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Investment in Intangible Assets and Economic Growth: Global Trends and Policy Implications Economic Growth, Industrial Policy

Author Sang-Ha Yoon, Jung Eun Yoon, Sunghun Cho, Jiyun Lee, Yaein Baek, and Nyeong Seon Son Series 24-07 Language Korean Date 2024.12.31

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Until the mid-20th century, tangible assets were the primary drivers of economic growth. However, with the rise of digital transformation and the Fourth Industrial Revolution, the role of intangible assets has grown significantly in the 21st century. Traditional forms of intangible assets—such as software, R&D, branding, intellectual property rights, design, and organizational capital—have become central to enhancing firm-level and macroeconomic competitiveness. More recently, advanced technologies such as artificial intelligence (AI), big data, and cloud computing are emerging as new forms of intangible capital that contribute to innovation, productivity, and growth. Against this backdrop, this report analyzes the role of intangible asset investment in both major economies and Korea, and provides policy insights by examining its relationship with key economic indicators such as growth, productivity, and firm performance.

This study is composed of four chapters, excluding the introduction and conclusion. Chapter 2 investigates the relationship between intangible asset investment and economic growth using EKIP data. The analysis confirms a positive correlation between intangible investment and productivity growth, although the strength of this relationship varies considerably across countries and industries. Growth accounting results show that while the contribution of tangible assets to growth is declining, the contribution of intangible assets is on the rise—particularly for those included in national accounts. A regression analysis further reveals that economies with higher concentration of intangible asset investment tend to experience stronger medium-term growth. Additionally, the impact of intangible investment appears to vary depending on the economic size of each country group.

Chapter 3 employs a macroeconomic model with optimal intangible asset investment by agents, calibrated separately to Korean and U.S. data. The results show that the distribution of intangible assets is less dispersed in Korea than in the U.S. Model simulations suggest that increasing intangible asset investment—driven by supportive policy—can lead Korea toward a pattern seen in the U.S., where larger firms expand employment and productivity rises. However, this transformation is also accompanied by a deterioration in income distribution, highlighting the trade-offs involved.

Chapter 4 analyzes the relationship between intangible assets and firm performance using data from the Korean Survey of Business Activities between 2006 and 2021. While the proportion of firms holding intangible assets and their total value have increased steadily, the ratio of intangible to total assets has declined since the mid-2010s. Regression results show that greater intangible asset holdings are associated with improvements in sales, labor productivity, and export performance, both in the short term (1 year) and medium to long term (5 years). The positive effects on sales and productivity are particularly persistent. While the impact on export value weakens over time, intangible assets consistently enhance a firm's likelihood of participating in export activities.

Chapter 5 examines the short-term effects of AI exposure on industries in Korea and the U.S. based on data from 2019 to 2022—prior to the widespread adoption of generative AI. In Korea, AI exposure is positively correlated with some employment indicators, but these correlations are not statistically significant once labor productivity is controlled for. Moreover, AI exposure shows a negative relationship with per capita sales and labor share, indicating that technology adoption does not affect all outcomes equally. In contrast, in the U.S., AI exposure is significantly and positively associated with employment, hourly wages, and labor compensation, suggesting that AI adoption may enhance employment and earnings even in the absence of direct productivity gains.

Based on these findings, the report presents several policy implications. First, given the increasing economic contribution of intangible assets, governments should strengthen tax incentives and public support for R&D, software development, and organizational capital—especially in manufacturing and ICT industries—while promoting digitalization in the financial sector. Second, to address the gap in intangible asset accumulation between large firms and SMEs, policies such as easing credit constraints, expanding tax benefits, and providing innovation vouchers for SMEs are needed. At the same time, expanded investment in intangible assets calls for stronger policies on worker retraining to mitigate income polarization. Third, given the demonstrated benefits of intangible assets for firm performance, it is essential to improve access to finance for SMEs through the development of intangible asset–backed lending and to establish policy frameworks for valuing new types of assets such as AI- and big data–driven technologies. Lastly, since the employment and wage effects of AI adoption differ across countries, Korea should focus on strengthening AI-related job training for existing workers, supporting the adoption of AI by SMEs and traditional industries, and implementing labor protection policies for AI-affected sectors.

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