This is a subset of Population projections

Population projections for Pacific Island Countries and territories from 1950 to 2050, by sex and by 5-years age groups.

About this dataset

Context

The dataset tabulates the data for the Colorado population pyramid, which represents the Colorado population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Colorado, is 26.0.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Colorado, is 22.6.
• Total dependency ratio for Colorado is 48.6.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Colorado is 4.4.

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

Variables / Data Columns

• Age Group: This column displays the age group for the Colorado population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Colorado for the selected age group is shown in the following column.
• Population (Female): The female population in the Colorado for the selected age group is shown in the following column.
• Total Population: The total population of the Colorado for the selected age group 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 Colorado Population by Age. You can refer the same here

This is a subset of Population projections

Population projections for Pacific Island Countries and territories from 1950 to 2050, by sex and by 5-years age groups.

About this dataset

Context

The dataset tabulates the data for the China, TX population pyramid, which represents the China population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for China, TX, is 25.5.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for China, TX, is 39.1.
• Total dependency ratio for China, TX is 64.6.
• Potential support ratio, which is the number of youth (working age population) per elderly, for China, TX is 2.6.

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

Variables / Data Columns

• Age Group: This column displays the age group for the China population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the China for the selected age group is shown in the following column.
• Population (Female): The female population in the China for the selected age group is shown in the following column.
• Total Population: The total population of the China for the selected age group 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 China Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the data for the Breckenridge, CO population pyramid, which represents the Breckenridge population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Breckenridge, CO, is 29.5.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Breckenridge, CO, is 30.2.
• Total dependency ratio for Breckenridge, CO is 59.7.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Breckenridge, CO is 3.3.

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

Variables / Data Columns

• Age Group: This column displays the age group for the Breckenridge population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Breckenridge for the selected age group is shown in the following column.
• Population (Female): The female population in the Breckenridge for the selected age group is shown in the following column.
• Total Population: The total population of the Breckenridge for the selected age group 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 Breckenridge Population by Age. You can refer the same here

As of 2023, the bulk of the Chinese population was aged between 25 and 59 years, amounting to around half of the population. A breakdown of the population by broad age groups reveals that around 61.3 percent of the total population was in working age between 16 and 59 years in 2023. Age cohorts below 25 years were considerably smaller, although there was a slight growth trend in recent years. Population development in China Population development in China over the past decades has been strongly influenced by political and economic factors. After a time of high fertility rates during the Maoist regime, China introduced birth-control measures in the 1970s, including the so-called one-child policy. The fertility rate dropped accordingly from around six children per woman in the 1960s to below two at the end of the 20th century. At the same time, life expectancy increased consistently. In the face of a rapidly aging society, the government gradually lifted the one-child policy after 2012, finally arriving at a three-child policy in 2021. However, like in most other developed countries nowadays, people in China are reluctant to have more than one or two children due to high costs of living and education, as well as changed social norms and private values. China’s top-heavy age pyramid The above-mentioned developments are clearly reflected in the Chinese age pyramid. The age cohorts between 30 and 39 years are the last two larger age cohorts. The cohorts between 15 and 24, which now enter childbearing age, are decisively smaller, which will have a negative effect on the number of births in the coming decade. When looking at a gender distribution of the population pyramid, a considerable gender gap among the younger age cohorts becomes visible, leaving even less room for growth in birth figures.

Contained within the 5th Edition (1978 to 1995) of the National Atlas of Canada is a sheet that has 2 maps and an inset map. The first map shows proportion of total population in 65 to 74 and 75 plus age groups for each Census Division in 1986. An inset map shows the same information for the area from Windsor to Quebec. The second map of Canada shows proportion under 15 by Census Division. Population pyramids of age / sex distributions for 1961 and 1986 shown for each province, territory and for Canada.

Abstract

Population and Housing Census of Bhutan 2005 collected data on demographic, education, health, migration, household and housing characteristics. It covered the entire country irrespective of human habitation or not and counted all structures, census house, households and people whether Bhutanese or non-Bhutanese residing in the country at a specific point of time. The Census was carried out for two days, 30 and 31 May, 2005. A total of 7500 enumerators, supervisors and administrators were involved.

General Objective The 2005 Census seeks to create an inventory of Bhutan's population size, socio-economic information, labour and demographic characteristics.

Specific Objectives: - to obtain an up-to date count of the population size, by age and sex - to obtain geographic distribution of the population by demographic and socio-economic characteristics - to provide frames for surveys and other statistical activities - to gather information about migration and fertility

Salient features of a census: 1. The population census forms an integral part of a country’s National Statistical System. 2. The census provides valuable benchmark data on a wide range of characteristics, a frame for statistical survey and data to compile a variety of social and economic indicators. These indicators must be comparable between areas within as well as with that of other countries. 3. The census provides the demographic, housing, social and economic data not provided by population registers. 4. Most importantly a census provides data at the smallest area level like a village. Extensive and detailed cross-classification is possible. This is not possible in a sample survey. 5. The population census has a legitimate methodology, which is acceptable internationally.

Geographic coverage

National

Analysis unit

Households, household members

Universe

The Census covered all de facto household members. It covered the entire country irrespective of human habitation or not and counted all structures, census house, households and people whether Bhutanese or non-Bhutanese residing in the country at a Census Night (Midnight of 30 May).

Kind of data

Census/enumeration data [cen]

Sampling procedure

Not Applicable

Mode of data collection

Face-to-face [f2f]

Research instrument

To develop the census questionnaires, consultative meetings were conducted with all ministries. This was followed by a workshop for all sector heads to finalise the contents of the census questionnaires. Necessary changes were incorporated into the census questionnaires based on the outcome of the workshops and consultative meetings. The questionnaires were pre-tested in the three regions of the country. After making all necessary changes the forms were printed in adequate numbers.

Form PHCB - 2A - Household List Update: This section collects data on village code, structure number, census house number, use of census house, serial number of household, name of household head, sex and age with geographical codes.

Form PHCB - 2B - Household Members List: This section collects information on household members, relationship, sex, age, member status, members absent and duration absent.

Form PHCB -2C - Individual Member Details: This section has three parts. Part A collects information on general demographic characteristics and migration. Part B collects information on education and employment and Part C collects information on fertility of women age 15-49 years.

Form PHCB - 2D - Household Information: This section has two parts. Part A collects information on housing conditions and facilities. Part B collects information on particulars of the deceased in the past twelve months.

Cleaning operations

Data editing was done in several stages. The first editing of data was done by the field supervisors and then followed by the manual editing at the dzongkhag level immediately after the field operation. The final manual editing was done at the centre by 20 Dzongkhag Statistical Officers, 1 Registration Officer and 28 graduates who were trained and deployed on temporary basis for three months.

Response rate

100% response rate.

Note: The Royal Government of Bhutan declared 30 May - 31 May, 2005, as public holidays.

Sampling error estimates

Since PHCB, 2005 involved complete enumeration of respondents, Sampling procedures were not applicable thus sampling errors were not computed.

Data appraisal

Standard tables and graphs were generated to assess the data reliability. This includes the computation of population pyramid, graphs of male and female population by single years of age, age and sex structure, age distribution of the household population.

According to the age distribution of China's population in 2024, approximately 68.6 percent of the population were in their working age between 15 and 64 years of age. Retirees aged 65 years and above made up about 15.6 percent of the total population. Age distribution in China As can be seen from this statistic, the age pyramid in China has been gradually shifting towards older demographics during the past decade. Mainly due to low birth rates in China, the age group of 0 to 14 year-olds has remained at around 16 to 17 percent since 2010, whereas the age groups 65 years and over have seen growth of nearly seven percentage points. Thus, the median age of the Chinese population has been constantly rising since 1970 and is forecast to reach 52 years by 2050. Accompanied by a slightly growing mortality rate of more than 7 per thousand, China is showing strong signs of an aging population. China's aging society The impact of this severe change in demographics is the subject of an ongoing scientific discussion. Rising standards of living in China contain the demand for better health care and pension insurance for retirees, which will be hard to meet with the social insurance system in China still being in its infancy. Per capita expenditure on medical care and services of urban households has grown more than ninefold since 2000 with a clear and distinctive upward trend for the near future. As for social security spending, public pension expenditure is forecast to take up approximately nine percent of China's GDP by 2050.

In 1800, the region of Germany was not a single, unified nation, but a collection of decentralized, independent states, bound together as part of the Holy Roman Empire. This empire was dissolved, however, in 1806, during the Revolutionary and Napoleonic eras in Europe, and the German Confederation was established in 1815. Napoleonic reforms led to the abolition of serfdom, extension of voting rights to property-owners, and an overall increase in living standards. The population grew throughout the remainder of the century, as improvements in sanitation and medicine (namely, mandatory vaccination policies) saw child mortality rates fall in later decades. As Germany industrialized and the economy grew, so too did the argument for nationhood; calls for pan-Germanism (the unification of all German-speaking lands) grew more popular among the lower classes in the mid-1800s, especially following the revolutions of 1948-49. In contrast, industrialization and poor harvests also saw high unemployment in rural regions, which led to waves of mass migration, particularly to the U.S.. In 1886, the Austro-Prussian War united northern Germany under a new Confederation, while the remaining German states (excluding Austria and Switzerland) joined following the Franco-Prussian War in 1871; this established the German Empire, under the Prussian leadership of Emperor Wilhelm I and Chancellor Otto von Bismarck. 1871 to 1945 - Unification to the Second World War The first decades of unification saw Germany rise to become one of Europe's strongest and most advanced nations, and challenge other world powers on an international scale, establishing colonies in Africa and the Pacific. These endeavors were cut short, however, when the Austro-Hungarian heir apparent was assassinated in Sarajevo; Germany promised a "blank check" of support for Austria's retaliation, who subsequently declared war on Serbia and set the First World War in motion. Viewed as the strongest of the Central Powers, Germany mobilized over 11 million men throughout the war, and its army fought in all theaters. As the war progressed, both the military and civilian populations grew increasingly weakened due to malnutrition, as Germany's resources became stretched. By the war's end in 1918, Germany suffered over 2 million civilian and military deaths due to conflict, and several hundred thousand more during the accompanying influenza pandemic. Mass displacement and the restructuring of Europe's borders through the Treaty of Versailles saw the population drop by several million more.

Reparations and economic mismanagement also financially crippled Germany and led to bitter indignation among many Germans in the interwar period; something that was exploited by Adolf Hitler on his rise to power. Reckless printing of money caused hyperinflation in 1923, when the currency became so worthless that basic items were priced at trillions of Marks; the introduction of the Rentenmark then stabilized the economy before the Great Depression of 1929 sent it back into dramatic decline. When Hitler became Chancellor of Germany in 1933, the Nazi government disregarded the Treaty of Versailles' restrictions and Germany rose once more to become an emerging superpower. Hitler's desire for territorial expansion into eastern Europe and the creation of an ethnically-homogenous German empire then led to the invasion of Poland in 1939, which is considered the beginning of the Second World War in Europe. Again, almost every aspect of German life contributed to the war effort, and more than 13 million men were mobilized. After six years of war, and over seven million German deaths, the Axis powers were defeated and Germany was divided into four zones administered by France, the Soviet Union, the UK, and the U.S.. Mass displacement, shifting borders, and the relocation of peoples based on ethnicity also greatly affected the population during this time. 1945 to 2020 - Partition and Reunification In the late 1940s, cold war tensions led to two distinct states emerging in Germany; the Soviet-controlled east became the communist German Democratic Republic (DDR), and the three western zones merged to form the democratic Federal Republic of Germany. Additionally, Berlin was split in a similar fashion, although its location deep inside DDR territory created series of problems and opportunities for the those on either side. Life quickly changed depending on which side of the border one lived. Within a decade, rapid economic recovery saw West Germany become western Europe's strongest economy and a key international player. In the east, living standards were much lower, although unemployment was almost non-existent; internationally, East Germany was the strongest economy in the Eastern Bloc (after the USSR), though it eventually fell behind the West by the 1970s. The restriction of movement between the two states also led to labor shortages in t...

The Consumer Pyramids is the largest survey of households in India. The survey contains record-level data that are delivered in the form of population estimates. The survey contains multiple databases that contain population estimates on household demographics, household income and expenses, borrowing by household, and household assets. The data also contain individual-level health status, financial inclusion, education level, and caste and literacy estimates. Demographic information collected include gender, age, religion, education, and occupation. Database Composition: The Consumer Pyramids Survey is conducted over the course of four-month periods or waves throughout the year totaling three rounds a year. This collection includes the following six databases: People of India; Household Income and Expenses; Household Amenities, Assets, and Liabilities; Household Expenses; Composition of Incomes at the member level; Composition of Incomes at the household level.

Abstract

Population and Housing Census of Bhutan 2005 collected data on demographic, eduation, health, migration, household and housing characteristics. It covered the entire country irrespective of human habitation or not and counted all structures, census house, households and people whether Bhutanese or non-Bhutanese residing in the country at a specific point of time. The Census was carried out for two days, 30 and 31 May, 2005. A total of 7500 enumerators, supervisors and administrators were involved.

General Objectives: The 2005 Census seeks to create an inventory of Bhutan's population size, socio-economic information, labour and demographic characteristics.

Specific Objectives: - to obtain an up-to date count of the population size, by age and sex - to obtain geographic distribution of the population by demographic and socio-economic characteristics - to provide frames for surveys and other statistical activities - to gather information about migration and fertility

Salient features of a census: 1. The population census forms an integral part of a country’s National Statistical System. 2. The census provides valuable benchmark data on a wide range of characteristics, a frame for statistical survey and data to compile a variety of social and economic indicators. These indicators must be comparable between areas within as well as with that of other countries. 3. The census provides the demographic, housing, social and economic data not provided by population registers. 4. Most importantly a census provides data at the smallest area level like a village. Extensive and detailed cross-classification is possible. This is not possible in a sample survey. 5. The population census has a legitimate methodology, which is acceptable internationally.

Geographic coverage

National, District (Dzongkhag), Sub-district (Gewogs), Urban (or Rural) areas.

Analysis unit

Individuals, Households, Gewogs, Dzongkhags, National

Universe

The Census covered all de facto household members. It covered the entire country irrespective of human habitation or not and counted all structures, census house, households and people whether Bhutanese or non-Bhutanese residing in the country at a Census Night ( Midngiht of 30 May)

Kind of data

Census/enumeration data [cen]

Sampling procedure

Not Applicable

Mode of data collection

Face-to-face [f2f]

Research instrument

To develop the census questionnaires, consultative meetings were conducted with all ministries. This was followed by a workshop for all sector heads to finalise the contents of the census questionnaires. Necessary changes were incorporated into the census questionnaires based on the outcome of the workshops and consultative meetings. The questionnaires were pre-tested in the three regions of the country. After making all necessary changes the forms were printed in adequate numbers.

Form PHCB - 2A - Household List Update: This section collects data on village code, structure number, census house number, use of census house, serial number of household, name of household head, sex and age with geographical codes. Form PHCB - 2B - Household Members List: This section collects information on household members, relationship, sex, age, member status, members absent and duration absent. Form PHCB -2C - Individual Member Details: This section has three parts. Part A collects information on general demographic characteristics and migration. Part B collects information on education and employment and Part C collects information on fertiliy of women age 15-49 years. Form PHCB - 2D - Household Informamtion: This section has two parts. Part A collects information on housing conditions and facilities. Part B collects information on particulars of the deceased in the past twelve months.

Cleaning operations

Data editing was done in several stages. The first editing of data was done by the field supervisors and then followed by the manual editing at the dzongkhag level immediately after the field operation. The final manual editing was done at the centre by 20 Dzongkhag Statistical Officers, 1 Registration Officer and 28 graduates who were trained and deployed on temporary basis for three months.

Response rate

100% response rate.

Note: The Royal Government of Bhutan declared 30 May - 31 May, 2005, as public holidays.

Sampling error estimates

Since PHCB, 2005 involved complete enumeration of respondents, Sampling procedures were not applicable thus sampling errors were not computed.

Data appraisal

Standard tables and graphs were generated to assess the data reliablity. This includes the computation of population pyramid, grapha of male and female population by single years of age, age and sex structure, age distribution of the household population.

This layer contains WorldPop's 100m resolution annual estimates of population density from the year 2000 to 2020. Usage notes: This layer is configured to be viewed only at a scale range for large-scale maps, i.e., zoomed into small areas of the world. Because the underlying data for this layer is relatively large and because raster pyramids cannot accurately represent aggregated population density, there are no pyramids. Thus, this layer may at times require 10 to 15 seconds to draw. We recommend using this layer in conjunction with WorldPop's 1-km resolution Population Density layer to create web maps that allow users to pan and zoom to wider areas; this web map contains an example of this combination. The population estimates in this layer are derived WorldPop's total population data, which use a Top-down unconstrained method which estimates the total population for each cell with a Random Forest-based dasymetric model (Stevens, F. R., Gaughan, A. E., Linard, C., & Tatem, A. J. (2015). Disaggregating census data for population mapping using random forests with remotely-sensed and ancillary data. PloS one, 10(2), e0107042) and converts these values to population density by dividing the number of people in each pixel by the pixel surface area. This diagram visually describes this model that uses known populated locations to analyze imagery to find similarly populated locations. The DOI for the original WorldPop.org total population population data is 10.5258/SOTON/WP00645.Recommended Citation: WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation. Accessed from https://worldpop.arcgis.com/arcgis/rest/services/WorldPop_Total_Population_100m/ImageServer, which was acquired from WorldPop in December 2021.

According to latest figures, the Chinese population decreased by 1.39 million to around 1.408 billion people in 2024. After decades of rapid growth, China arrived at the turning point of its demographic development in 2022, which was earlier than expected. The annual population decrease is estimated to remain at moderate levels until around 2030 but to accelerate thereafter. Population development in China China had for a long time been the country with the largest population worldwide, but according to UN estimates, it has been overtaken by India in 2023. As the population in India is still growing, the country is very likely to remain being home of the largest population on earth in the near future. Due to several mechanisms put into place by the Chinese government as well as changing circumstances in the working and social environment of the Chinese people, population growth has subsided over the past decades, displaying an annual population growth rate of -0.1 percent in 2024. Nevertheless, compared to the world population in total, China held a share of about 17 percent of the overall global population in 2024. China's aging population In terms of demographic developments, the birth control efforts of the Chinese government had considerable effects on the demographic pyramid in China. Upon closer examination of the age distribution, a clear trend of an aging population becomes visible. In order to curb the negative effects of an aging population, the Chinese government abolished the one-child policy in 2015, which had been in effect since 1979, and introduced a three-child policy in May 2021. However, many Chinese parents nowadays are reluctant to have a second or third child, as is the case in most of the developed countries in the world. The number of births in China varied in the years following the abolishment of the one-child policy, but did not increase considerably. Among the reasons most prominent for parents not having more children are the rising living costs and costs for child care, growing work pressure, a growing trend towards self-realization and individualism, and changing social behaviors.

Contained within the 3rd Edition (1957) of the Atlas of Canada is a map that shows three condensed maps of the percentage of population: under 20 years of age, 20-64 years of age, and over 64 years of age illustrated by the census division, circa 1951. Each of these maps is accompanied by a pie chart showing the percentage distribution by province and territory. The two remaining maps show urban and rural sex ratios using the number of males to 100 females by census division as of 1951. The rural sex ratio map is accompanied by a chart showing the ratio of males to 100 females by province and territory. As well, a chart accompanies the urban sex ratio map and shows the ratio of males to 100 females for chief urban centers. A set of age-sex pyramids that show the 1951 percentage distribution of males and females by quinquennial age groups for Canada, each province and the territories are also included.

Abstract

The 2022 Philippines National Demographic and Health Survey (NDHS) was implemented by the Philippine Statistics Authority (PSA). Data collection took place from May 2 to June 22, 2022.

The primary objective of the 2022 NDHS is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the NDHS collected information on fertility, fertility preferences, family planning practices, childhood mortality, maternal and child health, nutrition, knowledge and attitudes regarding HIV/AIDS, violence against women, child discipline, early childhood development, and other health issues.

The information collected through the NDHS is intended to assist policymakers and program managers in designing and evaluating programs and strategies for improving the health of the country’s population. The 2022 NDHS also provides indicators anchored to the attainment of the Sustainable Development Goals (SDGs) and the new Philippine Development Plan for 2023 to 2028.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49, and all children aged 0-4 resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling scheme provides data representative of the country as a whole, for urban and rural areas separately, and for each of the country’s administrative regions. The sample selection methodology for the 2022 NDHS was based on a two-stage stratified sample design using the Master Sample Frame (MSF) designed and compiled by the PSA. The MSF was constructed based on the listing of households from the 2010 Census of Population and Housing and updated based on the listing of households from the 2015 Census of Population. The first stage involved a systematic selection of 1,247 primary sampling units (PSUs) distributed by province or HUC. A PSU can be a barangay, a portion of a large barangay, or two or more adjacent small barangays.

In the second stage, an equal take of either 22 or 29 sample housing units were selected from each sampled PSU using systematic random sampling. In situations where a housing unit contained one to three households, all households were interviewed. In the rare situation where a housing unit contained more than three households, no more than three households were interviewed. The survey interviewers were instructed to interview only the preselected housing units. No replacements and no changes of the preselected housing units were allowed in the implementing stage in order to prevent bias. Survey weights were calculated, added to the data file, and applied so that weighted results are representative estimates of indicators at the regional and national levels.

All women age 15–49 who were either usual residents of the selected households or visitors who stayed in the households the night before the survey were eligible to be interviewed. Among women eligible for an individual interview, one woman per household was selected for a module on women’s safety.

For further details on sample design, see APPENDIX A of the final report.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Two questionnaires were used for the 2022 NDHS: the Household Questionnaire and the Woman’s Questionnaire. The questionnaires, based on The DHS Program’s model questionnaires, were adapted to reflect the population and health issues relevant to the Philippines. Input was solicited from various stakeholders representing government agencies, academe, and international agencies. The survey protocol was reviewed by the ICF Institutional Review Board.

After all questionnaires were finalized in English, they were translated into six major languages: Tagalog, Cebuano, Ilocano, Bikol, Hiligaynon, and Waray. The Household and Woman’s Questionnaires were programmed into tablet computers to allow for computer-assisted personal interviewing (CAPI) for data collection purposes, with the capability to choose any of the languages for each questionnaire.

Cleaning operations

Processing the 2022 NDHS data began almost as soon as fieldwork started, and data security procedures were in place in accordance with confidentiality of information as provided by Philippine laws. As data collection was completed in each PSU or cluster, all electronic data files were transferred securely via SyncCloud to a server maintained by the PSA Central Office in Quezon City. These data files were registered and checked for inconsistencies, incompleteness, and outliers. The field teams were alerted to any inconsistencies and errors while still in the area of assignment. Timely generation of field check tables allowed for effective monitoring of fieldwork, including tracking questionnaire completion rates. Only the field teams, project managers, and NDHS supervisors in the provincial, regional, and central offices were given access to the CAPI system and the SyncCloud server.

A team of secondary editors in the PSA Central Office carried out secondary editing, which involved resolving inconsistencies and recoding “other” responses; the former was conducted during data collection, and the latter was conducted following the completion of the fieldwork. Data editing was performed using the CSPro software package. The secondary editing of the data was completed in August 2022. The final cleaning of the data set was carried out by data processing specialists from The DHS Program in September 2022.

Response rate

A total of 35,470 households were selected for the 2022 NDHS sample, of which 30,621 were found to be occupied. Of the occupied households, 30,372 were successfully interviewed, yielding a response rate of 99%. In the interviewed households, 28,379 women age 15–49 were identified as eligible for individual interviews. Interviews were completed with 27,821 women, yielding a response rate of 98%.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors and (2) sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and in data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2022 Philippines National Demographic and Health Survey (2022 NDHS) to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2022 NDHS is only one of many samples that could have been selected from the same population, using the same design and identical size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95% of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2022 NDHS sample was the result of a multistage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed in SAS using programs developed by ICF. These programs use the Taylor linearization method to estimate variances for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

A more detailed description of estimates of sampling errors are presented in APPENDIX B of the survey report.

Data appraisal

Data Quality Tables

• Household age distribution
• Age distribution of eligible and interviewed women
• Age displacement at age 14/15
• Age displacement at age 49/50
• Pregnancy outcomes by years preceding the survey
• Completeness of reporting
• Observation of handwashing facility
• School attendance by single year of age
• Vaccination cards photographed
• Population pyramid
• Five-year mortality rates

See details of the data quality tables in Appendix C of the final report.

PLEASE NOTE: Following the consultation on the proposal to stop producing this report, it has been confirmed that this report will continue to be produced by NHS Digital. This is the first time this publication has been released in a new format and on a monthly basis. In addition, quarterly publications will include a topic of interest. This quarter the topic of interest is Office for National Statistics (ONS) population estimates compared to GP registration data using a population pyramid. Data are extracted each month as a snapshot in time from the GP Payments system maintained by NHS Digital. This release is an accurate snapshot as at 1 April 2017. Since April 2014, geographical references have been taken from 2011 census information. GP Practice; Clinical Commissioning Group (CCG); NHS England Region and NHS England Commissioning Region level data are released in single year of age (SYOA) and 5-year age bands, both of which finish at 95+, split by gender. Additional guidance has been included in this publication to show how the new datasets can be used. New Health Geography structure: Please note that this publication reflects NHS England's health geography structure as at 1 April 2017. This includes the move of 32 practices from NHS Cumbria CCG (01H) to NHS Lancashire North CCG (01K) In addition, NHS Lancashire North CCG is changing its name to NHS Morecambe Bay CCG (01K) and NHS Cumbria CCG to NHS North Cumbria CCG although the CCG codes will remain the same. Roundwell Medical Centre (D82023) GP practice is changing its CCG parent from NHS South Norfolk CCG (06Y) to NHS Norwich CCG (06W). NHS Central Manchester (00W), NHS North Manchester (01M) and NHS South Manchester (01N) combined to form NHS Manchester CCG with a new code of 14L. UPDATED 17 November 2017 These ONS CCG codes have been updated to reflect changes that took place on 1 April 2017 (No data have been affected) NHS North Cumbria E38000041 to E38000215 NHS Morecambe Bay CCG E38000093 to E38000216 NHS Norwich CCG E38000131 to E38000218 NHS South Norfolk CCG E38000159 to E38000219

Abstract

The main objectives of the 2010 Census of Population and Housing were: • To provide accurate and reliable information on the size, composition and distribution of the population of Zambia at the time of the census; • To provide information on the demographic and socioeconomic characteristics of the population of Zambia at the lowest administrative level - the ward; • To provide indicators for measuring progress towards national and international development goals in a timely and user friendly manner; • To provide information on the number and characteristics of households engaged in agriculture and other economic activities; • To provide an accurate sampling frame and sample weights for future inter-censal household and population based surveys; • To provide information identifying the number of eligible voters for the 2011 General Elections; • To provide a census that meets national and international standards and allows for comparability with other censuses; • To provide information on the housing characteristics of the population.

Universe

Census Enumerators went out visiting all buildings in Zambia whether completed, incomplete, abandoned, habitable and inhabitable for the purpose of identifying characteristics of all buildings, households and other human aspects. All persons who lived in the buildings were counted and detailed information pertaining to their characteristics obtained.

Sampling procedure

The Census mapping methodology in 2010 was Geographic Information System (GIS) driven with the use of Satellite Imagery in urban areas and Global Positioning System (GPS) in rural areas.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2010 Census used a single questionnaire to capture individual, household and housing characteristics from the population. The 2010 Census differs from the 2000 Census by including questions on deaths of Household Members during the 12 months period prior to the census enumeration, as well as cause of death for all reported deaths.

Included for the first time were questions on maternal deaths to women aged 12-49 years during the reference period (12 months prior to the Census). Questions were asked of female household members aged 12-49 years that were reported to have died during the reference period (12 months prior to the census), whether the death had occurred while the woman was pregnant, during childbirth or six weeks after the end of a pregnancy, regardless of the outcome of the pregnancy. Another new addition was the question on whether one was an Albino or not.

Cleaning operations

In April 2011, the Central Statistical Office started the data capture and processing of the 2010 Census questionnaires. Scanning of the 2010 Census questionnaires started in April 2011 and was successfully concluded in August 2011. The data capture used Optical Mark Reading (OMR) and Intelligent Character Recognition (ICR) technology in order to speed up the processing time. Data verification and development of edit and imputation specifications and programmes started in May and was completed in November 2011.

Data appraisal

Methods of evaluation applied were:

• Direct Method: Post Enumeration Survey (PES)- a sample of households is revisited after the census and data are again collected but on a smaller scale and later compared with that collected during the actual census. • Indirect Method: Comparison of data using both internal and external consistency checks. Internal consistency checks compare relationships of data within the same census data, whereas external consistency checks compare census data with data generated from other sources.

Coverage errors: • Omission or duplication of individuals, households, or housing units resulting in under or over enumeration. • Lack of accessibility or cooperation with respondents. • Lack of proper boundary descriptions on maps. Coverage errors can be measured by examining certain statistics such as growth rate, age composition, child woman ratio and dependency ratio.

Content errors: Content errors refer to instances where characteristics such as age, sex, marital status, economic activity, etc. of a person enumerated in a census or survey are incorrectly reported or tabulated. • Content errors are caused by either a respondent giving a wrong response or by an enumerator recording an incorrect response. • 2010 census errors were estimated by the use of the Myers' Index, Sex Ratios, Age Ratios and Population Pyramids.

For findings, please refer to the presentation on census data evaluation provided as external resources.

Over the last decade, Japan’s population has aged more and more, to the point where more than a quarter of Japanese were 65 years and older in 2022. Population growth has stopped and even reversed, since it’s been in the red for several years now.

It’s getting old

With almost 30 percent of its population being elderly inhabitants, Japan is considered the “oldest” country in the world today. Japan boasts a high life expectancy, in fact, the Japanese tend to live longer than the average human worldwide. The increase of the aging population is accompanied by a decrease of the total population caused by a sinking birth rate. Japan’s fertility rate has been below the replacement rate for many decades now, mostly due to economic uncertainty and thus a decreasing number of marriages.

Are the Japanese invincible?

There is no real mystery surrounding the ripe old age of so many Japanese. Their high average age is very likely due to high healthcare standards, nutrition, and an overall high standard of living – all of which could be adopted by other industrial nations as well. But with high age comes less capacity, and Japan’s future enemy might not be an early death, but rather a struggling social network.

In the middle of 2023, about 60 percent of the global population was living in Asia.The total world population amounted to 8.1 billion people on the planet. In other words 4.7 billion people were living in Asia as of 2023. Global populationDue to medical advances, better living conditions and the increase of agricultural productivity, the world population increased rapidly over the past century, and is expected to continue to grow. After reaching eight billion in 2023, the global population is estimated to pass 10 billion by 2060. Africa expected to drive population increase Most of the future population increase is expected to happen in Africa. The countries with the highest population growth rate in 2024 were mostly African countries. While around 1.47 billion people live on the continent as of 2024, this is forecast to grow to 3.9 billion by 2100. This is underlined by the fact that most of the countries wit the highest population growth rate are found in Africa. The growing population, in combination with climate change, puts increasing pressure on the world's resources.