The smallest country in the world is Vatican City, with a landmass of just **** square kilometers (0.19 square miles). Vatican City is an independent state surrounded by Rome. Vatican City is not the only small country located inside Italy. San Marino is another microstate, with a land area of ** square kilometers, making it the fifth-smallest country in the world. Many of these small nations have equally small populations, typically less than ************** inhabitants. However, the population of Singapore is almost *** million, and it is the twentieth smallest country in the world with a land area of *** square kilometers. In comparison, Jamaica is almost eight times larger than Singapore, but has half the population.

The Vatican City, often called the Holy See, has the smallest population worldwide, with only *** inhabitants. It is also the smallest country in the world by size. The islands Niue, Tuvalu, and Nauru followed in the next three positions. On the other hand, India is the most populous country in the world, with over *** billion inhabitants.

Population rankings of the world's smallest countries

The United States had the largest male population of the G7 between 2010 and 2022, reaching *** million that year. On the other hand, Canada had the smallest number of male inhabitants at ** million. Moreover, the number of men living in Japan has been constantly decreasing since 2010, from ** million to ** million, following an overall decrease in the Japanese population.

Context The current US Census Bureau world population estimate in June 2019 shows that the current global population is 7,577,130,400 people on earth, which far exceeds the world population of 7.2 billion in 2015. Our own estimate based on UN data shows the world's population surpassing 7.7 billion.

China is the most populous country in the world with a population exceeding 1.4 billion. It is one of just two countries with a population of more than 1 billion, with India being the second. As of 2018, India has a population of over 1.355 billion people, and its population growth is expected to continue through at least 2050. By the year 2030, the country of India is expected to become the most populous country in the world. This is because India’s population will grow, while China is projected to see a loss in population.

The following 11 countries that are the most populous in the world each have populations exceeding 100 million. These include the United States, Indonesia, Brazil, Pakistan, Nigeria, Bangladesh, Russia, Mexico, Japan, Ethiopia, and the Philippines. Of these nations, all are expected to continue to grow except Russia and Japan, which will see their populations drop by 2030 before falling again significantly by 2050.

Many other nations have populations of at least one million, while there are also countries that have just thousands. The smallest population in the world can be found in Vatican City, where only 801 people reside.

In 2018, the world’s population growth rate was 1.12%. Every five years since the 1970s, the population growth rate has continued to fall. The world’s population is expected to continue to grow larger but at a much slower pace. By 2030, the population will exceed 8 billion. In 2040, this number will grow to more than 9 billion. In 2055, the number will rise to over 10 billion, and another billion people won’t be added until near the end of the century. The current annual population growth estimates from the United Nations are in the millions - estimating that over 80 million new lives are added each year.

This population growth will be significantly impacted by nine specific countries which are situated to contribute to the population growing more quickly than other nations. These nations include the Democratic Republic of the Congo, Ethiopia, India, Indonesia, Nigeria, Pakistan, Uganda, the United Republic of Tanzania, and the United States of America. Particularly of interest, India is on track to overtake China's position as the most populous country by 2030. Additionally, multiple nations within Africa are expected to double their populations before fertility rates begin to slow entirely.

Content In this Dataset, we have Historical Population data for every Country/Territory in the world by different parameters like Area Size of the Country/Territory, Name of the Continent, Name of the Capital, Density, Population Growth Rate, Ranking based on Population, World Population Percentage, etc.

With 450,295 square kilometers, Sweden is the largest Nordic country by area size, followed by Finland and Norway. This makes it the fifth largest country in Europe. Meanwhile, Denmark is the smallest of the five Nordic countries with only 43,094 square kilometers, however, the Danish autonomous region of Greenland is significantly larger than any of the Nordic countries, and is almost double the size of the other five combined.

Population

Sweden is also the Nordic country with the largest population. 10.45 million people live in the country. Denmark, Finland, and Norway all have between five and six million inhabitants, whereas only 370,000 people live in Iceland. Meanwhile, Denmark has the highest population density of the five countries. Greenland is the most sparsely populated permanently-inhabited country in the world, followed by the regions of Svalbard and Jan Mayen.

Geography

The five Nordic countries vary geographically. While Denmark is mostly flat, its highest point only stretching around 170 meters above sea level, Norway's highest peak is nearly 2,500 meters high. Moreover, Finland is known for its many lakes and is often called the land of a thousand lakes, whereas Iceland is famous for its volcanoes.

The current US Census Bureau world population estimate in June 2019 shows that the current global population is 7,577,130,400 people on earth, which far exceeds the world population of 7.2 billion from 2015. Our own estimate based on UN data shows the world's population surpassing 7.7 billion.

China is the most populous country in the world with a population exceeding 1.4 billion. It is one of just two countries with a population of more than 1 billion, with India being the second. As of 2018, India has a population of over 1.355 billion people, and its population growth is expected to continue through at least 2050. By the year 2030, the country of India is expected to become the most populous country in the world. This is because India’s population will grow, while China is projected to see a loss in population.

The next 11 countries that are the most populous in the world each have populations exceeding 100 million. These include the United States, Indonesia, Brazil, Pakistan, Nigeria, Bangladesh, Russia, Mexico, Japan, Ethiopia, and the Philippines. Of these nations, all are expected to continue to grow except Russia and Japan, which will see their populations drop by 2030 before falling again significantly by 2050.

Many other nations have populations of at least one million, while there are also countries that have just thousands. The smallest population in the world can be found in Vatican City, where only 801 people reside.

In 2018, the world’s population growth rate was 1.12%. Every five years since the 1970s, the population growth rate has continued to fall. The world’s population is expected to continue to grow larger but at a much slower pace. By 2030, the population will exceed 8 billion. In 2040, this number will grow to more than 9 billion. In 2055, the number will rise to over 10 billion, and another billion people won’t be added until near the end of the century. The current annual population growth estimates from the United Nations are in the millions - estimating that over 80 million new lives are added each year.

This population growth will be significantly impacted by nine specific countries which are situated to contribute to the population growth more quickly than other nations. These nations include the Democratic Republic of the Congo, Ethiopia, India, Indonesia, Nigeria, Pakistan, Uganda, the United Republic of Tanzania, and the United States of America. Particularly of interest, India is on track to overtake China's position as the most populous country by the year 2030. Additionally, multiple nations within Africa are expected to double their populations before fertility rates begin to slow entirely.

Global life expectancy has also improved in recent years, increasing the overall population life expectancy at birth to just over 70 years of age. The projected global life expectancy is only expected to continue to improve - reaching nearly 77 years of age by the year 2050. Significant factors impacting the data on life expectancy include the projections of the ability to reduce AIDS/HIV impact, as well as reducing the rates of infectious and non-communicable diseases.

Population aging has a massive impact on the ability of the population to maintain what is called a support ratio. One key finding from 2017 is that the majority of the world is going to face considerable growth in the 60 plus age bracket. This will put enormous strain on the younger age groups as the elderly population is becoming so vast without the number of births to maintain a healthy support ratio.

Although the number given above seems very precise, it is important to remember that it is just an estimate. It simply isn't possible to be sure exactly how many people there are on the earth at any one time, and there are conflicting estimates of the global population in 2016.

Some, including the UN, believe that a population of 7 billion was reached in October 2011. Others, including the US Census Bureau and World Bank, believe that the total population of the world reached 7 billion in 2012, around March or April.

About this dataset

Context

The dataset tabulates the Lost Nation population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Lost Nation. The dataset can be utilized to understand the population distribution of Lost Nation by age. For example, using this dataset, we can identify the largest age group in Lost Nation.

Key observations

The largest age group in Lost Nation, IA was for the group of age 85+ years with a population of 44 (10.48%), according to the 2021 American Community Survey. At the same time, the smallest age group in Lost Nation, IA was the 15-19 years with a population of 7 (1.67%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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 in consideration
• Population: The population for the specific age group in the Lost Nation is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Lost Nation 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 Lost Nation Population by Age. You can refer the same here

The statistic shows the countries with the smallest sales per capita of music products worldwide in 2018. In India, the sales per capita of music products was **** U.S. dollars in 2018.

About this dataset

Context

The dataset tabulates the National City population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for National City. The dataset can be utilized to understand the population distribution of National City by age. For example, using this dataset, we can identify the largest age group in National City.

Key observations

The largest age group in National City, CA was for the group of age 30-34 years with a population of 4,656 (8.19%), according to the 2021 American Community Survey. At the same time, the smallest age group in National City, CA was the 80-84 years with a population of 1,002 (1.76%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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 in consideration
• Population: The population for the specific age group in the National City is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of National City 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 National City Population by Age. You can refer the same here

ZIP Codes are administrative codes generated by the United States Postal Service (USPS) that refer to the geographic area covered by a specific set of mail delivery routes. The U.S. Census Bureau calculates and distributes aggregated social, economic, and demographic information for the population associated with "ZIP Code Tabulation Areas" (ZCTAs), which are roughly analogous to ZIP Codes and serve as identifiers for specific neighborhoods and communities. These aggregated census data, however, are unable to account for changes in ZIP Code boundaries that occur between decennial censuses, leading to measurement error and missing data problems for scholars who attempt to use the aggregated ZCTA data. The purpose of this crosswalk file is to allow researchers to overcome this limitation, enabling them to appropriately link spatial reference information (ZIP Codes) with characteristics of the populations to which they refer. Most ZIP Codes do not change boundaries in a decade, but a large enough percentage do as to create a problem with missing or mis-specified data. Boundary changes typically involve one or more of the following three processes, although a small number of cases do not conform to these typologies: (1) two or more existing ZIP Codes are combined to create a single surviving ZIP Code, (2) an existing ZIP Code is divided into multiple resulting ZIP Codes, and (3) boundaries between two or more existing ZIP Codes are altered. Each of these types of changes alters the geographic area that a ZIP Code refers to, and as such, the spatial unit identified by the ZIP Code includes a different population, with a different array of characteristics. By linking the spatial units associated with ZIP Codes as these boundary changes are enacted, the research team can both prevent the loss of observations due to missing data, and more accurately measure social, demographic, and economic characteristics associated with each ZIP Code. This data set identifies changes in ZIP Code boundaries between 1990 and 2020, and provides numeric codes that cluster the ZIP Codes into the smallest geographic unit, or group of ZIP Codes, that are consistent across a decade: 1990 - 2000, 2000 - 2010, and 2010 - 2020. This "crosswalk" covers the contiguous United States, Alaska, Hawaii, and the District of Columbia. Since much administrative data is available with ZIP Code as the smallest identifiable geography, ZIP Codes are often used to embed observations from administrative data (patients, businesses, survey respondents, etc.) within their social, demographic, and economic contexts. However, ZIP Code boundaries change over time, resulting in measurement error (matching observations to the wrong contextual unit) or missing data (due to an observation reporting a ZIP Code that did not exist at the beginning of the observational period). These data were collected, and the crosswalk created, in an attempt to resolve these data quality issues.

The average for 2023 based on 196 countries was 0.51 percent. The highest value was in India: 17.94 percent and the lowest value was in Andorra: 0 percent. The indicator is available from 1960 to 2023. Below is a chart for all countries where data are available.

NOTE: The included files cover the data and replication code for each of the three working papers that comprise this dissertation. By the time these files are available, it is likely that the author will have updated versions of each of these files. If you are interested in using these data, please contact the author directly or visit his website for the most updated versions. Concerns about domestic authority shape how governments conduct their foreign policies. However, this influence is often difficult to observe in highly opaque, non-democratic political systems. In the first part of the dissertation, I investigate the link between domestic authority and foreign policy in the context of diplomacy and trade in late imperial China, a period that spans the Ming (1368-1644) and Qing (1644-1911) dynasties. I argue that international diplomacy can serve leaders’ domestic political needs when it is highly visible to relevant audiences; conducted with counterparts held in relatively high esteem domestically; when certain diplomatic practices are historically associated with regime authority; or when diplomacy is wielded by leaders with relatively low levels of legitimacy. Using an original dataset of over 5,000 Ming and Qing tribute exchanges, I demonstrate that Chinese emperors newly in power conducted a disproportionately high volume of diplomatic activity. I find weaker evidence that this effect was more salient among low-legitimacy emperors. An accompanying case study illustrates how the Yongle Emperor deployed tribute diplomacy as a tool for domestic authority consolidation. Turning to the trade policies of the same period, I argue that beyond leaders, other autocratic elites who participate in foreign policy making are motivated by similar authority concerns. Extant research on non-democratic trade policy has largely neglected this group of actors. I develop a theory that predicts variation in elite policy preferences based on top-down and bottom-up authority relations with the leader and local trading communities, respectively. To assess these claims, I introduce a dataset on the maritime trade preferences of several hundred individual elite officials in late imperial China created through 10 months of archival work in Beijing and Taipei. The data suggest that coastal provincial officials became key pro-trade advocates during the Qing dynasty. The findings offer an example of how trade preferences can vary within a non-democratic regime, and how historical cases can be especially useful for empirically studying these preferences. In the third paper, the dissertation then flips the focus from the domestic politics of Chinese foreign policy to how other states’ internal politics shape their engagement with contemporary China. I argue that leaders of small developing countries can seek greater domestic authority by acquiring “prestige projects,” defined as highly visible, nationally salient international development projects. After identifying a set of Chinese government-financed prestige projects using a new dataset on Chinese development finance, I show that these projects are overwhelmingly concentrated in the world’s poorest and smallest countries, and that their implementation may be associated with higher public support for recipient governments. I also find that China’s government supplies more prestige projects to states that increase their support for Chinese diplomatic objectives.

Abstract

Round 1 of the Afrobarometer survey was conducted from July 1999 through June 2001 in 12 African countries, to solicit public opinion on democracy, governance, markets, and national identity. The full 12 country dataset released was pieced together out of different projects, Round 1 of the Afrobarometer survey,the old Southern African Democracy Barometer, and similar surveys done in West and East Africa.

The 7 country dataset is a subset of the Round 1 survey dataset, and consists of a combined dataset for the 7 Southern African countries surveyed with other African countries in Round 1, 1999-2000 (Botswana, Lesotho, Malawi, Namibia, South Africa, Zambia and Zimbabwe). It is a useful dataset because, in contrast to the full 12 country Round 1 dataset, all countries in this dataset were surveyed with the identical questionnaire

Geographic coverage

Botswana Lesotho Malawi Namibia South Africa Zambia Zimbabwe

Analysis unit

Basic units of analysis that the study investigates include: individuals and groups

Kind of data

Sample survey data [ssd]

Sampling procedure

A new sample has to be drawn for each round of Afrobarometer surveys. Whereas the standard sample size for Round 3 surveys will be 1200 cases, a larger sample size will be required in societies that are extremely heterogeneous (such as South Africa and Nigeria), where the sample size will be increased to 2400. Other adaptations may be necessary within some countries to account for the varying quality of the census data or the availability of census maps.

The sample is designed as a representative cross-section of all citizens of voting age in a given country. The goal is to give every adult citizen an equal and known chance of selection for interview. We strive to reach this objective by (a) strictly applying random selection methods at every stage of sampling and by (b) applying sampling with probability proportionate to population size wherever possible. A randomly selected sample of 1200 cases allows inferences to national adult populations with a margin of sampling error of no more than plus or minus 2.5 percent with a confidence level of 95 percent. If the sample size is increased to 2400, the confidence interval shrinks to plus or minus 2 percent.

Sample Universe

The sample universe for Afrobarometer surveys includes all citizens of voting age within the country. In other words, we exclude anyone who is not a citizen and anyone who has not attained this age (usually 18 years) on the day of the survey. Also excluded are areas determined to be either inaccessible or not relevant to the study, such as those experiencing armed conflict or natural disasters, as well as national parks and game reserves. As a matter of practice, we have also excluded people living in institutionalized settings, such as students in dormitories and persons in prisons or nursing homes.

What to do about areas experiencing political unrest? On the one hand we want to include them because they are politically important. On the other hand, we want to avoid stretching out the fieldwork over many months while we wait for the situation to settle down. It was agreed at the 2002 Cape Town Planning Workshop that it is difficult to come up with a general rule that will fit all imaginable circumstances. We will therefore make judgments on a case-by-case basis on whether or not to proceed with fieldwork or to exclude or substitute areas of conflict. National Partners are requested to consult Core Partners on any major delays, exclusions or substitutions of this sort.

Sample Design

The sample design is a clustered, stratified, multi-stage, area probability sample.

To repeat the main sampling principle, the objective of the design is to give every sample element (i.e. adult citizen) an equal and known chance of being chosen for inclusion in the sample. We strive to reach this objective by (a) strictly applying random selection methods at every stage of sampling and by (b) applying sampling with probability proportionate to population size wherever possible.

In a series of stages, geographically defined sampling units of decreasing size are selected. To ensure that the sample is representative, the probability of selection at various stages is adjusted as follows:

The sample is stratified by key social characteristics in the population such as sub-national area (e.g. region/province) and residential locality (urban or rural). The area stratification reduces the likelihood that distinctive ethnic or language groups are left out of the sample. And the urban/rural stratification is a means to make sure that these localities are represented in their correct proportions. Wherever possible, and always in the first stage of sampling, random sampling is conducted with probability proportionate to population size (PPPS). The purpose is to guarantee that larger (i.e., more populated) geographical units have a proportionally greater probability of being chosen into the sample. The sampling design has four stages

A first-stage to stratify and randomly select primary sampling units;

A second-stage to randomly select sampling start-points;

A third stage to randomly choose households;

A final-stage involving the random selection of individual respondents

We shall deal with each of these stages in turn.

STAGE ONE: Selection of Primary Sampling Units (PSUs)

The primary sampling units (PSU's) are the smallest, well-defined geographic units for which reliable population data are available. In most countries, these will be Census Enumeration Areas (or EAs). Most national census data and maps are broken down to the EA level. In the text that follows we will use the acronyms PSU and EA interchangeably because, when census data are employed, they refer to the same unit.

We strongly recommend that NIs use official national census data as the sampling frame for Afrobarometer surveys. Where recent or reliable census data are not available, NIs are asked to inform the relevant Core Partner before they substitute any other demographic data. Where the census is out of date, NIs should consult a demographer to obtain the best possible estimates of population growth rates. These should be applied to the outdated census data in order to make projections of population figures for the year of the survey. It is important to bear in mind that population growth rates vary by area (region) and (especially) between rural and urban localities. Therefore, any projected census data should include adjustments to take such variations into account.

Indeed, we urge NIs to establish collegial working relationships within professionals in the national census bureau, not only to obtain the most recent census data, projections, and maps, but to gain access to sampling expertise. NIs may even commission a census statistician to draw the sample to Afrobarometer specifications, provided that provision for this service has been made in the survey budget.

Regardless of who draws the sample, the NIs should thoroughly acquaint themselves with the strengths and weaknesses of the available census data and the availability and quality of EA maps. The country and methodology reports should cite the exact census data used, its known shortcomings, if any, and any projections made from the data. At minimum, the NI must know the size of the population and the urban/rural population divide in each region in order to specify how to distribute population and PSU's in the first stage of sampling. National investigators should obtain this written data before they attempt to stratify the sample.

Once this data is obtained, the sample population (either 1200 or 2400) should be stratified, first by area (region/province) and then by residential locality (urban or rural). In each case, the proportion of the sample in each locality in each region should be the same as its proportion in the national population as indicated by the updated census figures.

Having stratified the sample, it is then possible to determine how many PSU's should be selected for the country as a whole, for each region, and for each urban or rural locality.

The total number of PSU's to be selected for the whole country is determined by calculating the maximum degree of clustering of interviews one can accept in any PSU. Because PSUs (which are usually geographically small EAs) tend to be socially homogenous we do not want to select too many people in any one place. Thus, the Afrobarometer has established a standard of no more than 8 interviews per PSU. For a sample size of 1200, the sample must therefore contain 150 PSUs/EAs (1200 divided by 8). For a sample size of 2400, there must be 300 PSUs/EAs.

These PSUs should then be allocated proportionally to the urban and rural localities within each regional stratum of the sample. Let's take a couple of examples from a country with a sample size of 1200. If the urban locality of Region X in this country constitutes 10 percent of the current national population, then the sample for this stratum should be 15 PSUs (calculated as 10 percent of 150 PSUs). If the rural population of Region Y constitutes 4 percent of the current national population, then the sample for this stratum should be 6 PSU's.

The next step is to select particular PSUs/EAs using random methods. Using the above example of the rural localities in Region Y, let us say that you need to pick 6 sample EAs out of a census list that contains a total of 240 rural EAs in Region Y. But which 6? If the EAs created by the national census bureau are of equal or roughly equal population size, then selection is relatively straightforward. Just number all EAs consecutively, then make six selections using a table of random numbers. This procedure, known as simple random sampling (SRS), will

About this dataset

Context

The dataset tabulates the National Park population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for National Park. The dataset can be utilized to understand the population distribution of National Park by age. For example, using this dataset, we can identify the largest age group in National Park.

Key observations

The largest age group in National Park, NJ was for the group of age 40-44 years with a population of 290 (9.46%), according to the 2021 American Community Survey. At the same time, the smallest age group in National Park, NJ was the 80-84 years with a population of 32 (1.04%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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 in consideration
• Population: The population for the specific age group in the National Park is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of National Park 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 National Park Population by Age. You can refer the same here

We collected agricultural census and survey information on the areas and yields of 175 crops from the smallest political units reasonably obtainable for 206 countries. Data availability varied for different crops within each country, with most countries having subnational statistics for some crops but national statistics for others. Subnational data are one or two administrative levels below the national (i.e., state/province and county/district). These include 2299 political units one level below the national from 150 countries, and 19,751 units two levels below for 73 countries. The largest single source of subnational data is Agro-MAPS, a joint project between the United Nations Food and Agriculture Organization (FAO), the International Food Policy Research Institute (IFPRI), and the Center for Sustainability and the Global Environment (SAGE).

Agro-MAPS is a collection of subnational statistics on crop area, production, and yield for most countries in the world. For some crops, however, the Agro-MAPS data was missing or insufficiently detailed. To cover these gaps, we collected additional data from national census agencies and agricultural surveys. In particular, we turned to additional censuses and surveys to ensure county level data for the largest countries, including Brazil, Argentina, Mexico, Canada, India, the United States, and China. In certain instances we were only able to obtain subnational information on major crop groups (e.g., fruits or vegetables), which we used to proportionally distribute national level data from FAO to the state or county level.

When subnational statistics were unavailable, we relied on national figures from the Food and Agriculture Organization’s statistical databases [FAO, 2006a]. We collected independent national level data for four countries that were absent from FAO: Afghanistan, Iraq, Somalia, and Taiwan.

Abstract

The Survey Assessment of Vietnamese Youth (SAVY) undertaken in late 2003 was a collaboration of the Ministry of Health, General Statistics Office with technical and financial support from the World Health Organization (WHO) and the United Nations Children’s Fund (UNICEF).

This is the first nationwide baseline survey of youth ever undertaken in Viet Nam. It mainly aims to collect data on various aspects of youth life in order to inform policy and programmes in the adolescent and youth health and development area.

SAVY reveals a positive picture of Vietnamese youth as they face both challenges and opportunities in a changing economic and social environment. Compared with young people in other Asian countries, Vietnamese youth display relatively less risky behaviour, are supported by protective factors and are optimistic and eager to build a prosperous country. However, this survey does reveal that some young people will encounter considerable challenges in their transition to adulthood, unless provided with support. It is important that parents, the community and the government, with the support of international agencies and young people, work together to ensure the healthy development of young people in Viet Nam.

The survey involved 7,584 youth aged 14-25 years from 42 provinces across the country, from the smallest rural hamlet to the largest cities. Using a household sample, youth were invited to a central location to complete both a face-to-face interview and a self-administered anonymous survey which contained sensitive questions young people could answer in private. What results is the most extensive understanding of the social life, attitudes and aspirations of young Vietnamese people today.

Survey Objectives - Provide information that can best inform future initiatives to promote the healthy development of youth across the country; - Inform policy and program development in the Adolescent and Youth Health area in the immediate future; and - Provide baseline data about Vietnamese youth to identify trends and patterns in the coming years.

Survey Content The questionnaire was designed through a very dynamic process, where experience from previous surveys was examined and opinion of young people ware actively solicited to ensure quality and relevance. The specific information collected through the questionnaire includes: Personal demographics Schooling, education Vocational training, Work and employment Puberty: knowledge and behaviors about reproductive health Dating and friendships HIV/AIDS Injury, illness and physical health Attitudes, perceptions and behaviors Social factors and emotional wellbeing Mass media Future aspirations

Survey Implementation SAVY is a collaborative effort between many agencies and young people. It is the result of extensive investment and parnership building between the Vietnamese Government through the Ministry of Health, the General Statistics Office, and United Nations agencies, notably The World Health Organisation and the United Nations Children's Fund. Several other organizations, from a variety of sectors, also contributed to the endeavor, notably the Ministry of Education and Training (MoET), the Central Youth Union (YU) and the Vietnam Women's Union (VWU). In order to ensure that the survey was methodologically sound, the East- West Centrer (Honolulu, Hawaii) provided intensive technical assisstance.

Survey Results Results from the surveys, including national reports, and micro level datasets. The dataset was formatted by *.sav (SPSS) and *.dta (STATA) More information and electronic files of SAVY, visit : http://www.moh.gov.vn/SKSS/Savy_htm/savy.htm

Geographic coverage

National

Analysis unit

Youth aged 14-25 years

Universe

The survey covered all youths aged 14-25 years resident in the household. The SAVY sample did not include Vietnamese youth not living with their families nor those living in military barracks, social protection centers, dormitories, re-education centers and drug treatment centers.

Kind of data

Sample survey data [ssd]

Sampling procedure

The SAVY sample is a national representative sample of youth (persons ages 14-25 years) living in households across the eight economic regions of Viet Nam. THe sample was drawn from the sub-sample of 45,000 households in the 2002 Viet Nam Living Standards Survey (VLSS 2002), within a multi-staged and stratified design. The youth in the SAVY sample design are sufficient to represent the nation as a whole, as well as the urban and rural separely. The largest cities (Hanoi and Ho Chi Minh) were over sampled in order to provide for increased statistical power in that segment of the total population of youth.

Forty-two out of 61 provinces were selected for the SAVY sample, using the probability proportional to size (PPS) method to maintain representativeness . At the next stage of sampling, enumeration areas (EAs) in each province were selected. In those EAs sampled, all youth aged 14 through 25 were identified (i.e, those born between 1978 and 1989) males and females, married and non married from the 20 households that had been selected for the VLSS2002. The youth cohort represents all youth, but not those living in special arrangements, such as barracks, re-education centers, social protection centers, factories and dormitories.

The 61 provinces in the VLSS 2002 sample included 2.250 EAS, and the 42 provinces selected for SAVY included 1643 EAs. From these, a total of 446 EAs were selected for the SAVY sample. These EAs contained 8920 households corresponding to a population of 40,140 (about 4.5 persons per household). Since youth aged 14-25 account for 24.5% of the total population (the figure in the 1999 census), the anticipated number of youth in the SAVY sample was approximately 9,835. If the mobilization rate (percentage of eligible youth actually interviewed) was 90% then the number of youth interviewed woul be estimated to be about 8,850. In the actual SAVY field experiece, the mobilization rate was 85% and the number of completed interviews was 7,584.

The sample is therefore representative, and provides sufficient cases for analysis at the national level within urban and rural sectors at the national level, by gender at the nation level, and for each of the regions. Further detail on the sampling methodology is provided in the Appendix of the Final Report.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was designed through a very dynamic process, where experience from previous surveys was examined and opinions of young people were actively solicited to ensure quality and relevance. This process also helped to define the methodology and implications for fieldwork planning.

A number of stakeholders’ agencies, including research institutes, were involved in the development of the questionnaire. This process ensured broad participation and ownership of the questionnaire and the survey.

The questionnaire design took place in two stages. In the first stage, experienced researchers, and others interested in the survey as stakeholders, were convened to a workshop by the MoH. Potential topics, and the possible phrasing of questions using the questionnaire bank from previous studies in the region as reference, were fully discussed. Since some of the topics were deemed to be more sensitive than others, it was recommended that the questionnaire should be organized into two parts, one for an interview and the other for self-completion. On the basis of that workshop, a draft questionnaire was created for review by the workshop members and numerous others in stakeholder agencies, as well as by young people through a series of consultations.

Eight focus group discussions were conducted in Hanoi and HCMC, with around 60 young people of different ages in the 14-25 range who were either married or unmarried and either attending or not attending school. Participants gave detailed feedback about the terminology, the ways in which questions were posed and the sequencing of the questions, as well as which specific questions or issues they would prefer to respond to on their own, rather than with an interviewer. This process resulted in the rephrasing of a number of questions and changes to the self-completed section.

Preliminary training was conducted for field-testing of the questionnaire. Participants came from the GSO Office in Tuyen Quang, Hue and HCMC, representing the north, south and central regions of Viet Nam. A group of 50 young males and females, either married or unmarried and either attending or not attending school, participated in the interviewers’ practice session. In the debriefing discussions, these young people expressed their feelings about the interviews, the questions asked, what they liked and did not like about the process, seating arrangements, ideas of what topics/issues they thought might still be missing in the draft questionnaire, and what they thought would be needed to make good interviewers. Field testing with around 180 young people from six communes in these three provinces then took place.

The second stage involved further vetting of questionnaire sections and was coordinated by the GSO. The review meeting following the field trips recommended the need for another field testing exercise, particularly because little experience had been gained from testing with urban young people and interviewing ethnic minority young people through interpreters. Following the second round of field-testing in Hanoi and Yen Bai, the feedback was incorporated to finalise the questionnaire for the interviewers training. At the training, further revision and refinement of

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Abstract

Different countries have different health outcomes that are in part due to the way respective health systems perform. Regardless of the type of health system, individuals will have health and non-health expectations in terms of how the institution responds to their needs. In many countries, however, health systems do not perform effectively and this is in part due to lack of information on health system performance, and on the different service providers.

The aim of the WHO World Health Survey is to provide empirical data to the national health information systems so that there is a better monitoring of health of the people, responsiveness of health systems and measurement of health-related parameters.

The overall aims of the survey is to examine the way populations report their health, understand how people value health states, measure the performance of health systems in relation to responsiveness and gather information on modes and extents of payment for health encounters through a nationally representative population based community survey. In addition, it addresses various areas such as health care expenditures, adult mortality, birth history, various risk factors, assessment of main chronic health conditions and the coverage of health interventions, in specific additional modules.

The objectives of the survey programme are to: 1. develop a means of providing valid, reliable and comparable information, at low cost, to supplement the information provided by routine health information systems. 2. build the evidence base necessary for policy-makers to monitor if health systems are achieving the desired goals, and to assess if additional investment in health is achieving the desired outcomes. 3. provide policy-makers with the evidence they need to adjust their policies, strategies and programmes as necessary.

Geographic coverage

The survey sampling frame must cover 100% of the country's eligible population, meaning that the entire national territory must be included. This does not mean that every province or territory need be represented in the survey sample but, rather, that all must have a chance (known probability) of being included in the survey sample.

There may be exceptional circumstances that preclude 100% national coverage. Certain areas in certain countries may be impossible to include due to reasons such as accessibility or conflict. All such exceptions must be discussed with WHO sampling experts. If any region must be excluded, it must constitute a coherent area, such as a particular province or region. For example if ¾ of region D in country X is not accessible due to war, the entire region D will be excluded from analysis.

Analysis unit

Households and individuals

Universe

The WHS will include all male and female adults (18 years of age and older) who are not out of the country during the survey period. It should be noted that this includes the population who may be institutionalized for health reasons at the time of the survey: all persons who would have fit the definition of household member at the time of their institutionalisation are included in the eligible population.

If the randomly selected individual is institutionalized short-term (e.g. a 3-day stay at a hospital) the interviewer must return to the household when the individual will have come back to interview him/her. If the randomly selected individual is institutionalized long term (e.g. has been in a nursing home the last 8 years), the interviewer must travel to that institution to interview him/her.

The target population includes any adult, male or female age 18 or over living in private households. Populations in group quarters, on military reservations, or in other non-household living arrangements will not be eligible for the study. People who are in an institution due to a health condition (such as a hospital, hospice, nursing home, home for the aged, etc.) at the time of the visit to the household are interviewed either in the institution or upon their return to their household if this is within a period of two weeks from the first visit to the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLING GUIDELINES FOR WHS

Surveys in the WHS program must employ a probability sampling design. This means that every single individual in the sampling frame has a known and non-zero chance of being selected into the survey sample. While a Single Stage Random Sample is ideal if feasible, it is recognized that most sites will carry out Multi-stage Cluster Sampling.

The WHS sampling frame should cover 100% of the eligible population in the surveyed country. This means that every eligible person in the country has a chance of being included in the survey sample. It also means that particular ethnic groups or geographical areas may not be excluded from the sampling frame.

The sample size of the WHS in each country is 5000 persons (exceptions considered on a by-country basis). An adequate number of persons must be drawn from the sampling frame to account for an estimated amount of non-response (refusal to participate, empty houses etc.). The highest estimate of potential non-response and empty households should be used to ensure that the desired sample size is reached at the end of the survey period. This is very important because if, at the end of data collection, the required sample size of 5000 has not been reached additional persons must be selected randomly into the survey sample from the sampling frame. This is both costly and technically complicated (if this situation is to occur, consult WHO sampling experts for assistance), and best avoided by proper planning before data collection begins.

All steps of sampling, including justification for stratification, cluster sizes, probabilities of selection, weights at each stage of selection, and the computer program used for randomization must be communicated to WHO

STRATIFICATION

Stratification is the process by which the population is divided into subgroups. Sampling will then be conducted separately in each subgroup. Strata or subgroups are chosen because evidence is available that they are related to the outcome (e.g. health, responsiveness, mortality, coverage etc.). The strata chosen will vary by country and reflect local conditions. Some examples of factors that can be stratified on are geography (e.g. North, Central, South), level of urbanization (e.g. urban, rural), socio-economic zones, provinces (especially if health administration is primarily under the jurisdiction of provincial authorities), or presence of health facility in area. Strata to be used must be identified by each country and the reasons for selection explicitly justified.

Stratification is strongly recommended at the first stage of sampling. Once the strata have been chosen and justified, all stages of selection will be conducted separately in each stratum. We recommend stratifying on 3-5 factors. It is optimum to have half as many strata (note the difference between stratifying variables, which may be such variables as gender, socio-economic status, province/region etc. and strata, which are the combination of variable categories, for example Male, High socio-economic status, Xingtao Province would be a stratum).

Strata should be as homogenous as possible within and as heterogeneous as possible between. This means that strata should be formulated in such a way that individuals belonging to a stratum should be as similar to each other with respect to key variables as possible and as different as possible from individuals belonging to a different stratum. This maximises the efficiency of stratification in reducing sampling variance.

MULTI-STAGE CLUSTER SELECTION

A cluster is a naturally occurring unit or grouping within the population (e.g. enumeration areas, cities, universities, provinces, hospitals etc.); it is a unit for which the administrative level has clear, nonoverlapping boundaries. Cluster sampling is useful because it avoids having to compile exhaustive lists of every single person in the population. Clusters should be as heterogeneous as possible within and as homogenous as possible between (note that this is the opposite criterion as that for strata). Clusters should be as small as possible (i.e. large administrative units such as Provinces or States are not good clusters) but not so small as to be homogenous.

In cluster sampling, a number of clusters are randomly selected from a list of clusters. Then, either all members of the chosen cluster or a random selection from among them are included in the sample. Multistage sampling is an extension of cluster sampling where a hierarchy of clusters are chosen going from larger to smaller.

In order to carry out multi-stage sampling, one needs to know only the population sizes of the sampling units. For the smallest sampling unit above the elementary unit however, a complete list of all elementary units (households) is needed; in order to be able to randomly select among all households in the TSU, a list of all those households is required. This information may be available from the most recent population census. If the last census was >3 years ago or the information furnished by it was of poor quality or unreliable, the survey staff will have the task of enumerating all households in the smallest randomly selected sampling unit. It is very important to budget for this step if it is necessary and ensure that all households are properly enumerated in order that a representative sample is obtained.

It is always best to have as many clusters in the PSU as possible. The reason for this is that the fewer the number of respondents in each PSU, the lower will be the clustering effect which

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