About this dataset

Context

The dataset tabulates the data for the International Falls, MN population pyramid, which represents the International Falls 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 International Falls, MN, is 22.5.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for International Falls, MN, is 43.8.
• Total dependency ratio for International Falls, MN is 66.3.
• Potential support ratio, which is the number of youth (working age population) per elderly, for International Falls, MN is 2.3.

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

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.

An interactive Story Map Series℠ explaining the links between the Demographic Transition Model and population pyramids (population structure) for almost all the countries in the world. It provides an excellent way to make spatial links with the demographic data. For example, each country is mapped using an interactive symbol representing its stage on the DTM. On clicking the symbol for any country, a pop-up provides a statement about its stage on the DTM and its 2018 population pyramid, provided by PopulationPyramid.net.The tabs in the Story Map Series℠ take the reader or presenter through an introduction and explanation of the DTM, followed by detail about particular places / countries currently at each stage including an example of anomalies which are less consistent with the model.The story map will be useful for a wide range of students and teachers of geography, demography and development at secondary and tertiary level.Credits and further study*Story Map template by Esri*Demographic Transition video by GeographyAllTheWay*Population structure diagrams from PopulationPyramid.net by Martin de Wulf based in Brussels, Belgium.*DTM diagram and population pyramid icons from Cool Geography *Population Education / PopEdBlog*BBC Bitesize Population growth and change*Thanks also to Ed Morgan of the ONS for very helpful feedback and further information.NB The DTM stages for each country are estimated and may be altered in due course.

In 2023, just under 42 percent of Sub-Saharan Africa's population was below the age of 15; in contrast, this figure was just 18 percent in Europe & Central Asia and in North America. Across these regions, the share of the population aged 65 and over inversely correlated with the younger population, in that the regions with the largest share aged under 15 had the smallest share aged over 64, and vice versa. For most regions, the share of the population aged between 15 and 64 years ranged between 64 and 65 percent, except for Sub-Saharan Africa where it was below 56 percent. These trends can largely be explained by looking at global demographic development.

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.

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.

Leslie matrix model of P. flavus BM population.

Estimates of total number of people per grid square broken down by sex and age groupings (including 0-1 and by 5-year up to 80+) in 2016. The dataset is available to download in Geotiff format at a resolution of 1km. The projection is Geographic Coordinate System, WGS84. The units are estimated number of male/female in each age group per grid square. The mapping approach is Pezzulo, C. et al. Sub-national mapping of population pyramids and dependency ratios in Africa and Asia. Sci. Data 4:170089 doi:10.1038/sdata.2017.89 (2017) Recommended citationWorldPop (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 (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00654

Dynamic quantitative prediction model of P. flavus WM population.

Time-specific life table of P. flavus BM 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.

About this dataset

Context

The dataset tabulates the White Earth population by age. The dataset can be utilized to understand the age distribution and demographics of White Earth.

Content

The dataset constitues the following three datasets

• White Earth, ND Age Group Population Dataset: A complete breakdown of White Earth age demographics from 0 to 85 years, distributed across 18 age groups
• White Earth, ND Age Cohorts Dataset: Children, Working Adults, and Seniors in White Earth - Population and Percentage Analysis
• White Earth, ND Population Pyramid Dataset: Age Groups, Male and Female Population, and Total Population for Demographics Analysis

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/.

Chart and table of population level and growth rate for the Cairo, Egypt metro area from 1950 to 2025.

Estimates of total number of people per grid square broken down by gender and age groupings (including 0-1 and by 5-year up to 80+) in 2020 for Armenia.The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator) . The projection is Geographic Coordinate System, WGS84. The units are estimated number of male/female in each age group per grid square. "NoData" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by Nieves, J. J et al. 2019 and 2020The mapping approach is Pezzulo, C. et al. Sub-national mapping of population pyramids and dependency ratios in Africa and Asia. Sci. Data 4:170089 doi:10.1038/sdata.2017.89 (2017)REFERENCES:- 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 (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00646- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https://doi.org/10.1016/j.compenvurbsys.2019.101444- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.- Pezzulo, C., Hornby, G., Sorichetta, A. et al. Sub-national mapping of population pyramids and dependency ratios in Africa and Asia. Sci Data 4, 170089 (2017). https://doi.org/10.1038/sdata.2017.89

The statistic shows the total population in Japan from 2020 to 2024, with projections up until 2030. In 2024, the total population of Japan amounted to around 123.89 million inhabitants. See the figures for the population of South Korea for comparison. Total population in Japan From steadily low fertility rates to a growing elderly population, it is no secret that Japan’s population is shrinking. Population growth rates jump around a little, but are currently following a declining trend. The post-war baby boom generation is now in the 65-and-over age group, and the percentage of the population in that category is expected to keep growing, as is indicated by a high median age and high life expectancy. Japan already has the highest percentage of its population over 65 in the world, and the aging population puts some pressure on the Japanese government to provide welfare services for more people as rising numbers leave the workforce. However, the amount of jobs opened up for the younger generations by the older generations leaving the workforce means that unemployment is kept to a minimum. Despite a jump in unemployment after the global recession hit in 2008, rates were almost back to pre-recession rates by 2013. Another factor affecting Japan is the number of emigrants to other countries. The United States absorbs a number of emigrants worldwide, so despite a stagnating birth rate, the U.S. has seen a steady rise in population.

In the world's most populous country, life expectancy has been continuously rising over the last decades, benefitting greatly from China's economic ascendance. In 2022, average life expectancy at birth in China reached about 78.6 years. Life expectancy at birth Life expectancy at birth refers to the average number of years a group of people born in the same year would live, assuming constant mortality rates. San Marino and Monaco had the highest life expectancy at birth, while China had reached a life expectancy above global average. People who were born in San Marino or Monaco in 2023 had a life expectancy of approximately 87 years or 86 years on average respectively. Demographic development in China Whereas average life expectancy at birth has been growing steadily, birth rates in China have been experiencing a slowdown. In 2024, about 6.77 babies had been born per 1,000 women in China, the second lowest point in the recent decade. As a result of low fertility rates and the extended life expectancy in China, the share of elderly people had been rising rapidly. The number of Chinese population aged 60 and older had more than doubled over the past three decades and is projected to reach its peak at 504 million in 2050. People aged 60 and older have been estimated to account for approximately one fourth of China’s total population by 2030, indicating a sharp climb from just around 13 percent in 2010. In order to pinpoint this massive shift in the age pyramid of China, an important indicator for measuring the pressure of aging population on productive population may be consulted. The old-age dependency ratio in China was expected to reach 52.3 percent in 2050.

In the Cook Islands in 2024, the population decreased by about 2.24 percent compared to the previous year, making it the country with the highest population decline rate in 2024. Of the 20 countries with the highest rate of population decline, the majority are island nations, where emigration rates are high (especially to Australia, New Zealand, and the United States), or they are located in Eastern Europe, which suffers from a combination of high emigration rates and low birth rates.

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.

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.

Chart and table of population level and growth rate for the Bhubaneswar, India metro area from 1950 to 2025.