In 1801 the population of France was estimated to be just under 20 million people, the number of women was 14 million, whereas the number of men was 13.3 million. The gap then widens in 1821 to 0.9 million, which is most likely a result of the Napoleonic Wars, and it then narrows during the rest of the century, shrinking to just 0.04 million in 1866.

Throughout the time shown in the graph the numbers of men and women seem to follow similar trends, however the period between 1911 and 1946 shows how drastically the numbers of men were affected by both World Wars. Between 1911 and 1921 the number of men dropped by 0.8 million, whereas the number of women grew by 0.4 million. The male population does grow again during the interwar years, however both populations drop between 1931 and 1946 due to the Second World War, with the number of males decreasing by just under one million and the number of females by 0.4 million. This graph does not show how many died in France during the wars, as the numbers would also be influenced by the birth and natural death rate, but it does give an insight into the long term affects it had on the population.

From 1946 onwards the population of France does grow steadily, and at a much faster rate than it did in the 19th century. The population grows from just under 40 million in 1946, to 65.7 million in 2020, with 31.2 and 33.2 million men and women respectively. This increase in growth comes as a result of an increased fertility rate as well as an increased rate of migration into the country. While the difference in the number of men and women did decrease after the war, reaching its lowest point of 1.1 million in 1975, the gap has widened again to over two million in 2020.

In 2025, the female population in France amounted to more than ** million. Like most of other European countries, France has a female population larger than its male population. Female population in France According to the source, the female population in France has been increasing since 2004. That year, there were more than ** million women in France, compared to **** million ten years later. Surprisingly, the total number of male births has always been higher than the total number of female births. However, life expectancy in the country is higher for women, and the proportion between men and women in France appears to stabilize over time. Women live longer than men Studies have shown that the life expectancy at birth is higher for females than for males. In 2023, a baby boy born in France had a life expectancy of 80 years, while it reached **** years for a baby girl. In Europe, as well as in France, the life expectancy gap between men and women is a consistent trend. Health issues and a riskier lifestyle could explain why women outlive men. In 2018, Madrid was the European city where both men and women had the longest life expectancy. It reached **** years for females and **** for males.

This graph shows the distribution of the French millennial population by age group as of 2025. That year, women aged between 15 and 39 accounted for almost ** percent of the French women population, while almost ** percent of the male population in France was made up of people aged between 15 and 39.

France FR: Sex Ratio at Birth: Male Births per Female Births data was reported at 1.052 Ratio in 2016. This stayed constant from the previous number of 1.052 Ratio for 2015. France FR: Sex Ratio at Birth: Male Births per Female Births data is updated yearly, averaging 1.052 Ratio from Dec 1962 (Median) to 2016, with 20 observations. The data reached an all-time high of 1.052 Ratio in 2016 and a record low of 1.052 Ratio in 2016. France FR: Sex Ratio at Birth: Male Births per Female Births data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s France – Table FR.World Bank: Population and Urbanization Statistics. Sex ratio at birth refers to male births per female births. The data are 5 year averages.; ; United Nations Population Division. World Population Prospects: 2017 Revision.; Weighted average;

The average age of the French population seems to be different according to gender, as told from the time line from 2010 to 2023. A general trend observed suggest that women had a higher average age over the years, compared to men. The average age of French men in 2023 (**** years) was lower than that of French women (**** years). This phenomenon could be explained by the fact that women have a higher life expectancy than men.

2019 situation
The school population aged 18 to 24, male and female population, and corresponding age group.

Consult the metadata

This scatter chart displays fertility rate (births per woman) against male population (people) in France. The data is about countries per year.

apa_tridas argentina bolivia censo-de-poblacio_n china colombia cuba demografi_a-y-poblacio_n demography-and-population dominican-republic ecuador espan_ola estadi_sticas estructura-y-situacio_n-de-la-poblacio_n females france francia hombres males marruecos morocco mujeres nacionalidad nationality oceani_a oceania other-american-countries other-asian-countries other-european-countries otros-pai_ses-de-ame_rica otros-pai_ses-de-asia otros-pai_ses-de-europa peru peru_ provinces provincias reino-unido repu_blica-dominicana rest-of-africa resto-de-africa romania rumani_a sex sexo spanish stateless-persons statistics structure-and-situation-of-the-population total ucrania ukraine united-kingdom venezuela

This statistic represents the number of people in the working population aged between 15 and 64 years old in France between 2006 and 2024, by gender. The population of working French women increased over this period, reaching over **** million in 2024. Men remained the majority, with a total of more than ***** million active men.

Between 1980 and 2023, the male and female prison populations more than doubled in France. There were more than ****** men and around ***** women in prison in France on January 1st, 2023, compared to ****** and ***** in 1980, respectively. During this entire period, the male prison population has been between ** and ** times greater than the female prison population.

This statistic shows the population distribution in France on January 1st, 2025, by age group. In 2025, people aged under 15 accounted for 16.7 percent of the total French population, whereas around 10 percent of the population were 75 years and older. By comparison, the number of members of the population over the age of 65 years has increased even more prominently, reaching 14.57 million in 2025. The number of people living in France has been steadily increasing since 1982, exceeding 68 million in 2025, having thus grown by seven percent during that time.

Abstract

Different countries have different health outcomes that are in part due to the way respective health systems perform. Regardless of the type of health system, individuals will have health and non-health expectations in terms of how the institution responds to their needs. In many countries, however, health systems do not perform effectively and this is in part due to lack of information on health system performance, and on the different service providers.

The aim of the WHO World Health Survey is to provide empirical data to the national health information systems so that there is a better monitoring of health of the people, responsiveness of health systems and measurement of health-related parameters.

The overall aims of the survey is to examine the way populations report their health, understand how people value health states, measure the performance of health systems in relation to responsiveness and gather information on modes and extents of payment for health encounters through a nationally representative population based community survey. In addition, it addresses various areas such as health care expenditures, adult mortality, birth history, various risk factors, assessment of main chronic health conditions and the coverage of health interventions, in specific additional modules.

The objectives of the survey programme are to: 1. develop a means of providing valid, reliable and comparable information, at low cost, to supplement the information provided by routine health information systems. 2. build the evidence base necessary for policy-makers to monitor if health systems are achieving the desired goals, and to assess if additional investment in health is achieving the desired outcomes. 3. provide policy-makers with the evidence they need to adjust their policies, strategies and programmes as necessary.

Geographic coverage

The survey sampling frame must cover 100% of the country's eligible population, meaning that the entire national territory must be included. This does not mean that every province or territory need be represented in the survey sample but, rather, that all must have a chance (known probability) of being included in the survey sample.

There may be exceptional circumstances that preclude 100% national coverage. Certain areas in certain countries may be impossible to include due to reasons such as accessibility or conflict. All such exceptions must be discussed with WHO sampling experts. If any region must be excluded, it must constitute a coherent area, such as a particular province or region. For example if ¾ of region D in country X is not accessible due to war, the entire region D will be excluded from analysis.

Analysis unit

Households and individuals

Universe

The WHS will include all male and female adults (18 years of age and older) who are not out of the country during the survey period. It should be noted that this includes the population who may be institutionalized for health reasons at the time of the survey: all persons who would have fit the definition of household member at the time of their institutionalisation are included in the eligible population.

If the randomly selected individual is institutionalized short-term (e.g. a 3-day stay at a hospital) the interviewer must return to the household when the individual will have come back to interview him/her. If the randomly selected individual is institutionalized long term (e.g. has been in a nursing home the last 8 years), the interviewer must travel to that institution to interview him/her.

The target population includes any adult, male or female age 18 or over living in private households. Populations in group quarters, on military reservations, or in other non-household living arrangements will not be eligible for the study. People who are in an institution due to a health condition (such as a hospital, hospice, nursing home, home for the aged, etc.) at the time of the visit to the household are interviewed either in the institution or upon their return to their household if this is within a period of two weeks from the first visit to the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLING GUIDELINES FOR WHS

Surveys in the WHS program must employ a probability sampling design. This means that every single individual in the sampling frame has a known and non-zero chance of being selected into the survey sample. While a Single Stage Random Sample is ideal if feasible, it is recognized that most sites will carry out Multi-stage Cluster Sampling.

The WHS sampling frame should cover 100% of the eligible population in the surveyed country. This means that every eligible person in the country has a chance of being included in the survey sample. It also means that particular ethnic groups or geographical areas may not be excluded from the sampling frame.

The sample size of the WHS in each country is 5000 persons (exceptions considered on a by-country basis). An adequate number of persons must be drawn from the sampling frame to account for an estimated amount of non-response (refusal to participate, empty houses etc.). The highest estimate of potential non-response and empty households should be used to ensure that the desired sample size is reached at the end of the survey period. This is very important because if, at the end of data collection, the required sample size of 5000 has not been reached additional persons must be selected randomly into the survey sample from the sampling frame. This is both costly and technically complicated (if this situation is to occur, consult WHO sampling experts for assistance), and best avoided by proper planning before data collection begins.

All steps of sampling, including justification for stratification, cluster sizes, probabilities of selection, weights at each stage of selection, and the computer program used for randomization must be communicated to WHO

STRATIFICATION

Stratification is the process by which the population is divided into subgroups. Sampling will then be conducted separately in each subgroup. Strata or subgroups are chosen because evidence is available that they are related to the outcome (e.g. health, responsiveness, mortality, coverage etc.). The strata chosen will vary by country and reflect local conditions. Some examples of factors that can be stratified on are geography (e.g. North, Central, South), level of urbanization (e.g. urban, rural), socio-economic zones, provinces (especially if health administration is primarily under the jurisdiction of provincial authorities), or presence of health facility in area. Strata to be used must be identified by each country and the reasons for selection explicitly justified.

Stratification is strongly recommended at the first stage of sampling. Once the strata have been chosen and justified, all stages of selection will be conducted separately in each stratum. We recommend stratifying on 3-5 factors. It is optimum to have half as many strata (note the difference between stratifying variables, which may be such variables as gender, socio-economic status, province/region etc. and strata, which are the combination of variable categories, for example Male, High socio-economic status, Xingtao Province would be a stratum).

Strata should be as homogenous as possible within and as heterogeneous as possible between. This means that strata should be formulated in such a way that individuals belonging to a stratum should be as similar to each other with respect to key variables as possible and as different as possible from individuals belonging to a different stratum. This maximises the efficiency of stratification in reducing sampling variance.

MULTI-STAGE CLUSTER SELECTION

A cluster is a naturally occurring unit or grouping within the population (e.g. enumeration areas, cities, universities, provinces, hospitals etc.); it is a unit for which the administrative level has clear, nonoverlapping boundaries. Cluster sampling is useful because it avoids having to compile exhaustive lists of every single person in the population. Clusters should be as heterogeneous as possible within and as homogenous as possible between (note that this is the opposite criterion as that for strata). Clusters should be as small as possible (i.e. large administrative units such as Provinces or States are not good clusters) but not so small as to be homogenous.

In cluster sampling, a number of clusters are randomly selected from a list of clusters. Then, either all members of the chosen cluster or a random selection from among them are included in the sample. Multistage sampling is an extension of cluster sampling where a hierarchy of clusters are chosen going from larger to smaller.

In order to carry out multi-stage sampling, one needs to know only the population sizes of the sampling units. For the smallest sampling unit above the elementary unit however, a complete list of all elementary units (households) is needed; in order to be able to randomly select among all households in the TSU, a list of all those households is required. This information may be available from the most recent population census. If the last census was >3 years ago or the information furnished by it was of poor quality or unreliable, the survey staff will have the task of enumerating all households in the smallest randomly selected sampling unit. It is very important to budget for this step if it is necessary and ensure that all households are properly enumerated in order that a representative sample is obtained.

It is always best to have as many clusters in the PSU as possible. The reason for this is that the fewer the number of respondents in each PSU, the lower will be the clustering effect which

France FR: Life Expectancy at Birth: Total data was reported at 82.273 Year in 2016. This stayed constant from the previous number of 82.273 Year for 2015. France FR: Life Expectancy at Birth: Total data is updated yearly, averaging 76.100 Year from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 82.671 Year in 2014 and a record low of 69.868 Year in 1960. France FR: Life Expectancy at Birth: Total data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s France – Table FR.World Bank: Health Statistics. Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life.; ; (1) United Nations Population Division. World Population Prospects: 2017 Revision, or derived from male and female life expectancy at birth from sources such as: (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.; Weighted average;

[Situation 2017] School population aged 18 to 24, male and female population, and corresponding age group.

Consult the metadata

alemania ambos-sexos argelia argentina bolivia born-abroad born-in-spain both-sexes brasil brazil bulgaria bulgary chile china colombia continuous-register-statistics cuba dominican-republic ecuador estadi_stica-del-padro_n-continuo estadi_sticas females france francia germany hombres italia italy males marruecos morocco mujeres municipalities municipios nacidos-en-el-extranjero nacidos-en-espan_a nigeria oceania pai_s-de-nacimiento pakista_n pakistan paraguay peru peru_ place-of-birth poland polonia portugal reino-unido repu_blica-dominicana rest-of-europe romania rumani_a rusia russia senegal sex sexo statistics total total-a_frica total-africa total-ame_rica total-america total-asia total-europa total-europa-no-comunitaria total-europe total-european-union total-oceani_a total-poblacio_n total-population total-unio_n-europea ucrania ukraine united-kingdom uruguay venezuela

Résumé

Le projet de recherche MAFE est une initiative de grande ampleur dont l'objectif est d'étudier les migrations entre l'Afrique subsaharienne et l'Europe. - Attention, la documentation des enquêtes MAFE est en langue anglaise. -

The MAFE project is a major research initiative focused on migration between Sub-Saharan Africa and Europe. It brings together ten European and African research centres working on international migration.

In the early XXIth Century, international migration from Sub-Saharan Africa to Europe has generated increasing public and policy attention. The flotilla of boats bringing would-be migrants to the Canary Islands, and attempts to reach Spanish territory in Ceuta and Mellila have drawn a rapid response from Europe in the form of new policy measures. Yet the scope, nature and likely development of Sub-Saharan African migration to Europe remained poorly understood, and, as a result, European polices may be ineffective. A major cause of this lack of understanding was the absence of comprehensive data on the causes of migration and circulation between Africa and Europe.

The MAFE project aimed at overcoming this lack of understanding by collecting unique data on the characteristics and behavior of migrants from Sub-Saharan countries to Europe. The key notion underpinning the project was that migration must not only be seen as a one-way flow from Africa to Europe. The argument was that return migration, circulation and transnational practices are significant and must be understood in order to design better migration policy.

The MAFE project focused on migration flows between Europe (Belgium, France, Italy, the Netherlands, Spain and the UK) and Senegal, the Democratic Republic of Congo and Ghana, which together accounted for over a quarter of all African migration to the EU at the time of the survey. In each of these "migration systems", the survey was designed to document four key areas: - Patterns of migration : *the socio-demographic characteristics of migrants, *the routes of migration from Africa to Europe, and *the patterns of return migration and circulation. - Determinants of migration: looking at departure, but also return and circulation and taking into account the whole set of possible destinations. - Migration and Development: MAFE documents some of the socio-economic changes driven by international migration, looking as often as possible at both ends of the Afro-European migration system, at the individual level. - Migrations and Families: the data collected by the MAFE project can be used to study all sorts of interactions between family formation and international migration. Although the survey was primarily designed to study international migration, it can also be used to study other phenomena, especially in Africa: domestic mobility, labor market participation, family formation, etc. Comparable data was collected in both 3 sending and 6 destination countries, i.e. in sub-Saharan Africa and in Europe. The data are longitudinal - including retrospective migration, education, work and family histories for individuals - and multi-level - (with data collected at the individual and household levels, in addition of macro-contextual data).

Please consult the official MAFE website for further details : https://mafeproject.site.ined.fr/en/

Geographic coverage

Six European countries and three African countries participated in the MAFE surveys. Data collection was carried out in both sending countries in Africa and destination countries in Europe, in order to constitute transnational samples. For MAFE Senegal, data was collected in Senegal (African part) and France, Italy and Spain (European part).

Analysis unit

Individual Household

Univers

SENEGAL Household: Households selected randomly from the updated list of households in the selected primary sampling units. Two strata were distinguished: the households with migrants and those without migrants. Individual: People aged 25-75 at the time of the survey, born in Senegal and who have/had Senegalese citizenship. This lower age limit was set in order to obtain informative life histories. By not including respondents younger than 25, the resources were used more effectively. The place of birth criterion was used to exclude people who were born out of their country of origin in order to exclude second generation migrants in Europe and to increase the homogeneity of sample. Up to two return migrants and partners of migrants, and one randomly selected other eligible person. Return migrants were eligible if their first departure was above at 18 or over.

EUROPE In all the European countries, the surveys were conducted among males and females who were aged 25 and over at the time of the surveys, and who were 18 or over when they had left Africa for the first time for at least one year. For MAFE Senegal, only migrants from Senegal were interviewed. This was a way to reinforce the homogeneity of the sample by excluding people of the 1.5 generation who are often "passive" migrants.

In theory, surveyed individuals must be representative of the whole population with these characteristics in the departure region and in the destination countries. The sample is composed of males and females. In Europe, in spite of a gender demographic disequilibrium, the objective was to include 50% of males and 50% of females in order to allow gender analyses.

Kind of data

survey data

Frequency of data collection

SENEGAL In Senegal, data collection activities started in November 2007 (selection of survey sites in Dakar and listing of households in the selected sites). They ended in September 2008 (data entry and data cleaning). Overall, 11 months were necessary to carry out all the activities related to data collection, and fieldwork lasted a little less than 6 months. Data collection was organized in two separate stages: the household survey was first conducted, and the biographic survey started after the household survey had been completed. The data collected in the household survey was used to prepare a sampling frame of individuals for the biographic survey; quick data entry of part of the questionnaires of the household survey was thus necessary before starting data collection for the biographic survey. Although this approach had advantages, it also lengthened the data collection process. This approach was not used for surveys in Ghana and DR Congo, where both surveys were conducted simultaneously.

EUROPE In France, Italy and Spain the surveys were conducted in 2008, before the start of the EU funded project. Data collection activities lasted approximately 6 months. Note: A second round was carried in Spain in 2010. About 400 Senegalese migrants were interviewed using exactly the same questionnaire. The data will be released in the future. For more information, contact: pau.baizan@upf.edu

Sampling procedure

Probability: Stratified

  SENEGAL

A three-stage stratified random sample was used. At the first stage, primary sampling units (census district) were selected randomly with varying probabilities. At the second stage, households were selected randomly in each of the selected primary sampling units (PSUs). At the third stage, individuals were selected within the households. a) Selection of primary sampling units (first stage) In the Senegal survey, the sample was designed to be probabilistic and representative of the Dakar region, and at the same time to maximize the chance of reaching households 'affected' by international migration (rare population). The sampling frame used to select the primary sampling units was the 2002 Population Census. The census districts (CD) -which are usually used as the primary sampling units in surveys in Senegal - have an average size of 100 households in urban areas. 60 primary sampling units were randomly selected at the first stage. This number of primary sampling units allows reaching a balance between a large dispersion of households (which decreases sampling errors) and a more concentrated sample (which reduces costs). The region of Dakar was divided into 10 strata of equal size, according to the % of migrant households within each of them (in average, 11.6% of the households were 'migrants'). 6 CD's per stratum were drawn, with a probability proportional to the number of households within each CD. In other words, census districts with a large number of migrants were more likely to be selected than those with low numbers of migrants. This approach increases the number of migrants interviewed in the individual survey, while still having a probabilistic sample representative of the target area. The listing of the households in the 60 selected primary sampling units was updated in order to select the sample of households. This stage was essential because a lot of changing occurred in some large neighbourhoods of Dakar since the previous census (2002), especially in suburban areas. This counting also allowed distinguishing between households with and without migrants. b) Selection of households (second stage) The following approach was used in MAFE-Senegal: - Households were selected randomly (using systematic random sampling) from the updated list of households in the selected PSUs. Two strata were distinguished: the households with migrants and those without migrants. A maximum of 50% of households with migrants were drawn in each district. Selected households that could not be reached (absence, refusals,…) were not replaced during the fieldwork. Replacement would distort the computation of sampling weights, and could also lead to bias the sample. To take account of refusals and absences

Work in progress

This dataset comes from https://github.com/scrouzet/covid19-incrementality If you want to get a fresh data update, please go to this repo.

covid19-incrementalite

Study on incrementality of COVID-19 effect. Objective : quantify death increase due to COVID-19 in France at a department level.

References

• https://medium.com/@davidbessis/coronavirus-the-core-metrics-we-should-be-looking-at-2ca09a3dc4b1

Data sources

Death data form INSEE (French Statistic Agency) : https://www.data.gouv.fr/fr/datasets/fichier-des-personnes-decedees/

Geography referential (commune and departement) : https://geo.api.gouv.fr

Population data time serie from INSEE : https://www.insee.fr/fr/statistiques/1893198

Methodology

Data preparation :

• Collection and concatenation of yearly death data (1990=> 2019)
• Deleted " replaced with white space
• retreated dates : removed lines with invalid birth or death dates (0,72% of cases)
• fitler death dates> 1970
• join with geography referential to assiciate commune with department
• correction of department for specific geographies (Lyon, Paris, Marseille with arrondissement code instead of comune code in INSEE File)
• Grouping by death department, death date, sex, age, year of observation

Colonnes du fichier : data/INSEE_deces_2010_2019.zip

• year of observation (annee_comptabilisation) = year of the yearly file where the death has been accounted for. (2019 = deces_2019.txt). Please note that a yearly file contains death dates from previous yeras due to delay in data collection at insee. All files must be concatenated to get a complete view of death for a given year. Duplicate records are already removed.
• sexe : 1 = Male / 2 = Female
• age : age at death time
• departement_deces : department code of the commune where death happend. Note that some department are not valid in the repository : 1% death in "99" departement, non significative death cases in "98" department. The department name and associated region is provided in the geography referential
• date_deces : death_date
• nb_deces : count of deceased person, grouped by (anneee_comptabilisation, sex, age, departement_code, date_deces)

Modélisation

WORK IN PROGRESS - Modélisation par classe d'age et par département - Retraitement de la canicule 2003 - redressement des données hebdo de l'INSEE pour estimer l'effet de décallage dans la remontée des information (délai entre survenance du délai et comptabilisation par l'INSEE)

Preprocessing

The pickle files in 'preprocessed' directory have been generated with data from the 'data' directory. See original repo for preprocessing method.

How to contribute

1. Fork this repo
2. Commit your code in your forked repo
3. Do a pull-request on the main repo and ask for code reviewers
4. Take into account the comments

For contributors 1. Make a branch "feat-short_name_feature" 2. Commit your code in this branch 3. Do a pull-request on the main repo and ask for code reviewers 4. Take into account the comments

The unemployment rate for women in France was *** percent in 2024, compared with *** percent for men. Since 2015, both female and male unemployment rates have decreased year-on-year, from *** percent for women and **** percent for men. Moreover, the gap between female and male unemployment rates has reduced quite significantly since 1996. Indeed, for that year, **** percent of the female active population was unemployed compared with only *** percent of the male active population, when in 2024 both rates were around ***** percent.

1. Economic activities such as logging and mineral extraction can result in the creation of new anthropogenic habitats that host specific biodiversity, including protected species. However, the legislation in many Western European countries requires the rehabilitation of ‘damaged’ areas following logging and mining operations, which can eliminate these early successional habitats. Conservation managers face a dilemma in these situations, but often lack knowledge about the impacts of environmental rehabilitation on the population dynamics of pioneer species and so are unable to take this into account in their actions. 2. We investigated the demography of a spatially structured population of an endangered amphibian (Bombina variegata) that uses waterbodies created by logging activities as breeding sites. Using capture–recapture (CR) data collected during a 9-year study period, we examined how the destruction of breeding patches due to environmental rehabilitation affected adult survival ...

Sex-ratio of Covid-19 death rates in France and South Africa (Male/Female).