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.

This dataset is about countries per year in France. It has 64 rows. It features 4 columns: country, fertility rate, and population.

This data collection consists of 161 selected social, demographic, and educational datasets for France in the period 1801-1897. The data were collected from published reports of three national statistical series: (1) National Censuses, (2) Vital Statistics, and (3) Primary Education. This project was supported by grants from the National Endowment for the Humanities and the National Science Foundation. The National Census data were derived from the quinquennial population censuses of France from 1801 to 1896 and were obtained from the Statistique Generale de la France. The data provide detailed social and economic information for the period 1851 to 1896. The data for 1801-1851 are less rich in subject matter coverage but do present some basic information on population characteristics. The National Census data in general describe the population, including the composition of the population by categories of age, sex, place of birth, marital status, religion, place of residence, and occupation. There is also some limited information on migration, transportation and communication, housing, and families. A large segment of the census data pertains to occupations of the population, specifying job classifications within professions, as well as information on non-employed household members that were dependent on employees in the various industries, in addition to enumerations of persons employed in various professions and trades. The Vital Statistics data files contain annual vital statistics for the French population. These data were obtained from two printed series, MOUVEMENT DE LA POPULATION (1801-1868), and STATISTIQUE ANNUELLE (1869-1897). The basic variables included in the vital statistics datasets record births, deaths, and marriages in France. Detailed cross-tabulations of these demographic indicators are presented for births, tabulated by sex, month, legitimacy status, and characteristics of the parents, and deaths, categorized by age and previous marital status of the partners. Additional cross-tabulations are provided for variables such as divorces, passports issued, medical personnel and hospitals, and a literacy indicator (signing of marriage certificates). The Primary Education data files provide information on primary schools and were obtained from the Statistique de l'enseignement Primaire. The data obtained from the series basically cover the period 1829-1897, although some recapitulative information for earlier years is also presented. The main focus of the data in this series is on primary schools, classes and buildings, enrollment, teachers, sources of funding and expenditure, and academic proficiency of the pupils. Additional information is included on literacy, teacher training (normal) schools, school age population, and libraries. A machine-readable French language codebook, describing the data items as well as the sources from which they were obtained, is provided with each dataset supplied. In addition, lists of the variables included in each dataset are included in Parts 162-164. See the related collection, DEMOGRAPHIC, SOCIAL, EDUCATIONAL AND ECONOMIC DATA FOR FRANCE, 1833-1925 (ICPSR 7529).

IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system. The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

This dataset is about countries per year in France. It has 64 rows. It features 4 columns: country, urban population living in areas where elevation is below 5 meters , and net migration.

France Population: Aged 90 to 94 data was reported at 628,685.000 Person in 2017. This records an increase from the previous number of 620,211.000 Person for 2016. France Population: Aged 90 to 94 data is updated yearly, averaging 375,813.500 Person from Dec 1998 (Median) to 2017, with 20 observations. The data reached an all-time high of 628,685.000 Person in 2017 and a record low of 293,086.000 Person in 2009. France Population: Aged 90 to 94 data remains active status in CEIC and is reported by French National Institute for Statistics and Economic Studies. The data is categorized under Global Database’s France – Table FR.G001: Population .

This dataset is about countries per year in France. It has 64 rows. It features 4 columns: country, vulnerable employment, and urban population living in areas where elevation is below 5 meters .

France Population: Aged 80 to 84 data was reported at 1,863,940.000 Person in 2017. This records a decrease from the previous number of 1,877,915.000 Person for 2016. France Population: Aged 80 to 84 data is updated yearly, averaging 1,643,862.000 Person from Dec 1990 (Median) to 2017, with 28 observations. The data reached an all-time high of 1,894,620.000 Person in 2014 and a record low of 886,757.000 Person in 1999. France Population: Aged 80 to 84 data remains active status in CEIC and is reported by French National Institute for Statistics and Economic Studies. The data is categorized under Global Database’s France – Table FR.G001: Population .

Context

[INSEE][1] is the official french institute gathering data of many types around France. It can be demographic (Births, Deaths, Population Density...), Economic (Salary, Firms by activity / size...) and more.
It can be a great help to observe and measure inequality in the french population.

Content

Four files are in the dataset :

• base_etablissement_par_tranche_effectif : give information on the number of firms in every french town, categorized by size , come from [INSEE][2].
  • CODGEO : geographique code for the town (can be joined with code_insee column from "name_geographic_information.csv')
  • LIBGEO : name of the town (in french)
  • REG : region number
  • DEP : depatment number
  • E14TST : total number of firms in the town
  • E14TS0ND : number of unknown or null size firms in the town
  • E14TS1 : number of firms with 1 to 5 employees in the town
  • E14TS6 : number of firms with 6 to 9 employees in the town
  • E14TS10 : number of firms with 10 to 19 employees in the town
  • E14TS20 : number of firms with 20 to 49 employees in the town
  • E14TS50 : number of firms with 50 to 99 employees in the town
  • E14TS100 : number of firms with 100 to 199 employees in the town
  • E14TS200 : number of firms with 200 to 499 employees in the town
  • E14TS500 : number of firms with more than 500 employees in the town

• name_geographic_information : give geographic data on french town (mainly latitude and longitude, but also region / department codes and names )

  • EU_circo : name of the European Union Circonscription
  • code_région : code of the region attached to the town
  • nom_région : name of the region attached to the town
  • chef.lieu_région : name the administrative center around the town
  • numéro_département : code of the department attached to the town
  • nom_département : name of the department attached to the town
  • préfecture : name of the local administrative division around the town
  • numéro_circonscription : number of the circumpscription
  • nom_commune : name of the town
  • codes_postaux : post-codes relative to the town
  • code_insee : unique code for the town
  • latitude : GPS latitude
  • longitude : GPS longitude
  • éloignement : i couldn't manage to figure out what was the meaning of this number

• net_salary_per_town_per_category : salaries around french town per job categories, age and sex

  • CODGEO : unique code of the town
  • LIBGEO : name of the town
  • SNHM14 : mean net salary
  • SNHMC14 : mean net salary per hour for executive
  • SNHMP14 : mean net salary per hour for middle manager
  • SNHME14 : mean net salary per hour for employee
  • SNHMO14 : mean net salary per hour for worker
  • SNHMF14 : mean net salary for women
  • SNHMFC14 : mean net salary per hour for feminin executive
  • SNHMFP14 : mean net salary per hour for feminin middle manager
  • SNHMFE14 : mean net salary per hour for feminin employee
  • SNHMFO14 : mean net salary per hour for feminin worker
  • SNHMH14 : mean net salary for man
  • SNHMHC14 : mean net salary per hour for masculin executive
  • SNHMHP14 : mean net salary per hour for masculin middle manager
  • SNHMHE14 : mean net salary per hour for masculin employee
  • SNHMHO14 : mean net salary per hour for masculin worker
  • SNHM1814 : mean net salary per hour for 18-25 years old
  • SNHM2614 : mean net salary per hour for 26-50 years old
  • SNHM5014 : mean net salary per hour for >50 years old
  • SNHMF1814 : mean net salary per hour for women between 18-25 years old
  • SNHMF2614 : mean net salary per hour for women between 26-50 years old
  • SNHMF5014 : mean net salary per hour for women >50 years old
  • SNHMH1814 : mean net salary per hour for men between 18-25 years old
  • SNHMH2614 : mean net salary per hour for men between 26-50 years old
  • SNHMH5014 : mean net salary per hour for men >50 years old

• population : [demographic][3] information in France per town, age, sex and living mode

  • NIVGEO : geographic level (arrondissement, communes...)
  • CODGEO : unique code for the town
  • LIBGEO : name of the town (might contain some utf-8 errors, this information has better quality name_geographic_information)
  • MOCO : cohabitation mode : [list and meaning available in Data description]
  • AGE80_17 : age category (slice of 5 years) | ex : 0 -> people between 0 and 4 years old
  • SEXE : sex, 1 for men | 2 for women
  • NB : Number of people in the category

• departments.geojson : contains the borders of french departments. From [Gregoire David (github)][4]

These datasets can be merged by : CODGEO = code_insee

Acknowledgements

The entire dataset has been created (and actualized) by INSEE, I just uploaded it on Kaggle after doing some jobs and checks ...

Abstract

In 2011, the EU-SILC instrument covered all EU Member States plus Iceland, Turkey, Norway, Switzerland and Croatia. EU-SILC has become the EU reference source for comparative statistics on income distribution and social exclusion at European level, particularly in the context of the "Program of Community action to encourage cooperation between Member States to combat social exclusion" and for producing structural indicators on social cohesion for the annual spring report to the European Council. The first priority is to be given to the delivery of comparable, timely and high quality cross-sectional data.

There are two types of datasets: 1) Cross-sectional data pertaining to fixed time periods, with variables on income, poverty, social exclusion and living conditions. 2) Longitudinal data pertaining to individual-level changes over time, observed periodically - usually over four years.

Social exclusion and housing-condition information is collected at household level. Income at a detailed component level is collected at personal level, with some components included in the "Household" section. Labor, education and health observations only apply to persons aged 16 and over. EU-SILC was established to provide data on structural indicators of social cohesion (at-risk-of-poverty rate, S80/S20 and gender pay gap) and to provide relevant data for the two 'open methods of coordination' in the field of social inclusion and pensions in Europe.

The 5th version 2011 Cross-Sectional User Database as released in July 2015 is documented here.

Geographic coverage

The survey covers following countries: Austria; Belgium; Bulgaria; Croatia; Cyprus; Czech Republic; Denmark; Estonia; Finland; France; Germany; Greece; Spain; Ireland; Italy; Latvia; Lithuania; Luxembourg; Hungary; Malta; Netherlands; Poland; Portugal; Romania; Slovenia; Slovakia; Sweden; United Kingdom; Iceland; Norway; Turkey; Switzerland

Small parts of the national territory amounting to no more than 2% of the national population and the national territories listed below may be excluded from EU-SILC: France - French Overseas Departments and territories; Netherlands - The West Frisian Islands with the exception of Texel; Ireland - All offshore islands with the exception of Achill, Bull, Cruit, Gorumna, Inishnee, Lettermore, Lettermullan and Valentia; United Kingdom - Scotland north of the Caledonian Canal, the Scilly Islands.

Analysis unit

• Households;
• Individuals 16 years and older.

Universe

The survey covered all household members over 16 years old. Persons living in collective households and in institutions are generally excluded from the target population.

Kind of data

Sample survey data [ssd]

Sampling procedure

On the basis of various statistical and practical considerations and the precision requirements for the most critical variables, the minimum effective sample sizes to be achieved were defined. Sample size for the longitudinal component refers, for any pair of consecutive years, to the number of households successfully interviewed in the first year in which all or at least a majority of the household members aged 16 or over are successfully interviewed in both the years.

For the cross-sectional component, the plans are to achieve the minimum effective sample size of around 131.000 households in the EU as a whole (137.000 including Iceland and Norway). The allocation of the EU sample among countries represents a compromise between two objectives: the production of results at the level of individual countries, and production for the EU as a whole. Requirements for the longitudinal data will be less important. For this component, an effective sample size of around 98.000 households (103.000 including Iceland and Norway) is planned.

Member States using registers for income and other data may use a sample of persons (selected respondents) rather than a sample of complete households in the interview survey. The minimum effective sample size in terms of the number of persons aged 16 or over to be interviewed in detail is in this case taken as 75 % of the figures shown in columns 3 and 4 of the table I, for the cross-sectional and longitudinal components respectively.

The reference is to the effective sample size, which is the size required if the survey were based on simple random sampling (design effect in relation to the 'risk of poverty rate' variable = 1.0). The actual sample sizes will have to be larger to the extent that the design effects exceed 1.0 and to compensate for all kinds of non-response. Furthermore, the sample size refers to the number of valid households which are households for which, and for all members of which, all or nearly all the required information has been obtained. For countries with a sample of persons design, information on income and other data shall be collected for the household of each selected respondent and for all its members.

At the beginning, a cross-sectional representative sample of households is selected. It is divided into say 4 sub-samples, each by itself representative of the whole population and similar in structure to the whole sample. One sub-sample is purely cross-sectional and is not followed up after the first round. Respondents in the second sub-sample are requested to participate in the panel for 2 years, in the third sub-sample for 3 years, and in the fourth for 4 years. From year 2 onwards, one new panel is introduced each year, with request for participation for 4 years. In any one year, the sample consists of 4 sub-samples, which together constitute the cross-sectional sample. In year 1 they are all new samples; in all subsequent years, only one is new sample. In year 2, three are panels in the second year; in year 3, one is a panel in the second year and two in the third year; in subsequent years, one is a panel for the second year, one for the third year, and one for the fourth (final) year.

According to the Commission Regulation on sampling and tracing rules, the selection of the sample will be drawn according to the following requirements:

1. For all components of EU-SILC (whether survey or register based), the crosssectional and longitudinal (initial sample) data shall be based on a nationally representative probability sample of the population residing in private households within the country, irrespective of language, nationality or legal residence status. All private households and all persons aged 16 and over within the household are eligible for the operation.
2. Representative probability samples shall be achieved both for households, which form the basic units of sampling, data collection and data analysis, and for individual persons in the target population.
3. The sampling frame and methods of sample selection shall ensure that every individual and household in the target population is assigned a known and non-zero probability of selection.
4. By way of exception, paragraphs 1 to 3 shall apply in Germany exclusively to the part of the sample based on probability sampling according to Article 8 of the Regulation of the European Parliament and of the Council (EC) No 1177/2003 concerning

Community Statistics on Income and Living Conditions. Article 8 of the EU-SILC Regulation of the European Parliament and of the Council mentions: 1. The cross-sectional and longitudinal data shall be based on nationally representative probability samples. 2. By way of exception to paragraph 1, Germany shall supply cross-sectional data based on a nationally representative probability sample for the first time for the year 2008. For the year 2005, Germany shall supply data for one fourth based on probability sampling and for three fourths based on quota samples, the latter to be progressively replaced by random selection so as to achieve fully representative probability sampling by 2008. For the longitudinal component, Germany shall supply for the year 2006 one third of longitudinal data (data for year 2005 and 2006) based on probability sampling and two thirds based on quota samples. For the year 2007, half of the longitudinal data relating to years 2005, 2006 and 2007 shall be based on probability sampling and half on quota sample. After 2007 all of the longitudinal data shall be based on probability sampling.

Detailed information about sampling is available in Quality Reports in Related Materials.

Mode of data collection

Mixed

Abstract

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems.

By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

Geographic coverage

National Coverage

Analysis unit

Individual

Universe

The target population is the civilian, non-institutionalized population 15 years and above.

Kind of data

Sample survey data [ssd]

Frequency of data collection

Triennial

Sampling procedure

As in the first edition, the indicators in the 2014 Global Findex are drawn from survey data covering almost 150,000 people in more than 140 economies-representing more than 97 percent of the world's population. The survey was carried out over the 2014 calendar year by Gallup, Inc. as part of its Gallup World Poll, which since 2005 has continually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 140 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. The set of indicators will be collected again in 2017.

Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or is the customary methodology. In most economies the fieldwork is completed in two to four weeks. In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households by means of the Kish grid. In economies where cultural restrictions dictate gender matching, respondents are randomly selected through the Kish grid from among all eligible adults of the interviewer's gender.

In economies where telephone interviewing is employed, random digit dialing or a nationally representative list of phone numbers is used. In most economies where cell phone penetration is high, a dual sampling frame is used. Random selection of respondents is achieved by using either the latest birthday or Kish grid method. At least three attempts are made to reach a person in each household, spread over different days and times of day.

The sample size in France was 1,000 individuals.

Mode of data collection

Other [oth]

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup Inc. also provided valuable input. The questionnaire was piloted in multiple countries, using focus groups, cognitive interviews, and field testing. The questionnaire is available in 142 languages upon request.

Questions on cash withdrawals, saving using an informal savings club or person outside the family, domestic remittances, school fees, and agricultural payments are only asked in developing economies and few other selected countries. The question on mobile money accounts was only asked in economies that were part of the Mobile Money for the Unbanked (MMU) database of the GSMA at the time the interviews were being held.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Asli Demirguc-Kunt, Leora Klapper, Dorothe Singer, and Peter Van Oudheusden, “The Global Findex Database 2014: Measuring Financial Inclusion around the World.” Policy Research Working Paper 7255, World Bank, Washington, D.C.

INSEE zoning comprising a commune or a group of municipalities which includes in its territory a built-up area of at least 2,000 inhabitants where no dwelling is separated from the nearest to more than 200 metres. In addition, each municipality concerned has more than half of its population in this built-up area. The concept of urban unity is based on the continuity of the building and the number of inhabitants. An urban unit is a municipality or group of municipalities with a continuous building area (no cut-off of more than 200 metres between two buildings) with at least 2,000 inhabitants. If the urban unit is located in a single municipality, it is referred to as an isolated city. If the urban unit extends over several municipalities, and each of these municipalities concentrates more than half of its population in the continuous built-up area, it is referred to as a multi-communal agglomeration. Code “size of the urban unit” (based on the municipal population in the 2007 census for UU 2010): 0-Rural 1-Urban units from 2 000 to 4 999 inhabitants 2-Urban units from 5,000 to 9,999 inhabitants 3-Urban units from 10,000 to 19,999 inhabitants 4-Urban units from 20,000 to 49,999 inhabitants 5-Urban units from 50,000 to 99,999 inhabitants 6-Urban units from 100,000 to 199 999 inhabitants 7-Urban units from 200 000 to 1,999 999 inhabitants 8-Agglomeration of Paris Code “Urban unit type” 0-Unit of rural municipalities of the department 1-Single city or single-community urban unit 2-Intra-departmental agglomeration 3-Inter-departmental agglomeration 4-Interregional agglomeration 5-International agglomeration

Abstract

The fourth edition of the Global Findex offers a lens into how people accessed and used financial services during the COVID-19 pandemic, when mobility restrictions and health policies drove increased demand for digital services of all kinds.

The Global Findex is the world's most comprehensive database on financial inclusion. It is also the only global demand-side data source allowing for global and regional cross-country analysis to provide a rigorous and multidimensional picture of how adults save, borrow, make payments, and manage financial risks. Global Findex 2021 data were collected from national representative surveys of about 128,000 adults in more than 120 economies. The latest edition follows the 2011, 2014, and 2017 editions, and it includes a number of new series measuring financial health and resilience and contains more granular data on digital payment adoption, including merchant and government payments.

The Global Findex is an indispensable resource for financial service practitioners, policy makers, researchers, and development professionals.

Geographic coverage

National coverage

Analysis unit

Individual

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most developing economies, Global Findex data have traditionally been collected through face-to-face interviews. Surveys are conducted face-to-face in economies where telephone coverage represents less than 80 percent of the population or where in-person surveying is the customary methodology. However, because of ongoing COVID-19 related mobility restrictions, face-to-face interviewing was not possible in some of these economies in 2021. Phone-based surveys were therefore conducted in 67 economies that had been surveyed face-to-face in 2017. These 67 economies were selected for inclusion based on population size, phone penetration rate, COVID-19 infection rates, and the feasibility of executing phone-based methods where Gallup would otherwise conduct face-to-face data collection, while complying with all government-issued guidance throughout the interviewing process. Gallup takes both mobile phone and landline ownership into consideration. According to Gallup World Poll 2019 data, when face-to-face surveys were last carried out in these economies, at least 80 percent of adults in almost all of them reported mobile phone ownership. All samples are probability-based and nationally representative of the resident adult population. Phone surveys were not a viable option in 17 economies that had been part of previous Global Findex surveys, however, because of low mobile phone ownership and surveying restrictions. Data for these economies will be collected in 2022 and released in 2023.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households. Each eligible household member is listed, and the hand-held survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In traditionally phone-based economies, respondent selection follows the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies where mobile phone and landline penetration is high, a dual sampling frame is used.

The same respondent selection procedure is applied to the new phone-based economies. Dual frame (landline and mobile phone) random digital dialing is used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digital dialing is used in economies with limited to no landline presence (less than 20 percent).

For landline respondents in economies where mobile phone or landline penetration is 80 percent or higher, random selection of respondents is achieved by using either the latest birthday or household enumeration method. For mobile phone respondents in these economies or in economies where mobile phone or landline penetration is less than 80 percent, no further selection is performed. At least three attempts are made to reach a person in each household, spread over different days and times of day.

Sample size for France is 1000.

Mode of data collection

Landline and mobile telephone

Research instrument

Questionnaires are available on the website.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, DC: World Bank.

This dataset presents the vintage legal populations, produced by INSEE, at the municipal level, from the year 2006.

The legal vintage populations of a year N come into force on 1^st January of the year N+3. Their statistical reference date is 1^st January N+3 and take into account the territorial limits of the municipalities as defined for year N+3.

. (for example)

The applied population definition is as follows:

OpenStreetMap exports for use in GIS applications.

This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) :

tags['place'] IN ('isolated_dwelling', 'town', 'village', 'hamlet', 'city')

Features may have these attributes:

• name
• place
• population
• is_in
• source

This dataset is one of many "https://data.humdata.org/organization/hot">OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

Prepared by ICPSR under a project to automate major portions of the Statistique Generale de la France, this is a collection of demographic, social, education, economic, population, and vital statistics data for France, 1833-1925. This conversion project is a continuation of one conducted in 1972, for which a similar data collection was created, SOCIAL, DEMOGRAPHIC, AND EDUCATIONAL DATA FOR FRANCE, 1801-1897 (ICPSR 0048). The project to collect and prepare these data was sponsored by two French and two American groups: ICPSR and the Center for Western European Studies at the University of Michigan, and the Fourth and Sixth Sections of the Ecole Pratique des Hautes Etudes and Conseil National de la Recherches Scientifique in France. Both collections include data recorded at the departement, arrondissement, chef-lieu, and ville level. In this collection, materials from the vital statistics series were prepared for selected years rather than for each year in the period from 1900-1925. The years that were chosen clustered around the quinquennial censuses and also included (because of the violent demographic dislocations produced by World War I) each year in the 1914-1919 period. In addition, some vital statistics for the nineteenth century (1836-1850, 1880, and 1892) obtained from fugitive published volumes that could not be located during the course of the 1972 project were prepared. The 136 datasets in this collection contain: (1) French population, economic, and social data obtained from the quenquennial censuses of 1901, 1906, 1911, and 1921, that detail the composition of the population by categories of age, sex, nativity, marital status, religion, place of residence, and occupation, (2) industrial census data for the years 1861-1896, (3) data on primary education in France for 1833, 1901, and 1906, as well as data on secondary and higher education in France for the years 1836-1850, 1880, and 1892, and (4) data from a separate series of annual vital statistics (Mouvement de la Population) that cover the years 1836-1850, 1892, and 1900-1925, citing births, deaths, and marriages in the nation.

INSEE zoning comprising a commune or a group of municipalities which includes in its territory a built-up area of at least 2,000 inhabitants where no dwelling is separated from the nearest to more than 200 metres. In addition, each municipality concerned has more than half of its population in this built-up area. The concept of urban unity is based on the continuity of the building and the number of inhabitants. An urban unit is a municipality or group of municipalities with a continuous building area (no cut-off of more than 200 metres between two buildings) with at least 2,000 inhabitants. If the urban unit is located in a single municipality, it is referred to as an isolated city. If the urban unit extends over several municipalities, and each of these municipalities concentrates more than half of its population in the continuous built-up area, it is referred to as a multi-communal agglomeration. Code “size of the urban unit” (based on the municipal population in the 2007 census for UU 2010): 0-Rural 1-Urban units from 2 000 to 4 999 inhabitants 2-Urban units from 5,000 to 9,999 inhabitants 3-Urban units from 10,000 to 19,999 inhabitants 4-Urban units from 20,000 to 49,999 inhabitants 5-Urban units from 50,000 to 99,999 inhabitants 6-Urban units from 100,000 to 199 999 inhabitants 7-Urban units from 200 000 to 1,999 999 inhabitants 8-Agglomeration of Paris Code “Urban unit type” 0-Unit of rural municipalities of the department 1-Single city or single-community urban unit 2-Intra-departmental agglomeration 3-Inter-departmental agglomeration 4-Interregional agglomeration 5-International agglomeration

This dataset contains administrative polygons grouped by country (admin-0) with the following subdivisions according to Who's On First placetypes:
- macroregion (admin-1 including region)
- region (admin-2 including state, province, department, governorate)
- macrocounty (admin-3 including arrondissement)
- county (admin-4 including prefecture, sub-prefecture, regency, canton, commune)
- localadmin (admin-5 including municipality, local government area, unitary authority, commune, suburb)

The dataset also contains human settlement points and polygons for:
- localities (city, town, and village)
- neighbourhoods (borough, macrohood, neighbourhood, microhood)

The dataset covers activities carried out by Who's On First (WOF) since 2015. Global administrative boundaries and human settlements are aggregated and standardized from hundreds of sources and available with an open CC-BY license. Who's On First data is updated on an as-need basis for individual places with annual sprints focused on improving specific countries or placetypes. Please refer to the README.md file for complete data source metadata. Refer to our blog post for explanation of field names.

Data corrections can be proposed using Write Field, an web app for making quick data edits. You’ll need a Github.com account to login and propose edits, which are then reviewed by the Who's On First community using the Github pull request process. Approved changes are available for download within 24-hours. Please contact WOF admin about bulk edits.

OpenStreetMap exports for use in GIS applications.

This theme includes all OpenStreetMap features in this area matching:

place IN ('isolated_dwelling','town','village','hamlet','city')

Features may have these attributes:

• name
• is_in
• source
• place
• population

This dataset is one of many "https://data.humdata.org/organization/hot">OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

Situation 2016

Active municipal population from 1962 to 2016 (at 1/4). The active population includes employed workers and the unemployed (excluding contingent soldiers).

In 1999 the population census immediately distinguished between employment and non-employment situations. This a priori excluded recording mixed situations such as students who worked a few hours to finance their studies or retirees who supplemented their pension with a few hours of work.

Taking into account these mixed situations in the 2006 census artificially inflates the number of active people, particularly at extreme ages ( before 25 years and after 55 years).

To be able to make comparisons with previous censuses, it is therefore preferable to only work on workers aged between 25 and 54 years old because they are the least impacted by changes in concepts. : 17% of working people under the age of 25 are in a mixed situation, 9% of those aged 55 and over and only 4% of those aged 25-54.