon, “Discourse of the post-COVID 19 New Deal in South Korea,” East Asia, vol. 38, no. 4, pp. 373–388, 2021, doi: https://doi.org/10.1007/s12140-021-09363-0
[18] OECD, Digital Government Review of Korea: Harnessing Digital and Data to Transform Government, OECD Digital Government Studies. Paris, France: OECD Publishing, 2025, doi: https://doi.org/10.1787/9defc197-en
[19] World Bank, “Korea digital development program (KoDi),” World Bank Group, Washington, DC, 2024. Available: https://documents1.worldbank.org/curated/en/099062624042513249/pdf/P17725617de67b06189bb1336b0bf33b56.pdf
[20] J. W. Creswell and V. L. Plano Clark, Designing and conducting mixed methods research, 3rd ed. Sage Publications, 2018.
[21] W. K. Newey and K. D. West, “A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix,” Econometrica, vol. 55, no. 3, pp. 703–708, 1987, doi: https://doi.org/10.2307/1913610
[22] J. K. Kruschke, Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan, 2nd ed. Burlington, MA: Academic Press, 2014. doi: https://doi.org/10.1016/B978-0-12-405888-0.00008-8
[23] E.-J. Wagenmakers, “A practical solution to the pervasive problems of p values,” Psychonomic Bulletin & Review, vol. 14, no. 5, pp. 779–804, 2007, doi: https://doi.org/10.3758/BF03194105
[24] A. Przeworski and H. Teune, The logic of comparative social inquiry. New York: Wiley-Interscience, 1970.
[25] Global Reporting Initiative, “GRI standards,” Global Reporting Initiative, Amsterdam, 2024. Available: https://www.globalreporting.org/standards/
[26] Task Force on Climate-related Financial Disclosures, Recommendations of the Task Force on Climate-related Financial Disclosures: Final Report. Basel, Switzerland: Financial Stability Board, Jun. 2017. Available: https://www.fsb-tcfd.org/recommendations/
[27] United Nations, “Sustainable development goals,” United Nations, New York, 2015. Available: https://sdgs.un.org/
[28] J. Haskel and S. Westlake, Capitalism Without Capital: The Rise of the Intangible Economy. Princeton, NJ, USA: Princeton University Press, 2017, doi: https://doi.org/10.1515/9781400888320
[29] M. Mazzucato, The entrepreneurial state: Debunking public vs. Private sector myths. Anthem Press, 2013.
[30] D. Rodrik, “Industrial policy for the twenty-first century,” in One Economics, Many Recipes: Globalization, Institutions, and Economic Growth. Princeton, NJ, USA: Princeton University Press, 2007, pp. 99–152, doi: https://doi.org/10.1515/9781400829354
[31] European Parliament and Council of the European Union, “Regulation (EU) 2024/1689 of 13 June 2024 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act),” Official Journal of the European Union, L series, Jul. 2024. Available: https://eur-lex.europa.eu/eli/reg/2024/1689/oj
[32] United Nations Development Programme, “Korean New Deal for the post-COVID-19 era,” UNDP Seoul Policy Centre, Seoul, 2020. Available: https://www.undp.org/seoul-policy-centre/news/korean-new-deal-post-covid-19-era
[33] R. M. Solow, “Technical change and the aggregate production function,” Review of Economics and Statistics, vol. 39, no. 3, pp. 312–320, 1957, doi: https://doi.org/10.2307/1926047
[34] R. C. Feenstra, R. Inklaar, and M. P. Timmer, “The next generation of the Penn World Table,” American Economic Review, vol. 105, no. 10, pp. 3150–3182, 2015, doi: https://doi.org/10.1257/aer.20130954
[35] D. Coyle, J. McHale, I. Bournakis, and J.-C. Mei, “Recent trends in firm-level total factor productivity in the UK: New measures, new puzzles,” Economica, vol. 91, no. 364, pp. 1320–1348, 2024, doi: https://doi.org/10.1111/ecca.12541
[36] R. Wade, “Governing the market: Economic theory and the role of government in East Asian industrialization,” in Governing the market, 2nd ed., Princeton, NJ: Princeton University Press, 2004.
[37] T. Hale et al., “A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker),” Nature Human Behaviour, vol. 5. pp. 529–538, 2021. doi: https://doi.org/10.1038/s41562-021-01079-8
[38] J. L. Bernal, S. Cummins, and A. Gasparrini, “Interrupted time series regression for the evaluation of public health interventions: A tutorial,” International Journal of Epidemiology, vol. 46, no. 1, pp. 348–355, 2017, doi: https://doi.org/10.1093/ije/dyw098
[39] J. N. Rouder, P. L. Speckman, D. Sun, R. D. Morey, and G. Iverson, “Bayesian t tests for accepting and rejecting the null hypothesis,” Psychonomic Bulletin & Review, vol. 16, no. 2, pp. 225–237, 2009, doi: https://doi.org/10.3758/PBR.16.2.225
[40] M. D. Lee and E.-J. Wagenmakers, Bayesian Cognitive Modeling: A Practical Course. Cambridge, U.K.: Cambridge University Press, 2014, doi: https://doi.org/10.1017/CBO9781139087759
[41] J. K. Kruschke, “Rejecting or accepting parameter values in Bayesian estimation,” Advances in Methods and Practices in Psychological Science, vol. 1, no. 2, pp. 270–280, 2018, doi: https://doi.org/10.1177/2515245918771304
[42] World Bank, “World development indicators,” World Bank Group, Washington, DC, 2024. Available: https://databank.worldbank.org/source/world-development-indicators
[43] J. M. Wooldridge, Econometric analysis of cross section and panel data. Cambridge, MA: MIT Press, 2002.
[44] Stan Development Team, “Stan Modeling Language Users Guide and Reference Manual, Version 2.34.” 2024. Available: https://mc-stan.org/users/documentation/
[45] C. W. J. Granger and P. Newbold, “Spurious regressions in econometrics,” Journal of Econometrics, vol. 2, no. 2, pp. 111–120, 1974, doi: https://doi.org/10.1016/0304-4076(74)90034-7
[46] European Commission, “Horizon Europe - research and innovation,” European Commission, 2024. Available: https://research-and-innovation.ec.europa.eu/funding/funding-opportunities/funding-programmes-and-open-calls/horizon-europe_en
[47] Government of Vietnam, “Decision No. 749/QD-TTg approving the program for national digital transformation by 2025, with orientations towards 2030,” Hanoi, Vietnam, Jun. 2020. Available: https://vanban.chinhphu.vn/portal/page/portal/chinhphu/hethongvanban?class_id=2&_page=1&mode=detail&document_id=200163
[48] L. Floridi et al., “AI4People—an ethical framework for a good AI society: Opportunities, risks, principles, and recommendations,” Minds and Machines, vol. 28, no. 4, pp. 689–707, 2018, doi: https://doi.org/10.1007/s11023-018-9482-5
