Patterns of educational attainment vary greatly across countries, and across population groups within countries. In some countries, virtually all children complete basic education whereas in others large groups fall short. The primary purpose of this database, and the associated research program, is to document and analyze these differences using a compilation of a variety of household-based data sets: Demographic and Health Surveys (DHS); Multiple Indicator Cluster Surveys (MICS); Living Standards Measurement Study Surveys (LSMS); as well as country-specific Integrated Household Surveys (IHS) such as Socio-Economic Surveys.As shown at the website associated with this database, there are dramatic differences in attainment by wealth. When households are ranked according to their wealth status (or more precisely, a proxy based on the assets owned by members of the household) there are striking differences in the attainment patterns of children from the richest 20 percent compared to the poorest 20 percent.In Mali in 2012 only 34 percent of 15 to 19 year olds in the poorest quintile have completed grade 1 whereas 80 percent of the richest quintile have done so. In many countries, for example Pakistan, Peru and Indonesia, almost all the children from the wealthiest households have completed at least one year of schooling. In some countries, like Mali and Pakistan, wealth gaps are evident from grade 1 on, in other countries, like Peru and Indonesia, wealth gaps emerge later in the school system.The EdAttain website allows a visual exploration of gaps in attainment and enrollment within and across countries, based on the international database which spans multiple years from over 120 countries and includes indicators disaggregated by wealth, gender and urban/rural location. The database underlying that site can be downloaded from here.

Mali ML: Prevalence of Overweight: Weight for Height: % of Children Under 5 data was reported at 1.900 % in 2015. This records an increase from the previous number of 1.000 % for 2010. Mali ML: Prevalence of Overweight: Weight for Height: % of Children Under 5 data is updated yearly, averaging 2.100 % from Dec 1987 (Median) to 2015, with 6 observations. The data reached an all-time high of 4.700 % in 2006 and a record low of 0.500 % in 1987. Mali ML: Prevalence of Overweight: Weight for Height: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Mali – Table ML.World Bank: Health Statistics. Prevalence of overweight children is the percentage of children under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's new child growth standards released in 2006.; ; UNICEF, WHO, World Bank: Joint child malnutrition estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology.; Linear mixed-effect model estimates; Estimates of overweight children are also from national survey data. Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues

![![https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F8278d442527f2405600742b9cf53b897%2Fshare-of-the-world-population-with-at-least-basic-education.png?generation=1704312437101665&alt=media" alt="">]

The world has made substantial progress in increasing basic levels of education Access to education is now seen as a fundamental right – in many cases, it’s the government’s duty to provide it.

But formal education is a very recent phenomenon. In the chart, we see the share of the adult population – those older than 15 – that has received some basic education and those who haven’t.

In the early 1800s, fewer than 1 in 5 adults had some basic education. Education was a luxury; in all places, it was only available to a small elite.

But you can see that this share has grown dramatically, such that this ratio is now reversed. Less than 1 in 5 adults has not received any formal education.

This is reflected in literacy data, too: 200 years ago, very few could read and write. Now most adults have basic literacy skills.

This table contains 30 series, with data for years 1961 - 1971 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Unit of measure (1 items: Persons ...) Geography (1 items: Canada ...) Children born to ever-married women (10 items: Number of children born to ever-married women 15 years of age and over; total; Number of children born to ever-married women aged 15-19 years; Number of children born to ever-married women aged 20-24 years; Number of children born to ever-married women aged 25-29 years ...) Type of area (3 items: Total urban and rural areas; Rural; Urban ...).

Will all children be able to read by 2030? The ability to read with comprehension is a foundational skill that every education system around the world strives to impart by late in primary school—generally by age 10. Moreover, attaining the ambitious Sustainable Development Goals (SDGs) in education requires first achieving this basic building block, and so does improving countries’ Human Capital Index scores. Yet past evidence from many low- and middle-income countries has shown that many children are not learning to read with comprehension in primary school. To understand the global picture better, we have worked with the UNESCO Institute for Statistics (UIS) to assemble a new dataset with the most comprehensive measures of this foundational skill yet developed, by linking together data from credible cross-national and national assessments of reading. This dataset covers 115 countries, accounting for 81% of children worldwide and 79% of children in low- and middle-income countries. The new data allow us to estimate the reading proficiency of late-primary-age children, and we also provide what are among the first estimates (and the most comprehensive, for low- and middle-income countries) of the historical rate of progress in improving reading proficiency globally (for the 2000-17 period). The results show that 53% of all children in low- and middle-income countries cannot read age-appropriate material by age 10, and that at current rates of improvement, this “learning poverty” rate will have fallen only to 43% by 2030. Indeed, we find that the goal of all children reading by 2030 will be attainable only with historically unprecedented progress. The high rate of “learning poverty” and slow progress in low- and middle-income countries is an early warning that all the ambitious SDG targets in education (and likely of social progress) are at risk. Based on this evidence, we suggest a new medium-term target to guide the World Bank’s work in low- and middle- income countries: cut learning poverty by at least half by 2030. This target, together with improved measurement of learning, can be as an evidence-based tool to accelerate progress to get all children reading by age 10.

For further details, please refer to https://thedocs.worldbank.org/en/doc/e52f55322528903b27f1b7e61238e416-0200022022/original/Learning-poverty-report-2022-06-21-final-V7-0-conferenceEdition.pdf

United States US: Prevalence of Wasting: Weight for Height: Female: % of Children Under 5 data was reported at 0.700 % in 2012. This records an increase from the previous number of 0.500 % for 2009. United States US: Prevalence of Wasting: Weight for Height: Female: % of Children Under 5 data is updated yearly, averaging 0.550 % from Dec 1991 (Median) to 2012, with 6 observations. The data reached an all-time high of 0.800 % in 2005 and a record low of 0.100 % in 2001. United States US: Prevalence of Wasting: Weight for Height: Female: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Health Statistics. Prevalence of wasting, female, is the proportion of girls under age 5 whose weight for height is more than two standard deviations below the median for the international reference population ages 0-59.; ; World Health Organization, Global Database on Child Growth and Malnutrition. Country-level data are unadjusted data from national surveys, and thus may not be comparable across countries.; Linear mixed-effect model estimates; Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF, www.childinfo.org). Estimates of child malnutrition, based on prevalence of underweight and stunting, are from national survey data. The proportion of underweight children is the most common malnutrition indicator. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition.

The Global Subnational Prevalence of Child Malnutrition dataset consists of estimates of the

percentage of children with weight-for-age z-scores that are more than two standard deviations below the median of the NCHS/CDC/WHO

International Reference Population. Data are reported for the most recent year with subnational information available at the time of

development. The data products include a shapefile (vector data) of percentage rates, grids (raster data) of rates (per thousand in

order to preserve precision in integer format), the number of children under five (the rate denominator), and the number of

underweight children under five (the rate numerator), and a tabular dataset of the same and associated data. This dataset is produced

by the Columbia University Center for International Earth Science Information Network (CIESIN).

The Poverty Mapping Project: Global Subnational Prevalence of Child Malnutrition data set consists of estimates of the percentage of children with weight-for-age z-scores that are more than two standard deviations below the median of the NCHS/CDC/WHO International Reference Population. Data are reported for the most recent year with subnational information available at the time of development. The data products include a shapefile (vector data) of percentage rates, grids (raster data) of rates (per thousand in order to preserve precision in integer format), the number of children under five (the rate denominator), and the number of underweight children under five (the rate numerator), and a tabular data set of the same and associated data. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).

About this dataset

Context

The dataset tabulates the White Earth 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 White Earth. 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 42 (49.41% 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 White Earth population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in White Earth is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the White Earth 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 White Earth Population by Age. You can refer the same here

Description

This comprehensive dataset provides a wealth of information about all countries worldwide, covering a wide range of indicators and attributes. It encompasses demographic statistics, economic indicators, environmental factors, healthcare metrics, education statistics, and much more. With every country represented, this dataset offers a complete global perspective on various aspects of nations, enabling in-depth analyses and cross-country comparisons.

Key Features

Country: Name of the country.

Density (P/Km2): Population density measured in persons per square kilometer.

Abbreviation: Abbreviation or code representing the country.

Agricultural Land (%): Percentage of land area used for agricultural purposes.

Land Area (Km2): Total land area of the country in square kilometers.

Armed Forces Size: Size of the armed forces in the country.

Birth Rate: Number of births per 1,000 population per year.

Calling Code: International calling code for the country.

Capital/Major City: Name of the capital or major city.

CO2 Emissions: Carbon dioxide emissions in tons.

CPI: Consumer Price Index, a measure of inflation and purchasing power.

CPI Change (%): Percentage change in the Consumer Price Index compared to the previous year.

Currency_Code: Currency code used in the country.

Fertility Rate: Average number of children born to a woman during her lifetime.

Forested Area (%): Percentage of land area covered by forests.

Gasoline_Price: Price of gasoline per liter in local currency.

GDP: Gross Domestic Product, the total value of goods and services produced in the country.

Gross Primary Education Enrollment (%): Gross enrollment ratio for primary education.

Gross Tertiary Education Enrollment (%): Gross enrollment ratio for tertiary education.

Infant Mortality: Number of deaths per 1,000 live births before reaching one year of age.

Largest City: Name of the country's largest city.

Life Expectancy: Average number of years a newborn is expected to live.

Maternal Mortality Ratio: Number of maternal deaths per 100,000 live births.

Minimum Wage: Minimum wage level in local currency.

Official Language: Official language(s) spoken in the country.

Out of Pocket Health Expenditure (%): Percentage of total health expenditure paid out-of-pocket by individuals.

Physicians per Thousand: Number of physicians per thousand people.

Population: Total population of the country.

Population: Labor Force Participation (%): Percentage of the population that is part of the labor force.

Tax Revenue (%): Tax revenue as a percentage of GDP.

Total Tax Rate: Overall tax burden as a percentage of commercial profits.

Unemployment Rate: Percentage of the labor force that is unemployed.

Urban Population: Percentage of the population living in urban areas.

Latitude: Latitude coordinate of the country's location.

Longitude: Longitude coordinate of the country's location.

Potential Use Cases

Analyze population density and land area to study spatial distribution patterns.

Investigate the relationship between agricultural land and food security.

Examine carbon dioxide emissions and their impact on climate change.

Explore correlations between economic indicators such as GDP and various socio-economic factors.

Investigate educational enrollment rates and their implications for human capital development.

Analyze healthcare metrics such as infant mortality and life expectancy to assess overall well-being.

Study labor market dynamics through indicators such as labor force participation and unemployment rates.

Investigate the role of taxation and its impact on economic development.

Explore urbanization trends and their social and environmental consequences.

Distribution of the household population by Children's body mass index (BMI) - World Health Organization (WHO) classification system, by sex and age group.

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

Data here cover child labor, gender issues, refugees, and asylum seekers. Children in many countries work long hours, often combining studying with work for pay. The data on their paid work are from household surveys conducted by the International Labour Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. Gender disparities are measured using a compilation of data on key topics such as education, health, labor force participation, and political participation. Data on refugees are from the United Nations High Commissioner for Refugees complemented by statistics on Palestinian refugees under the mandate of the United Nations Relief and Works Agency.

Child malnutrition joint country dataset (UNICEF, WHO, World Bank Group)

Definitions:
Severe Wasting: Percentage of children aged 0–59 months who are below minus three standard deviations from median weight-for-height of the WHO Child Growth Standards.
Wasting – Moderate and severe: Percentage of children aged 0–59 months who are below minus two standard deviations from median weight-for-height of the WHO Child Growth Standards.
Overweight – Moderate and severe: Percentage of children aged 0-59 months who are above two standard deviations from median weight-for-height of the WHO Child Growth Standards.
Stunting – Moderate and severe: Percentage of children aged 0–59 months who are below minus two standard deviations from median height-for-age of the WHO Child Growth Standards.
Underweight – Moderate and severe: Percentage of children aged 0–59 months who are below minus two standard deviations from median weight-for-age of the World Health Organization (WHO) Child Growth Standards.

license: apache-2.0 tags: - africa - sustainable-development-goals - world-health-organization - development

  Children under 5 years of age whose births have been registered with a civil authority (%)





  Dataset Description

This dataset provides country-level data for the indicator "16.9.1 Children under 5 years of age whose births have been registered with a civil authority (%)" across African nations, sourced from the World Health Organization's (WHO) data… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/children-under-5-years-of-age-whose-births-have-been-registe-for-african-countries.

SA2 based data for Number of Children Ever Born, in Place of Enumeration Profile (PEP), 2016 Census. Count of females aged 15 years and over (excludes overseas visitors), categorised by the number of children they have given birth to. The data is by SA2 2016 boundaries. Periodicity: 5-Yearly. Note: There are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals. For more information visit the data source: http://www.abs.gov.au/census.

Objective(s): The 2024 Pediatric Sepsis Data Challenge provides an opportunity to address the lack of appropriate mortality prediction models for LMICs. For this challenge, we are asking participants to develop a working, open-source algorithm to predict in-hospital mortality and length of stay using only the provided synthetic dataset. The original data used to generate the real-world data (RWD) informed synthetic training set available to participants was obtained from a prospective, multisite, observational cohort study of children with suspected sepsis aged 6 months to 60 months at the time of admission to hospitals in Uganda. For this challenge, we have created a RWD-informed synthetically generated training data set to reduce the risk of re-identification in this highly vulnerable population. The synthetic training set was generated from a random subset of the original data (full dataset A) of 2686 records (70% of the total dataset - training dataset B). All challenge solutions will be evaluated against the remaining 1235 records (30% of the total dataset - test dataset C). Data Description: Report describing the comparison of univariate and bivariate distributions between the Synthetic Dataset and Test Dataset C. Additionally, a report showing the maximum mean discrepancy (MMD) and Kullback–Leibler (KL) divergence statistics. Data dictionary for the synthetic training dataset containing 148 variables. NOTE for restricted files: If you are not yet a CoLab member, please complete our membership application survey to gain access to restricted files within 2 business days. Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator at sepsiscolab@bcchr.ca or visit our website.

About this dataset

Context

The dataset tabulates the Earth 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 Earth. 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 447 (47.71% 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 Earth population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Earth is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Earth 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 Earth Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the Globe 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 Globe. 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 4,499 (62.23% 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 Globe population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Globe is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Globe 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 Globe Population by Age. You can refer the same here

There is a need to understand the growth and burden of malnutrition in children with cerebral palsy (CP) in order to design appropriate inclusive nutrition strategies. We compared the nutritional status and four-year longitudinal growth of a population-based cohort of children and young people (C&Y) with CP (n=97; 2-17 years; 55/42 M/F), and an age and sex matched group without CP (n=91; 2-17y; 50/41 M/F) in rural Uganda. The cohorts were assessed in 2015 and 2019 for weight, height, social demographic characteristics, and feeding related factors. Nutritional status was determined using the World Health Organization (WHO) Z-scores. Wilcoxon sign rank and Mann-Whitney tests were used to test within and between group differences. Multivariable linear regression was used to determine predictors of the change in growth. Approximately two thirds (62/97 (64%)) of C&Y with CP were malnourished (with

EEG signals from 60 users have been recorded whose age range lies between 6 and 55 years. Among all, there were 25 females and 35 male users. In general, all the participants were either school children or belonged to the socioeconomic cross section of the population with no medical history. The EEG recordings were acquired from all 14 electrodes operating at a sampling rate of 128 Hz. During recording, the participants were asked to comfortably sit on the chair with clear thoughts and a relaxed state.