Korea
Regional economic trends
Employment and unemployment rates in regions
In Korea, regional disparities in unemployment rates are moderate compared to other OECD countries. While in Seoul Capital Area and Gyeongnam Region 3.3% of the working force was unemployed in 2022Q2, the share was 2% in Jeju.
Meanwhile, the difference in employment rate between the regions with the highest (Jeju) and lowest (Gyeongnam Region) employment rates reached 10 percentage points in 2022.
Note: Harmonised employment and unemployment rates, aged 15 and over. The OECD median corresponds to the median employment rate in large regions.
Source: OECD (2022), “Short-term regional statistics”, OECD Regional Statistics (database)
The first year of COVID-19 on GDP per capita
The first year of COVID-19 resulted in a decrease in GDP per capita in most Korean regions. Jeju, a region with a GDP per capita -22% below the national average (32 295 vs. 41 432 USD PPP), experienced the largest decrease in GDP among Korean regions, of approximately -7%.
Note: GDP per capita is measured in constant prices and constant PPPs, reference year 2015. Constant prices are calculated using national deflators. The OECD median corresponds to the median decline in GDP per capita observed across OECD large regions over the period.
Source: OECD (2022), “Regional economy”, OECD Regional Statistics (database)
Trends in regional economic disparities in the last decade
Differences between Korean regions in terms of GDP per capita have remained relatively stable over the past nine years, with the richest 20% of regions reporting a GDP per capita 1.2 times higher than the poorest 20% of regions.
Note: The GDP per capita of the top and bottom 20% regions are defined as those with the highest/lowest GDP per capita until the equivalent of 20% of the national population is reached. A ratio of 2 means the richest regions have a GDP per capita twice as large as the poorest regions. The indicator is calculated using large regions, except for Latvia and Estonia, where small regions are used instead. Irish GDP underwent an upwards revision in 2016. Care is advised in its interpretation.
Source: OECD (2022), “Regional economy”, OECD Regional Statistics (database)
Productivity trends in the last decade
Between 2010 and 2019, Seoul Capital Area and Gyeongnam Region experienced the highest and lowest productivity growth in Korea, respectively. Seoul Capital Area saw a labour productivity increase of 2.2% per year, above the OECD average of 0.9%1. During the same period, Gyeongnam Region experienced more modest growth in measured labour productivity, averaging 0.8% per year.
All Korean regions experienced a decline in labour productivity between 2019 and 2020. Jeju experienced the largest decline, with a drop of 7.6%
Note: Regional Gross Value Added (GVA) per worker, in USD, constant prices, constant PPP, base year 2015.
Source: OECD (2022), “Regional economy”, OECD Regional Statistics (database)
Well-being, liveability and inclusion in regions
Regional well-being
Korea faces stark regional disparities across one well-being dimensions, with the starkest disparities in terms of civic engagement, community and jobs.
Note: Regional indices provide a first comparative glance of well-being in OECD regions. The figure shows the relative ranking of the regions with the best and worst outcomes in the eleven well-being dimensions, relative to all OECD regions. The eleven dimensions are ordered by decreasing regional disparities in the country. Each well-being dimension is measured by the indicators in the table below.
Relative to other OECD regions, Korea performs best in the access to services dimension, with all of Korean regions lying in the top 20% of OECD regions.
The top 20% of Korean regions rank above the OECD median region in 10 out of 14 well-being indicators, performing best in terms of population with at least upper secondary education and households with broadband access.
Note: Regional well-being indices are affected by the availability and comparability of regional data across OECD countries. The indicators used to create the indices can therefore vary across OECD publications as new information becomes available. For more visuals, visit https://www.oecdregionalwellbeing.org.
The digital divide
Fixed Internet connections in Korean cities and rural areas deliver speeds significantly faster than the OECD average (38% and 30%, respectively). This gap (8 percentage points) is smaller than in most other OECD countries.
Note: Cities and rural areas are identified according to the degree of urbanisation (OECD et al. 2021). Internet speed measurements are based on speed tests performed by users around the globe via the Ookla Speedtest platform. As such, data may be subject to testing biases (e.g. fast connections being tested more frequently), or to strategic testing by ISPs in specific markets to boost averages. For a more comprehensive picture of Internet quality and connectivity across places, see OECD (2022), “Broadband networks of the future”.
Source: OECD calculations based on Speedtest by Ookla Global Fixed and Mobile Network Performance Maps for 2020Q4.
The average speed of fixed Internet connections is above the OECD average in all Korean regions. Within the country, residents of Seoul Capital Area, Chungcheong Region and Gangwon Region experience the fastest connections.
Demographic trends in regions and cities
Population projections by type of regions across OECD countries
Between 2020 and 2040, the population of Korea is expected to grow in regions near a metropolitan area and decrease in other types of regions. Regions near a metropolitan area are expected to see the greatest change, with their population increasing, on average, by 9 % over the next two decades.
Note: Lines represent the population projection in OECD countries per type of region (Fadic et al. 2019). Korea is highlighted in blue.
Dependency rate and proportion of the elderly
In the coming two decades, the share of the elderly population in Korea is expected to increase across all types of regions.
The elderly dependency rate2 in Korea is also lower than the OECD average (26.8 %) in most regions, ranging from 30.6% in Gangwon Region to 19.8% in Seoul Capital Area.
Population in cities
Between 2010 and 2021, 50% of cities in Korea experienced a decline in population. Population growth ranged from -0.8% per year in Iksan to 2.3% per year in Naju.
Note: Cities refer to functional urban areas (Dijkstra, Poelman, and Veneri 2019). Population counts for the functional urban area are aggregated from administrative, municipal-level, data.
Over the past decade, the population has grown the most in Korean cities with 100 000 to 250 000 inhabitants. Cities with more than 1.5 million inhabitants have seen their population shrink, on average.
Environmental challenges in regions and cities
Greenhouse gas emissions in regions
Since 1990, production-based greenhouse gas emissions have increased in all Korean regions. Chungcheong Region (325%) and Jeju (38%) experienced the largest and lowest increase in emissions, respectively.
On average, Korean regions increased their emissions by 2.69% per year between 1990 and 2018. This is below the 3.85% yearly reduction rate needed to reach the Korea target of a 40% reduction in emissions by 2030, with respect to 2018 levels.
Note: Bubbles are proportional to per capita greenhouse gas emissions, not to the overall level of greenhouse gas emissions in the region.
Source: OECD calculations, based on the Emissions Database for Global Atmospheric Research (European Commission. Joint Research Centre. 2019).
In 2018, greenhouse gas emissions per capita in Korea were largest in Gangwon Region, Chungcheong Region and Jeolla Region. Power accounts for the largest share of greenhouse gas emissions in the three regions.
Note: Regions with low population counts may rank high in greenhouse gas emissions per capita while contributing relatively little to overall emissions in the country.
Urban heat island effect
In Korean cities, the difference in temperature between cities and their surrounding areas (i.e. urban heat island intensity) reaches 3.5 degrees Celsius (°C). The largest effect is observed in Pohang and Chuncheon, two cities that are, on average, 5.3°C and 4.9°C warmer than their surrounding areas, respectively.
Note: The Urban Heat Island Intensity (UHI) index is defined as the difference in land surface temperature between built-up areas and non-built-up areas within functional urban areas. This index can be affected by the type of vegetation and climate in non-built-up areas.
Source: OECD calculations, based on land surface temperature data from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) (Wan, Hook, and Hulley 2021a, 2021b)
References
Source of administrative boundaries: © OECD, © EuroGeographics, National Statistical Offices, © UN-FAO Global Administrative Unit Layers (GAUL)
Footnotes
International comparability in 2019 and 2020 is limited because of methodological differences in the calculation of employment counts during the height of the COVID-19 economic crisis.↩︎
The elderly dependency rate compares the number of elderly people at an age when they are generally economically inactive (i.e. aged 65 and over), to the number of people of working age (i.e. 15-64 years old).↩︎