About this dataset

Context

The dataset tabulates the population of Town And Country by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Town And Country. The dataset can be utilized to understand the population distribution of Town And Country by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Town And Country. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Town And Country.

Key observations

Largest age group (population): Male # 60-64 years (538) | Female # 45-49 years (537). 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 groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Town And Country population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Town And Country is shown in the following column.
• Population (Female): The female population in the Town And Country is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Town And Country for each age group.

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 Town And Country Population by Gender. You can refer the same here

The average for 2024 based on 176 countries was 51.13 percent. The highest value was in Madagascar: 82.56 percent and the lowest value was in Yemen: 4.91 percent. The indicator is available from 1990 to 2024. Below is a chart for all countries where data are available.

Population, female (% of total population) in World was reported at 49.72 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. World - Population, female (% of total) - actual values, historical data, forecasts and projections were sourced from the World Bank on September of 2025.

Series Name: Proportion of countries with systems to track and make public allocations for gender equality and women's empowerment (percent)Series Code: SG_GEN_EQPWNRelease Version: 2020.Q2.G.03This dataset is the part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 5.c.1: Proportion of countries with systems to track and make public allocations for gender equality and women’s empowermentTarget 5.c: Adopt and strengthen sound policies and enforceable legislation for the promotion of gender equality and the empowerment of all women and girls at all levelsGoal 5: Achieve gender equality and empower all women and girlsFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

The Second World War had a sever impact on gender ratios across European countries, particularly in the Soviet Union. While the United States had a balanced gender ratio of one man for every woman, in the Soviet Union the ratio was below 5:4 in favor of women, and in Soviet Russia this figure was closer to 4:3.

As young men were disproportionately killed during the war, this had long-term implications for demographic development, where the generation who would have typically started families in the 1940s was severely depleted in many countries.

About this dataset

Context

The dataset tabulates the population of Country Club Hills by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Country Club Hills. The dataset can be utilized to understand the population distribution of Country Club Hills by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Country Club Hills. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Country Club Hills.

Key observations

Largest age group (population): Male # 10-14 years (639) | Female # 15-19 years (1,080). 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 groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Country Club Hills population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Country Club Hills is shown in the following column.
• Population (Female): The female population in the Country Club Hills is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Country Club Hills for each age group.

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 Gender. You can refer the same here

The Bahamas had the highest female employment-to-population ratio in the world in 2023 at 90 percent of the women in working age in some form of employment. Iceland had the second highest female employment rate of the countries, reaching 81 percent.

About this dataset

Context

The dataset tabulates the population of Country Club Heights by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Country Club Heights. The dataset can be utilized to understand the population distribution of Country Club Heights by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Country Club Heights. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Country Club Heights.

Key observations

Largest age group (population): Male # 55-59 years (22) | Female # 45-49 years (15). 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 groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Country Club Heights population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Country Club Heights is shown in the following column.
• Population (Female): The female population in the Country Club Heights is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Country Club Heights for each age group.

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 Heights Population by Gender. You can refer the same here

Context

Women's football is rapidly gaining popularity. More money, more fans, more female players. FIFA plans to increase the number of women playing football by almost 20 times.

And we will be watching the countries compete :)

Content

Base table "fifa_ranking-LASTDATE"

• country_full — country full name
• country_abrv — country abbreviation
• rank — current country rank
• total_points — current total points
• previous_points — total points in last rating
• rank_change — how rank has changed since the last publication
• confederation — FIFA confederations
• rank_date — date of rating calculation

Hint

You will find it interesting to compare the successes of the national teams in men's and women's football. Here is the data with the men's national teams — Men's Ranking

The average for 2021 based on 58 countries was 92.58 percent. The highest value was in Romania: 100 percent and the lowest value was in Afghanistan: 42 percent. The indicator is available from 1970 to 2023. Below is a chart for all countries where data are available.

Global Data: GDP, Life Expectancy & More

This dataset comprises 204 entries and 38 attributes, providing a comprehensive analysis of key economic and social indicators across various countries. It includes a diverse range of metrics, allowing for in-depth exploration of global trends related to GDP, education, health, and environmental factors.

Key Features:

• GDP: Gross Domestic Product (in current US dollars), representing the total economic output of a country.
• Sex Ratio: The ratio of males to females in the population, highlighting demographic trends.
• Life Expectancy: Average lifespan for males and females, an essential indicator of healthcare quality.
• Education Enrollment Rates: Data on primary, secondary, and post-secondary education enrollment for males and females, reflecting educational attainment.
• Unemployment Rate: Percentage of the labor force that is unemployed, indicating economic health.
• Homicide Rate: Number of homicides per 100,000 population, providing insight into safety and crime levels.
• Urban Population Growth: Rate of growth in urban populations, illustrating migration trends.
• CO2 Emissions: Carbon dioxide emissions per capita, an important measure of environmental impact.
• Forested Area: Percentage of land covered by forests, indicating biodiversity and environmental health.
• Tourist Numbers: Total number of international visitors, which can reflect a country's tourism potential.

Applications and Uses:

1. Research and Analysis: Ideal for researchers studying the correlation between economic performance and social indicators. This dataset can help identify trends and patterns relevant to global development.

2. Policy Development: Policymakers can utilize this data to inform decisions on education, healthcare, and environmental policies, aiming to improve national outcomes.

3. Machine Learning and Data Science: Data scientists can apply machine learning techniques to predict economic trends, analyze social impacts, or classify countries based on various indicators.

4. Educational Purposes: Suitable for students and educators in fields like economics, sociology, and environmental science for practical data analysis exercises.

5. Visualization Projects: Perfect for creating compelling visualizations that illustrate relationships between different metrics, aiding in public understanding and engagement.

By leveraging this dataset, users can uncover insights into how different factors influence a country's development, making it a valuable resource for diverse applications across various fields.

Zimbabwe had the most expensive mobile internet in Africa as of 2023. One gigabyte cost on average ***** U.S. dollars in the African country, the highest worldwide. Overall, the cost of mobile data varied significantly across the continent. South Sudan and the Central African Republic also recorded elevated prices for mobile data, positioning among the ** countries with the highest prices for data globally. By contrast, one gigabyte cost **** U.S. dollars in Malawi, the lowest average price registered in Africa. Determinants for high pricing On average, one gigabyte of mobile internet in Sub-Saharan Africa amounted to **** U.S. dollars in 2023, one of the highest worldwide, according to the source. In Northern Africa, the price for mobile data was far lower, **** U.S. dollars on average. Few factors influence the elevated prices of mobile data in Africa, such as high taxation and the lack of infrastructure. In 2021, around **** percent of the population in Sub-Saharan Africa lived within a range of ** kilometers from fiber networks. Mobile connectivity Over *** million people are estimated to be connected to the mobile internet in Africa as of 2022. The coverage gap has decreased in the continent but remained the highest worldwide in 2022. That year, ** percent of the population in Sub-Saharan Africa lived in areas not covered by a mobile broadband network. Additionally, the adoption of mobile internet is not equitable, as it is more accessible to men than women as well as more spread in urban than rural areas.

Statistical open data on LAU regions of Slovakia, Czech Republic, Poland, Hungary (and other countries in the future). LAU1 regions are called counties, okres, okresy, powiat, járás, járási, NUTS4, LAU, Local Administrative Units, ... and there are 733 of them in this V4 dataset. Overall, we cover 733 regions which are described by 137.828 observations (panel data rows) and more than 1.760.229 data points.

This LAU dataset contains panel data on population, on age structure of inhabitants, on number and on structure of registered unemployed. Dataset prepared by Michal Páleník. Output files are in json, shapefiles, xls, ods, json, topojson or CSV formats. Downloadable at zenodo.org.

This dataset consists of:

data on unemployment (by gender, education and duration of unemployment),

data on vacancies,

open data on population in Visegrad counties (by age and gender),

data on unemployment share.

Combined latest dataset

dataset of the latest available data on unemployment, vacancies and population

dataset includes map contours (shp, topojson or geojson format), relation id in OpenStreetMap, wikidata entry code,

it also includes NUTS4 code, LAU1 code used by national statistical office and abbreviation of the region (usually license plate),

source of map contours is OpenStreetMap, licensed under ODbL

no time series, only most recent data on population and unemployment combined in one output file

columns: period, lau, name, registered_unemployed, registered_unemployed_females, disponible_unemployed, low_educated, long_term, unemployment_inflow, unemployment_outflow, below_25, over_55, vacancies, pop_period, TOTAL, Y15-64, Y15-64-females, local_lau, osm_id, abbr, wikidata, population_density, area_square_km, way

Slovakia – SK: 79 LAU1 regions, data for 2024-10-01, 1.659 data,

Czech Republic – CZ: 77 LAU1 regions, data for 2024-10-01, 1.617 data,

Poland – PL: 380 LAU1 regions, data for 2024-09-01, 6.840 data,

Hungary – HU: 197 LAU1 regions, data for 2024-10-01, 2.955 data,

13.071 data in total.

column/number of observations description SK CZ PL HU

period period (month and year) the data is for 79 77 380 197

lau LAU code of the region 79 77 380 197

name name of the region in local language 79 77 380 197

registered_unemployed number of unemployed registered at labour offices 79 77 380 197

registered_unemployed_females number of unemployed women 79 77 380 197

disponible_unemployed unemployed able to accept job offer 79 77 0 0

low_educated unmployed without secondary school (ISCED 0 and 1) 79 77 380 197

long_term unemployed for longer than 1 year 79 77 380 0

unemployment_inflow inflow into unemployment 79 77 0 0

unemployment_outflow outflow from unemployment 79 77 0 0

below_25 number of unemployed below 25 years of age 79 77 380 197

over_55 unemployed older than 55 years 79 77 380 197

vacancies number of vacancies reported by labour offices 79 77 380 0

pop_period date of population data 79 77 380 197

TOTAL total population 79 77 380 197

Y15-64 number of people between 15 and 64 years of age, population in economically active age 79 77 380 197

Y15-64-females number of women between 15 and 64 years of age 79 77 380 197

local_lau region's code used by local labour offices 79 77 380 197

osm_id relation id in OpenStreetMap database 79 77 380 197

abbr abbreviation used for this region 79 77 380 0

wikidata wikidata identification code 79 77 380 197

population_density population density 79 77 380 197

area_square_km area of the region in square kilometres 79 77 380 197

way geometry, polygon of given region 79 77 380 197

Unemployment dataset

time series of unemployment data in Visegrad regions

by gender, duration of unemployment, education level, age groups, vacancies,

columns: period, lau, name, registered_unemployed, registered_unemployed_females, disponible_unemployed, low_educated, long_term, unemployment_inflow, unemployment_outflow, below_25, over_55, vacancies

Slovakia – SK: 79 LAU1 regions, data for 334 periods (1997-01-01 ... 2024-10-01), 202.082 data,

Czech Republic – CZ: 77 LAU1 regions, data for 244 periods (2004-07-01 ... 2024-10-01), 147.528 data,

Poland – PL: 380 LAU1 regions, data for 189 periods (2005-03-01 ... 2024-09-01), 314.100 data,

Hungary – HU: 197 LAU1 regions, data for 106 periods (2016-01-01 ... 2024-10-01), 104.408 data,

768.118 data in total.

column/number of observations description SK CZ PL HU

period period (month and year) the data is for 26 386 18 788 71 772 20 882

lau LAU code of the region 26 386 18 788 71 772 20 882

name name of the region in local language 26 386 18 788 71 772 20 882

registered_unemployed number of unemployed registered at labour offices 26 386 18 788 71 772 20 882

registered_unemployed_females number of unemployed women 26 386 18 788 62 676 20 882

disponible_unemployed unemployed able to accept job offer 25 438 18 788 0 0

low_educated unmployed without secondary school (ISCED 0 and 1) 11 771 9855 41 388 20 881

long_term unemployed for longer than 1 year 24 253 9855 41 388 0

unemployment_inflow inflow into unemployment 26 149 16 478 0 0

unemployment_outflow outflow from unemployment 26 149 16 478 0 0

below_25 number of unemployed below 25 years of age 11 929 9855 17 100 20 881

over_55 unemployed older than 55 years 11 929 9855 17 100 20 882

vacancies number of vacancies reported by labour offices 11 692 18 788 62 676 0

Population dataset

time series on population by gender and 5 year age groups in V4 counties

columns: period, lau, name, gender, TOTAL, Y00-04, Y05-09, Y10-14, Y15-19, Y20-24, Y25-29, Y30-34, Y35-39, Y40-44, Y45-49, Y50-54, Y55-59, Y60-64, Y65-69, Y70-74, Y75-79, Y80-84, Y85-89, Y90-94, Y_GE95, Y15-64

Slovakia – SK: 79 LAU1 regions, data for 28 periods (1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023), 152.628 data,

Czech Republic – CZ: 78 LAU1 regions, data for 24 periods (2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023), 125.862 data,

Poland – PL: 382 LAU1 regions, data for 29 periods (1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023), 626.941 data,

Hungary – HU: 197 LAU1 regions, data for 11 periods (2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023), 86.680 data,

992.111 data in total.

column/number of observations description SK CZ PL HU

period period (month and year) the data is for 6636 5574 32 883 4334

lau LAU code of the region 6636 5574 32 883 4334

name name of the region in local language 6636 5574 32 883 4334

gender gender (male or female) 6636 5574 32 883 4334

TOTAL total population 6636 5574 32 503 4334

Y00-04 inhabitants between 00 to 04 years inclusive 6636 5574 32 503 4334

Y05-09 number of inhabitants between 05 to 09 years of age 6636 5574 32 503 4334

Y10-14 number of people between 10 to 14 years inclusive 6636 5574 32 503 4334

Y15-19 number of inhabitants between 15 to 19 years of age 6636 5574 32 503 4334

Y20-24 number of people between 20 to 24 years inclusive 6636 5574 32 503 4334

Y25-29 number of inhabitants between 25 to 29 years of age 6636 5574 32 503 4334

Y30-34 inhabitants between 30 to 34 years inclusive 6636 5574 32 503 4334

Y35-39 number of inhabitants between 35 to 39 years of age 6636 5574 32 503 4334

Y40-44 inhabitants between 40 to 44 years inclusive 6636 5574 32 503 4334

Y45-49 number of inhabitants younger than 49 and older than 45 years 6636 5574 32 503 4334

Y50-54 inhabitants between 50 to 54 years inclusive 6636 5574 32 503 4334

Y55-59 number of inhabitants between 55 to 59 years of age 6636 5574 32 503 4334

Y60-64 inhabitants between 60 to 64 years inclusive 6636 5574 32 503 4334

Y65-69 number of inhabitants younger than 69 and older than 65 years 6636 5574 32 503 4334

Y70-74 inhabitants between 70 to 74 years inclusive 6636 5574 24 670 4334

Y75-79 number of inhabitants between 75 to 79 years of age 6636 5574 24 670 4334

Y80-84 number of people between 80 to 84 years inclusive 6636 5574 24 670 4334

Y85-89 number of inhabitants younger than 89 and older than 85 years 6636 5574 0 0

Y90-94 inhabitants between 90 to 94 years inclusive 6636 5574 0 0

Y_GE95 number of people 95 years or older 6636 3234 0 0

Y15-64 number of people between 15 and 64 years of age, population in economically active age 6636 5574 32 503 4334

Notes

more examples at www.iz.sk

NUTS4 / LAU1 / LAU codes for HU and PL are created by me, so they can (and will) change in the future; CZ and SK NUTS4 codes are used by local statistical offices, so they should be more stable

NUTS4 codes are consistent with NUTS3 codes used by Eurostat

local_lau variable is an identifier used by local statistical office

abbr is abbreviation of region's name, used for map purposes (usually cars' license plate code; except for Hungary)

wikidata is code used by wikidata

osm_id is region's relation number in the OpenStreetMap database

Example outputs

you can download data in CSV, xml, ods, xlsx, shp, SQL, postgis, topojson, geojson or json format at 📥 doi:10.5281/zenodo.6165135

Counties of Slovakia – unemployment rate in Slovak LAU1 regions

Regions of the Slovak Republic

Unemployment of Czechia and Slovakia – unemployment share in LAU1 regions of Slovakia and Czechia

interactive map on unemployment in Slovakia

Slovakia – SK, Czech Republic – CZ, Hungary – HU, Poland – PL, NUTS3 regions of Slovakia

download at 📥 doi:10.5281/zenodo.6165135

suggested citation: Páleník, M. (2024). LAU1 dataset [Data set]. IZ Bratislava. https://doi.org/10.5281/zenodo.6165135

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.

Around the world, an increasing number of people, predominantly women, are choosing to undergo cosmetic surgery – despite the associated health risks. This study aimed to promote a better cross-cultural understanding of the correlates and predictors of favorable attitudes toward cosmetic surgery among women in China (an Eastern country where cosmetic surgery is increasing most rapidly) and the Netherlands (a Western country). Questionnaire data were obtained from 763 adult women; 245 were Chinese women in China (Mage = 29.71), 265 were Chinese women in the Netherlands (Mage = 25.81), and 253 were Dutch women (Mage = 29.22). Facial appearance concerns and materialistic belief were significant predictors of favorable attitudes towards cosmetic surgery for all three cultural groups. Body appreciation was a significant positive predictor among Chinese women in both China and the Netherlands, whereas age and beauty-ideal internalization were significant positive predictors only among Chinese women in China. The findings and their implications are discussed with respect to the characteristics of Chinese culture that could explain the identified differences between Chinese and Dutch women.

Population: Female: VR: Republic of Chuvashia data was reported at 628,058.000 Person in 2023. This records a decrease from the previous number of 630,553.000 Person for 2022. Population: Female: VR: Republic of Chuvashia data is updated yearly, averaging 679,174.000 Person from Dec 1989 (Median) to 2023, with 35 observations. The data reached an all-time high of 727,230.000 Person in 1992 and a record low of 628,058.000 Person in 2023. Population: Female: VR: Republic of Chuvashia data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Demographic and Labour Market – Table RU.GA010: Population: Female: by Region.

About this dataset

Context

The dataset tabulates the population of Country Club by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Country Club. The dataset can be utilized to understand the population distribution of Country Club by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Country Club. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Country Club.

Key observations

Largest age group (population): Male # 35-39 years (226) | Female # 10-14 years (214). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Country Club population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Country Club is shown in the following column.
• Population (Female): The female population in the Country Club is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Country Club for each age group.

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 Population by Gender. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Country Life Acres by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Country Life Acres. The dataset can be utilized to understand the population distribution of Country Life Acres by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Country Life Acres. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Country Life Acres.

Key observations

Largest age group (population): Male # 60-64 years (11) | Female # 60-64 years (7). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Country Life Acres population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Country Life Acres is shown in the following column.
• Population (Female): The female population in the Country Life Acres is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Country Life Acres for each age group.

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 Life Acres Population by Gender. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Hill Country Village by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Hill Country Village across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of male population, with 52.04% of total population being male. 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.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Hill Country Village is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Hill Country Village 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 Hill Country Village Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Gratis by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Gratis across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of female population, with 50.0% of total population being female. 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.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Gratis is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Gratis 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 Gratis Population by Race & Ethnicity. You can refer the same here