The total population in France was estimated at 68.4 million people in 2024, according to the latest census figures and projections from Trading Economics. This dataset provides the latest reported value for - France Population - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

About this dataset

Context

The dataset tabulates the population of French Settlement by race. It includes the population of French Settlement across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of French Settlement across relevant racial categories.

Key observations

The percent distribution of French Settlement population by race (across all racial categories recognized by the U.S. Census Bureau): 94.80% are white, 0.92% are Black or African American and 4.28% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the French Settlement
• Population: The population of the racial category (excluding ethnicity) in the French Settlement is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of French Settlement total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for French Settlement Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the population of French Lick by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for French Lick. The dataset can be utilized to understand the population distribution of French Lick by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in French Lick. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for French Lick.

Key observations

Largest age group (population): Male # 40-44 years (154) | Female # 40-44 years (106). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the French Lick population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the French Lick is shown in the following column.
• Population (Female): The female population in the French Lick is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in French Lick for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for French Lick Population by Gender. You can refer the same here

Employment Rate in France increased to 69.50 percent in the first quarter of 2025 from 69.10 percent in the fourth quarter of 2024. This dataset provides - France Employment Rate- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Projection of total population 2024-2050 Territorial entities: arrondissements (Wallonie), départements (Lorraine), Grand-Duché (Luxembourg), Kreise (Saarland, Rheinland-Pfalz) Statistical data sources: Destatis, Eurostat, Statbel, STATEC, Statistisches Amt Saarland, Statistisches Landesamt Rheinland-Pfalz. Calculations: OIE/IBA 2024 Geodata sources: ACT Luxembourg, IGN France, GeoBasis-DE / BKG, NGI-Belgium. Harmonization: SIG-GR / GIS-GR 2024 Link to interactive map: https://map.gis-gr.eu/theme/main?version=3&zoom=8&X=708580&Y=6429642&lang=fr&rotation=0&layers=2425&opacities=1&bgLayer=basemap_2015_global Link to Geocatalog: https://geocatalogue.gis-gr.eu/geonetwork/srv/eng/catalog.search#/metadata/05879c75-1c5f-4eea-be23-53e27662fb16 This dataset is published in the view service (WMS) available at: https://ws.geoportail.lu/wss/service/GR_Population_projection_WMS/guest with layer name(s): -Projection_20_64_years_2024_2050

Unemployment Rate in France increased to 7.40 percent in the first quarter of 2025 from 7.30 percent in the fourth quarter of 2024. This dataset provides the latest reported value for - France Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The number of employed persons in France increased to 28066 Thousand in the first quarter of 2025 from 27867 Thousand in the fourth quarter of 2024. This dataset provides - France Employed Persons - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Population density 2024 (inhabitants per km²) per municipality Statistical data sources: INSEE Grand Est, IWEPS, Statistisches Landesamt Rheinland-Pfalz, Statistisches Amt Saarland Geodata sources: ACT Luxembourg 2024, IGN France 2022, GeoBasis-DE / BKG 2024, NGI-Belgium 2024. Harmonization: SIG-GR / GIS-GR 2024 Link to interactive map: https://map.gis-gr.eu/theme/main?version=3&zoom=8&X=708580&Y=6429642&lang=fr&rotation=0&layers=2434&opacities=1&bgLayer=basemap_2015_global Link to Geocatalog: https://geocatalogue.gis-gr.eu/geonetwork/srv/eng/catalog.search#/metadata/4ba433fb-6c1e-459f-89ca-a2914eedfdaa This dataset is published in the view service (WMS) available at: https://ws.geoportail.lu/wss/service/GR_Pop_density_WMS/guest with layer name(s): -Pop_density_2024

Home Ownership Rate in France decreased to 61.20 percent in 2024 from 63.10 percent in 2023. This dataset provides the latest reported value for - France Home Ownership Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This table contains data from the census of the municipal population since 2013 in the French municipalities. Collection Context The data is uploaded to the [INSEE] website(https://www.insee.fr/fr/accueil) and then integrated into a repository database to make it available to GIS users and departmental agents. The municipal population includes persons: * having their habitual residence in the territory of the municipality, in a dwelling or community; * detained in the penal institutions of the municipality; * homeless persons registered in the territory of the municipality; * usually residing in a mobile dwelling registered in the territory of the municipality. The municipal population of a group of municipalities is equal to the sum of the municipal populations of the municipalities that make up it. The concept of municipal population now corresponds to the concept of population used in statistics. It does not contain double accounts: every person living in France is counted once and only once. In 1999, the concept of a population without double counting corresponded to the notion of a statistical population. The concept of municipal population is defined by Decree No. 2003-485 published in the Official Journal of 8 June 2003 on the population census (source INSEE). Collection method Every year, the table is updated. A new field is created and filled in with the data from the last census of the municipal population. Attributes | field | Alias ▲ Type | – | – — | ‘objectID’ | | ‘integer’ | ‘Reg’ | | ‘char’ ▲ | ‘DEP’ | | ‘char’ ▲ | ‘CV’ | | ‘char’ ▲ | ‘CODGEO’ | | ‘char’ ▲ | ‘libgeo’ | | ‘char’ ▲ | ‘p13_pop’ | | ‘double’ ▲ | ‘p14_pop’ | | ‘double’ ▲ | ‘p15_pop’ | | ‘integer’ ▲ | ‘p16_pop’ | Municipal population 2019 – Census 2016 ⋆ ‘integer’ ⋆ | ‘p17_pop’ | Municipal population 2020 – Census 2017 ⋆ ‘integer’ ⋆ | ‘p18_pop’ | Municipal population 2021 – 2018 Census ▲ ‘integer’ ⋆ | ‘p19_pop’ | Municipal population 2022 – 2019 Census ▲ ‘integer’ ⋆ | ‘p20_pop’ | Municipal population 2023 – 2020 census ⋆ ‘integer’ | ‘p21_pop’ | Municipal population 2024 – Census 2021 ⋆ ‘integer’ -’ For more information, see the metadata on the Isogeo catalog.

State of education of the population aged 2 to 30 by sex and age group. This data is produced from the 2021 population census according to the geography in force on 1 January 2024. The population census makes it possible to know the diversity and evolution of the population of France. INSEE thus provides statistics on inhabitants and dwellings, their number and characteristics: breakdown by sex and age, occupations, housing conditions, modes of transport, commuting, etc. Age groups: * 02-05 years * 06-10 years * 11-14 years * 15-17 years * 15-24 years * 18-24 years * 25-29 years * 30 years and over For more information on the variables, you can consult the definitions by clicking on this link For more methodological information, you can consult the fact sheets "Tips for the use of census results" by clicking on the link For more information on the use of the data, you can consult this document

This upload contains two Geopackage files of raw data used for urban analysis in the outskirts of Lille and Nice, France. 
The data include building footprints (layer "building"), roads (layer "road"), and administrative boundaries (layer "adm_boundaries")
extracted from version 3.3 of the French dataset BD TOPO®3 (IGN, 2023) for the municipalities of Santes, Hallennes-lez-Haubourdin,
Haubourdin, and Emmerin in northern France (Geopackage "DPC_59.gpkg") and Drap, Cantaron and La Trinité in southern France 
(Geopackage "DPC_06.gpkg").

Metadata for these layers is available here: https://geoservices.ign.fr/sites/default/files/2023-01/DC_BDTOPO_3-3.pdf

Additionally, this upload contains the results of the following algorithms available in GitHub (https://github.com/perezjoan/emc2-WP2?tab=readme-ov-file)

1. The identification of main streets using the QGIS plugin Morpheo (layers "road_morpheo" and "buffer_morpheo") 
https://plugins.qgis.org/plugins/morpheo/ 

2. The identification of main streets in local contexts – connectivity locally weighted (layer "road_LocRelCon")

3. Basic morphometry of buildings (layer "building_morpho")

4. Evaluation of the number of dwellings within inhabited buildings (layer "building_dwellings")

5. Projecting population potential accessible from main streets (layer "road_pop_results")

Project website: http://emc2-dut.org/

Publications using this sample data: 
Perez, J. and Fusco, G., 2024. Potential of the 15-Minute Peripheral City: Identifying Main Streets and Population Within Walking Distance. In: O. Gervasi, B. Murgante, C. Garau, D. Taniar, A.M.A.C. Rocha and M.N. Faginas Lago, eds. Computational Science and Its Applications – ICCSA 2024 Workshops. ICCSA 2024. Lecture Notes in Computer Science, vol 14817. Cham: Springer, pp.50-60. https://doi.org/10.1007/978-3-031-65238-7_4.

Acknowledgement. This work is part of the emc2 project, which received the grant ANR-23-DUTP-0003-01 from the French National Research Agency (ANR) within the DUT Partnership.

Active population over 15 years of age by socio-professional categories. This data is produced from the 2021 population census according to the geography in force on 1 January 2024. The population census makes it possible to know the diversity and evolution of the population of France. INSEE thus provides statistics on inhabitants and dwellings, their number and characteristics:breakdown by sex and age, occupations, housing conditions, modes of transport, commuting, etc. Age groups: * 02-05 years * 06-10 years * 11-14 years * 15-17 years * 18-24 years * 25-29 years * 30 years and over For more information on the variables, you can consult the definitions by clicking on this link For more methodological information, you can consult the fact sheets "Tips for the use of census results" by clicking on the link For more information on the use of the data, you can consult this document

This table contains data from the census of the municipal population in the French departments. Censuses for the following years are present in the table: * 1968 * 1975 * 1982 * 1990 * from 2006 to 2021 Collection Context The data is uploaded to the [INSEE] website(https://www.insee.fr/fr/accueil) and then integrated into a repository database to make it available to GIS users and departmental agents. The municipal population includes persons: * having their habitual residence in the territory of the municipality, in a dwelling or community; * detained in the penal institutions of the municipality; * homeless persons registered in the territory of the municipality; * usually residing in a mobile dwelling registered in the territory of the municipality. The municipal population of a group of municipalities is equal to the sum of the municipal populations of the municipalities that make up it. The concept of municipal population now corresponds to the concept of population used in statistics. It does not contain double accounts: every person living in France is counted once and only once. In 1999, the concept of a population without double counting corresponded to the notion of a statistical population. The concept of municipal population is defined by Decree No. 2003-485 published in the Official Journal of 8 June 2003 on the population census (source INSEE). Collection method Every year, the table is updated. A new field is created and filled in with the data from the last census of the municipal population. Attributes | field | Alias ▲ Type | – | – — | ‘objectID’ | Unique identifier ‘integer’ | ‘Reg’ | Region code ⋆ ‘char’ -’ | ‘DEP’ | Department Number ▲ ‘char’ — | ‘dep_name’ | Department name ▲ ‘char’ | ‘superf’ | Area ▲ ‘double’ ⋆ | ‘d68_pop’ | Census 1968 ‘integer’ | ‘d75_pop’ | Census 1975 ‘integer’ | ‘d82_pop’ | Census 1982 ‘integer’ | ‘d90_pop’ | Census 1990 ‘integer’ | ‘p99_pop’ | Census 1999 ‘integer’ | ‘p06_pop’ | Census 2006 ‘integer’ | ‘p07_pop’ | Census 2007 ‘integer’ | ‘p08_pop’ | Census 2008 ‘integer’ | ‘p09_pop’ | Census 2009 ‘integer’ | ‘p10_pop’ | Census 2010 ‘integer’ | ‘p11_pop’ | Census 2011 ‘integer’ | ‘p12_pop’ | Municipal population 2015 – Census 2012 ‘integer’ | ‘p13_pop’ | Municipal population 2016 – Census 2013 ⋆ ‘integer’ ⋆ | ‘p14_pop’ | Municipal population 2017 – 2014 Census ⋆ ‘integer’ — | ‘p15_pop’ | Municipal population 2018 – Census 2015 ⋆ ‘integer’ ⋆ | ‘p16_pop’ | Municipal population 2019 – Census 2016 ⋆ ‘integer’ ⋆ | ‘p17_pop’ | Municipal population 2020 – Census 2017 ⋆ ‘integer’ ⋆ | ‘p18_pop’ | Municipal population 2021 – 2018 Census ▲ ‘integer’ ⋆ | ‘p19_pop’ | Municipal population 2022 – 2019 Census ▲ ‘integer’ ⋆ | ‘p20_pop’ | Municipal population 2023 – 2020 census ⋆ ‘integer’ | ‘p21_pop’ | Municipal population 2024 – Census 2021 ⋆ ‘integer’ -’ For more information, see the metadata on the Isogeo catalog.

Population density 2023 (inhabitants per km²), Lorraine: 2021 Territorial entities: arrondissements (Lorraine, Wallonie), cantons (Luxembourg), Kreise (Saarland, Rheinland-Pfalz) Statistical data sources: Destatis, INSEE, Statbel, STATEC. Harmonization: IBA / OIE 2024 Geodata sources: GeoBasis-DE / BKG, IGN France, NGI-Belgium, ACT Luxembourg. Harmonization: SIG-GR / GIS-GR 2024 Link to interactive map: https://map.gis-gr.eu/theme/main?version=3&zoom=8&X=708580&Y=6429642&lang=fr&rotation=0&layers=2418&opacities=1&bgLayer=basemap_2015_global Link to Geocatalog: https://geocatalogue.gis-gr.eu/geonetwork/srv/eng/catalog.search#/metadata/3ed89eb1-9a37-4b86-b793-126411751345 This dataset is published in the view service (WMS) available at: https://ws.geoportail.lu/wss/service/GR_Pop_density_WMS/guest with layer name(s): -Pop_density_2023

Change in working age population (20-64 years) 2000-2023 (Lorraine: 1999-2021) Territorial entities: arrondissements (Lorraine, Wallonie), Grand Duchy (Luxembourg), Kreise (Saarland, Rheinland-Pfalz) Statistical data sources: Destatis, INSEE, Statbel, STATEC. Calculations: OIE/IBA 2024 Geodata sources: ACT Luxembourg, IGN France, GeoBasis-DE / BKG, NGI-Belgium. Harmonization: SIG-GR / GIS-GR 2024 Link to interactive map: https://map.gis-gr.eu/theme/main?version=3&zoom=8&X=708580&Y=6429642&lang=fr&rotation=0&layers=2423&opacities=1&bgLayer=basemap_2015_global Link to Geocatalog: https://geocatalogue.gis-gr.eu/geonetwork/srv/eng/catalog.search#/metadata/669552f7-f5cf-402a-9429-e3df779d2cf5 This dataset is published in the view service (WMS) available at: https://ws.geoportail.lu/wss/service/GR_Pop_change_20_64year_olds_WMS/guest with layer name(s): -Pop_change_20_64years_2000_2023

Dataset Description

Authors: Ludovic Moncla, Katherine McDonough and Denis Vigier in the framework of the GEODE project. Data source: ARTFL Encyclopédie Project, University of Chicago Github repository: https://github.com/GEODE-project/ner-spancat-edda Language: French License: cc-by-nc-4.0 Zenodo repository: https://zenodo.org/records/10530177

Dataset Summary This dataset contains semantic annotations (at the token and span levels) for named entities (such as Spatial, Person, and MISC), nominal entities, as well as nested named entities, spatial relations, and other relevant information within French encyclopedic entries.

The span tagset is as follows: - NC-Spatial: a common noun that identifies a spatial entity (nominal spatial entity) including natural features, e.g. ville, la rivière, royaume. - NP-Spatial: a proper noun identifying the name of a place (spatial named entities), e.g. France, Paris, la Chine. - ENE-Spatial: nested spatial entity , e.g. ville de France, royaume de Naples, la mer Baltique. - Relation: spatial relation, e.g. dans, sur, à 10 lieues de. - Latlong: geographic coordinates, e.g. Long. 19. 49. lat. 43. 55. 44. - NC-Person: a common noun that identifies a person (nominal spatial entity), e.g. roi, l'empereur, les auteurs. - NP-Person: a proper noun identifying the name of a person (person named entities), e.g. Louis XIV, Pline, les Romains. - ENE-Person: nested people entity, e.g. le czar Pierre, roi de Macédoine - NP-Misc: a proper noun identifying entities not classified as spatial or person, e.g. l'Eglise, 1702, Pélasgique. - ENE-Misc: nested named entity not classified as spatial or person, e.g. l'ordre de S. Jacques, la déclaration du 21 Mars 1671. - Head: entry name - Domain-Mark: words indicating the knowledge domain (usually after the head and between parenthesis), e.g. Géographie, Geog., en Anatomie.

Supported Tasks

token-classification or span-classification: The dataset can be used to train a model for token-classification or span-classification. It is more specifically designed for spatial role labelling. A spacy custom spancat model is available at: https://huggingface.co/GEODE/fr_spacy_custom_spancat_edda.

Dataset Structure The dataset is provided as JSONL files¹ where each row follows the following structure:

{ "text": "ILLESCAS, (Géog.) petite ville d'Espagne <...> ", "meta": {"volume": 8, "head": "ILLESCAS", "author": "unsigned", "domain_article": "Géographie", "domain_paragraph": "Géographie", "article": 2637, "paragraph": 1}, "tokens": [{"text": "ILLESCAS", "start": 0, "end": 8, "id": 0, "ws": false}, {"text": ",", "start": 8, "end": 9, "id": 1, "ws": true}, {"text": "(", "start": 10, "end": 11, "id": 2, "ws": false}, {"text": "Géog", "start": 11, "end": 15, "id": 3, "ws": false}, {"text": ".", "start": 15, "end": 16, "id": 4, "ws": false}, {"text": ")", "start": 16, "end": 17, "id": 5, "ws": true}, {"text": "petite", "start": 18, "end": 24, "id": 6, "ws": true}, {"text": "ville", "start": 25, "end": 30, "id": 7, "ws": true}, {"text": "d'", "start": 31, "end": 33, "id": 8, "ws": false}, {"text": "Espagne", "start": 33, "end": 40, "id": 9, "ws": false}, {"text": ",", "start": 40, "end": 41, "id": 10, "ws": true} <...>], "spans": [{"text": "ILLESCAS", "start": 0, "end": 8, "token_start": 0, "token_end": 0, "label": "Head"}, {"text": "Géog.", "start": 11, "end": 16, "token_start": 3, "token_end": 4, "label": "Domain-mark"}, {"text": "petite ville", "start": 18, "end": 30, "token_start": 6, "token_end": 7, "label": "NC-Spatial"}, {"text": "petite ville d'Espagne", "start": 18, "end": 40, "token_start": 6, "token_end": 9, "label": "ENE-Spatial"}, {"text": "petite ville d'Espagne, dans la nouvelle Castille", "start": 18, "end": 67, "token_start": 6, "token_end": 14, "label": "ENE-Spatial"}, {"text": "Espagne", "start": 33, "end": 40, "token_start": 9, "token_end": 9, "label": "NP-Spatial"}, <...>] }

Each data contains four main fields:

text: plain text of a paragraph. meta: metadata from the ARTFL Encyclopédie about the paragraph such volume, article, paragraph id, headword, etc. tokens: list of tokens, with their text, id, start and end position at the character level. spans: list of spans (i.e., annotations), with their text, label, start and end position at the character level.

spaCy binary files are also available on the Github and Zenodo repositories.

Data Splits The dataset consists of 2200 paragraphs randomly selected out of 2001 Encyclopédie's entries. All paragraphs were written in French and are distributed as follows among the Encyclopédie knowledge domains:

The spans/entities were labeled by the project team along with using pre-labelling with early models to speed up the labelling process. A train/val/test split was used. Validation and test sets are composed of 200 paragraphs each: 100 classified as 'Géographie' and 100 from another knowledge domain. The datasets have the following breakdown of tokens and spans/entities.

Additional Information Dataset Curators List of people involved in annotating the dataset: * Ludovic Moncla (@ludovicmoncla), INSA Lyon, CNRS, LIRIS UMR 5205 * Katherine McDonough (@kmcdono2, Lancaster University & The Alan Turing Institute

Cite this work

Moncla, L., Vigier, D., & McDonough, K. (2024). GeoEDdA: A Gold Standard Dataset for Geo-semantic Annotation of Diderot & d’Alembert’s Encyclopédie. In proceedings of the 2nd International Workshop on Geographic Information Extraction from Texts (GeoExT'24), ECIR Conference, Glasgow, UK.

Acknowledgement The authors are grateful to the ASLAN project (ANR-10-LABX-0081) of the Université de Lyon, for its financial support within the French program "Investments for the Future" operated by the National Research Agency (ANR). Data courtesy the ARTFL Encyclopédie Project, University of Chicago.

The Gross Domestic Product per capita in France was last recorded at 39117.48 US dollars in 2023. The GDP per Capita in France is equivalent to 310 percent of the world's average. This dataset provides the latest reported value for - France GDP per capita - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Share of the working age population (20-64 years) in total population 2023 (Lorraine: 2021) Territorial entities: arrondissements (Lorraine, Wallonie), Grand Duchy (Luxembourg), Kreise (Saarland, Rheinland-Pfalz) Statistical data sources: Destatis; INSEE; Statbel, STATEC. Calculations: OIE/IBA 2024 Geodata sources: ACT Luxembourg, IGN France, GeoBasis-DE / BKG, NGI-Belgium. Harmonization: SIG-GR / GIS-GR 2024 Link to interactive map: https://map.gis-gr.eu/theme/main?version=3&zoom=8&X=708580&Y=6429642&lang=fr&rotation=0&layers=2421&opacities=1&bgLayer=basemap_2015_global Link to Geocatalog: https://geocatalogue.gis-gr.eu/geonetwork/srv/eng/catalog.search#/metadata/b146769a-bbe9-4861-b94e-323916c46ae6 This dataset is published in the view service (WMS) available at: https://ws.geoportail.lu/wss/service/GR_Population_share_20_64year_olds_WMS/guest with layer name(s): -Pop_share_20_64years_2023

Households Debt in France decreased to 95.35 percent of gross income in 2023 from 101.84 percent in 2022. This dataset provides - France Households Debt To Income- actual values, historical data, forecast, chart, statistics, economic calendar and news.