OECD Regions and Cities at a Glance 2024

Throughout the publication, regional disparities in different domains are analysed through the persistence of disparities across regions and cities over space and time. More precisely, the report proposes several approaches to measure regional disparities:

• The straightforward difference between the maximum and minimum regional values in a country (regional range).

• Ranking regions by the value of an indicator and taking the ratio (or the difference) between the highest value representing 20% of the population and the lowest value of the regions representing 20% of the population. This approach assesses regional disparities that are less sensitive to possible outliers and cross-country differences in the size of regions.

• Using standard composite indices, such as the Theil general entropy index, which reflects inequality among all regions.

• Summarising spatial disparities by type of territory. This includes using the degree of urbanisation or OECD typology on access to cities in small administrative regions (Territorial Level 3, TL3).

The cut-off date for data included in the publication was 8 September 2024. Due to the time lag of subnational statistics, the last available year is generally 2023 for demographic and labour market and 2022 for socio-economic indicators.

This publication features statistical indicators at three scales: i) administrative regions; ii) functional urban areas (FUAs), composed of local units, and iii) areas defined from grid cells of regular size. The table below summarises the different geographic areas for which the publication reports indicators.

Administrative regions are the regional boundaries within a country as organised by governments. As such, they refer to areas that are often under the responsibility of a particular subnational government or to the scale targeted by a specific policy implemented at the national or subnational level. Regions are classified into two scales, large (Territorial Level 2, TL2) and small (Territorial Level 3, TL3), which ensures comparability across countries.

The report uses the definition of FUA developed by the European Commission (EC) and the OECD (Dijkstra, Poelman and Veneri, 2019). FUAs consist of cities (local units where at least half of the population live in clusters of densely populated grid cells with at least 50 000 inhabitants) and adjacent local units with high levels of commuting (travel-to-work flows) towards the cities. A minimum threshold for the population size of FUAs is set at 50 000 inhabitants. The definition identifies 1 271 FUAs of different sizes in all OECD member countries except Costa Rica and three accession countries. Large FUAs correspond to FUAs with a population of over 1.5 million inhabitants, while midsize FUAs range from 250 000 to 1.5 million inhabitants. Small FUAs have populations between 100 000 and 250 000 inhabitants and very small FUAs contain between 50 000 and 100 000 inhabitants.

Common thresholds and similar geographical units across countries were defined to establish this cross‑country methodology. These units and thresholds may not correspond to the ones chosen in the national definitions. Therefore, the resulting FUAs may differ from the ones derived from national definitions and the OECD functional urban delimitation may not capture all of the local factors and dynamics in the same way as national definitions.

Regions within the 38 OECD countries are classified on 2 territorial levels reflecting the administrative organisation of countries. The 433 OECD large (TL2) regions represent the first administrative tier of subnational government, for example, the Ontario province in Canada. There are 2 414 OECD small (TL3) regions, each TL3 being contained in a TL2 region (except for the United States). For example, the TL2 region of Aragon in Spain encompasses three TL3 regions: Huesca, Teruel and Zaragoza. TL3 regions correspond to administrative regions, except for Australia, Canada, Germany and the United States. All of the regions are defined within national borders.

This classification – which, for European countries, is largely consistent with the Eurostat Nomenclature of Territorial Units for Statistics (NUTS) 2021 classification – facilitates greater comparability of geographic units at the same territorial level. Indeed, these two levels, which are officially established and relatively stable in all member countries, are used as a framework for implementing regional policies in most countries.

For accession countries, the report identifies only TL2 regions for Brazil and Peru, and TL2 and TL3 regions for Bulgaria, Croatia and Romania (derived from the European NUTS).

For analyses conducted at the TL3 level, data are aggregated at the metropolitan level if multiple TL3 regions are linked to the same FUA. Metropolitan regions are defined by combining TL3 regions where 50% or more of the population resides in FUAs with at least 250 000 inhabitants. This aggregation helps mitigate distortions caused by commuting patterns. The small TL3 regions connected to FUAs can be found in the OECD Territorial Correspondence Table (OECD, 2023).

This report uses shortened labels in figures when relevant. The OECD territorial correspondence table includes a shortened name to enhance readability of graphs, besides the official name and the English or French translation. In shortened names, terms such as “Region”, “District”, or “Province” are omitted, while the terms “North”, “West”, “East”, “South”, “Central”, ‘Island’ are abbreviated to N., W., E., S., C., I., respectively.

The report refers to “capital-city regions” as TL2 or TL3 regions that include the administrative capital city of each country.

The OECD typology for small regions (TL3) by access to cities helps to assess differences in socio‑economic trends in regions – both within and across countries – by controlling for the presence/absence of metropolitan areas and the extent to which the latter is accessible by the population living in each region (Fadic et al., 2019). According to such typology, TL3 regions are classified as “metropolitan” if more than half of their population live in an FUA of at least 250 000 inhabitants and as “non-metropolitan” otherwise. A “metropolitan region” becomes a “large metropolitan region” if the FUA, accounting for more than half of the regional population, has over 1.5 million inhabitants.

In turn, the typology further classifies “non-metropolitan” regions based on the size of the FUA that is most accessible to the regional population. “Non-metropolitan” TL3 regions are sub-classified into three possible types:

• With access to a FUA with at least 250 000 inhabitants (>250K), if at least half of the regional population can reach an FUA of at least 250 000 inhabitants within a 60-minute car ride.

• With access to a FUA with less than 250 000 inhabitants (<250K), if at least half of the regional population can reach an FUA of between 50 000 and 250 000 inhabitants within a 60-minute car ride.

• Remote, if reaching the closest FUA by car takes more than 60 minutes for more than half of the regional population.

The method relies on publicly available grid-level population data and localised information on driving conditions. In this report, the five types of regions identified are sometimes aggregated into three classes only, as indicated in the table below.

The degree of urbanisation classifies local units as: i) “cities” or “densely populated areas”; ii) “towns and semi-dense areas” or “intermediate density areas”; and iii) “rural areas” or “thinly populated areas” based on population density, population size and contiguity using 1 km² grid cells (EC et al., 2020). Each local unit belongs exclusively to one of these three classes. Classification is carried out in two steps:

• Step 1: Identifying three types of grid cells

  • An urban centre (or a high-density cluster) consists of contiguous grid cells with a density of at least 1 500 inhabitants per square kilometre (km²) and has a population of at least 50 000 inhabitants. Gaps in this cluster are filled and edges are smoothed. If needed, cells that are 50% built-up can be added.

  • An urban cluster (or moderate density clusters) consists of contiguous grid cells with a density of at least 300 inhabitants per km² and a population of at least 5 000 inhabitants.

  • Rural grid cells (mostly low-density cells) are cells that do not belong to an urban cluster. Most of these will have a density below 300 inhabitants per km².

• Step 2: Classifying local units

  This step is an overlay of Step‑1 classification onto local units:

  • Cities (or densely populated areas): local units that have at least 50% of their population in urban centres

  • Towns and semi-dense areas (or intermediate density areas): local units that have less than 50% of their population in urban centres and less than 50% of their population in rural grid cells

  • Rural areas (or thinly populated areas): local units that have at least 50% of their population in rural grid cells

Chapter 1 discusses real economic performance in regions, accounting for the impact of inflation on producers and households in regions and cities and ongoing demographic change. It also provides key indicators of productivity, competitiveness and innovation in regions and cities.

Chapter 2 highlights disparities in worker participation, the rise of remote work and migrant integration, focusing on the varying effects on women and migrants. It also addresses the growing need for green skills and the evolving role of higher education in preparing workers for future demands.

Chapter 3 analyses the progress of OECD regions and cities towards achieving climate neutrality by 2050, indicators of greenhouse gas emissions, sectoral drivers of emissions and climate impacts, and climate projections for different emission scenarios.

Chapter 4 analyses trends in household income inequality within regions and differences in healthcare performance across regions. The chapter also presents accessibility indicators of childcare and education, public transport. Finally, it discusses Internet connectivity across regions and cities.

The OECD Database on Regions, Cities and Local Areas on the OECD Data Explorer (https://data-explorer.oecd.org/) provides the raw data and indicators presented in Regions and Cities at a Glance, along with metadata. This platform enables users to explore the data in more depth than is available in this publication and is regularly updated.

Regions and Cities at a Glance uses the OECD’s StatLink service. A URL below each table and figure leads to a corresponding Excel file containing the underlying data for the indicator. These URLs are stable and will not change.

Region and cities indicators can be further explored in the OECD Regions and Cities Atlas data visualisation tool (https://regions-cities-atlas.oecd.org/), while local areas indicators are available on the Local Data Hub (https://localdatahub-pp.oecd.org/). Thematic data visualisation tools also allow for measurement of the distance to United Nations Sustainable Development Goals in regions and cities (https://www.oecd-local-sdgs.org/) and assess how regions perform in terms of well-being (https://www.oecdregionalwellbeing.org/).

Dijkstra, L., H. Poelman and P. Veneri (2019), “The EU-OECD definition of a functional urban area”, OECD Regional Development Working Papers, Vol. 2019/11, https://doi.org/10.1787/d58cb34d-en.

EC et al. (2020), “A recommendation on the method to delineate cities, urban and rural areas”, Background document, https://unstats.un.org/unsd/statcom/51st-session/documents/BG-Item3j-Recommendation-E.pdf.

Fadic, M. et al. (2019), “Classifying small (TL3) regions based on metropolitan population, low density and remoteness”, OECD Regional Development Working Papers, No. 2019/06, OECD Publishing, Paris, https://doi.org/10.1787/b902cc00-en.

OECD (2023), OECD Territorial Correspondance Table, OECD, Paris, https://stats.oecd.org/wbos/fileview2.aspx?IDFile=db68c5c3-5fd5-465c-b25b-b50aa14c2da1.