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

France FR: Population: Total data was reported at 67,118,648.000 Person in 2017. This records an increase from the previous number of 66,859,768.000 Person for 2016. France FR: Population: Total data is updated yearly, averaging 58,009,594.000 Person from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 67,118,648.000 Person in 2017 and a record low of 46,814,237.000 Person in 1960. France FR: Population: 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.WDI: Population and Urbanization Statistics. Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. The values shown are midyear estimates.; ; (1) United Nations Population Division. World Population Prospects: 2017 Revision. (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.; Sum; Relevance to gender indicator: disaggregating the population composition by gender will help a country in projecting its demand for social services on a gender basis.

France FR: Population: Growth data was reported at 0.386 % in 2017. This records a decrease from the previous number of 0.399 % for 2016. France FR: Population: Growth data is updated yearly, averaging 0.570 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 1.412 % in 1962 and a record low of 0.079 % in 1991. France FR: Population: Growth 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.WDI: Population and Urbanization Statistics. Annual population growth rate for year t is the exponential rate of growth of midyear population from year t-1 to t, expressed as a percentage . Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.; ; Derived from total population. Population source: (1) United Nations Population Division. World Population Prospects: 2017 Revision, (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;

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.

Comprehensive demographic dataset for LA France, , SC, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

About this dataset

Context

The dataset presents the median household income across different racial categories in French Lick. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of French Lick population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 80.91% of the total residents in French Lick. Notably, the median household income for White households is $49,722. Interestingly, despite the White population being the most populous, it is worth noting that Black or African American households actually reports the highest median household income, with a median income of $124,643. This reveals that, while Whites may be the most numerous in French Lick, Black or African American households experience greater economic prosperity in terms of median household income.

https://i.neilsberg.com/ch/french-lick-in-median-household-income-by-race.jpeg" alt="French Lick median household income diversity across racial categories">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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 of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in French Lick.
• Median household income: Median household income, adjusting for inflation, presented in 2022-inflation-adjusted dollars

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 median household income by race. You can refer the same here

France FR: Birth Rate: Crude: per 1000 People data was reported at 11.700 Ratio in 2016. This records a decrease from the previous number of 12.000 Ratio for 2015. France FR: Birth Rate: Crude: per 1000 People data is updated yearly, averaging 13.800 Ratio from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 18.700 Ratio in 1961 and a record low of 11.700 Ratio in 2016. France FR: Birth Rate: Crude: per 1000 People 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.WDI: Population and Urbanization Statistics. Crude birth rate indicates the number of live births occurring during the year, per 1,000 population estimated at midyear. Subtracting the crude death rate from the crude birth rate provides the rate of natural increase, which is equal to the rate of population change in the absence of migration.; ; (1) United Nations Population Division. World Population Prospects: 2017 Revision. (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;

France FR: Death Rate: Crude: per 1000 People data was reported at 8.800 Ratio in 2016. This records a decrease from the previous number of 8.900 Ratio for 2015. France FR: Death Rate: Crude: per 1000 People data is updated yearly, averaging 9.500 Ratio from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 11.400 Ratio in 1960 and a record low of 8.300 Ratio in 2007. France FR: Death Rate: Crude: per 1000 People 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.WDI: Population and Urbanization Statistics. Crude death rate indicates the number of deaths occurring during the year, per 1,000 population estimated at midyear. Subtracting the crude death rate from the crude birth rate provides the rate of natural increase, which is equal to the rate of population change in the absence of migration.; ; (1) United Nations Population Division. World Population Prospects: 2017 Revision. (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;

Dataset

The dataset (dataset.csv) comes from a service from which anyone present on the French territory benefits without social, cultural or administrative distinction (with or without papers). Nationalities have only been inferred from individuals' last names.

Task Details

The text below is based on an article from the French Observatory for Immigration and Demography entitled: The « Great Replacement »: Fantasy or Reality? The notion of « great replacement » in France now haunts editorials, social networks and major audiovisual media platforms, but places of power and simple family discussions. The importance of migratory flows, coupled with the birth rate of immigrants or of immigrant origin, resulted in 11% of the population residing in France being immigrant in 2017 and 25% being of immigrant origin - counting children of the second generation from immigration - according to figures from the French Office for Immigration and Integration (OFII) published in October 2018. This represents a quarter of the French population. And these are all stocks - that is, what is and not what will be in the future, as a result of migratory flows and future births. However, it is necessary to take into account the fertility differential between women descending from indigenous peoples (less than 1.8 children per woman on average in 2017), women descending from immigrants (2.02 children per woman on average) and immigrant women (2.73 children per woman on average). This fertility varies greatly according to the origin of the women: 3.6 children per woman on average for Algerian immigrants, 3.5 children per woman for Tunisian immigrants, 3.4 children per woman for Moroccan immigrants and 3.1 children per woman for Turkish immigrants, which is higher than the fertility of their country of origin (respectively 3; 2.4; 2.2; 2.1). Over the same twenty-year period, between 1998 and 2018: • The number of births to children with both French parents fell by 13.7%. • The number of births of children with at least one foreign parent increased by 63.6% • The number of births to children with both foreign parents increased by 43%. In 2018, almost a third of children born (31.4%) had at least one parent born abroad. While a part of the French political class remains in denial about this phenomenon and its consequences, officials in other countries source of immigration, have openly claimed this contemporary mode of conquest since the 70s: 1974, former Algerian President Houari Boumedienne said in a U.N. speech: “One day, millions of men will leave the Southern Hemisphere to go to the Northern Hemisphere. And they will not go there as friends. The wombs of our women will give us victory.” A precisely anti-France hatred is even cultivated by certain African states for which France happens to be the perfect scapegoat for the failure of their successive policies. For Algeria, this hatred even goes so far as to be included in its national anthem (cf. [Wikipedia] National anthem of Algeria).

Expected Submission

Using the data provided, support a diagnosis on the current state and future of the French civilization. And if the replacement of the French population and its customs a fantasy or reality?

Further help

• 2020 France population pyramide : https://www.insee.fr/fr/statistiques/2381472
• For cartographic representations, the projection RGF93 / Lambert93, reference EPSG:2154 or IGNF:LAMB93 should be favored.

Our Point of Interest (POI) data supports various location intelligence projects and facilitates the development of precise mapping and navigation tools, location analysis, address validation, and much more. Gain access to highly accurate, clean, and globally scaled POI data featuring over 164 million verified locations across 220 countries. We have been providing this data to companies worldwide for 30 years.

• Develop mapping and navigation tools and software.
• Identify new areas and locations suitable for business development.
• Analyze the presence of competitors and nearby populations.
• Optimize routes to enhance delivery efficiency.
• Evaluate property values based on nearby infrastructure.
• Support disaster management by identifying high-risk areas.
• Promote your products and services using geotargeting strategies.

Our use cases demonstrate how our data has been beneficial and helped our customers in several key areas: 1. Gaining a Competitive Edge: Utilize point of interest (POI) data to analyze competitors, identify high-opportunity areas, and attract more customers. 2. Enhancing Customer Journeys: Leverage location intelligence to provide personalized, real-time recommendations that boost customer engagement. 3. Optimizing Store Expansion: Select the most profitable locations by analyzing foot traffic, demographics, and competitor insights. 4. Streamlining Deliveries: Improve fulfillment accuracy through address validation, reducing failed shipments and increasing customer satisfaction. 5. Driving Smarter Campaigns: Use geospatial insights to effectively target the right audiences, enhance outreach, and maximize campaign impact.

TThe ERS International Macroeconomic Data Set provides historical and projected data for 181 countries that account for more than 99 percent of the world economy. These data and projections are assembled explicitly to serve as underlying assumptions for the annual USDA agricultural supply and demand projections, which provide a 10-year outlook on U.S. and global agriculture. The macroeconomic projections describe the long-term, 10-year scenario that is used as a benchmark for analyzing the impacts of alternative scenarios and macroeconomic shocks.

Explore the International Macroeconomic Data Set 2015 for annual growth rates, consumer price indices, real GDP per capita, exchange rates, and more. Get detailed projections and forecasts for countries worldwide.

Annual growth rates, Consumer price indices (CPI), Real GDP per capita, Real exchange rates, Population, GDP deflator, Real gross domestic product (GDP), Real GDP shares, GDP, projections, Forecast, Real Estate, Per capita, Deflator, share, Exchange Rates, CPI

Afghanistan, Albania, Algeria, Angola, Antigua and Barbuda, Argentina, Armenia, Australia, Austria, Azerbaijan, Bahamas, Bahrain, Bangladesh, Barbados, Belarus, Belgium, Belize, Benin, Bhutan, Bolivia, Bosnia and Herzegovina, Botswana, Brazil, Brunei, Bulgaria, Burkina Faso, Burundi, Côte d'Ivoire, Cabo Verde, Cambodia, Cameroon, Canada, Central African Republic, Chad, Chile, China, Colombia, Congo, Costa Rica, Croatia, Cuba, Cyprus, Denmark, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Eritrea, Estonia, Eswatini, Ethiopia, Fiji, Finland, France, Gabon, Gambia, Georgia, Germany, Ghana, Greece, Grenada, Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, Hungary, Iceland, India, Indonesia, Iran, Iraq, Ireland, Israel, Italy, Jamaica, Japan, Jordan, Kazakhstan, Kenya, Kuwait, Kyrgyzstan, Laos, Latvia, Lebanon, Lesotho, Liberia, Libya, Lithuania, Luxembourg, Madagascar, Malawi, Malaysia, Maldives, Mali, Malta, Mauritania, Mauritius, Mexico, Moldova, Mongolia, Morocco, Mozambique, Myanmar, Namibia, Nepal, Netherlands, New Zealand, Nicaragua, Niger, Nigeria, Norway, Oman, Pakistan, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russia, Rwanda, Samoa, Saudi Arabia, Senegal, Serbia, Seychelles, Sierra Leone, Singapore, Slovakia, Slovenia, Solomon Islands, South Africa, Spain, Sri Lanka, Sudan, Suriname, Sweden, Switzerland, Syria, Tajikistan, Tanzania, Thailand, Togo, Tonga, Trinidad and Tobago, Tunisia, Turkey, Turkmenistan, Uganda, Ukraine, United Arab Emirates, United Kingdom, Uruguay, Uzbekistan, Vanuatu, Venezuela, Vietnam, Yemen, Zambia, Zimbabwe, WORLD Follow data.kapsarc.org for timely data to advance energy economics research. Notes:

Developed countries/1 Australia, New Zealand, Japan, Other Western Europe, European Union 27, North America

Developed countries less USA/2 Australia, New Zealand, Japan, Other Western Europe, European Union 27, Canada

Developing countries/3 Africa, Middle East, Other Oceania, Asia less Japan, Latin America;

Low-income developing countries/4 Haiti, Afghanistan, Nepal, Benin, Burkina Faso, Burundi, Central African Republic, Chad, Democratic Republic of Congo, Eritrea, Ethiopia, Gambia, Guinea, Guinea-Bissau, Liberia, Madagascar, Malawi, Mali, Mozambique, Niger, Rwanda, Senegal, Sierra Leone, Somalia, Tanzania, Togo, Uganda, Zimbabwe;

Emerging markets/5 Mexico, Brazil, Chile, Czech Republic, Hungary, Poland, Slovakia, Russia, China, India, Korea, Taiwan, Indonesia, Malaysia, Philippines, Thailand, Vietnam, Singapore

BRIICs/5 Brazil, Russia, India, Indonesia, China; Former Centrally Planned Economies

Former centrally planned economies/7 Cyprus, Malta, Recently acceded countries, Other Central Europe, Former Soviet Union

USMCA/8 Canada, Mexico, United States

Europe and Central Asia/9 Europe, Former Soviet Union

Middle East and North Africa/10 Middle East and North Africa

Other Southeast Asia outlook/11 Malaysia, Philippines, Thailand, Vietnam

Other South America outlook/12 Chile, Colombia, Peru, Bolivia, Paraguay, Uruguay

Indicator Source

Real gross domestic product (GDP) World Bank World Development Indicators, IHS Global Insight, Oxford Economics Forecasting, as well as estimated and projected values developed by the Economic Research Service all converted to a 2015 base year.

Real GDP per capita U.S. Department of Agriculture, Economic Research Service, Macroeconomic Data Set, GDP table and Population table.

GDP deflator World Bank World Development Indicators, IHS Global Insight, Oxford Economics Forecasting, as well as estimated and projected values developed by the Economic Research Service, all converted to a 2015 base year.

Real GDP shares U.S. Department of Agriculture, Economic Research Service, Macroeconomic Data Set, GDP table.

Real exchange rates U.S. Department of Agriculture, Economic Research Service, Macroeconomic Data Set, CPI table, and Nominal XR and Trade Weights tables developed by the Economic Research Service.

Consumer price indices (CPI) International Financial Statistics International Monetary Fund, IHS Global Insight, Oxford Economics Forecasting, as well as estimated and projected values developed by the Economic Research Service, all converted to a 2015 base year.

Population Department of Commerce, Bureau of the Census, U.S. Department of Agriculture, Economic Research Service, International Data Base.

France FR: Refugee Population: by Country or Territory of Origin data was reported at 49.000 Person in 2017. This records a decrease from the previous number of 54.000 Person for 2016. France FR: Refugee Population: by Country or Territory of Origin data is updated yearly, averaging 88.500 Person from Dec 1994 (Median) to 2017, with 24 observations. The data reached an all-time high of 286.000 Person in 2005 and a record low of 15.000 Person in 1995. France FR: Refugee Population: by Country or Territory of Origin 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.WDI: Population and Urbanization Statistics. Refugees are people who are recognized as refugees under the 1951 Convention Relating to the Status of Refugees or its 1967 Protocol, the 1969 Organization of African Unity Convention Governing the Specific Aspects of Refugee Problems in Africa, people recognized as refugees in accordance with the UNHCR statute, people granted refugee-like humanitarian status, and people provided temporary protection. Asylum seekers--people who have applied for asylum or refugee status and who have not yet received a decision or who are registered as asylum seekers--are excluded. Palestinian refugees are people (and their descendants) whose residence was Palestine between June 1946 and May 1948 and who lost their homes and means of livelihood as a result of the 1948 Arab-Israeli conflict. Country of origin generally refers to the nationality or country of citizenship of a claimant.; ; United Nations High Commissioner for Refugees (UNHCR), Statistics Database, Statistical Yearbook and data files, complemented by statistics on Palestinian refugees under the mandate of the UNRWA as published on its website. Data from UNHCR are available online at: www.unhcr.org/en-us/figures-at-a-glance.html.; Sum;

Context

The outbreak of COVID19 pushed Kaggle to launch several competitions to better understand how the new virus spreads.

The data provided by this competition is not only divided by nation (China, US, Canada...), but also sometimes by province/state/dependency/territory (California, Hubei, French Guiana, Saskatchewan...).

Although there are already several Kaggle datasets that provide population estimates by nation, I couldn't find any that provided a population estimate for each one of the constituent states ("provinces/states") included in the data for the 2nd week COVID19 Global Forecasting competition. So here they are, packaged in a super simple two-column CSV file.

Content

Each row in this dataset is a rough estimate of the population in each of the constituent states that appear in the COVID19 Global Forecasting competition. Each row is, of course, one of these inner states. By "constituent state" I mean one of: - the 54 United States of America - the 33 Chinese provinces - 10 Canadian provinces (plus a territory, Northwest Territories) - 11 French overseas territories - 10 British overseas territories - 6 Australian states (plus 2 internal territories) - 5 constituent countries of the Kingdom of the Netherlands - 2 autonomous Danish territories (Faroe Islands and Greenland)

In total, 134 states are listed.

Acknowledgements

The population estimates were collected from the following sources: - Australia: Wikipedia - Canada: worldpopulationreview.com - China: another Kaggle dataset - Denmark: worldpopulationreview.com - France: worldometers.info (retrieved 2020-04-02, 18:00 UTC) - Netherlands: worldometers.info (retrieved 2020-04-02, 18:00 UTC) - US: worldpopulationreview.com - Guam: worldpopulationreview.com - UK: worldometers.info (retrieved 2020-04-02, 18:00 UTC)

French Social Contact Data

A Study of 1755 Participants and their Household Contacts (2015)

By [source]

About this dataset

This dataset provides a comprehensive exploration of contacts and interactions among 1755 participants in France in 2015, giving insights into the social behaviour of French households. With detailed information on contact locations, the gender and ages of contacts, the frequency and duration of interactions with each contact, as well as the number of people within a household, this data set covers a variety of factors which govern human interaction. By analyzing this data set we can better understand how social networks are formed among families and individuals in different communities. It is an essential guide to understanding how behaviour has changed over time and across different cultures. This dataset allows us to gain new perspectives on how various factors shape our relationships with others at home or out in society

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For more datasets, click here.

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How to use the dataset

This dataset provides a comprehensive collection of data regarding household contacts, social contact networks, and other individual characteristics in France in 2015. The data was collected by Antoine Beraud and his research team between July 2014 and February 2015.

This dataset is ideal for use as an exploratory tool to investigate how different contact factors (age, gender, location, frequency of contact) interact with each other. It can also be used to investigate variations in social contact behavior across France's cities or regions. Additionally, this dataset can be used to study the influence of certain individual characteristics (e.g., age or gender) on one's overall pattern of social contacts and household compositions.

Here are some useful tips for using this dataset: • Explore patterns such as how ages interact with frequency of contacts within your analyses
• Consider grouping participants across different metropolitian areas when studying regional variations
• Make sure to identify any outliers when looking at average values across the board
• Focus on exploring specific sections before looking at the larger picture

Research Ideas

• Measuring the impact of different types of contact within a household such as gender, age range and frequency on the risk of infection spread in France
• Examining correlations between sociodemographic factors such as household size, geographical location and contact patterns in France.
• Analyzing how changing physical distancing restrictions affects contact patterns by comparing pre-pandemic data with current social isolation trends

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: 2015_Beraud_France_contact_common.csv | Column name | Description | |:--------------------|:--------------------------------------------------------------| | cnt_age_exact | The exact age of the contact. (Numeric) | | cnt_age_est_min | The estimated minimum age of the contact. (Numeric) | | cnt_age_est_max | The estimated maximum age of the contact. (Numeric) | | cnt_gender | The gender of the contact. (Categorical) | | cnt_home | The frequency of contact at home. (Numeric) | | cnt_work | The frequency of contact at work. (Numeric) | | cnt_school | The frequency of contact at school. (Numeric) | | cnt_transport | The frequency of contact on public transport. (Numeric) | | cnt_leisure | The frequency of contact during leisure activities. (Numeric) | | cnt_otherplace | The frequency of contact at other places. (Numeric) | | frequency_multi | The frequency of contact with multiple people. (Numeric) | | phys_contact | Whether physical contact occurred. (Categorical) | | duration_multi | The duration of contact with multiple people. (Numeric) |

**File: 2015_Beraud_France_hh_...

Demographics Analysis with Consumer Edge Credit & Debit Card Transaction Data

Consumer Edge is a leader in alternative consumer data for public and private investors and corporate clients. CE Transact Signal is an aggregated transaction feed that includes consumer transaction data on 100M+ credit and debit cards, including 14M+ active monthly users. Capturing online, offline, and 3rd-party consumer spending on public and private companies, data covers 12K+ merchants and deep demographic and geographic breakouts. Track detailed consumer behavior patterns, including retention, purchase frequency, and cross shop in addition to total spend, transactions, and dollars per transaction.

Consumer Edge’s consumer transaction datasets offer insights into industries across consumer and discretionary spend such as: • Apparel, Accessories, & Footwear • Automotive • Beauty • Commercial – Hardlines • Convenience / Drug / Diet • Department Stores • Discount / Club • Education • Electronics / Software • Financial Services • Full-Service Restaurants • Grocery • Ground Transportation • Health Products & Services • Home & Garden • Insurance • Leisure & Recreation • Limited-Service Restaurants • Luxury • Miscellaneous Services • Online Retail – Broadlines • Other Specialty Retail • Pet Products & Services • Sporting Goods, Hobby, Toy & Game • Telecom & Media • Travel

This data sample illustrates how Consumer Edge data can be used to compare demographics breakdown (age and income excluded in this free sample view) for one company vs. a competitor for a set period of time (Ex: How do demographics like wealth, ethnicity, children in the household, homeowner status, and political affiliation differ for Walmart vs. Target shopper?).

Inquire about a CE subscription to perform more complex, near real-time demographics analysis functions on public tickers and private brands like: • Analyze a demographic, like age or income, within a state for a company in 2023 • Compare all of a company’s demographics to all of that company’s competitors through most recent history

Consumer Edge offers a variety of datasets covering the US and Europe (UK, Austria, France, Germany, Italy, Spain), with subscription options serving a wide range of business needs.

Use Case: Demographics Analysis

Problem A global retailer wants to understand company performance by age group.

Solution Consumer Edge transaction data can be used to analyze shopper transactions by age group to understand: • Overall sales growth by age group over time • Percentage sales growth by age group over time • Sales by age group vs. competitors

Impact Marketing and Consumer Insights were able to: • Develop weekly reporting KPI's on key demographic drivers of growth for company-wide reporting • Reduce investment in underperforming age groups, both online and offline • Determine retention by age group to refine campaign strategy • Understand how different age groups are performing compared to key competitors

Corporate researchers and consumer insights teams use CE Vision for:

Corporate Strategy Use Cases • Ecommerce vs. brick & mortar trends • Real estate opportunities • Economic spending shifts

Marketing & Consumer Insights • Total addressable market view • Competitive threats & opportunities • Cross-shopping trends for new partnerships • Demo and geo growth drivers • Customer loyalty & retention

Investor Relations • Shareholder perspective on brand vs. competition • Real-time market intelligence • M&A opportunities

Most popular use cases for private equity and venture capital firms include: • Deal Sourcing • Live Diligences • Portfolio Monitoring

Public and private investors can leverage insights from CE’s synthetic data to assess investment opportunities, while consumer insights, marketing, and retailers can gain visibility into transaction data’s potential for competitive analysis, understanding shopper behavior, and capturing market intelligence.

Most popular use cases among public and private investors include: • Track Key KPIs to Company-Reported Figures • Understanding TAM for Focus Industries • Competitive Analysis • Evaluating Public, Private, and Soon-to-be-Public Companies • Ability to Explore Geographic & Regional Differences • Cross-Shop & Loyalty • Drill Down to SKU Level & Full Purchase Details • Customer lifetime value • Earnings predictions • Uncovering macroeconomic trends • Analyzing market share • Performance benchmarking • Understanding share of wallet • Seeing subscription trends

Fields Include: • Day • Merchant • Subindustry • Industry • Spend • Transactions • Spend per Transaction (derivable) • Cardholder State • Cardholder CBSA • Cardholder CSA • Age • Income • Wealth • Ethnicity • Political Affiliation • Children in Household • Adults in Household • Homeowner vs. Renter • Business Owner • Retention by First-Shopped Period ...

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;

Explore gender statistics related to families and households, including data on both sexes, percent of total for both sexes, total live births, population, and residential information. Access valuable insights and trends for countries such as Portugal, Belgium, Spain, France, Italy, United Kingdom, United States, and many more.

Both sexes, Percent of Total for Both Sexes, Total Live Births, Population, Residential, birth

Portugal, Belgium, Spain, Bosnia and Herzegovina, France, Denmark, Italy, Uzbekistan, Bulgaria, United Kingdom, Slovenia, Czechia, Poland, Ukraine, Latvia, Sweden, Iceland, Armenia, Georgia, Canada, Montenegro, Hungary, United States, Andorra, Republic of Moldova, Croatia, Malta, San Marino, Turkmenistan, Azerbaijan, Kyrgyzstan, North Macedonia, Russian Federation, Greece, Luxembourg, Monaco, Slovakia, Norway, Tajikistan, Albania, Liechtenstein, Serbia, Switzerland, Lithuania, Estonia, Turkiye, Cyprus, Germany, Finland, Ireland, Israel, Kazakhstan, Austria, Belarus, Netherlands, RomaniaFollow data.kapsarc.org for timely data to advance energy economics research.Source: UNECE Statistical Database, compiled from national and international (Eurostat, UN Statistics Division Demographic Yearbook, WHO European health for all database and UNICEF TransMONEE) official sources.Definition: A live birth is the complete expulsion or extraction from its mother of a product of conception, irrespective of the duration of pregnancy, which after such separation breathes or shows any other evidence of life such as beating of the heart, pulsation of the umbilical cord or definite movement of voluntary muscles, whether or not the umbilical cord has been cut or the placenta is attached.General note: Data come from registers, unless otherwise specified. In years 2003 and before, the number of live births for girl child and boy child may not add up to the number for both sexes (Total) due to the rounding up of numbers.

Abstract

This dataset, a product of the Trade Team - Development Research Group, is part of a larger effort in the group to measure the extent of the brain drain as part of the International Migration and Development Program. It measures international skilled migration for the years 1975-2000.

The methodology is explained in: "Tendance de long terme des migrations internationals. Analyse à partir des 6 principaux pays recerveurs", Cécily Defoort.

This data set uses the same methodology as used in the Docquier-Marfouk data set on international migration by educational attainment. The authors use data from 6 key receiving countries in the OECD: Australia, Canada, France, Germany, the UK and the US.

It is estimated that the data represent approximately 77 percent of the world’s migrant population.

Bilateral brain drain rates are estimated based observations for every five years, during the period 1975-2000.

Geographic coverage

Australia, Canada, France, Germany, UK and US

Kind of data

Aggregate data [agg]

Mode of data collection

Other [oth]

This dataset presents information on historical central government revenues for 31 countries in Europe and the Americas for the period from 1800 (or independence) to 2012. The countries included are: Argentina, Australia, Austria, Belgium, Bolivia, Brazil, Canada, Chile, Colombia, Denmark, Ecuador, Finland, France, Germany (West Germany between 1949 and 1990), Ireland, Italy, Japan, Mexico, New Zealand, Norway, Paraguay, Peru, Portugal, Spain, Sweden, Switzerland, the Netherlands, the United Kingdom, the United States, Uruguay, and Venezuela. In other words, the dataset includes all South American, North American, and Western European countries with a population of more than one million, plus Australia, New Zealand, Japan, and Mexico. The dataset contains information on the public finances of central governments. To make such information comparable cross-nationally we have chosen to normalize nominal revenue figures in two ways: (i) as a share of the total budget, and (ii) as a share of total gross domestic product. The total tax revenue of the central state is disaggregated guided by the Government Finance Statistics Manual 2001 of the International Monetary Fund (IMF) which provides a classification of types of revenue, and describes in detail the contents of each classification category. Given the paucity of detailed historical data and the needs of our project, we combined some subcategories. First, we are interested in total tax revenue (centaxtot), as well as the shares of total revenue coming from direct (centaxdirectsh) and indirect (centaxindirectsh) taxes. Further, we measure two sub-categories of direct taxation, namely taxes on property (centaxpropertysh) and income (centaxincomesh). For indirect taxes, we separate excises (centaxexcisesh), consumption (centaxconssh), and customs(centaxcustomssh).

For a more detailed description of the dataset and the coding process, see the codebook available in the .zip-file.

Purpose:

This dataset presents information on historical central government revenues for 31 countries in Europe and the Americas for the period from 1800 (or independence) to 2012. The countries included are: Argentina, Australia, Austria, Belgium, Bolivia, Brazil, Canada, Chile, Colombia, Denmark, Ecuador, Finland, France, Germany (West Germany between 1949 and 1990), Ireland, Italy, Japan, Mexico, New Zealand, Norway, Paraguay, Peru, Portugal, Spain, Sweden, Switzerland, the Netherlands, the United Kingdom, the United States, Uruguay, and Venezuela. In other words, the dataset includes all South American, North American, and Western European countries with a population of more than one million, plus Australia, New Zealand, Japan, and Mexico. The dataset contains information on the public finances of central governments. To make such information comparable cross-nationally we have chosen to normalize nominal revenue figures in two ways: (i) as a share of the total budget, and (ii) as a share of total gross domestic product. The total tax revenue of the central state is disaggregated guided by the Government Finance Statistics Manual 2001 of the International Monetary Fund (IMF) which provides a classification of types of revenue, and describes in detail the contents of each classification category. Given the paucity of detailed historical data and the needs of our project, we combined some subcategories. First, we are interested in total tax revenue (centaxtot), as well as the shares of total revenue coming from direct (centaxdirectsh) and indirect (centaxindirectsh) taxes. Further, we measure two sub-categories of direct taxation, namely taxes on property (centaxpropertysh) and income (centaxincomesh). For indirect taxes, we separate excises (centaxexcisesh), consumption (centaxconssh), and customs(centaxcustomssh).

The American mink (Neovison vison) is known as a successful invader in Europe, impacting native species' population sizes and habitats. This study investigates the genetic structure and diversity of American mink populations in France over two decades (1997-2016). The analysis involves feral and farmed mink sampled from various regions, using ten autosomal microsatellite loci for genotyping. The objective is to identify genetic clusters, especially between feral and captive individuals, and assess changes in genetic structure over time. Results reveal high genetic diversity and low inbreeding within populations, with evidence of genetic structure influenced by both farm releases and feral colonization. The study highlights the reflection of the genetic structure in farm populations in the feral populations within the first period (1997-2007), and a decline of a lineage over time in the second period (2007-2016) with the emergence of a new genetic cluster, potentially influenced by factors such as selection, phenotypic changes, and interactions with pathogens. Overall, this research contributes to understanding the dynamics of American mink populations in France and their genetic variability, emphasizing the importance of ongoing monitoring and management efforts to mitigate the impact of this invasive species.