OECD subnational regions are classified into two scales: large regions, referred to as Territorial Level 2 (TL2), and small regions, referred to as Territorial Level 3 (TL3). Large regions generally refer to the first government level after the national or federal one. Small regions are contained within large regions and, in the case of European countries, correspond to the NUTS3 nomenclature. For more details, check OECD territorial grid description.

The concept of Functional Urban Areas (FUA) allows to compare cities across countries by also taking into account their areas of influence. Developed in collaboration with the European Union and endorsed at the 2020 Statistical Commission of the United Nations, FUAs consist of densely populated cities together with their surrounding commuting zones. This definition looks at the full extent of cities’ labour markets to capture the economic boundaries of cities. FUAs are generally the aggregation of municipalities or local areas. For more details on how Functional Urban Areas are defined, check here.

To address the need for local statistics, the OECD is now maintaining a database and reference grid of municipalities and local areas for all OECD countries, that complements at local level the existing OECD subnational regions and Functional Urban Areas. These geographical levels are chosen based on the following principles:

• Administrative units corresponding to local governments, i.e. the lowest tier of administration within a given country;
• Units for which data is made available by National Statistical Offices on different topics, including demography, housing, transport and municipal finance;
• Units used as building blocks for Functional Urban Areas (FUAs) and Cities;
• Units appropriate for the implementation of the degree of urbanisation classification (see Territorial Organisation section);
• Subdivisions of other OECD territorial levels (TL2 and TL3 regions)

In European countries, Eurostat maintains a system of Local Administrative Units (LAU) compatible with the European Nomenclature of Territorial Units (NUTS). For these countries, the OECD grid of municipalities and local areas aligns with the LAUs.

In the United States, local governments vary significantly across states resulting in different forms of local areas. Most states have in general two tiers of local governments: counties and municipalities. Counties, also known as “boroughs” in Alaska and “parishes” in Louisiana, are the largest territorial unit within a state. Counties are divided into townships or municipalities which can assume various names depending on the state - cities, towns, villages, or boroughs. Townships are most often found in the northern and midwestern parts of the country. In 2022, the US was composed of 3 031 county governments and 35 705 subcounty governments (19 491 municipal governments and 16 214 township governments). Some areas do not have an incorporated municipal government and are governed directly by the county government. The official boundaries corresponding to the subcounty governments are provided in two different layers by the US Census Bureau: “county subdivisions” for township governments and “places” for municipal governments. To reflect this complexity, four geographical levels are included for the US in the OECD system of local areas: counties, county subdivisions, places and zipcodes.

As local area boundaries tend to change regularly, and to facilitate time series analysis, indicators collected at the municipal and local area levels and presented in the OECD Local Data Portal are harmonised and reported using the most recent geography. The harmonisation process is based on the relative distribution of population within local areas. Coefficients are applied to the indicators of former local areas that were either merged or split, depending on the share of the population that live in the current local area.

The table below provides for each country the geographical levels used in the OECD system of municipalities and local areas. This table highlights descriptive statistics on the number of units, average population and average territorial area across local areas in each country.

The total population refers to the permanent resident population.

The population by age groupis provided by sex for 3 age groups: younger than 14 years old, between 15 and 64 years old, and older than 65 years old.

The sex ratio is define as the ratio of male population divided by female population.

Population change corresponds to the total percentage change over the last 5 years or 10 years of data available. This change can be broken down into two components: natural change, which accounts for births and deaths, and net migration, which reflects the balance of people moving in and out of an area over the period:

The OECD Local Data Portal also provides indicators on the change in population share by age group in the last 5 years, measured in percentage points (pp.). This helps assess the pace of ageing in a municipality or local area.

The crude birth rate corresponds to the number of live births per 1000 population and death rate to the number of deaths per 1000 population. The general fertility rate is defined as the number of live births per 1000 woman aged 15-44 years old. And natural change is the difference between the number of births and deaths.

Indicators related to natural change are based on the cumulative number of births and deaths over the last 5 years of data available.

Migration indicators include foreign-born population and migration flows related to internal (people moving within the country) and international (people moving across country borders) movements of people. As with population and natural change, these statistics are computed based on cumulative flows over the last 5 years of data available.

The land use and land cover indicators are based on the WorldCover dataset. WorldCover is a global land cover product at 10 m resolution for 2021 based on both Sentinel-1 and Sentinel-2 data, containing 11 land cover classes and independently validated with a global overall accuracy of about 76.7%. The land cover classes available are:

• Tree cover
• Shrubland
• Grassland
• Cropland
• Built-up
• Bare / sparse vegetation
• Snow and ice
• Permanent water bodies
• Herbaceous wetland
• Mangroves
• Moss and lichen

WorldCover is also used in the OECD Local Data Portal to estimate green areas which are defined as the sum of the following land cover classes: trees, shrubland and grassland. The indicators displayed in the portal related to green areas are the share of green areas (in % of the total area) and green areas per capita (in m² per capita).

Tree cover loss is obtained using Hansen Global Forest Change v1.11 dataset, which provides global forest extent and change from 2000 to 2023 at 30m resolution based on Landsat satellite images. Tree cover loss is defined as a stand-replacement disturbance (a change from a forest to non-forest state).

Air pollution exposure is estimated using Plume Labs air pollution exposure grids for 3 air pollutants: nitrogen dioxide (NO₂), fine particulate matters (PM2.5) and coarse particulate matters (PM10). The OECD obtained this dataset thanks to the Development Data Partnership, a collaboration between international organisations and technology companies, facilitating the efficient and responsible use of third-party data in international development. Average population exposure to air pollution is estimated at the local scale by computing the population-weighted average air pollutant concentration, using the Global Human Settlement Layer population grid (GHS-POP).

Protected areas are delineated using the World Database on Protected Areas - WDPA (UNEP-WCMC and IUCN, 2024), a comprehensive global database on terrestrial and marine protected areas. It is a joint project between UN Environment Programme and the International Union for Conservation of Nature (IUCN), and is managed by UN Environment Programme World Conservation Monitoring Centre (UNEPWCMC), in collaboration with governments, non-governmental organisations, academia and industry. Protected areas are classified into 100% terrestrial, coastal: marine and terrestrial, and 100% marine. In the OECD Local Data Portal, marine protected areas are excluded.

The wind power potential corresponds to the mean wind power density, which serves as a quantitative measure of wind energy available at a specific location. It is expressed in W/m². This indicator does not account for land availability for wind turbine installation.

The solar power potential is measured by the photovoltaic power potential, indicating the solar energy available at a specific location. It represents the power generated per unit of installed PV capacity over the long term, measured in kilowatt-hours per kilowatt-peak (kWh/kWp). This indicator does not consider the availability of roofs or land for solar panel installation.

Cooling Degree Days (CDD) are a measure of how hot the temperature was during a period of days, which serves to quantify the demand for energy needed to cool buildings. It is here defined as the sum of the difference between the daily mean temperature and the reference temperature of 22°C for days during which the mean temperature is higher than 22°C. Cooling Degree Days are assessed based on ERA5-Land using the average annual air temperature at 2 meters height.

Heating Degree Days (HDD) are a measure of how cold the temperature was during a period of days, which serves to quantify the demand for energy needed to heat buildings. It is here defined as the sum of the difference between the reference temperature of 15°C daily and the mean temperature for days during which the mean temperature is lower than 15°C. Heating Degree Days are assessed based on ERA5-Land using the average annual air temperature at 2 meters height.

Vehicle indicators include the number of private passenger vehicles (per 1000 people), along with the percentage of these vehicles that are electric or hybrid. Passenger vehicles are defined as road vehicles, excluding motorcycles, intended for the carriage of passengers and with less than nine seats including the driver.

The main modes of transport used by workers for commuting to work are categorised into 5 types: car, scooter, public transport (including company buses), bicycle or walking. Workers working from home are excluded from this indicator.

Public transport performance is measured using publicly available General Transit Feed Specification (GTFS) data. GTFS data is a common format for public transportation schedules and associated geographic information. Public transport performance is defined at the population grid level. For each grid cell, it corresponds to the share of people living within an 8 km radius of the local area who can access the area by public transport within 30 min. The public transport performance for a local area or municipality corresponds to the population-weighted average or the grid-level public transport performance.

Commuting flows track the movement of workers from their place of residence to their workplace. This includes several indicators: the share of residents working within their local area, the share of residents commuting to a different area than their area of residence, and the net flow rate of workers relative to the resident population.

A private household refers to a person or group of persons who occupy the same dwelling. It may consist of one person or several persons who have common housekeeping arrangements. The OECD Local Data Portal reports indicators on the number of households, the average size of households, and the share of households that are single-person households.

The transaction price indicates the average price of transacted dwellings in the local area in the most recent available year, while the transaction price per square meter reports the average price adjusted for the size of the dwelling. All house price indicators are expressed in current US dollars and adjusted for differences in purchasing power to allow for cross-country comparisons.

The energy sources used for heating in dwellings include electricity, natural gas, district heating, fuel oil, coal, biomass, and other sources.

Education accessibility indicators include the average distance to the closest school and childcare centre in the local area. The distance is defined as the population-weighted great-circle distance to the closest facility, expressed in meters (m).

Healthcare accessibility indicators include the average distance to the closest hospital and to the closest pharmacy in the local area. The distance is defined as the population-weighted great-circle distance to the closest facility, expressed in meters (m).

The source of the data on municipal government finance is the OECD Disaggregated Municipal Finance Database (Disaggregated MUNIFI), which covers 28 countries from the OECD and the EU, with data from 2010 to 2021. The full database can be accessed on the OECD Data Explorer here: Disaggregated MUNIFI.

The MUNIFI database was developed as part of the OECD and European Commission (DG REGIO) project “Strengthening analytical frameworks and data on subnational government finance and public employment”, with the results published in June 2024. This project developed two databases on municipal public finance: one for individual municipalities in a country (Disaggregated MUNIFI) and for the entire municipal government sector (Aggregated MUNIFI). From 2021 to 2024, the project also collected data on regional public finance (REGOFI) and subnational public employment (SUBEMP). Altogether, these databases encompass 37 OECD and EU countries, covering data for over 550 regional/state-level governments and over 120 000 municipal-level governments.

The methodology consisted of two main steps: data inventory and engagement with national stakeholders, followed by data processing and verification. The first step involved liaising with National Statistics Institutes, national ministries and other national and subnational stakeholders, to inventory available data for each country, and develop standardised data queries. The second step involved creating tables of equivalence to align local finance accounting in each country with national accounts standards, ensuring comparability, and verifying the data with national stakeholders.

Please note that cross-country comparisons should be made with caution, as the degree of decentralisation and the responsibilities of subnational governments vary greatly across the sample. For detailed information on country-specific aspects of subnational finance, multi-level governance frameworks, and the distribution of responsibilities across levels of government, please visit the country profiles in the World Observatory on Subnational Government Finance and Investment (SNG-WOFI). For any questions on the MUNIFI database, please contact: Antti Moisio.

Municipal governments represent the lowest self-governing administration level within a country´s government. In the System of National Accounts, municipal governments are part of the “local government” sector (S.1313), which also includes other local government bodies such as regional and intermediate governments in countries with several layers of subnational governments. The MUNIFI database focuses exclusively on the self-governing municipal governments.

Total municipal government expenditure includes current expenditure (compensation of employees, intermediate consumption, social expenditure, subsidies and other current transfers, taxes, financial charges, adjustments) and capital expenditure. Here, the data is provided in USD PPP per capita, while in MUNIFI, all data is available also in national currencies.

Total capital expenditure includes capital transfers and direct investment. This indicator is calculated by dividing total municipal capital expenditure by total municipal expenditure and is expressed as a percentage.

Education expenditure is a Classification of Function of Government (COFOG) category that includes expenditure on pre-primary and primary education, secondary education, post-secondary and non-tertiary education, tertiary education and education not definable by level. Please note that the ratio of expenditure on education in total expenditure made by municipal government is especially prone to cross-country differences, depending on the allocation of responsibilities across levels of government. As such, comparing municipalities in different countries should be done with caution.

Total municipal government revenue includes tax revenue, transfers (current and capital grants and subsidies), tariffs and fees, property income and social contributions. This data is provided in USD PPP per capita.

Tax revenue includes tax corporate income tax, personal income tax, value added taxes, property taxes, and other tax revenues. It includes both own-source tax revenue (or “autonomous”) and tax revenue shared between central and subnational governments. Data varies depending on the revenue raising powers assigned to municipal governments as well as on the categorisations used in the municipal accounts. This indicator is calculated by dividing municipal tax revenue by total municipal revenue and is expressed as a percentage.

Grant revenue includes both current and capital transfers received by municipal governments. This indicator is calculated by dividing total municipal grant revenue by total municipal revenue and is expressed as a percentage.