KDI - Korea Development Institute - RESEARCH - Research Areas

Summary

Policy Study KOR Analysis of Commuting Efficiency based on Urban Spatial Development and Transport Infrastructure Supply Policies

Commuting, which bridges residences and workplaces, is a fundamental trip that supports daily life and economic activities, while maintaining close ties to labor market accessibility, productivity, and social quality of life. The government has made continuous efforts to reduce commuting distances and times by alleviating the spatial imbalance between residential and employment facilities and expanding the housing supply for diverse social classes near job-dense areas.

The purpose of this study is to empirically analyze the impact of urban spatial development and transport infrastructure supply policies on commuting efficiency. To this end, we established commuting efficiency indicators―including average and external commute times, external commute ratios, and car usage rates for external commutes―at the level of cities (si), counties (gun), and districts (gu) (hereafter si-gun-gu). By constructing a staggered Difference-in-Differences (DiD) model that accounts for the varying implementation timelines of urban development and rail projects across different regions, this research analyzes the heterogeneity of commuting efficiency improvements according to the timing of development and supply.

Based on the staggered DiD model, which considers the interaction effects between rail and housing land supply, the results reveal that despite the steady expansion of physical infrastructure―such as urban development, transport expansion, and public transit improvements―over the past 20 years, the actual effects on improving commuting efficiency have been limited. The average commute time across nationwide si-gun-gu slightly increased from 23.02 minutes in 2000 to 24.18 minutes in 2020. Furthermore, the car usage rate for external commutes across these si-gun-gu regions rose significantly from 54.61% to 69.69%, and the external commuting ratio also saw a slight increase from 23.18% to 24.75% during the same period.

The empirical findings suggest that to enhance commuting efficiency in the future, a policy shift is required: moving away from the traditional focus on expanding transport and land supply toward Transportation Demand Management (TDM) and the refinement of urban spatial structures. Specifically, car demand management policies―such as congestion charging, adjusting parking fees to realistic levels, and expanding bus-only lanes―should be strengthened. Concurrently, public transit service quality, including fare systems, transfer convenience, accessibility, and reliability, must be improved to actively encourage transit use. Additionally, strategies to distribute transport demand through mixed-use urban developments that promote jobs-housing proximity are essential.

Furthermore, to improve commuting efficiency, policies should focus on creating polycentric urban structures to alleviate commute concentration in specific areas, establishing walkable and bikeable communities through mixed-use development, and strengthening jobs-housing proximity via Transit-Oriented Development (TOD). Moreover, "soft" policies such as telecommuting, flexible work hours, decentralized office spaces, and staggered commuting should be implemented in parallel to reduce the necessity of travel or mitigate peak-hour congestion.

Commuting efficiency cannot be improved solely by the supply of land and transport facilities. This is because commute times are influenced by a complex interplay of factors, including induced demand, jobs-housing imbalances, and individual mobility behaviors. Therefore, the policy focus must shift fundamentally toward improving spatial accessibility between housing and employment opportunities, prioritizing demand management and structural urban improvements over simple infrastructure expansion.

Summary

Policy Study KOR On City Size Distribution: Determinants, Spatial Policies, and Welfare

This study employs a quantitative spatial general equilibrium model to estimate the determinants of city size distribution in South Korea and quantify the effects of regional policies, evaluating the optimality of its current population distribution.

Defining 161 cities and counties nationwide as cities and analyzing data from 2005 to 2019, the study finds that differentials in total factor productivity (TFP) drove up the population share of the Seoul Metropolitan Area (SMA), while amenities and urban accommodation costs worked in the opposite direction. In particular, the TFP decline among non-SMA manufacturing cities, such as Geoje, Gumi, and Ulsan, during the 2010s accelerated concentration in the SMA. Although regional development initiatives in this period, including innovation cities and Sejong City, improved local infrastructure and supported moderate population growth, weak TFP gains limited their impact on alleviating SMA concentration.

Counterfactual policy experiments that designate seven non-SMA cities as regional hubs reveal that restoring the SMA population share to its 2000 level would require substantial improvements in TFP and urban accommodation costs. The upper bound of policy costs justifiable without welfare loss is limited, and even with such investments, the SMA would still account for nearly half the national population. The optimal spatial policy analysis further suggests that efficient utilization of agglomeration economies favors population concentration in major cities across the country. With population reallocating not only to Seoul but also to major non-SMA cities, such as Busan, Daegu, and Gwangju, the SMA concentration may not rise beyond its 2019 level.

These findings offer three policy implications. First, for regional development policies to be effective, they must include measures that boost regional productivity. Second, even under optimistic scenarios, reducing the SMA share below 40% remains challenging, suggesting that policy goals should be realistic and strategically focused. Third, to prevent further SMA concentration, proactive spatial restructuring through the development of regional hub cities could provide a path forward consistent with the Pareto optimum.

Summary

KDI FOCUS Why the Capital Region Keeps Growing: Urban Accounting and Spatial Policy Implications

Population concentration in the Seoul Metropolitan Area has continued unabated since the 1970s. In the 2010s, diverging productivity drove this trend―gains in semiconductor and knowledge-based sectors in the capital region versus losses in manufacturing-oriented regional cities. Planned city projects, including Sejong, heavily prioritized infrastructure provision, yielding limited productivity growth and only modest population inflows. To achieve balanced development, spatial policy should move beyond infrastructure investment to boosting regional productivity. Furthermore, narrowing the divide between the capital region and outer regions may necessitate accepting some disparities within outer areas.

- Despite three decades of balanced national development plans, SMA concentration has intensified unabated.

- Cities draw population by providing both economic and non-economic advantages.

- Cities cannot grow indefinitely, as congestion intensifies with expansion.

- City size increases as productivity and amenities rise and accommodation costs fall.

- Through population mobility, local changes in city characteristics spill over nationwide.

- Productivity in the SMA not only exceeded non-SMA levels but also grew at a faster rate.

- Amenities were consistently higher in non-SMA regions, a pattern strengthened over 15 years.

- Urban accommodation costs were substantially lower in the SMA, only marginally narrowing the regional gap.

- The SMA’s growing productivity advantage drove further concentration toward the region from 2005 to 2019.

- Reversing the concentration toward the SMA remains elusive without closing the regional productivity gap.

- Industrial restructuring in the 2010s led to productivity declines in non-SMA industrial cities.

- Without the productivity decline in non-SMA industrial cities, concentration toward the SMA would have been moderated.

- Had productivity in the industrial cities grown at the national average rate, it could have reversed the trend of SMA concentration.

- Substantial productivity gains in regional hubs are essential to reduce the SMA population share.

- Productivity gains achieved through fiscal investment involve high uncertainty and therefore require a cautious, selective approach.

- SMA concentration since the 2000s has been closely tied to its growing relative productivity advantage.

- Substantial regional productivity gains can meaningfully offset SMA concentration.

- Spatial policy should pivot from infrastructure provision toward driving tangible regional productivity gains.

- Restructuring regional spatial configurations around large hubs can help reduce the gap with the SMA.

- Central coordination is crucial for policy efficacy, and decentralization must be aligned with resource allocation efficiency.

Summary

Policy Study KOR Transport Policy for Megacity Regions: Insights from Commuting and Travel Patterns

This study employs a network community detection approach to identify megacity regions in South Korea based on commuting and travel flows between municipalities―cities (si), counties (gun), and districts (gu)―and defined boundaries. The analysis finds 16 megacity regions in 2000, 19 in 2005, 16 in 2010, 17 in 2015, and 15 in 2020.

Comparing these findings with the designated metropolitan areas under the Special Act on Metropolitan Area Transport Management, the study proposes revisions to these designations. Specifically, the Busan-Ulsan area should be split into two regions, Busan-Gyeongnam and Ulsan-Pohang-Gyeongju. Additionally, Changnyeong-gun, currently part of the Daegu area, should be reassigned to the Busan-Gyeongnam area, and Gimcheon-si should be incorporated into the Daegu area. For the Gwangju area, expansion could be considered with additional municipalities of Yeongam, Muan, Mokpo, Gokseong, Haenam, Yeonggwang, Gangjin, Jangheung, Wando, and Jindo. The Daejeon area could also consider adding Buyeo, Yeongdong (Chungbuk-do), and Muju (Jeonbuk-do), while Cheongju and Boeun in the existing Daejeon area appear to form a different cluster. Furthermore, given population size and constituent municipalities, the Jeonbuk area―encompassing Jeonju, Wanju, Iksan, Gimje, Gunsan, Jeongeup, Seocheon, Namwon, Buan, Imsil, Gochang, Jinan, Sunchang, and Jangsu―should be recognized as an additional metropolitan area under the Special Act.

This study also identifies anchor and hub cities (nodal points) within each megacity region and proposes a framework for establishing a polycentric spatial structure, a key feature of megacity development. For instance, within the Seoul Metropolitan Area (A), Gangnam and Jung-gu in Seoul serve as major inflow nodes, while Seongnam and Suwon in Gyeonggi-do are major outflow nodes. To support megacity development, it is necessary to build a transport system that connects these nodes with surrounding municipalities.

Among large-scale transport infrastructure projects for megacity development, railway service expansion is identified as the most feasible option, with the greatest potential to reshape the centrality of cities, counties, and districts. Accordingly, this study assesses the impact and sustainability of railway services on the centrality. The findings indicate that, outside the Seoul Metropolitan Area (SMA) and excluding regional metropolitan cities, railway expansion consistently increases inflow degree centrality, reinforcing the crucial role of rail networks in fostering polycentric megacities.

Based on these findings, transport infrastructure recommendations for megacity development are proposed. First, the definition of metropolitan transport under the Special Act should be expanded from 'travel between cities and provinces within a metropolitan area' to travel between cities, counties, and districts that form integrated living and economic areas.' This expansion would provide a legal basis for transport infrastructure provision to support megacity development. Regarding funding mechanisms, the study proposes strengthening the user pays principle, including expanding both the entities responsible for formulating metropolitan transport improvement plans and the entities subject to transport facility charges. In particular, to secure funding for railway construction and capture land value gains around transit-oriented developments (TOD), the study suggests an integrated approach that effectively aligns railway TOD with railway construction projects.

Lastly, an integrated governance framework should be established to coordinate transport projects for megacity development. To that end, it emphasizes the importance of setting shared objectives and consolidating authority and responsibilities within a flexible governance structure.

Summary

Research Monograph KOR A Study on Regional Inequalities andRegional Development Strategy

Among OECD member countries, South Korea has a wide disparity in Gross Regional Domestic Product (GRDP) in absolute terms, with the gap widening further between 2015 and 2020. A similar analysis of per capita GRDP, which accounts for individual income distribution, ranks Korea higher―indicating a wider disparity― than the United States and Japan in terms of both absolute and relative terms. Overall, Korea faces significant disparities both between and within regions compared to its peers. Against this backdrop, this study empirically analyzes the current state and key drivers of the inter- and intra-regional disparities, providing policy insights for balanced regional development. It consists of two parts: (1) an examination of the impact of inter-regional disparities on population movement, and (2) an analysis of intra-regional income inequality.

Chapter 2 examines factors influencing representative indices for four key sectors―employment, education, housing infrastructure, and childcare―selected for their high relevance to the Korean context. The analysis covers 227 cities, districts, and counties across Korea from 2013 to 2022. This study empirically assesses how these four sectors influence total population (stock) and population inflow (flow) while considering mobility patterns by age group.

Findings show that better local conditions in employment and housing infrastructure contribute to growth in total population and population inflow. By age group, enhanced education and childcare services have a positive effect on attracting individuals in their 30s and 40s. In the Seoul Metropolitan Area, improvements in employment, education, housing, and childcare serve as primary drivers of population inflows. However, in non-metropolitan areas, even improvements across all four sectors do not significantly influence population inflows or mobility patterns across their administrative boundaries. Overall, the effects of sectoral improvements on population growth and mobility appear asymmetric between the Seoul Metropolitan Area and non-metropolitan areas. The stronger effects observed in the Seoul Metropolitan Area are likely attributable to differences in sectoral quality or a prevailing preference for the metropolitan area, where strengthening the key sectors in non-metropolitan areas alone may not be sufficient to drive residential relocation.

Based on the findings, the study presents the following policy recommendations for regional development. First, regional development strategies should adopt a coordinated, multi-sectoral investment approach, concurrently targeting dimensions like job creation, education, housing, and childcare, alongside a strategy to comprehensively enhance living conditions. To date, policy efforts have focused on single-sector investments, which have had limited effects on population inflows. Given the differing impacts on population inflows between metropolitan and non-metropolitan areas, narrowly focused investments alone are unlikely to serve as effective incentives for attracting people, highlighting broad-based investments as a vital policy tool for boosting regional appeal.

Second, strengthening cultural, educational, and social welfare infrastructure that reflects local characteristics is essential for improving residents' quality of life. Mobility patterns show that within non-metropolitan areas, movements mainly occur within the same cities and provinces, with limited inflows into other non-metropolitan areas. However, outflows to the metropolitan area increase when metropolitan conditions improve, suggesting the need to develop non-metropolitan areas into more viable and attractive living environments that can better compete with the metropolitan area in both quantity and quality.
Third, non-metropolitan areas need a sustainable, long-term regional development model led by local governments, together with a paradigm shift in the existing balanced regional development policy. Since qualitative improvements across all sectors take time, regional development strategies should move away from short-term population growth targets and prioritize long-term effectiveness based on regional strengths and weaknesses.

In conclusion, comprehensive improvements in sectors like employment, education, housing, and childcare through a population mobility-centered approach can help non-metropolitan areas to transform into more attractive places to live and work, complemented by tailored development strategies that reflect local characteristics.

Chapter 3 examines trends in income inequality at both national and regional levels in Korea. Using Gini coefficient decomposition analysis, the study assesses the extent to which inter- and intra-regional disparities contribute to overall income inequality. It further explores the relationship between city/provincial-level income inequality and per capita Gross Regional Domestic Product (GRDP) through a regression analysis covering 24 years (1998~2021). Additionally, the contribution of different income sources to national and regional income inequality is analyzed, with a focus on variations by age group of household heads, categorized as young (39 years and under), middle-aged (4064 years), and elderly (65 years and older).

The key findings on income inequality are as follows. First, decomposing the overall Gini coefficient into inter- and intra-regional components shows that inter-regional inequality has declined over time. In contrast, intra-regional inequality has remained relatively stable, making it a more significant factor in overall inequality. Analysis of the relationship between intra-regional income inequality and per capita GRDP at the city/provincial level based on such trends reveals an "N-shaped" pattern, where income inequality initially increases with economic growth, then decreases, and subsequently rises again. Additionally, higher average age of household heads, higher labor income across all age groups, and higher social insurance income among older adults are associated with lower income inequality. Notably, in non-metropolitan areas, the negative relationship between labor income and inequality is more pronounced.

Next, analysis of the contribution of different income sources to inequality indicates that reducing disparities across income categories―labor, real estate, other, transfer, social insurance, and financial―would be crucial for alleviating overall income inequality in the listed order. Over time, labor income inequality has worsened in the metropolitan area compared to non-metropolitan areas. Among young household heads, the contribution of labor income inequality to overall inequality was initially lower in the metropolitan area than in non-metropolitan areas, but this trend reversed around the mid-2010s and has remained higher since then. For older households, while no consistent pattern emerges, labor income inequality in non-metropolitan areas has been steadily increasing since the early 2000s.

Findings in Chapter 3 show that income inequality during economic growth follows an N-shaped pattern rather than an inverted U-shaped pattern, indicating that rising income inequality is not merely a transitional phase of economic growth but may persist or even intensify depending on various socioeconomic conditions. Therefore, continual monitoring of income inequality trends and efforts to mitigate its deepening should be integral to national and regional development strategies. Moreover, based on the analysis of income source contributions to inequality, reducing labor income disparities among young people is critical for alleviating overall income inequality in the metropolitan area. In non-metropolitan areas, the increasing labor income inequality among older adults requires particular attention. While labor income constitutes a relatively small share of their total income, its growing inequality could have a significant impact, especially given the faster population aging in non-metropolitan areas. This underscores the need to strengthen stable labor income sources for older adults in non-metropolitan regions.

Summary

Policy Study KOR Strategies for Upscaling Regional Innovation Clusters: Empricial Analysis and Case Studies

This study investigates the growth dynamics of businesses within regional clusters through empirical analysis and identifies the success factors of regional innovation clusters through case studies. Adopting a practical approach, the empirical analysis uses the data from industrial complexes―recognized as the foundation of Korea’s industrial cluster policy―in connection with the establishment level data from Mining and Manufacturing Survey. The empirical analysis addresses three questions: (1) Do businesses within clusters demonstrate superior growth performance compared to those outside? (2) What specific attributes of cluster-based businesses contribute to their growth patterns? In particular, do they demonstrate linkages with regional innovation capacity? (3) How does cluster membership influence recovery from economic crises such as the global financial crisis?

The case studies focus on Pangyo Techno Valley and the Wonju Medical Device Industry Cluster, analyzing their current status, success factors, and challenges to derive policy insights.

Key findings and policy implications are summarized as follows:

First, businesses located in industrial complexes outperform those outside both in employment and production. According to the analysis model, the businesses in industrial complexes demonstrated approximately 3~5% additional growth in employment, production, and value-added during the investigated period. This trend is consistent across most types of industrial complexes. However, their performance shows no significant correlation with the region’s innovation capacity, suggesting limited impact from the collaboration with regional innovation actors on business outcomes.

Second, there are no additional effects from the government’s cluster policies, such as Free Economic Zones and Research and Development (R&D) Special Zones, at least within the scope of the empirical analysis. While further research is needed, these findings indicate that overlapping designations and investments may dilute the effectiveness of existing cluster policies, calling for more cautious and strategic planning in future cluster policy implementation. As the Korean government is introducing Opportunity Development Zones, it should evaluate the effectiveness of previous policies and better utilize already-invested resources by connecting with existing clusters.

Third, businesses within regional clusters exhibit stronger recovery from external shocks, such as economic crises, than those outside. This advantage appears to stem from the agglomeration of industries within clusters, facilitating information sharing and access to resources. These attributes are noteworthy for building sustainable and resilient regional economic ecosystems. Moreover, the case studies reveal that the establishment and development of regional innovation clusters rely on key success factors, including leadership by regional stakeholders and the capacity to develop and manage these clusters―specifically, planning led by local actors, tight-knit collaboration among innovation actors, and cooperative governance to mobilize resources. While generalizing these findings requires caution, they provide valuable insights for fostering successful regional innovation clusters.

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