This dataset provides values for RETIREMENT AGE MEN reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Inspiration

Which countries have the most social contacts in the world? In particular, do countries with more social contacts among the elderly report more deaths caused by a pandemic caused by a respiratory virus?

Context

With the emergence of the COVID-19 pandemic, reports have shown that the elderly are at a higher risk of dying than any other age groups. 8 out of 10 deaths reported in the U.S. have been in adults 65 years old and older. Countries have also began to enforce 2km social distancing to contain the pandemic.

To this end, I wanted to explore the relationship between social contacts among the elderly and its relationship with the number of COVID-19 deaths across countries.

Content

This dataset includes a subset of the projected social contact matrices in 152 countries from surveys Prem et al. 2020. It was based on the POLYMOD study where information on social contacts was obtained using cross-sectional surveys in Belgium (BE), Germany (DE), Finland (FI), Great Britain (GB), Italy (IT), Luxembourg (LU), The Netherlands (NL), and Poland (PL) between May 2005 and September 2006.

This dataset includes contact rates from study participants ages 65+ for all countries from all sources of contact (work, home, school and others).

I used this R code to extract this data:

load('../input/contacts.Rdata') # https://github.com/kieshaprem/covid19-agestructureSEIR-wuhan-social-distancing/blob/master/data/contacts.Rdata
View(contacts)
contacts[["ALB"]][["home"]]
contacts[["ITA"]][["all"]]
rowSums(contacts[["ALB"]][["all"]])
out1 = data.frame(); for (n in names(contacts)) { x = (contacts[[n]][["all"]])[16,]; out <- rbind(out, data.frame(x)) }
out2 = data.frame(); for (n in names(contacts)) { x = (contacts[[n]][["all"]])[15,]; out <- rbind(out, data.frame(x)) }
out3 = data.frame(); for (n in names(contacts)) { x = (contacts[[n]][["all"]])[14,]; out <- rbind(out, data.frame(x)) }
m1 = data.frame(t(matrix(unlist(out1), nrow=16)))
m2 = data.frame(t(matrix(unlist(out2), nrow=16)))
m3 = data.frame(t(matrix(unlist(out3), nrow=16)))
rownames(m1) = names(contacts)
colnames(m1) = c("00_04", "05_09", "10_14", "15_19", "20_24", "25_29", "30_34", "35_39", "40_44", "45_49", "50_54", "55_59", "60_64", "65_69", "70_74", "75_79")
rownames(m2) = rownames(m1)
rownames(m3) = rownames(m1)
colnames(m2) = colnames(m1)
colnames(m3) = colnames(m1)
write.csv(zapsmall(m1),"contacts_75_79.csv", row.names = TRUE)
write.csv(zapsmall(m2),"contacts_70_74.csv", row.names = TRUE)
write.csv(zapsmall(m3),"contacts_65_69.csv", row.names = TRUE)

Rows names correspond to the 3 letter country ISO code, e.g. ITA represents Italy. Column names are the age groups of the individuals contacted in 5 year intervals from 0 to 80 years old. Cell values are the projected mean social contact rate.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1139998%2Ffa3ddc065ea46009e345f24ab0d905d2%2Fcontact_distribution.png?generation=1588258740223812&alt=media" alt="">

Acknowledgements

Thanks goes to Dr. Kiesha Prem for her correspondence and her team for publishing their work on social contact matrices.

References

• The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: a modelling study
• Projecting social contact matrices in 152 countries using contact surveys and demographic data
• Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases (POLYMOD study)

Related resources

• My starter notebook
• http://www.socialcontactdata.org/
• https://www.kaggle.com/tsubasatwi/close-contact-status-of-corona-in-japan
• Facebook Data for Good Mobility Dashboard

This dataset displays countries that had ten percent or more of their population age 65 and older. This data was collecte through agingstats.gov.

This dataset provides values for RETIREMENT AGE WOMEN reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

About this dataset

Context

The dataset tabulates the Country Club Hills population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Country Club Hills. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 10,211 (62.19% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the Country Club Hills population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Country Club Hills is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Country Club Hills is shown in the following column.

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 Country Club Hills Population by Age. You can refer the same here

The OECD Ageing and Employment Policies Project is part of the Organisation for Economic Co-operation and Development (OECD) and forms a collection of data on work and employment regarding older people, as well as of policy reviews in order to encourage greater labour market participation for the elderly. Most data come from the report "Live longer, Work longer" and country-specific scoreboards for respectively 21 and 36 OECD member countries. Here we focus on statistical data.

This dataset was created to support the 2016 DIA (Related publication only available in Spanish). The accelerated aging process that countries in Latin America and the Caribbean are undergoing imposes unprecedented pressures on the long-term care sector. In this context, the growing demand for care from the elderly population occurs alongside a reduction in the availability of informal care. Governments in the region must prepare to address these pressures by supporting the provision of care services to alleviate social exclusion in old age. The Inter-American Development Bank has created an Observatory on Aging and Care — the focus of this policy brief — aimed at providing decision-makers with information to design policies based on available empirical evidence. In this initial phase, the Observatory seeks to document the demographic situation of countries in the region, the health of their elderly population, their limitations and dependency status, as well as their main socioeconomic characteristics. The goal is to estimate the care needs countries in the region will face. This brief summarizes the key findings from an initial analysis of the data. The results highlight the scale of the problem. The figures speak for themselves: in the region, 11% of the population aged 60 and older is dependent. Both the magnitude and intensity of dependency increase with age. Women are the most affected across all age groups. This policy brief is part of a series of studies on dependency care, including works by Caruso, Galiani, and Ibarrarán (2017); Medellín et al. (2018); López-Ortega (2018); and Aranco and Sorio (2018).

Context

This dataset contains information about Anime scraped from Anime Planet on 28/06/2023. It contains information about anime (episodes, aired date, rating, genre, etc.), and favorite anime based on the countries and top countries that watch the most anime.

• The scraped program of this dataset is in Anime.Quest GitHub repository.
• Tableau visualization of this dataset can also be found in Anime Quest: Visualization.

Content

The dataset contains 3 files:

📁 anime_data.csv: 1. Name: Full name of the anime 2. Media Type: TV, Web, Movie, etc. 3. Episodes: Total episodes of the anime 4. Studio: Name of the studios of the anime, from most recent to oldest. 5. Start Year: Release Year of the anime 6. End Year: Last year of the anime airing 7. Ongoing: Is the anime currently airing or not? True or False. 8. Release Season: Spring, Fall, Winter, and Summer 9. Rating: The global rating ranges from 0 to 5. 10. Rank: Global ranking of the anime 11. Members: Total members of the anime 12. Genre: The category of the anime 13. Creator: Creator of the anime

📁 anime_top_by_country_data.csv: 1. Country: Individual country name 2. Most Popular: The most popular anime in the country 3. 2nd Place: Second-most popular anime in the country 4. 3rd Place: Third-most popular anime in the country 5. 4th Place: Fourth-most popular anime in the country 6. 5th Place: The fifth-most popular anime in the country

📁 anime_watching_data.csv: 1. Rank: Ranking of countries based on the number of anime viewers 2. Country: Individual country name 3. Population: Total population of the country 4. Percentage of People Watching: Percentage of people watching anime in the country 5. Number of People Watching: Total number of people watching anime in the country

Acknowledgements

The website Anime Planet was used to scrape this dataset. Please include citations for this dataset if you use it in your own research.

Inspiration

This dataset can be used to find the factors determining an anime's rating and ranking. Additionally, it can be used to make anime recommendations. The pattern can be observed in anime.

Top Covid19 Countries and Health Demographic Trend

Context

This is a time-series trend data collection with a series of json files primarily focused on countries most impacted by Covid-19. The tree formatted time series data should be able to enable various different kinds of analysis to answer questions about what may make a country's health system vulnerable to Covid-19 and what health demographics may help reducing the impact.

Demographic metrics

Healthcare GDP Expenditure 
Healthcare Employment
Hospital Bed Capacity
Air Pollution and Death Rate
Chronic illnesses and DALYs(Disability-Adjusted Life Years)
Body Weight 
Elderly(Aged 65+) Population
CT Scanner Density
Tobacco Consumption(Smoker population %)

More metrics can be added upon request.

Data Normalization

The raw CSV includes many different types of measurements such as number, percentage and per 1 million population. This data normalizes the time_series data by selecting data that is more about density, and number per capita data rather than absolute numbers. This could help doing comparison among nations since they may vary significantly on population.

Content

Most of the JSON files contain time_series data. For people who want to use the data as country metadata, the most-recent data attribute is collected in top_countries_latest_fact_summary.json

The JSON data focuses on the above mentioned demographic areas in a simple tree schema { Country_name: { metric_name:[ List of {year, value, unit} ] } }

Data source & License

The data is sourced from OECD(https://stats.oecd.org/) and GDHX(http://ghdx.healthdata.org/). The json files with prefix "gbd_" are from GDHX

Following citation is needed for using GDHX data:

GBD Results tool: Use the following to cite data included in this download: Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2017 (GBD 2017) Results. Seattle, United States: Institute for Health Metrics and Evaluation (IHME), 2018. Available from http://ghdx.healthdata.org/gbd-results-tool.

Inspiration

• Where does US rank in term of Healthcare/Preventive spending in GDP, hospital bed/ICU bed/physician density and long-term illness? In which areas can US do more to prevent future Cov-19 crisis?

• Is there correlation in a nation's medical preparedness and the rate of growth in confirmation, death rate and recovery rate? From GBD data graphs, it seems that Dalys(DALYs (Disability-Adjusted Life Years), rate per 100k) can divided nations into different camps.

• How does death rate from Cov-19 correlate with Death rate related to Cardiovascular diseases and Chronic respiratory diseases?

• What trends can we discover in various nation's health demographics over time? Are some areas getting better while others getting worse?

• With time span from 2010 to 2018, this dataset can also correlate with data related to recent outbreaks such as seasonal flus, Avian influenza, etc.

Example Notebook

With some quick analysis, it shows that the US actually ranks higher than China for DALYs(Disability-adjusted life years) caused by Chronic Respiratory conditions, which could be due to seasonal allergies. It seems counter-intuitive that this may suggest that countries with cleaner air may have higher burden of people with Chronic Respiratory conditions that may have made them more vulnerable in the Covid-19 crisis.

Example Kernel: https://www.kaggle.com/timxia/bar-chart-comparison-of-countries https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4802460%2F2fce05195108856422b437316f34e837%2FTobacco.png?generation=1585936274243838&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4802460%2Fe8db14764a47a8bce48fa79bdfdfb0f1%2FChronicDisease.png?generation=1585936274372639&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4802460%2Fc534d40af042b9a503325f41c49b83cb%2FAirPollution.png?generation=1585936274337626&alt=media" alt="">

Time series data for the statistic Age dependency ratio, old (% of working-age population) and country Luxembourg. Indicator Definition:Age dependency ratio, old, is the ratio of older dependents--people older than 64--to the working-age population--those ages 15-64. Data are shown as the proportion of dependents per 100 working-age population.The indicator "Age dependency ratio, old (% of working-age population)" stands at 22.51 as of 12/31/2024, the highest value at least since 12/31/1961, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes an increase of 2.94 percent compared to the value the year prior.The 1 year change in percent is 2.94.The 3 year change in percent is 6.09.The 5 year change in percent is 8.14.The 10 year change in percent is 10.17.The Serie's long term average value is 19.77. It's latest available value, on 12/31/2024, is 13.87 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/1960, to it's latest available value, on 12/31/2024, is +44.55%.The Serie's change in percent from it's maximum value, on 12/31/2024, to it's latest available value, on 12/31/2024, is 0.0%.

The Older Persons and Informal Caregivers Survey - Minimum DataSet (TOPICS-MDS) is a public data repository which contains information on the physical and mental health and well-being of older persons and informal caregivers and their care use across the Netherlands. The database was developed at the start of The National Care for the Elderly Programme (‘Nationaal Programma Ouderenzorg’ - NPO) on behalf of the Organisation of Health Research and Development (ZonMw - The Netherlands), in part to ensure uniform collection of outcome measures, thus promoting comparability between studies.Since September 2014, TOPICS-MDS data are also collected within the ZonMw funded ‘Memorabel’ programme, that is specifically aimed at improving the quality of life for people with dementia and the care and support provided to them. In Memorabel round 1 through 4, 11 different research projects have collected TOPICS-MDS data, which has resulted in a pooled database with cross-sectional and (partly) longitudinal data of 1,400 older persons with early onset or advanced dementia and about 950 informal caregivers. Out of these numbers, a number of 919 concerns care receiver - caregiver dyads of whom information on both the care receiver and caregiver is available.More background information on both NPO and Memorabel 1-4 can be found in the overall information on TOPICS-MDS under the tab ‘Data files’ in DANS EASY (doi.org/10.17026/dans-xvh-dbbf).At the moment, 3 different research projects have collected data for TOPICS-MDS Memorabel 5.The 'TOPICS-MDS Memorabel 5 care receiver' dataset, as part of the Memorabel 5 database, contains no informal caregiver data, only care receiver (older person) data. The dataset includes data on age, gender, country of birth, level of education, marital status and living situation of the care receiver, as well as data on physical and emotional health and well-being, quality of life, daily functioning and use of care, such as GP visits, home care, day care/treatment and admittance in a hospital, home for the aged or nursing home. Date Submitted: 2023-10-05

The here presented perceived age data span birth cohorts from the years 1877 to 2014. Since 2012 the database has grown to now contain around 200,000 perceived age guesses. More than 4000 citizen scientists from over 120 countries of origin have uploaded ~5000 facial photographs. Beyond ageing research, the data present a wealth of possibilities to study how humans guess ages and to use this knowledge for instance in advancing and testing emerging applications of artificial intelligence and deep learning algorithms. In many developed countries, human life expectancy has doubled over the last 180 years from ~40 to ~80 years. Underlying this great advance is a change in how we age, yet our understanding of this change remains limited. Here we present a unique database rich with possibilities to study the human ageing process: the AgeGuess.org database on people’s perceived and chronological ages. Perceived age (i.e. how old one looks to others) correlates with biological age, a measure of a person’s health condition in comparison to the average of same-aged peers. Determining biological age usually involves elaborate molecular and cellular biomarkers. Using instead perceived age as a biomarker of biological age enables us to collect large amounts of data on biological age through a citizen science project, where people upload pictures of themselves and guess the ages of other people. It furthermore allows to collect data retrospectively, because people can upload photographs of themselves when they were younger or of their parents and grandparents. We can thus study the temporal variation in the gap between perceived age and chronological age to address questions such as whether we now age slower or delay ageing until older ages. The data are collected via the webpage at www.ageguess.org, which is accessible worldwide. Therefore, the data collection spans ~120 countries.

Abstract The scope of this study is violence perpetrated against the elderly. It aims to analyze the international scientific production on violence against the elderly. It involved bibliometric research carried out in the ISI Web of Knowledge/Web of ScienceTM database, in which the search terms “elder,”violence” or “abuse” and “health care” were used, in the period between the years 1991 and 2016. The data were analyzed considering the evolution of the annual publications, the journals with the highest number of records, the authors with the highest number of publications, the number of articles distributed by authors’ country of origin, and articles with the highest impact. A total of 267 published records in 174 different journals indexed to the database in question were identified and were written by 901 authors with links to 410 institutions located in 39 countries. In the descriptive analysis of the content of the top journals on the topic and of the most cited articles there was potential for the development of the topic, since there is a need for more data on interventions in cases of violence against the elderly, with a multidisciplinary approach, as well as conducting more research on clinical manifestations, quality of life and its economic impact on the use of health services.

Time series data for the statistic Age dependency ratio, old (% of working-age population) and country Cayman Islands. Indicator Definition:Age dependency ratio, old, is the ratio of older dependents--people older than 64--to the working-age population--those ages 15-64. Data are shown as the proportion of dependents per 100 working-age population.The indicator "Age dependency ratio, old (% of working-age population)" stands at 12.03 as of 12/31/2024, the highest value since 12/31/1972. Regarding the One-Year-Change of the series, the current value constitutes an increase of 5.84 percent compared to the value the year prior.The 1 year change in percent is 5.84.The 3 year change in percent is 17.55.The 5 year change in percent is 27.70.The 10 year change in percent is 54.23.The Serie's long term average value is 9.39. It's latest available value, on 12/31/2024, is 28.09 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/2009, to it's latest available value, on 12/31/2024, is +72.62%.The Serie's change in percent from it's maximum value, on 12/31/1969, to it's latest available value, on 12/31/2024, is -6.26%.

Azerbaijan number dataset helps your business find new customers easily and grow effectively.. Reaching the right people can boost your sales and help you make more return on investment (ROI). This list can also help you explore different markets in Azerbaijan. Besides, when you use our cell phone marketing list the right way, your business can grow instantly. All in all, investing in our Azerbaijan number dataset is a smart choice for your business. It gives you all the details you need to reach a larger audience, helping you connect with those who are more likely to buy. When you have the right information, you can reach your business goals and see your business become successful. Azerbaijan phone data is a helpful list of valid contact numbers that can boost your marketing efforts. Moreover, the accuracy rate of our phone database is more than 95%. As a result, most of the numbers are correct and active. Also, our data has a low bounce rate, which ensures fewer failed calls or messages. We update and verify each number regularly. Therefore, you won’t find duplicates or errors. This makes our service reliable and efficient. Overall, our Azerbaijan phone data only contains the latest information. This valid contact list helps you reach real customers who want to have your products or services. As a result, you can trust that you’re reaching the right people to promote your products or services. We don’t sell old phone numbers. Instead, we always provide our clients with the newest phone leads. Azerbaijan phone number list can help your direct marketing campaigns be more successful. This list connects you with potential B2B and B2C customers from all over the country. Moreover, it has a population of around 1.4 million and 11 million phone users. Some people have more than one phone, so it is a smart idea to use telemarketing in Azerbaijan. Moreover, Azerbaijan phone number list makes it easy to send messages and share special offers. Also, you can make ads that catch people’s eyes by using easy words that everyone knows. This helps you attract new customers and stay in touch with the ones you already have. Here List to Data helps you find phone numbers for your business.

ObjectiveTo explore the burden and trend of osteoarthritis (OA) at different sites in middle-aged and elderly people (45 years and older) from 1990 to 2021.MethodsAge-standardized incidence rates, prevalence rates, disability-adjusted life years (Daly) rates and average annual percent change were used to quantify the disease burden and trend of OA at different sites. Decomposition analysis was conducted to explore the impact of three population-level determinants on the burden of OA and the distribution of OA burden inequality in the Socio-Demographic Index (SDI) across countries.ResultsThe age-standardized prevalence rate had increased by 8.9%, and the OA cases had increased by 2.41 times compared to 1990. The incidence and prevalence of knee, hip and hand OA decreased sequentially, while high SDI regions tended to have higher age-standardized incidence rates, prevalence rates, and Daly rates. Decomposition analysis revealed that 85.9% of the increase in OA age-standardized Daly rates was attributable to population growth. This increase was most pronounced in high SDI populations for hip OA and middle SDI populations for knee and hand OA. From 1990 to 2021, the inequality in overall OA burden between countries had decreased. The absolute inequality gap for hand OA had narrowed the most significantly (45.3%), which followed by knee OA (11.9%), while the inequality gap for hip OA has slightly increased.ConclusionIn summary, all parts of the OA burden in middle-aged and elderly people had steadily increased from 1990 to 2021, which calls to implement personalized prevention targeting different parts of OA.

Coverage of Social Insurance Programs in Richest Quintile

Percent of Population Eligible

By data.world's Admin [source]

About this dataset

This dataset offers a unique insight into the coverage of social insurance programs for the wealthiest quintile of populations around the world. It reveals how many individuals in each country are receiving support from old age contributory pensions, disability benefits, and social security and health insurance benefits such as occupational injury benefits, paid sick leave, maternity leave, and more. This data provides an invaluable resource to understand the health and well-being of those most financially privileged in society – often having greater impact on decision making than other groups. With up-to-date figures from 2019-05-11 this dataset is invaluable in uncovering where there is work to be done for improved healthcare provision in each country across the world

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

• Understand the context: Before you begin analyzing this dataset, it is important to understand the information that it provides. Take some time to read the description of what is included in the dataset, including a clear understanding of the definitions and scope of coverage provided with each data point.

• Examine the data: Once you have a general understanding of this dataset's contents, take some time to explore its contents in more depth. What specific questions does this dataset help answer? What kind of insights does it provide? Are there any missing pieces?

• Clean & Prepare Data: After you've preliminarily examined its content, start preparing your data for further analysis and visualization. Clean up any formatting issues or irregularities present in your data set by correcting typos and eliminating unnecessary rows or columns before working with your chosen programming language (I prefer R for data manipulation tasks). Additionally, consider performing necessary transformations such as sorting or averaging values if appropriate for the findings you wish to draw from your analysis.

• Visualize Results: Once you've cleaned and prepared your data, use visualizations such as charts, graphs or tables to reveal patterns within it that support specific conclusions about how insurance coverage under social programs vary among different groups within society's quintiles - based on age groups etc.. This type of visualization allows those who aren't familiar with programming to process complex information quickly and accurately than when displayed numerically in tabular form only!

5 Final Analysis & Export Results: Finally export your visuals into presentation-ready formats (e.g., PDFs) which can be shared with colleagues! Additionally use these results as part of a narrative conclusion report providing an accurate assessment and meaningful interpretation about how social insurance programs vary between different members within society's quintiles (i..e., accordingest vs poorest), along with potential policy implications relevant for implementing effective strategies that improve access accordingly!

Research Ideas

• Analyzing the effectiveness of social insurance programs by comparing the coverage levels across different geographic areas or socio-economic groups;
• Estimating the economic impact of social insurance programs on local and national economies by tracking spending levels and revenues generated;
• Identifying potential problems with access to social insurance benefits, such as racial or gender disparities in benefit coverage

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: coverage-of-social-insurance-programs-in-richest-quintile-of-population-1.csv

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit data.world's Admin.

The Multilinks project explores how demographic changes shape intergenerational solidarity, well-being and social integration. The project examines a) multiple linkages in families (e.g. transfers up and down family lineages, interdependencies between older and younger family members); b) multiple linkages across time (measures at different points in time, at different points in the individual and family life course); c) multiple linkages between, on the one hand, national and regional contexts (e.g. policy regimes, economic circumstances, normative climate, religiosity) and, on the other hand, individual behaviour, well-being and values.

The conceptual approach builds on three key premises. First, ageing affects all age groups: the young, the middle-aged and the old. Second, there are critical interdependencies between family generations as well as between men and women. Third, we must recognize and distinguish analytical levels: the individual, the dyad (parent-child, partners), family, region, historical generation and country.

The database aims to map how the state, in form of public policies and legal norms, defines and regulates intergenerational obligations within the family. What is the contribution of public authorities to support and secure financial and care needs for the young and the elderly in the family? In what ways the state assumes that intergenerational responsibilities are a family matter? In order to answer these questions the database includes a dual intergenerational perspective: upwards generations; from children to parents; and downwards; from parents to children. It looks across a variety of social policies and also includes legal obligations to support. It entails over 70 indicators on social policy rights, legal obligations to support, and care service usage. It offers a structured access to the public support for families with children and for elderly people within 30 European countries for 2004 and 2009.

The research project MULTILINKS (How demographic changes shape intergenerational solidarity, well-being, and social integration: A Multilinks framework) existed from 2009 to 2011. It has received funding from the European Union's Seventh Framework Programme (FP7/2007-2011) under grant agreement n° 217523.

After the end of the project the results were made available as a web application and as individual datasets together with the documentation files by the WZB (http://multilinks-database.wzb.eu). Since 2020, this website no longer exists. The single datasets and reports are available here unchanged.

However, the web application, together with the documents, is still available through the "Gender & Generations Programme (GGP)" and the French Institute for Demographic Research (INED). There you will find further information, additional descriptive variables and full possibilities to explore and navigate through the database. For more details see: https://www.ggp-i.org/data/multilinks-database/

Time series data for the statistic Age dependency ratio, old (% of working-age population) and country Botswana. Indicator Definition:Age dependency ratio, old, is the ratio of older dependents--people older than 64--to the working-age population--those ages 15-64. Data are shown as the proportion of dependents per 100 working-age population.The indicator "Age dependency ratio, old (% of working-age population)" stands at 6.32 as of 12/31/2024, the highest value since 12/31/1978. Regarding the One-Year-Change of the series, the current value constitutes an increase of 1.53 percent compared to the value the year prior.The 1 year change in percent is 1.53.The 3 year change in percent is 3.84.The 5 year change in percent is 3.39.The 10 year change in percent is 12.66.The Serie's long term average value is 6.48. It's latest available value, on 12/31/2024, is 2.50 percent lower, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/2011, to it's latest available value, on 12/31/2024, is +16.43%.The Serie's change in percent from it's maximum value, on 12/31/1970, to it's latest available value, on 12/31/2024, is -28.36%.

Time series data for the statistic Age dependency ratio, old (% of working-age population) and country Kiribati. Indicator Definition:Age dependency ratio, old, is the ratio of older dependents--people older than 64--to the working-age population--those ages 15-64. Data are shown as the proportion of dependents per 100 working-age population.The indicator "Age dependency ratio, old (% of working-age population)" stands at 6.94 as of 12/31/2024, the highest value since 12/31/1977. Regarding the One-Year-Change of the series, the current value constitutes an increase of 2.68 percent compared to the value the year prior.The 1 year change in percent is 2.68.The 3 year change in percent is 6.73.The 5 year change in percent is 8.66.The 10 year change in percent is 16.18.The Serie's long term average value is 6.75. It's latest available value, on 12/31/2024, is 2.82 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/2004, to it's latest available value, on 12/31/2024, is +17.65%.The Serie's change in percent from it's maximum value, on 12/31/1960, to it's latest available value, on 12/31/2024, is -24.77%.