Worldwide, Papua New Guinea was the country with the highest rural population in terms of share of the country's population. As of 2023, ***** percent of the Asian country's inhabitants lived in rural areas. Burundi followed in second with ***** percent, whereas ***** percent of Liechtenstein's population lived in rural areas that year. Over the past decades, the share of the global population living in rural areas decreased.
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The average for 2023 based on 47 countries was 26.9 percent. The highest value was in Liechtenstein: 85.38 percent and the lowest value was in Gibraltar: 0 percent. The indicator is available from 1960 to 2023. Below is a chart for all countries where data are available.
In 2023, approximately ** percent of the population in Papua New Guinea were living in rural areas. In comparison, approximately ***** percent of the population in Japan were living in rural areas that year. Urbanization and development Despite the desirable outcomes that urbanization entails, these rapid demographic shifts have also brought about unintended changes. For instance, in countries like India, rapid urbanization has led to unsustainable and crowded cities, with **** of the urban population in India estimated to live in slums. In China, population shifts from rural to urban areas have aggravated regional economic disparities. For example, the migration of workers into coastal cities has made possible the creation of urban clusters of immense economic magnitude, with the Yangtze River Delta city cluster accounting for about a ******of the country’s gross domestic product. Megacities and their future Home to roughly 60 percent of the world’s population, the Asia-Pacific region also shelters most of the globe’s largest urban agglomerations. Megacities, a term used for cities or urban areas with a population of over ten million people, are characterized by high cultural diversity and advanced infrastructure. As a result, they create better economic opportunities, and they are often hubs of innovation. For instance, many megacities in the Asia-Pacific region offer high local purchasing power to their residents. Despite challenges like pollution, income inequality, or the rising cost of living, megacities in the Asia-Pacific region have relatively high population growth rates and are expected to expand.
The share of people living in rural areas decreases with a higher level of income. In 2024, less than ********* of the population in high-income countries lived in rural areas, whereas the figures were around ***percent among people living in the least developed and low-income countries. In 2023, Papua New Guinea was the country where the highest share of the population lived in rural areas.
This statistic shows the ten countries with the largest increase in the size of the rural population between 2018 and 2015. Based on forecasted population figures, the rural population of Ethiopia is projected to be around ** million more in 2050 than it was in 2018.
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The average for 2023 based on 24 countries was 37.14 percent. The highest value was in Saint Lucia: 80.83 percent and the lowest value was in Bermuda: 0 percent. The indicator is available from 1960 to 2023. Below is a chart for all countries where data are available.
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The average for 2023 based on 47 countries was 53.88 percent. The highest value was in Burundi: 85.22 percent and the lowest value was in Gabon: 8.97 percent. The indicator is available from 1960 to 2023. Below is a chart for all countries where data are available.
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The Rural Access Index (RAI) is a measure of access, developed by the World Bank in 2006. It was adopted as Sustainable Development Goal (SDG) indicator 9.1.1 in 2015, to measure the accessibility of rural populations. It is currently the only indicator for the SDGs that directly measures rural access.The RAI measures the proportion of the rural population that lives within 2 km of an all-season road. An all-season road is one that is motorable all year, but may be temporarily unavailable during inclement weather (Roberts, Shyam, & Rastogi, 2006). This dataset implements and expands on the most recent official methodology put forward by the World Bank, ReCAP's 2019 RAI Supplemental Guidelines. This is, to date, the only publicly available application of this method at a global scale.MethodologyReCAP's methodology provided new insight on what makes a road all-season and how this data should be handled: instead of removing unpaved roads from the network, the ones that are classified as unpaved are to be intersected with topographic and climatic conditions and, whenever there’s an overlap with excess precipitation and slope, a multiplying factor ranging from 0% to 100% is applied to the population that would access to that road. This present dataset developed by SDSN's SDG Transformation Centre proposes that authorities ability to maintain and remediate road conditions also be taken into account.Data sourcesThe indicator relies on four major items of geospatial data: land cover (rural or urban), population distribution, road network extent and the “all-season” status of those roads.Land cover data (urban/rural distinction)Since the indicator measures the acess rural populations, it's necessary to define what is and what isn't rural. This dataset uses the DegUrba Methodology, proposed by the United Nations Expert Group on Statistical Methodology for Delineating Cities and Rural Areas (United Nations Expert Group, 2019). This approach has been developed by the European Commission Global Human Settlement Layer (GHSL-SMOD) project, and is designed to instil some consistency into the definitions based on population density on a 1-km grid, but adjusted for local situations.Population distributionThe source for population distribution data is WorldPop. This uses national census data, projections and other ancillary data from countries to produce aggregated, 100 m2 population data. Road extentTwo widely recognized road datasets are used: the real-time updated crowd-sourced OpenStreetMap (OSM) or the GLOBIO’s 2018 GRIP database, which draws data from official national sources. The reasons for picking the latter are mostly related to its ability to provide information on the surface (pavement) of these roads, to the detriment of the timeliness of the data, which is restrained to the year 2018. Additionally, data from Microsoft Bing's recent Road Detection project is used to ensure completeness. This dataset is completely derived from machine learning methods applied over satellite imagery, and detected 1,165 km of roads missing from OSM.Roads’ all-season statusThe World Bank's original 2006 methodology defines the term all-season as “… a road that is motorable all year round by the prevailing means of rural transport, allowing for occasional interruptions of short duration”. ReCAP's 2019 methodology makes a case for passability equating to the all-season status of a road, along with the assumption that typically the wet season is when roads become impassable, especially so in steep roads that are more exposed to landslides.This dataset follows the ReCAP methodology by creating an passability index. The proposed use of passability factors relies on the following three aspects:• Surface type. Many rural roads in LICs (and even in large high-income countries including the USA and Australia) are unpaved. As mentioned before, unpaved roads deteriorate rapidly and in a different way to paved roads. They are very susceptible to water ingress to the surface, which softens the materials and makes them very vulnerable to the action of traffic. So, when a road surface becomes saturated and is subject to traffic, the deterioration is accelerated. • Climate. Precipitation has a significant effect on the condition of a road, especially on unpaved roads, which predominate in LICs and provide much of the extended connectivity to rural and poor areas. As mentioned above, the rainfall on a road is a significant factor in its deterioration, but the extent depends on the type of rainfall in terms of duration and intensity, and how well the roadside drainage copes with this. While ReCAP suggested the use of general climate zones, we argue that better spatial and temporal resolutions can be acquired through the Copernicus Programme precipitation data, which is made available freely at ~30km pixel size for each month of the year.• Terrain. The gradient and altitude of roads also has an effect on their accessibility. Steep roads become impassable more easily due to the potential for scour during heavy rainfall, and also due to slipperiness as a result of the road surface materials used. Here this is drawn from slope calculated from SRTM Digital Terrain data.• Road maintenance. The ability of local authorities to remediate damaged caused by precipitation and landslides is proposed as a correcting factor to the previous ones. Ideally this would be measured by the % of GDP invested in road construction and maintenance, but this isn't available for all countries. For this reason, GDP per capita is adopted as a proxy instead. The data range is normalized in such a way that a road maxed out in terms of precipitation and slope (accessibility score of 0.25) in a country at the top of the GDP per capita range is brought back at to the higher end of the accessibility score (0.95), while the accessibility score of a road meeting the same passability conditions in a country which GDP per capita is towards the lower end is kept unchanged.Data processingThe roads from the three aforementioned datasets (Bing, GRIP and OSM) are merged together to them is applied a 2km buffer. The populations falling exclusively on unpaved road buffers are multiplied by the resulting passability index, which is defined as the normalized sum of the aforementioned components, ranging from 0.25 to. 0.9, with 0.95 meaning 95% probability that the road is all-season. The index applied to the population data, so, when calculated, the RAI includes the probability that the roads which people are using in each area will be all-season or not. For example, an unpaved road in a flat area with low rainfall would have an accessibility factor of 0.95, as this road is designed to be accessible all year round and the environmental effects on its impassability are minimal.The code for generating this dataset is available on Github at: https://github.com/sdsna/rai
In 2024, Belize had the highest share of the population living in rural areas in Central America, with over half the residents. Followed closely behind by Guatemala, with almost ** percent of the population in rural regions. In 2022, Nicaragua ranked as the third most populated country in the region, with over *********** inhabitants.
This statistic shows the twenty countries with the largest rural populations worldwide in 2018. In 2018, the rural population of India was around ***** million people.
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Rural population (% of total population) in India was reported at 63.13 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. India - Rural population - actual values, historical data, forecasts and projections were sourced from the World Bank on August of 2025.
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Oman OM: Rural Population data was reported at 762,201.000 Person in 2017. This records a decrease from the previous number of 774,333.000 Person for 2016. Oman OM: Rural Population data is updated yearly, averaging 625,841.000 Person from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 786,442.000 Person in 2014 and a record low of 461,255.000 Person in 1960. Oman OM: Rural Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Oman – Table OM.World Bank.WDI: Population and Urbanization Statistics. Rural population refers to people living in rural areas as defined by national statistical offices. It is calculated as the difference between total population and urban population. Aggregation of urban and rural population may not add up to total population because of different country coverages.; ; World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects: 2018 Revision.; Sum;
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The average for 2022 based on 73 countries was 45 percent. The highest value was in Hungary: 100 percent and the lowest value was in Democratic Republic of the Congo: 0.5 percent. The indicator is available from 2000 to 2022. Below is a chart for all countries where data are available.
The Afrobarometer project assesses attitudes and public opinion on democracy, markets, and civil society in several sub-Saharan African.This dataset was compiled from the studies in Round 3 of the Afrobarometer survey, conducted from 2005-2006 in 18 African countries (Benin, Botswana, Cape Verde, Ghana, Kenya, Lesotho, Madagascar, Malawi, Mali, Mozambique, Namibia, Nigeria, Senegal, South Africa, Tanzania, Uganda, Zambia, Zimbabwe).
The Afrobarometer surveys have national coverage
Botswana Lesotho Malawi Namibia South Africa Zambia Zimbabwe Ghana Mali Nigeria Tanzania Uganda Cape Verde Mozambique Senegal Kenya Benin Madagascar
Basic units of analysis that the study investigates include: individuals and groups
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 survey data [ssd]
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
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Rural population (% of total population) in Nigeria was reported at 44.97 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. Nigeria - Rural population - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.
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
Botswana Lesotho Malawi Namibia South Africa Zambia Zimbabwe
Basic units of analysis that the study investigates include: individuals and groups
Sample survey data [ssd]
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
India's total population reached nearly 1.43 billion people as of 2023, making the country by far the most populous throughout the Asia-Pacific region. Contrastingly, Micronesia had a total population of around 115 thousand people in the same year. The demographics of APAC Asia-Pacific, made up of many different countries and regions, is the most populated region across the globe. Being home to a significant number of megacities, and with the population ever-increasing, the region is unsurprisingly expected to have the largest urban population by 2050. However, as of 2021, the majority of Asia-Pacific countries had rural populations greater than 50 percent. Population densities Despite China being the most populated country across the region, it fell in the middle of Asia-Pacific regions in terms of population density. On the other hand, Macao, Singapore, and Hong Kong all had the highest population densities across the Asia-Pacific region. These three Asia-Pacific regions also ranked among the top four densest populations worldwide.
The project uses public opinion polling to gather and then analyze a sample that represents the entire population of each of four different countries of Central Asia: Kazakhstan, Kyrgyzstan, Tajikistan, and Uzbekistan.
The project uses public opinion polling to gather and then analyze a sample that represents the entire population of the country.
Sample survey data [ssd]
For all four Central Asian countries in this survey, the sampling procedure is a three-stage stratified clustered one. Census data on the territorial dispersion of the population is used as the base to start the sampling methodology. The sampling procedure takes the total population of the country, considers geographic units within the country as either urban or rural, and then develops random procedures to select who to survey in three stages: first by randomly selected smaller geographic urban and units in each province (the primary sampling units or PSUs), second randomly chosing households within these units, and third, to randomly select which household member to interview in each household.
The sampling frame used to divide these four countries into smaller geographic units to randomly sample from differs slightly for each Central Asian country, based on differences in data availability on the population of the country and its dispersion. Subsequent sections explain the sampling methodology used and how this sampling frame differs in each country. Then all four countries have PSUs, random selection of households, and random sampling of individuals within households using the same methods.
Uzbekistan has 12 provinces, the Republic of Karakalpakstan, and the city of Tashkent. Each province has several districts for a total of 168 districts in the country. Each district has a number of cities, small towns and villages. Of the 233 cities and small towns in Uzbekistan, 76 cities are subordinated directly to provinces due to their importance. The population of Uzbekistan was 25,523,000 people, of which 9,410,700 (37%) were urban residents, and the 16,112,300 (63%) were rural residents as of May 2002. Several districts, practically inaccessible from an absence of transportation or remote location, are excluded from the sampling frame. These two cities, one small town, and one district in Navoi have a population of 95,300, 0.9% of the urban population and 0.1% of the rural population of the country - a total of 0.4% of the population of Uzbekistan is excluded from the sampling frame.
The sampling frame for Uzbekistan has primary sampling units (PSUs) of two types: - MK ("Mahallinskiy Komitet") - town makhalla committee. Makhallas are the traditional neighborhood committees which have been revived (and in some urban areas artificially created) by the Uzbek government; - SSG ("Selskiy Skhod Grazhdan") - village council. This type has been used for rural areas in all recent surveys.
The sampling scheme then has the following three standard stages: - proportionate stratification by population of provinces; - for all provinces (include Tashkent city as urban stratum): - proportionate stratification by urban/rural population within provinces; - PPS-sampling of PSUs within urban/rural strata; - sequential random sampling of households (Secondary Sampling Units - SSUs) in selected PSUs; - Kish grid based sampling of respondents. Thus, the sampling is three-stage stratified clustered sampling.
There are 63 PSUs are selected from the sampling frames, with the number of respondents to be interviewed in each varying between 17 and 29 in different PSUs.
The sample distribution by the main demographic characteristics can be compared with data of Statistical Department of Republic of Uzbekistan from January 1, 2002.
Face-to-face [f2f]
To perform questioning, the following documents have been prepared (attached): - Questionnaire (in Uzbek, in Russian and in Karakalpak languages). - Sets of cards (in Uzbek, in Russian and in Karakalpak languages). - Forms of the respondent's sampling and records of the households' visits with Kish's cards (in Russian and Uzbek languages). - Forms of the households' sampling in selected points of questioning (in Russian language). - Sampling instructions (in Russian and Uzbek languages). - Instructions on households and respondents' sampling (in Russian and Uzbek languages). - Examples how to fill in sampling forms - Covering letter to local authorities of 2 types (in Cyrillic and in Latin).
During the fieldwork, 766 cases of non-response were registered (non-eligible units are excluded from this count). The average response rate is about 66% (1,500 of 2,266 attempts). Generally, the non-response case was registered if an interviewer had made up to two failed callbacks. the response rate in rural areas is higher than in urban areas. In Tashkent city very much high level of refusals is observed (response rate barely about 38%). This is caused mainly by the following factors: a) rural residents are more willing to cooperate; b) they are less active in sense of movement, therefore more reachable; c) the theme of interview sets people on the alert; d) population registration and register maintenance in cities are generally worse which leads to poor quality sampling frames. The influence of first two factors is aligned lately because of a falling of a scale of living of people.
40% of all the causes in the urban areas is the "household members refused contacting respondent" (cause 7), as compared with the corresponding 31.2% in the rural areas. This cause has the most spread for urban people and the second at the prevalence for rural areas (about 31% of all causes of non-response), because the theme of interview (the internal politic, interethnic problem etc.) makes people mistrustful and situation with the criminality (especially in the cities) is very complicated. Otherwise, cause 10 ("not at home for a long time") is second at the prevalence for urban areas (about 37%) and first for rural areas (about 39% of all non-response causes). This cause is spread for urban and rural people because they migrate in searches of earnings. The similar reasons called cause 3 "nobody at home" and 4 "respondent was not at home by that time" (8.2% and 2,3% for urban and 5.1% and 3.6% for rural areas accordingly). Besides for these causes there is one more explanation - employment of urban population and "cotton campaign" for rural population. The causes 6, 8, and 9 met not frequently. Therefore we may not make any conclusions. The sampling frame quality is revealed by comparing the share of cause 11 "address was not found, does not exist"- 4.8% in the urban areas versus 6.4% in the rural. In the urban areas 2.8% of the non-response are "Address is not residential" (cause 12). In the rural areas this cause makes 4.2% of all causes of non-response. In most cases it originates from that a household, in order to get an additional land plot from a makhalla committee for running subsidiary economy, declares itself to be actually consisting of two households - parents' and a new, young one. Then the makhalla committee registers a new household and allocates a plot. However, this "household" continues living with the parents, making the new address not residential. Most urban cases are connected with fitting apartments for small offices, cafes, renting to foreigners, etc. More apartments in the cities are thrown (owners have left in searching of earnings).
Like many large countries, Indonesia has difficulty attracting doctors to service in rural and remote areas. To guide the creation of incentives for service in these areas, the authors analyze two sets of data about physicians: 1) the locations chosen by graduating medical students before and after a major change in the incentive system, and 2) survey data on choices among hypothetical assignments differing in compensation, career prospects, and amenities at various locations. Their findings suggest that: a) The current policy of offering specialist training is incentive enough to make doctors from Java willing to serve in remote areas. (It is not necessary to also offer a civil service appointment.) But providing specialist training as an incentive to work in remote areas is not only expensive, but potentially inefficient, since specialist practice and rural public health management require different skills and attitudes. b) Moderately (but not extremely) remote areas can be staffed using modest cash incentives. c) Doctors from the Outer Islands are far more willing to serve in remote areas than their counterparts from Java. So, it may be worthwhile increasing the representation of Outer Island students in medical schools (perhaps through scholarships and assistance in pre-university preparation).
The Problem: Providing health personnel to rural and remote areas
Health problems are often the most acute in rural and remote areas, especially in developing countries. But it is difficult to get health professionals to serve in these areas. Understandably, most physicians prefer to settle in urban areas offering opportunities for professional development, education and other amenities for their families, and attractive employment opportunities. As a result, there is a mismatch between the geographic distribution of physicians and the perceived need for them.
Context: Indonesia and other large countries
The geographic distribution of physicians is of particular concern for Indonesia. Indonesia's vast size and difficult geography present a tremendous challenge to health services delivery. It is difficult to place doctors in remote island, mountain, or forest locations with few amenities, no opportunities for private practice, and poor communications with the rest of the country. In addition, Indonesia's development goals strongly emphasize equity across regions, with particular stress on improving health status in the most remote and poorly served areas. The country's success in placing health centers in all of its more than 6000 subdistricts only increases the challenge of ensuring that those centers are staffed. These problems are not unique to Indonesia. The geographic distribution of doctors has been a concern in the US, Canada, Norway, and many other countries.
How can we persuade doctors to serve in remote areas? How do we find out? One method is to offer incentive packages. For this to be affordable, it is necessary to fine-tune the incentives so as to be attractive as possible.
What incentives should be offered? Cash? Career development? Housing? How long should tours of duty be? How should the incentives differ according to the difficulty of the posting? These questions can be answered by experiment. But experiments are expensive and difficult to set up, and require years for evaluation.
An alternative is to use survey techniques to assess doctors' reactions to potential incentive packages. This approach, often used in commercial marketing, is here applied to policy analysis.
The surveys
Three surveys of Indonesian physicians' preferences concerning postings and incentives were conducted: - Medical students' actual choice of postings to satisfy their compulsory contract service requirement. We describe the impact of a major change in the incentives to serve in very remote areas. - Medical students' choices among hypothetical bundles of incentives and post characteristics. Using survey techniques, we can asssess the attractiveness of a large range of yet-untried options. - Doctors currently performing contract service at rural and remote health centers were surveyed about conditions under which they would renew their contract.
Survey of Medical Students
Final year medical students were surveyed at fourteen medical schools. The purpose of the survey was to assess students' preferences over hypothetical assignments differing in locational amenities, compensation packages, and career paths.
Survey of Serving Doctors
The mail-out survey of serving doctors was designed to complement the survey of graduating medical students. Contract doctors (recent graduates performing compulsory service) were surveyed at health centers nationwide, excluding urban areas, and very remote areas. The purpose of the mail-out survey was to determine the conditions under which serving doctors would be willing to extend their contracts. At the time of the survey, such extensions were not allowed. It was hypothesized that - at least for a subset of doctors - modest additional incentives might elicit a substantial increase in willingness to extend. The preferences of pre-service medical students are based on very fragmentary information, and these preferences may well change as a result of field experience.
Survey of Medical Students: Final year medical students were surveyed at fourteen medical schools.
Survey of Serving Doctors: The mail-out survey of serving doctors was designed to complement the survey of graduating medical students. Contract doctors (recent graduates performing compulsory service) were surveyed at health centers nationwide, excluding urban areas, and very remote areas.
Sample survey data [ssd]
Mail Questionnaire [mail]
See the following appendixes to the Working paper:
In 2024, the rural population in France reached 17.96 percent of the total. In 2023, Europe was ranked the third continent worldwide in terms of degree of urbanization. 75 percent of the European population was living in cities in 2023, but this figure is expected to decrease by 2050. In France, studies have shown that most of the population lives in urban areas, but many French citizens seem to aspire to live in the countryside. France: an urban country From 2006 to 2020, the share of French residents living in rural areas kept decreasing, going from roughly 22.6 percent in 2006 to slightly more than 19 percent in 2020. According to Insee, a municipality is rural when it does not reach the threshold of 2,000 inhabitants. In France, more than 13 million individuals were living in the countryside in 2016. In comparison, the urban population amounted to 53 million people that same year and reached more than 80 percent of the total in 2022. The advantages of the countryside A survey conducted by Ifop in 2018 showed that 41 percent of French people wanted to live in a rural town. Despite common beliefs, the countryside appears to have a lot to offer. In addition to a more pleasant living environment, the employment situation seems to be more advantageous in French rural areas. In 2020, the percentage of unemployed people reached 5.5 percent in rural areas, compared to less than nine percent in cities. Similarly, the percentage of labor force participants is higher in rural areas.
Worldwide, Papua New Guinea was the country with the highest rural population in terms of share of the country's population. As of 2023, ***** percent of the Asian country's inhabitants lived in rural areas. Burundi followed in second with ***** percent, whereas ***** percent of Liechtenstein's population lived in rural areas that year. Over the past decades, the share of the global population living in rural areas decreased.