Description:

*The World Development Indicators (WDI) is a premier compilation of cross-country comparable data about development. It provides a broad range of economic, social, environmental, and governance indicators to support analysis and decision-making for development policies. The dataset includes indicators from different countries, spanning multiple decades, enabling researchers and policymakers to understand trends and progress in development goals such as poverty reduction, education, healthcare, and infrastructure.*

*The dataset is a collection of multiple CSV files providing information on global indicators, countries, and time-series data. It is structured as follows:*

1. series:
Contains metadata for various indicators, including their descriptions, definitions, and other relevant information. This file acts as a reference for understanding what each indicator represents.

2. country_series:
Establishes relationships between countries and specific indicators. It provides additional metadata, such as contextual descriptions of indicator usage for particular countries.

3. countries:
Includes detailed information about countries, such as country codes, region classifications, income levels, and other geographical or socio-economic attributes.

4. footnotes:
Provides supplementary notes and additional context for specific data points in the main dataset. These notes clarify exceptions, limitations, or other special considerations for particular entries.

5. main_data:
The core dataset containing the actual indicator values for countries across different years. This file forms the backbone of the dataset and is used for analysis.

6. series_time:
Contains time-related metadata for indicators, such as their start and end years or periods of data availability.

*This dataset is ideal for analyzing global development trends, comparing country-level statistics, and studying the relationships between different socio-economic indicators over time.*

Columns and Examples:

Series Code:

Description: Unique code identifying the data series.

Example: AG.LND.AGRI.K2 (Agricultural land, sq. km).

Topic:

Description: Category under which the indicator is classified.

Example: Environment: Land use.

Indicator Name:

Description: Full name describing what the indicator measures.

Example: Agricultural land (sq. km).

Short Definition:

Description: A brief explanation of the indicator (if available).

Example: Not applicable for all indicators.

Long Definition:

Description: Detailed explanation of the indicator’s meaning and methodology.

Example: "Agricultural land refers to the share of land area that is arable, under permanent crops, or under permanent pastures."

Unit of Measure:

Description: Unit in which the data is expressed.

Example: Square kilometers.

Periodicity:

Description: How frequently the data is collected or reported.

Example: Annual.

Base Period:

Description: The reference period used for comparison, if applicable.

Example: Often not specified.

Other Notes:

Description: Additional context or remarks about the data.

Example: "Data for former states are included in successor states."

Aggregation Method:

Description: Method used to combine data for groups (e.g., regions).

Example: Weighted average.

Limitations and Exceptions:

Description: Constraints or exceptions in the data.

Example: "Data may not be directly comparable across countries due to different definitions."

Notes from Original Source:

Description: Remarks provided by the data source.

Example: Not specified for all indicators.

General Comments:

Description: Broad remarks about the dataset or indicator.

Example: Not available in all cases.

Source:

Description: Organization providing the data.

Example: Food and Agriculture Organization.

Statistical Concept and Methodology:

Description: Explanation of how the data was generated.

Example: "Agricultural land is calculated based on land area classified as arable."

Development Relevance:

Description: Importance of the indicator for development.

Example: "Agricultural land availability impacts food security and rural livelihoods."

Related Source Links:

Description: URLs to related information sources (if any).

Example: Not specified.

Other Web Links:

Description: Additional web resources.

Example: Not specified.

Related Indicators:

Description: Indicators conceptually related...

The CLEAR Water Dashboard aims at supporting Water teams to inform the standardized diagnostic framework for Climate and Economic Analyses of Resilience in Water in Country Climate and Development Reports. The CLEAR Water Dashboard curates and assembles more than twenty global data sets from recognized institutions/outfits. The source and definition for each of the indicators in the tool are displayed in the page entitled Sources and Definitions. Additional data sets will be added when available.

This dataset provides economic indicators for FUAs of more than 250 000 inhabitants, including GDP, GDP per capita, jobs and labour productivity.

   <h3>Data sources and methodology</h3>
   <p align="justify">
   When economic statistics are unavailable at a more granular level than the FUA (e.g. municipal level), indicators are estimated by adjusting regional (OECD TL2 and TL3 regions) values to FUA boundaries, based on the population distribution in each region. Regional values (GDP and jobs) in TL3 regions are used as data inputs and combined with gridded population data <a href=https://doi.org/10.2760/098587>(European Commission, GHSL Data Package 2023)</a>. FUA boundaries are intersected with TL3 borders to compute the share of the regional population that lives within FUAs in each region. This share is then applied to the variable of interest (e.g. GDP) and allocated to the FUA. In case several regions intersect the FUA, the adjusted values of intersecting regions are summed. For countries where TL3-level data is not available, data for TL2 regions is used. This approach assumes that the variable of interest has the same spatial distribution as population. Therefore, the modelled indicators should be interpreted with caution.<br /><br />
   When a more granular level is available, data is aggregated for each FUA. For example in the United States, GDP estimates are available at the county-level (<a href=https://www.bea.gov/data/employment/employment-county-metro-and-other-areas>US Bureau of Economic Analysis</a>), and then aggregated by FUA.
   </p>

   <h3>Defining FUAs and cities</h3>
   <p align="justify">The OECD, in cooperation with the EU, has developed a harmonised <a href="https://www.oecd.org/en/data/datasets/oecd-definition-of-cities-and-functional-urban-areas.html">definition of functional urban areas</a> (FUAs) to capture the economic and functional reach of cities based on daily commuting patterns <a href=https://doi.org/10.1787/9789264174108-en>(OECD, 2012)</a>. FUAs consist of:
   <ol>
   <li><b>A city</b> – defined by urban centres in the degree of urbanisation, adapted to the closest local administrative units to define a city.</li>
   <li><b>A commuting zone</b> – including all local areas where at least 15% of employed residents work in the city.</li>
   </ol>
   The delineation process includes:
   <ul>
   <li>Assigning municipalities surrounded by a single FUA to that FUA.</li>
   <li>Excluding non-contiguous municipalities.</li>
   </ul>
   The definition identifies 1 285 FUAs and 1 402 cities in all OECD member countries except Costa Rica and three accession countries.</p>
   <h3>Cite this dataset</h3>
   <p>OECD Regions, cities and local areas database (<a href="http://data-explorer.oecd.org/s/1e5">Economy - FUAs</a>), <a href=http://oe.cd/geostats>http://oe.cd/geostats</a></p>

   <h3>Further information</h3>
   <ul> 
   <li> <a href=https://localdataportal.oecd.org/>OECD Local Data Portal </a> </li>
   <li> <a href=https://www.oecd.org/en/publications/oecd-regions-and-cities-at-a-glance-2024_f42db3bf-en.html/>OECD Regions and Cities at a Glance </a> </li>
   </ul>
   <p align="justify">For questions and/or comments, please email <a href="mailto:CitiesStat@oecd.org">CitiesStat@oecd.org</a>

Democracy Timeseries Data Release 3.0, January 2009

This dataset is in a country-year case format, suitable for time-series analysis. It contains data on the social, economic and political characteristics of 191 nations with over 600 variables from 1971 to 2007. It merges the indicators of democracy by Freedom House, Vanhanen, Polity IV, and Cheibub and Gandhi, plus selected institutional classifications and also socio-economic indicators from the World Bank. New variables including the KOF Globalization Index and the new Norris-Inglehart Cosmopolitan Index. Note that you should check the original codebooks for the meaning and definition of each of the variables. The period for each series also varies. Note that the Excel version is for Office 2007 only. This is the dataset used in the book, Driving Democracy.

January 2009

Stored in Stata, SPSS, Excel and CSV.

The IMF-adapted ND-GAIN index is an adaptation of the original index, adjusted by IMF staff to replace the Doing Business (DB) Index, used as source data in the original ND-GAIN, because the DB database has been discontinued by the World Bank in 2020 and it is no longer allowed in IMF work. The IMF-adapted ND-GAIN is an interim solution offered by IMF staff until the ND-GAIN compilers will review the methodology and replace the DB index.Sources: ND-GAIN; Findex - The Global Findex Database 2021; Worldwide Governance Indicators; IMF staff calculations. Category: AdaptationData series: IMF-Adapted ND-GAIN IndexIMF-Adapted Readiness scoreReadiness score, GovernanceReadiness score, IMF-Adapted EconomicReadiness score, SocialVulnerability scoreVulnerability score, CapacityVulnerability score, EcosystemsVulnerability score, ExposureVulnerability score, FoodVulnerability score, HabitatVulnerability score, HeathVulnerability score, SensitivityVulnerability score, WaterVulnerability score, InfrastructureMetadata:The IMF-adapted ND-GAIN Country Index uses 75 data sources to form 45 core indicators that reflect the vulnerability and readiness of 192 countries from 2015 to 2021. As the original indicator, a country's IMF-adapted ND-GAIN score is composed of a Readiness score and a Vulnerability score. The Readiness score is measured using three sub-components – Economic, Governance and Social. In the original ND-GAIN database, the Economic score is built on the DB index, while in the IMF-adapted ND-GAIN, the DB Index is replaced with a composite index built using the arithmetic mean of “Borrowed from a financial institution (% age 15+)” from The Global Financial Index database (FINDEX_BFI) and “Government effectiveness” from the Worldwide Governance Indicators database (WGI_GE). The Vulnerability, Social and Governance scores do not contain any DB inputs and, hence, have been sourced from the original ND-GAIN database. Methodology:The procedure for data conversion to index is the same as the original ND-GAIN and follows three steps: Step 1. Select and collect data from the sources (called “raw” data), or compute indicators from underlying data. Some data errors (i.e., tabulation errors coming from the source) are identified and corrected at this stage. If some form of transformation is needed (e.g., expressing the measure in appropriate units, log transformation to better represent the real sensitivity of the measure etc.) it happens also at this stage. Step 2. At times some years of data could be missing for one or more countries; sometimes, all years of data are missing for a country. In the first instance, linear interpolation is adopted to make up for the missing data. In the second instance, the indicator is labeled as "missing" for that country, which means the indicator will not be considered in the averaging process. Step 3. This step can be carried out after of before Step 2 above. Select baseline minimum and maximum values for the raw data. These encompass all or most of the observed range of values across countries, but in some cases the distribution of the observed raw data is highly skewed. In this case, ND-GAIN selects the 90-percentile value if the distribution is right skewed, or 10-percentile value if the distribution is left skewed, as the baseline maximum or minimum. Based on this procedure, the IMF–Adapted ND-GAIN Index is derived as follows: i. Replace the original Economic score with a composite index based on the average of WGI_GE and cubic root of FINDEX_BFI1, as follows:IMF-Adapted Economic = ½ · (WGI_GE) + ½ · (FINDEX_BFI)1/3 (1) The IMF-adapted Readiness and overall IMF-adapted ND-GAIN scores are then derived as: IMF-Adapted ND-GAIN Readiness = 1/3 · ( IMF-Adapted Economic + Governance + Social) IMF-Adapted ND-GAIN = ½·( IMF-Adapted ND-GAIN Readiness+ND-GAIN Vulnerability) ii. In case of missing data for one of the indicators in (1), IMF-Adapted ND-GAIN Economic would be based on the value of the available indicator. In case none of the two indicators is available, the IMF-Adapted Economic score would not be produced but the IMF-Adapted ND-GAIN Readiness would be computed as average of the Governance and Social scores. This approach, that replicates the approach used to derive the original ND-GAIN indexes in case of missing data, ensures that the proposed indicator has the same coverage as the original ND-GAIN database.
1 Given that the FINDEX_BFI data are positively skewed, a cubic root transformation has been implemented to induce symmetry.

This dataset provides economic indicators aggregated at national level and broken down by territorial typology according to the population's access to cities.

Data source and definition

The indicators include GDP, GDP per capita, gross value added, employment at place of work and labour productivity by type of territory. Data is collected from Eurostat (reg_eco10) for EU countries and via delegates of the OECD Working Party on Territorial Indicators (WPTI), as well as from national statistical offices' websites.

The indicators are aggregated data at the national level, using the typology of small (TL3) regions to calculate totals or averages for all metropolitan large regions, metropolitan midsize regions, near a midsize/large FUA regions, near a small FUA regions and remote regions.

Territorial typology on the population's access to cities

Territorial typologies helps to assess differences in socio-economic trends in regions, both within and across countries and to highlight the specific issues faced by each type of region.

The OECD territorial typology on access to cities uses the concept of functional urban areas (FUA) – composed of urban centres and their commuting areas – and classifies small (TL3) regions (Fadic et al., 2019) according to the following criteria:

• Metropolitan regions, if more than half of the population live in a FUA. Metropolitan regions are further classified into: metropolitan large, if more than half of the population live in a (large) FUA of at least 1.5 million inhabitants; and metropolitan midsize, if more than half of the population live in a (midsize) FUA of at 250 000 to 1.5 million inhabitants.
• Non-metropolitan regions, if less than half of the population live in a midsize/large FUA. These regions are further classified according to their level of access to FUAs of different sizes: near a midsize/large FUA if more than half of the population live within a 60-minute drive from a midsize/large FUA (of more than 250 000 inhabitants) or if the TL3 region contains more than 80% of the area of a midsize/large FUA; near a small FUA if the region does not have access to a midsize/large FUA and at least half of its population have access to a small FUA (i.e. between 50 000 and 250 000 inhabitants) within a 60-minute drive, or contains 80% of the area of a small FUA; and remote, otherwise.

List of OECD regions and typologies are presented in the OECD Territorial correspondence table (xlsx). Maps of OECD regions are presented in the OECD Territorial grid (pdf).

Cite this dataset

OECD Regions and Cities databases http://oe.cd/geostats

Further information

• The interactive web-tool OECD Regions and Cities Statistical Atlas
• The report OECD Regions and Cities at a Glance

Contact: RegionStat@oecd.org

This table provides an international comparison of the performance of the economy. This is done by means of a number of broadly accepted economic indicators as gross domestic product and employed labour force. These indicators are complemented by a number of indicators on the quality of life and ecological sustainability.

Note: Comparable definitions are used to facilitate international comparisons of the figures. The definitions used here sometimes differ from definitions used by Statistics Netherlands. The figures in this table can differ from Dutch figures presented elsewhere on the website of Statistics Netherlands.

Data available for: 1960, 1970, 1980 and from 1990 up to 2012.

Status of the figures: The external sources of these data frequently supply adjusted figures on preceding periods. These adjusted data are not mentioned as such in the table.

Changes as of 1 March 2018: This table has been discontinued.

When will new figures be published? No longer applicable.