The world population surpassed eight billion people in 2022, having doubled from its figure less than 50 years previously. Looking forward, it is projected that the world population will reach nine billion in 2038, and 10 billion in 2060, but it will peak around 10.3 billion in the 2080s before it then goes into decline. Regional variations The global population has seen rapid growth since the early 1800s, due to advances in areas such as food production, healthcare, water safety, education, and infrastructure, however, these changes did not occur at a uniform time or pace across the world. Broadly speaking, the first regions to undergo their demographic transitions were Europe, North America, and Oceania, followed by Latin America and Asia (although Asia's development saw the greatest variation due to its size), while Africa was the last continent to undergo this transformation. Because of these differences, many so-called "advanced" countries are now experiencing population decline, particularly in Europe and East Asia, while the fastest population growth rates are found in Sub-Saharan Africa. In fact, the roughly two billion difference in population between now and the 2080s' peak will be found in Sub-Saharan Africa, which will rise from 1.2 billion to 3.2 billion in this time (although populations in other continents will also fluctuate). Changing projections The United Nations releases their World Population Prospects report every 1-2 years, and this is widely considered the foremost demographic dataset in the world. However, recent years have seen a notable decline in projections when the global population will peak, and at what number. Previous reports in the 2010s had suggested a peak of over 11 billion people, and that population growth would continue into the 2100s, however a sooner and shorter peak is now projected. Reasons for this include a more rapid population decline in East Asia and Europe, particularly China, as well as a prolongued development arc in Sub-Saharan Africa.

1Historical estimates for 1–1950 CE are from refs. [72]–[78]. The UN global census data for 1955–2012 is from ref. [24], which provides an open-access web-based summary of these data. The historical world population estimates are also summarized by Cohen [23] in his Appendix 2. Note that the average population values—which are used to calculate (the distance for a given model population trajectory from the average population estimate/census value for the 1750–2012 data)—exclude duplicate estimates, in which a later study relies on an earlier study's result (e.g., Kremer's extensive use of the earlier estimates from McEvedy & Jones [74], [77]).

Abstract

The fourth Population and Housing Census (PHC4) was based on the characteristics of persons with disabilities under the following three broad headings: (i) The number, prevalence, types, and causes of disability, (ii) The demographic, social and economic characteristics of persons with disabilities, (iii) The characteristics of household heads with disabilities and the living standards of their households. The disability measure used in the 2012 Census was based on the International Classification of Functioning, Disability and Health (ICF) and used the concept of activity limitations e.g. difficulty seeing, hearing, speaking, walking/climbing and learning/concentrating to identify persons with disabilities.

The 2012 PHC was carried out by the National Institute of Statistics of Rwanda (NISR). Field work was conducted from August 16th to August 30th 2012, financial support was provided by the Government of Rwanda, The World Bank (WBG), UKAID, the European Union (EU), One UN, United Nations Population Fund (UNFPA), United Nations Development Programme (UNDP), United Nations Children's Fund (UNICEF) and UN Women.

The specific objectives of the PHC include: 1. The number of persons with disabilities and the prevalence of the different types of disability. 2. The causes of these disabilities. 3. The background characteristics (profile) of persons with disabilities. 4. The household headship rate among people with disabilities. 5. The characteristics of heads of household with disabilities. 6. The household characteristics and the living conditions of households headed by persons with disabilities compared to those headed by persons without a disability.

Geographic coverage

National

Analysis unit

Individuals

Kind of data

Census/enumeration data [cen]

Sampling procedure

As disability affects only a rather small percentage of the population, Census data were particularly valuable in providing detailed evidence on the demographic and socio-economic characteristics of this population group. Sample surveys, unless specifically targeting the population with disabilities, tend to have insufficient sample sizes to examine types and causes of disabilities as well as detailed cross-tabulations of characteristics of the population with disabilities.

Overall, 446,453 persons with disabilities aged 5 and above live in Rwanda according to the 2012 Census, out of which 221,150 are male and 225,303 are female. The count of persons with disabilities by province reflects the geographical distribution of the population in general, with the largest number being found in the Southern Province (122,319) and the lowest in Kigali City (32,170). For the same reason, the number of persons with disabilities is higher in rural areas than in urban areas.

Mode of data collection

Face-to-face [f2f]

Research instrument

Two different types of questionnaires were administered. One for private households and one for institutional households. The questionnaire for private households contained a person record, a household record and a mortality record. The questionnaire for institutional households contained only a person record.

Over the past 23 years, there were constantly more men than women living on the planet. Of the 8.06 billion people living on the Earth in 2023, 4.05 billion were men and 4.01 billion were women. One-quarter of the world's total population in 2024 was below 15 years.

CSD Projection: Population: RPA: Mid Year: All data was reported at 8,469.000 Person th in 2041. This records an increase from the previous number of 8,446.500 Person th for 2040. CSD Projection: Population: RPA: Mid Year: All data is updated yearly, averaging 7,937.100 Person th from Jun 2011 (Median) to 2041, with 31 observations. The data reached an all-time high of 8,469.000 Person th in 2041 and a record low of 7,071.600 Person th in 2011. CSD Projection: Population: RPA: Mid Year: All data remains active status in CEIC and is reported by Census and Statistics Department. The data is categorized under Global Database’s Hong Kong SAR – Table HK.G005: Population: 2012-2041: GHS: RPA: Projection: Census and Statistics Department.

LANDSCAN GLOBAL 2012

Contact: Human Geography, Geospatial Science and Human Security Division, Oak Ridge National Laboratory

Address: landscan@ornl.gov

Online Resource: https://landscan.ornl.gov

Standard Name: ISO 19139 Geographic Information - Metadata - Implementation Specification

Standard Version: 2007

Title: LandScan Global 2012

Publication Date: 2013-07-01

Creation Date: Human Geography, Geospatial Science and Human Security Division, Oak Ridge National Laboratory

Other Citation Details: https://doi.org/10.48690/1524215

Abstract: Using an innovative approach that combines Geographic Information Science, remote sensing technology, and machine learning algorithms, ORNL’s LandScan is the community standard for global population distribution. At 30 arc-second (approximately 1 km) resolution, LandScan is the finest resolution global population distribution data available representing an “ambient population” (average over 24 hours). The LandScan algorithm, an R&D 100 Award Winner, uses spatial data, high-resolution imagery exploitation, and a multi-variable dasymetric modeling approach to disaggregate census counts within an administrative boundary. LandScan population data are spatially explicit - unlike tabular Census data. Since no single population distribution model can account for the differences in spatial data availability, quality, scale, and accuracy as well as the differences in cultural settlement practices, LandScan population distribution models are tailored to match the data conditions and geographical nature of each individual country and region. By modeling an ambient population, LandScan Global captures the full potential activity space of people throughout the course of the day and night rather than just a residential location.

Purpose: LandScan Global was developed on behalf of the U.S. federal government and is used for rapid consequence and risk assessment as well as emergency planning and management.

Credit: Human Geography, Geospatial Science and Human Security Division, Oak Ridge National Laboratory; US DOD

​

Creative Commons Attribution 4.0 International License

https://landscan.ornl.gov/licensing

Oak Ridge National Laboratory

Abstract

The STEP (Skills Toward Employment and Productivity) Measurement program is the first ever initiative to generate internationally comparable data on skills available in developing countries. The program implements standardized surveys to gather information on the supply and distribution of skills and the demand for skills in labor market of low-income countries.

The uniquely-designed Household Survey includes modules that measure the cognitive skills (reading, writing and numeracy), socio-emotional skills (personality, behavior and preferences) and job-specific skills (subset of transversal skills with direct job relevance) of a representative sample of adults aged 15 to 64 living in urban areas, whether they work or not. The cognitive skills module also incorporates a direct assessment of reading literacy based on the Survey of Adults Skills instruments. Modules also gather information about family, health and language.

Geographic coverage

The STEP target population is the urban population aged 15 to 64 included. Sri Lanka sampled both urban and rural areas. Areas are classified as rural or urban based on each country's official definition.

Analysis unit

The units of analysis are the individual respondents and households. A household roster is undertaken at the start of the survey and the individual respondent is randomly selected among all household members aged 15 to 64 included. The random selection process was designed by the STEP team and compliance with the procedure is carefully monitored during fieldwork.

Universe

The target population for the Sri Lanka STEP survey comprised all non-institutionalized persons 15 to 64 years of age (inclusive) living in private dwellings in urban and rural areas of Sri Lanka at the time of data collection. Exclusions The target population excludes: - Foreign diplomats and non-nationals working for international organizations; - People in institutions such as hospitals or prisons; - Collective dwellings or group quarters; - Persons living outside the country at the time of data collection, e.g., students at foreign universities; - Persons who are unable to complete the STEP assessment due to a physical or mental condition, e.g., visual impairment or paralysis.

The sample frame for the selection of first stage sample units was the Census 2011/12

Kind of data

Sample survey data [ssd]

Sampling procedure

The Sri Lanka sample size was 2,989 households. The sample design is a 5 stage stratified sample design. The stratification variable is Urban-Rural indicator.

First Stage Sample The primary sample unit (PSU) is a Grama Niladari (GN) division. The sampling objective was to conduct interviews in 200 GNs, consisting of 80 urban GNs and 120 rural GNs. Because there was some concern that it might not be possible to conduct any interviews in some initially selected GNs (e.g. due to war, conflict, or inaccessibility, for some other reason), the sampling strategy also called for the selection of 60 extra GNs (i.e., 24 urban GNs and 36 rural GNs) to be held in reserve for such eventualities. Hence, a total of 260 GNs were selected, consisting of 200 'initial' GNs and 60 'reserve' GNs. Two GNS from the initial sample of GNs were not accessible and reserve sampled GNs were used instead. Thus a total of 202 GNs were activated for data collection, and interviews were conducted in 200 GNs. The sample frame for the selection of first stage sample units was the list of GNs from the Census 2011/12. Note: The sample of first stage sample units was selected by the Sri Lanka Department of Census & Statistics (DCS) and provided to the World Bank. The DCS selected the GNs with probability proportional to size (PPS), where the measure of size was the number of dwellings in a GN.

Second Stage Sample The second stage sample unit (SSU) is a GN segment, i.e., GN BLOCK. One GN Block was selected from each activated PSU (i.e., GN). According to the Sri Lanka survey firm, each sampled GN was divided into a number of segments, i.e., GN Blocks, with approximately the same number of households, and one GN Block was selected from each sampled GN.

Third Stage Sample The third stage sample unit is a dwelling. The sampling objective was to obtain interviews at 15 dwellings within each selected SSU.

Fourth Stage Sample The fourth stage sample unit is a household. The sampling objective was to select one household within each selected third stage dwelling.

Fifth Stage Sample The fourth stage sample unit is an individual aged 15-64 (inclusive). The sampling objective was to select one individual with equal probability from each selected household.

Please refer to the Sri Lanka STEP Survey Weighting Procedures Summary for additional information on sampling.

Mode of data collection

Face-to-face [f2f]

Research instrument

The STEP survey instruments include: (i) A Background Questionnaire developed by the WB STEP team. (ii) A Reading Literacy Assessment developed by Educational Testing Services (ETS).

All countries adapted and translated both instruments following the STEP Technical Standards: 2 independent translators adapted and translated the Background Questionnaire and Reading Literacy Assessment, while reconciliation was carried out by a third translator. - The survey instruments were both piloted as part of the survey pretest. - The adapted Background Questionnaires are provided in English as external resources. The Reading Literacy Assessment is protected by copyright and will not be published.

Cleaning operations

STEP Data Management Process 1. Raw data is sent by the survey firm 2. The WB STEP team runs data checks on the Background Questionnaire data. - ETS runs data checks on the Reading Literacy Assessment data. - Comments and questions are sent back to the survey firm. 3. The survey firm reviews comments and questions. When a data entry error is identified, the survey firm corrects the data. 4. The WB STEP team and ETS check the data files are clean. This might require additional iterations with the survey firm. 5. Once the data has been checked and cleaned, the WB STEP team computes the weights. Weights are computed by the STEP team to ensure consistency across sampling methodologies. 6. ETS scales the Reading Literacy Assessment data. 7. The WB STEP team merges the Background Questionnaire data with the Reading Literacy Assessment data and computes derived variables.

Detailed information data processing in STEP surveys is provided in the 'Guidelines for STEP Data Entry Programs' document provided as an external resource. The template do-file used by the STEP team to check the raw background questionnaire data is provided as an external resource.

Response rate

The response rate for Sri Lanka (urban and rural) was 63%. (See STEP Methodology Note Table 4).

Sampling error estimates

A weighting documentation was prepared for each participating country and provides some information on sampling errors. Weighting documentation is provided as an external resource.

There is a large body of research on utilizing online activity as a survey of political opinion to predict real world election outcomes. There is considerably less work, however, on using this data to understand topic-specific interest and opinion amongst the general population and specific demographic subgroups, as currently measured by relatively expensive surveys. Here we investigate this possibility by studying a full census of all Twitter activity during the 2012 election cycle along with the comprehensive search history of a large panel of Internet users during the same period, highlighting the challenges in interpreting online and social media activity as the results of a survey. As noted in existing work, the online population is a non-representative sample of the offline world (e.g., the U.S. voting population). We extend this work to show how demographic skew and user participation is non-stationary and difficult to predict over time. In addition, the nature of user contributions varies substantially around important events. Furthermore, we note subtle problems in mapping what people are sharing or consuming online to specific sentiment or opinion measures around a particular topic. We provide a framework, built around considering this data as an imperfect continuous panel survey, for addressing these issues so that meaningful insight about public interest and opinion can be reliably extracted from online and social media data.

Abstract

Tunisia Rural Youth Survey (RYS) was implemented in 2012 in rural areas, building on the data collection in urban areas. The survey was conceived by a group of Tunisian professors and students, called Projet Citoyen, from various universities in Tunisia, particularly from Ecole Superieure des Sciences Economiques et Commerciales de Tunis (ESSECT). Motivated by the observed differences between different parts of the country, including neighborhoods in the Grand Tunis area, the aim of the survey was to scientifically understand urban inequality, with a specific focus on economic opportunities for young people. This effort led to collaboration between the Tunisian National Statistical Office (Institut National de la Statistique or INS), the General Commissariat for Regional Development, and the World Bank. The INS provided the sampling frame, the commissariat, as the main government counterpart, provided guidance for the scope of the survey and its urban focus, and the World Bank provided technical and financial support.

Geographic coverage

Rural Areas The first survey region covered the coast and included coastal governorates in the north and east of the country. The second survey region covered the south and included the southern governorates. The third survey region covered the rural interior of Tunisia and included the remote areas of central and western Tunisia, including the Algerian border.

Universe

The survey covered youth population aged 15-29 years.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Household Survey has a sample size of 1,400 households the entire rural area of Tunisia, as defined by the Tunisian Statistical Office, Institut National de la Statistique (INS). For the purpose of sampling, administrative governorates were grouped into 3 Survey Regions. The data is representative on the level of these Survey Regions, which largely correspond to socio-economically and geographically distinct rural zones. The first survey region covers the Coast and includes coastal governorates in the North and East of the country. The second survey region covers South and includes the southern governorates. The third survey region is covers the rural Interior of Tunisia and includes the remote areas of central and western Tunisia, incl. the Algerian border.

The sample was drawn from the latest available census, the 2004 General Census of Population and Housing, provided by the INS. This census also provided the sampling frame for the corresponding Urban and Peri-Urban Youth Survey. For determining the number of households in rural areas, proportionality of the possible locations was used to ensure representativeness. Because of the overall research focus on youth, the sampling design ensures representativeness of youth population, which is defined by ages 15-29. The proportionality to youth population size is based on the disaggregation of Tunisia into Enumeration Areas (EA). Each EA contains about 100-120 households. In total 70 EAs were randomly selected, with 29 EAs along the Coast, 10 EAs in the South, and 31 EAs in the Interior survey regions. The relative distribution between the survey regions corresponds to their respective shares of youth population. From each of these 70 EAs, 20 households were randomly selected, leading to a total sample size of 1,400 households.

The random sampling of PSUs was performed by experts from the INS, who were also responsible for the sample frame. The drawing of 20 households from each PSU is processed on a systematic and clearly defined approach. A random-walk procedure was conducted for each of the PSUs of the sample, which included 2 separate starting points at opposing ends of the east-west dimension of each PSU, and moving towards the population center of the PSU to allow a full coverage of both centrally and remotely located households.

Mode of data collection

Face-to-face [f2f]

Abstract

The STEP (Skills Toward Employment and Productivity) Measurement program is the first ever initiative to generate internationally comparable data on skills available in developing countries. The program implements standardized surveys to gather information on the supply and distribution of skills and the demand for skills in labor market of low-income countries.

The uniquely-designed Household Survey includes modules that measure the cognitive skills (reading, writing and numeracy), socio-emotional skills (personality, behavior and preferences) and job-specific skills (subset of transversal skills with direct job relevance) of a representative sample of adults aged 15 to 64 living in urban areas, whether they work or not. The cognitive skills module also incorporates a direct assessment of reading literacy based on the Survey of Adults Skills instruments. Modules also gather information about family, health and language.

Geographic coverage

13 major metropolitan areas: Bogota, Medellin, Cali, Baranquilla, Bucaramanga, Cucuta, Cartagena, Pasto, Ibague, Pereira, Manizales, Monteira, and Villavicencio.

Analysis unit

The units of analysis are the individual respondents and households. A household roster is undertaken at the start of the survey and the individual respondent is randomly selected among all household members aged 15 to 64 included. The random selection process was designed by the STEP team and compliance with the procedure is carefully monitored during fieldwork.

Universe

The target population for the Colombia STEP survey is all non-institutionalized persons 15 to 64 years old (inclusive) living in private dwellings in urban areas of the country at the time of data collection. This includes all residents except foreign diplomats and non-nationals working for international organizations.

The following groups are excluded from the sample: - residents of institutions (prisons, hospitals, etc.) - residents of senior homes and hospices - residents of other group dwellings such as college dormitories, halfway homes, workers' quarters, etc. - persons living outside the country at the time of data collection.

Kind of data

Sample survey data [ssd]

Sampling procedure

Stratified 7-stage sample design was used in Colombia. The stratification variable is city-size category.

First Stage Sample The primary sample unit (PSU) is a metropolitan area. A sample of 9 metropolitan areas was selected from the 13 metropolitan areas on the sample frame. The metropolitan areas were grouped according to city-size; the five largest metropolitan areas are included in Stratum 1 and the remaining 8 metropolitan areas are included in Stratum 2. The five metropolitan areas in Stratum 1 were selected with certainty; in Stratum 2, four metropolitan areas were selected with probability proportional to size (PPS), where the measure of size was the number of persons aged 15 to 64 in a metropolitan area.

Second Stage Sample The second stage sample unit is a Section. At the second stage of sample selection, a PPS sample of 267 Sections was selected from the sampled metropolitan areas; the measure of size was the number of persons aged 15 to 64 in a Section. The sample of 267 Sections consisted of 243 initial Sections and 24 reserve Sections to be used in the event of complete non-response at the Section level.

Third Stage Sample The third stage sample unit is a Block. Within each selected Section, a PPS sample of 4 blocks was selected; the measure of size was the number of persons aged 15 to 64 in a Block. Two sample Blocks were initially activated while the remaining two sample Blocks were reserved for use in cases where there was a refusal to cooperate at the Block level or cases where the block did not belong to the target population (e.g., parks, and commercial and industrial areas).

Fourth Stage Sample The fourth stage sample unit is a Block Segment. Regarding the Block segmentation strategy, the Colombia document 'FINAL SAMPLING PLAN (ARD-397)' states "According to the 2005 population and housing census conducted by DANE, the average number of dwellings per block in the 13 large cities or metropolitan areas was approximately 42 dwellings. Based on this finding, the defined protocol was to report those cases in which 80 or more dwellings were present in a given block in order to partition block using a random selection algorithm." At the fourth stage of sample selection, 1 Block Segment was selected in each selected Block using a simple random sample (SRS) method.

Fifth Stage Sample The fifth stage sample unit is a dwelling. At the fifth stage of sample selection, 5582 dwellings were selected from the sampled Blocks/Block Segments using a simple random sample (SRS) method. According to the Colombia document 'FINAL SAMPLING PLAN (ARD-397)', the selection of dwellings within a participant Block "was performed differentially amongst the different socioeconomic strata that the Colombian government uses for the generation of cross-subsidies for public utilities (in this case, the socioeconomic stratum used for the electricity bill was used). Given that it is known from previous survey implementations that refusal rates are highest amongst households of higher socioeconomic status, the number of dwellings to be selected increased with the socioeconomic stratum (1 being the poorest and 6 being the richest) that was most prevalent in a given block".

Sixth Stage Sample The sixth stage sample unit is a household. At the sixth stage of sample selection, one household was selected in each selected dwelling using an SRS method.

Seventh Stage Sample The seventh stage sample unit was an individual aged 15-64 (inclusive). The sampling objective was to select one individual with equal probability from each selected household.

Sampling methodologies are described for each country in two documents and are provided as external resources: (i) the National Survey Design Planning Report (NSDPR) (ii) the weighting documentation (available for all countries)

Mode of data collection

Face-to-face [f2f]

Research instrument

The STEP survey instruments include:

• The background questionnaire developed by the World Bank (WB) STEP team
• Reading Literacy Assessment developed by Educational Testing Services (ETS).

All countries adapted and translated both instruments following the STEP technical standards: two independent translators adapted and translated the STEP background questionnaire and Reading Literacy Assessment, while reconciliation was carried out by a third translator.

The survey instruments were piloted as part of the survey pre-test.

The background questionnaire covers such topics as respondents' demographic characteristics, dwelling characteristics, education and training, health, employment, job skill requirements, personality, behavior and preferences, language and family background.

The background questionnaire, the structure of the Reading Literacy Assessment and Reading Literacy Data Codebook are provided in the document "Colombia STEP Skills Measurement Survey Instruments", available in external resources.

Cleaning operations

STEP data management process:

1) Raw data is sent by the survey firm 2) The World Bank (WB) STEP team runs data checks on the background questionnaire data. Educational Testing Services (ETS) runs data checks on the Reading Literacy Assessment data. Comments and questions are sent back to the survey firm. 3) The survey firm reviews comments and questions. When a data entry error is identified, the survey firm corrects the data. 4) The WB STEP team and ETS check if the data files are clean. This might require additional iterations with the survey firm. 5) Once the data has been checked and cleaned, the WB STEP team computes the weights. Weights are computed by the STEP team to ensure consistency across sampling methodologies. 6) ETS scales the Reading Literacy Assessment data. 7) The WB STEP team merges the background questionnaire data with the Reading Literacy Assessment data and computes derived variables.

Detailed information on data processing in STEP surveys is provided in "STEP Guidelines for Data Processing", available in external resources. The template do-file used by the STEP team to check raw background questionnaire data is provided as an external resource, too.`

Response rate

An overall response rate of 48% was achieved in the Colombia STEP Survey.

Abstract

Cambodia Enterprise Survey 2012 (also known as Investment Climate Survey 2012) was conducted by the World Bank Cambodia country office and Asian Development Bank between February 2012 and February 2013. The survey formed analytical background for the Investment Climate Assessment (ICA) prepared by the World Bank in partnership with the government of Cambodia. The assessment was completed in August 2014.

The objectives of the 2014 Cambodia ICA are to provide up-to-date and fact-based analysis of the business environment for development partners, policymakers in the government, private sector, civil society, and outline priorities for improving business environment and suggest possible policy options for achieving them.

Cambodia Enterprise Survey 2012 was not conducted under the supervision of World Bank's Enterprise Analysis Unit, as other Enterprise Surveys, and therefore small variations in methodology are present.

Data from 472 registered establishments was analyzed. Stratified random sampling was used to select the surveyed businesses. Data was collected using face-to-face interviews.

The topics covered include firm characteristics, access to finance, sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is an establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The universe of the study, is manufacturing, trade, tourism, and selected services. In terms of the International Standard Industrial Classification (Rev. 4) the following groups are included: manufacturing (group C), construction (group F), wholesale and retail trade (group G), transportation and storage (group H), accommodation and food services activities (group I), travel agency, tour operator, reservation service and related activity (79) and computer programming, consultancy and related activities (62). Note that this definition excludes agriculture (group A), mining and quarrying (group B), energy and water supply (groups D and E), and all other services (groups J to U) except for IT (62) and travel agency, tour operator, reservation service and related activity (79) which were included in the population under study.

Kind of data

Sample survey data [ssd]

Sampling procedure

Four levels of stratification were used in this country: sector, establishment size, location and formal status.

Sector stratification was designed in the following way: the universe was stratified into 5 sectors: (1) agroprocessing consisting of manufacture of food, beverages and tobacco, manufacture of wood and wood products and manufacture of rubber products (ISIC Rev. 4 codes 10-12 and 16), (2) manufacturing except agroprocessing (ISIC Rev. 4 group C except 10-12 and 16), (3) trade (ISIC Rev. 4 group G), (4) tourism (ISIC Rev. 4 group I and 79), and (5) other (ISIC Rev. 4 groups F and H and 62).

Size stratification was defined following the standardized definition for the rollout: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposes, the number of employees was defined on the basis of reported number of persons engaged daily in the last week as this was the only information available in the sampling frame.

Location stratification was defined in the five major urban economic centers: Phnom Penh, Siem Reap, Kampong Cham, Sihanouk Ville, and Battambang.

Stratification by formal status is done by distinguishing between firms that have the required registration with the Ministry of Commerce (formal firms) and those that lack the registration (informal firms).

The Establishment Listing 2009 (EL 2009), which was conducted during February-March 2009 by the National Institute of Statistics and the Ministry of Planning of Cambodia, was used as the sampling frame. The EL 2009 aimed at compiling basic statistics on establishments and constructing a comprehensive list of establishments. The establishment list was later used as a frame for the 2011 Economic Census.

The sample of firms that were interviewed for Cambodia Enterprise Survey 2007 was also used in the survey.

In order to have a sufficient number of firms outside Phnom Penh in the sample, firms in Battambang, Siem Reap, Kampong Cham, Sihanouk Ville were oversampled proportionally in each stratum defined by sector, size, and formality, such that the total number of sampled firms from Battambang, Siem Reap, Kampong Cham, Sihanouk Ville was approximately 50% in each of the strata (less if not enough firms outside Phnom Penh are available).

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire included most questions from the traditional Enterprise Survey Core Module. But there were some differences.

First, the survey collected more detailed information on some elements of the investment climate, such as firm registration (question 113), interest in the stock market (questions 102-106), and assessment of different investment locations (questions 107-108).

Second, detailed questions on revenues from supplying products/services and trade and the costs of inputs were asked (questions 132-135). It was found that some firms had difficulty providing this information for the whole year, but they were able to provide this information for subperiods. Also given poor bookkeeping in a lot of Cambodia businesses, firms were asked for the revenues and raw material costs for their main three products and other (remaining) products rather than for the total revenues and raw materials directly.

Third, detailed questions were asked on investment in and replacement values of machinery and equipment (questions 138 and 140). Firms were asked to provide information on components rather than total values, as firms had otherwise even more difficulty answering this question.

Response rate

Survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Attempts were made to contact the establishment for interview at different times/days of the week before a replacement establishment (within the same stratum) was selected for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific quota.

The number of contacted establishments per realized interview was 2.56. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) as well as difficulties to locate firms and changes in sector activity. The number of refusals per contact actually made was 0.32.

Abstract

The Integrated Living Conditions Survey (ILCS), conducted annually by the NSS National Statistical Service of the Republic of Armenia, formed the basis for monitoring living conditions in Armenia. The ILCS is a universally recognized best-practice survey for collecting data to inform about the living standards of households. The ILCS comprises comprehensive and valuable data on the welfare of households and separate individuals which gives the NSS an opportunity to provide the public with up to date information on the population’s income, expenditures, the level of poverty and the other changes in living standards on an annual basis.

Geographic coverage

Urban and rural communities

Analysis unit

• Households;
• Individuals.

Kind of data

Sample survey data [ssd]

Sampling procedure

During the 2001-2003 surveys two-stage random sample was used; the first stage covered the selection of settlements - cities and villages, while the second stage was focused on the selection of households in these settlements. The surveys were conducted on the principle of monthly rotation of households by clusters (sample units). In 2002 and 2003 the number of households was 387 with the sample covering 14 cities and 30 villages in 2002 and 17 cities and 20 villages in 2003.

During the 2004-2006 surveys the sampling frame for the ILCS was built using the database of addresses for the 2001 Population Census; the database was developed with the World Bank technical assistance. The database of addresses of all households in Armenia was divided into 48 strata including 12 communities of Yerevan city. The households from other regions (marzes) were grouped according to the following three categories: big towns with 15,000 and more population; villages, and other towns. Big towns formed 16 strata (the only exception was the Vayots Dzor marz where there are no big towns). The villages and other towns formed 10 strata each. According to this division, a random, two-step sample stratified at marz level was developed. All marzes, as well as all urban and rural settlements were included in the sample population according to the share of population residing in those settlements as percent to the total population in the country. In the first step, the settlements, i.e. primary sample units, were selected: 43 towns out of 48 or 90 percent of all towns in Armenia were surveyed during the year; also 216 villages out of 951 or 23 percent of all villages in the country were covered by the survey. In the second step, the respondent households were selected: 6,816 households (5,088 from urban and 1,728 from rural settlements). As a result, for the first time since 1996 survey data were representative at the marz level.

During the 2007-2012 surveys the sampling frame for ILCS was designed according to the database of addresses for the 2001 Population Census, which was developed with the World Bank technical assistance. The sample consisted of two parts: core sample and oversample.

1) For the creation of core sample, the sample frame (database of addresses of all households in Armenia) was divided into 48 strata including 12 communities of Yerevan city. The households from other regions (marzes) were grouped according to three categories: large towns (with population of 15000 and higher), villages and other towns. Large towns formed by 16 groups (strata), while the villages and towns formed by 10 strata each. According to that division, a random, two-step sample stratified at the marz level was developed. All marzes, as well as all urban and rural settlements were included in the sample population according to the share of households residing in those settlements as percent to the total households in the country. In the first step, using the PPS method the enumeration units (i.e., primary sample units to be surveyed during the year) were selected. 2007 sample includes 48 urban and 18 rural enumeration areas per month. 2) The oversample was drawn from the list of villages included in MCA-Armenia Rural Roads Rehabilitation Project. The enumeration areas of villages that were already in the core sample were excluded from that list. From the remaining enumeration areas 18 enumeration areas were selected per month. Thus, the rural sample size was doubled. 3) After merging the core sample and oversample, the survey households were selected in the second step. 656 households were surveyed per month, from which 368 from urban and 288 from rural settlements. Each month 82 interviewers had conducted field work, and their workload included 8 households per month. In 2007 number of surveyed households was 7,872 (4,416 from urban and 3,456 from rural areas).

For the survey 2013 the sample frame for ILCS was designed in accordance with the database of addresses of all private households in the country developed on basis of the 2001 Population Census results, with the technical assistance of the World Bank. The method of systematic representative probability sampling was used to frame the sample. For the purpose of drawing the sample, the sample frame was divided into 32 strata including 12 communities of Yerevan City (currently, the administrative districts). According to this division, a two-tier sample was drawn stratified by regions and by Yerevan. All regions and Yerevan, as well as all urban and rural communities were included in the sample in accordance to the shares of their resident households within the total number of households in the country. In the first round, enumeration areas - that is primary sample units to be surveyed during the year - were selected. The ILCS 2013 sample included 32 enumeration areas in urban and 16 enumeration areas in rural communities per month. The households to be surveyed were selected in the second round. A total of 432 households were surveyed per month, of which 279 and 153 households from urban and rural communities, respectively. Every month 48 interviewers went on field work with a workload of 9 households per month.

The sample frame for 2014-2016 was designed in accordance with the database of addresses of all private households in the country developed on basis of the 2011 Population Census results, with the technical assistance of the World Bank. The method of systematic representative probability sampling was used to frame the sample.
For drawing the sample, the sample frame was divided into 32 strata including 12 communities of Yerevan City (currently, the administrative districts). According to this division, a two-tier sample was drawn stratified by regions and by Yerevan. All regions and Yerevan, as well as all urban and rural communities were included in the sample in accordance to the shares of their resident households within the total number of households in the country. In the first round, enumeration areas - that is primary sample units to be surveyed during the year - were selected. The ILCS 2014 sample included 30 enumeration areas in urban and 18 enumeration areas in rural communities per month. The method of representative probability sampling was used to frame the sample. At regional level, all communities were grouped into two categories - towns and villages. According to this division, a two-tier sample was drawn stratified by regions and by Yerevan. All regions and Yerevan, as well as all rural and urban communities were included in the sample in accordance to the shares of their resident households within the total number of households in the country. In the first round, enumeration districts - that is primary sample units to be surveyed during the year - were selected. The ILCS 2015 sample included 30 enumeration districts in urban and 18 enumeration districts in rural communities per month.

Mode of data collection

Face-to-face [f2f]

Research instrument

The Questionnaire is filled in by the interviewer during the least five visits to households per month. During face-to-face interviews with the household head or another knowledgeable adult member, the interviewer collects information on the composition and housing conditions of the household, the employment status, educational level and health condition of the members, availability and use of land, livestock, and agricultural machinery, monetary and commodity flows between households, and other information.

The 2012 survey questionnaire had the following sections: (1) "List of Household Members", (2) "Migration", (3) "Housing and Dwelling Conditions", (4) "Employment", (5) "Education", (6) "Agriculture", (7) "Food Production", (8) "Monetary and Commodity Flows between Households", (9) "Health (General) and Healthcare", (10) "Debts", (11) "Subjective Assessment of Living Conditions", (12) "Provision of Services", (13) "Social Assistance", (14) "Households as Employers for Service Personnel", and (15) "Household Monthly Consumption of Energy Resources".

The Diary is completed directly by the household for one month. Every day the household would record all its expenditures on food, non-food products and services, also giving a detailed description of such purchases; e.g. for food products the name, quantity, cost, and place of purchase of the product is recorded. Besides, the household records its consumption of food products received and used from its own land and livestock, as well as from other sources (e.g. gifts, humanitarian aid). Non-food products and services purchased or received for free are also recorded in the diary. Then, the household records its income received during the month. At the end of the month, information on rarely used food products, durable goods and ceremonies is recorded, as well. The records in the diary are verified by the interviewer in the course of 5

This statistic depicts the age distribution in the United States from 2014 to 2024. In 2024, about 17.32 percent of the U.S. population fell into the 0-14 year category, 64.75 percent into the 15-64 age group and 17.93 percent of the population were over 65 years of age. The increasing population of the United States The United States of America is one of the most populated countries in the world, trailing just behind China and India. A total population count of around 320 million inhabitants and a more-or-less steady population growth over the past decade indicate that the country has steadily improved its living conditions and standards for the population. Leading healthier lifestyles and improved living conditions have resulted in a steady increase of the life expectancy at birth in the United States. Life expectancies of men and women at birth in the United States were at a record high in 2012. Furthermore, a constant fertility rate in recent years and a decrease in the death rate and infant mortality, all due to the improved standard of living and health care conditions, have helped not only the American population to increase but as a result, the share of the population younger than 15 and older than 65 years has also increased in recent years, as can be seen above.

The datasets are curated from the Tanzania National Bureau of Statistics (NBS) 2012 Population and Housing Census (PHC) of Tanzania which was preceded by the preparatory geographic work, which involved field visiting of all regions, districts, wards/shehia, villages/mitaa, localities and sub-villages in the country, primarily to create and delineate Enumeration Area boundaries (EAs) so as to produce maps required for census operations. The most important principle followed in delineating an EA was that under no circumstance should an EA overlap the existing administrative boundaries of regions, districts, wards/shehia or villages/mitaa. Adherence to this principle was necessary since the census results were to be presented at the level of these administrative units. The National Bureau of Statistics (NBS) intends to provide a geo-database with spatial and non-spatial information at five levels of geography, to facilitate presentation of data from censuses and other surveys. These levels are regional (level one), district (level two), ward/shehia (level three), villages/mitaa (level four) and enumeration areas (level five). Levels one and two have been put onto the NBS website in June, 2013 for use by various stakeholders, and the web-page will be updated to include other levels of shapefiles when they are ready for use. To learn more, please visit website http://www.nbs.go.tz/nbstz/index.php/english/statistics-by-subject/popul...

In 2023, the population of Vietnam reached approximately 100.3 million, indicating an increase by nearly a million people from the previous year. Vietnam is among the most populated countries in the Asia-Pacific region.

Abstract

The World Values Survey (www.worldvaluessurvey.org) is a global network of social scientists studying changing values and their impact on social and political life, led by an international team of scholars, with the WVS association and secretariat headquartered in Stockholm, Sweden.

The survey, which started in 1981, seeks to use the most rigorous, high-quality research designs in each country. The WVS consists of nationally representative surveys conducted in almost 100 countries which contain almost 90 percent of the world’s population, using a common questionnaire. The WVS is the largest non-commercial, cross-national, time series investigation of human beliefs and values ever executed, currently including interviews with almost 400,000 respondents. Moreover the WVS is the only academic study covering the full range of global variations, from very poor to very rich countries, in all of the world’s major cultural zones.

The WVS seeks to help scientists and policy makers understand changes in the beliefs, values and motivations of people throughout the world. Thousands of political scientists, sociologists, social psychologists, anthropologists and economists have used these data to analyze such topics as economic development, democratization, religion, gender equality, social capital, and subjective well-being. These data have also been widely used by government officials, journalists and students, and groups at the World Bank have analyzed the linkages between cultural factors and economic development.

Geographic coverage

National

Analysis unit

Household Individual

Universe

National Population, Both sexes,18 and more years.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample size: 1030.

Multistage probability sampling up to households with quotas of sex and age. In each selected location (first stage unit), a census radio sample is taken (second stage unit) – previously ordered according to the social and economic level (house head educational level)‐. In each census radio, blocks are selected at random, and following a pre‐established route routine, five (5) interviews are made in different houses. Final sampling unit selection in each house is carried out taking into account age and sex quotas, according to the population census. interviewed. Total number of clusters was 206 and the the sample size was 1030. Substitution was permitted. Household selection: Interviewers are asked to stand in the left superior extreme of each block. From this point, they have to count 5 households and interview number 5. If the interview is successful: they will have to count again 5 households and interview number 5 (10 of the total) and so on. If the interview is unsuccessful, for substitution they will have to try with number 6, 7, 8 and so on. Respondent selection: If the person who answers the door matches the sex and age quota requirements and is willing to answer the questionnaire, he/ she is interviewed. If there is not a match or there is unwillingness, we ask for a willing and matching person. If rejected, the next household, according to the procedure described above is selected. For more on the sampling procedure refer to the Sampling methodology in the related materials.

Mode of data collection

Face-to-face [f2f]

Research instrument

For each wave, suggestions for questions are solicited by social scientists from all over the world and a final master questionnaire is developed in English. Since the start in 1981 each successive wave has covered a broader range of societies than the previous one. Analysis of the data from each wave has indicated that certain questions tapped interesting and important concepts while others were of little value. This has led to the more useful questions or themes being replicated in future waves while the less useful ones have been dropped making room for new questions.

The questionnaire is translated into the various national languages and in many cases independently translated back to English to check the accuracy of the translation. In most countries, the translated questionnaire is pre-tested to help identify questions for which the translation is problematic. In some cases certain problematic questions are omitted from the national questionnaire.

WVS requires implementation of the common questionnaire fully and faithfully, in all countries included into one wave. Any alteration to the original questionnaire has to be approved by the EC. Omission of no more than a maximum of 12 questions in any given country can be allowed.

Response rate

• Total number of starting names/addresses: 5425
• No contact at selected address: 1949
• No contact with selected person: 782
• Refusal at selected address: 815
• Full productive interview: 1030
• Partially productive interview: 15
• Households in which quota was achieved: 839

This dataset includes the yearly population count as recorded by the World Bank from the year 1960 to 2012. The population count for the years 2015 and 2020 are forecasted data.

Abstract

The Saint Lucia Multiple Indicator Cluster Survey (MICS) is a nationally representative household survey developed under the guidance of the United Nations Children's Fund (UNICEF) to provide internationally comparable and up-to-date information on the country's children and women. The survey measure key indicators used to monitor progress towards the Millennium Development Goals (MDGs) and will assist in policy decisions and government interventions.

The Saint Lucia MICS was conducted in 2012 as part of the fourth global round of MICS (MICS4), with the implementing agencies within the Government of Saint Lucia being the Ministry of Social Transformation, Local Government and Community Empowerment (MoST) and the Central Statistics Office (CSO) in collaboration with the Ministry of Health, Wellness, Human Services and Gender Relations (MoH), Ministry of Education, Human Resource Development and Labour (MoE) and other government departments as well as non-government agencies.

The Saint Lucia MICS was conducted using a sample of 2,000 households from both rural and urban areas in all the country's districts. Information was collected from 1,718 households about 1,253 women aged 15-49 years and 291 children under the age of 5 living in the households. A set of three questionnaires - a household questionnaire, a questionnaire for women aged 15-49years and a questionnaire for children under 5 - was used to conduct face-to-face interviews, and each yielded response rates of over 90 percent.

Geographic coverage

National

Analysis unit

• individuals
• households

Universe

The survey covered all de jure household members (usual residents), all women aged between 15-49 years, all children under 5 living in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The primary objective of the sample design for the Saint Lucia Multiple Indicator Cluster Survey (MICS) was to produce statistically reliable estimates of most indicators both at the national level and for urban and rural areas.

There are 10 geographic districts in Saint Lucia. Five of these districts contain less than 3,000 households: Canaries (786 households), Anse la Raye (2,162 households), Soufriere (2,875 households), Choiseul (2,069 households) and Laborie (2,180 households). Due to the small size of so many districts it is not realistic to provide estimates at the district level. There is no obvious grouping of districts into a smaller sub-set of three or four regions, which would have made sampling more manageable. Thus urban and rural population were selected as the sampling strata for the purpose of the MICS.

The 2010 Population and Household Census is used as the sample frame for the Saint Lucia MICS and census EDs are defined as the primary sampling units (PSUs)/ clusters. These were selected from each of the sampling strata by using systematic pps (probability proportional to size) sampling procedures based on the estimated sizes of the enumeration districts (clusters) from the 2010 Census.

There were no obvious sources of data that could provide indicative values of some of the key MICS indicators. The CSO has not conducted any previous surveys of this nature, although the Core Wealth Indicator Questionnaire Survey (CWIQ) conducted in 2004 provided estimates showed almost 100 percent coverage for prenatal care and for professional attendance at delivery.

The average number of households selected per cluster was determined as 20 households based on a number of considerations including the design effect, the budget available and the time that would be needed per team to complete one cluster. Dividing the total number of households (2,000) by the number of sample households per cluster, it was calculated that 100 sample clusters would be selected.

The 2010 Population and Household Census was used as the sample frame for the selection of clusters. Census ED/clusters were defined as primary sampling units (PSUs) and selected from each of the sampling strata by using systematic pps sampling procedures, based on the estimated sizes of the enumeration areas from the 2010 Census.

To select the sample of clusters, EDs/clusters within each stratum were listed in order by district and by ED/cluster number within each district. In cases where larger EDs/clusters had been subdivided previously, these parts were listed next to each other (even if they did not have adjacent ED numbers). EDs/clusters with less than 20 households were combined with the ED/cluster immediately preceding them in the list, and if the small ED/cluster was the first ED/cluster shown in a district it was combined with the next ED/cluster on the list. The first stage of sampling was completed by selecting the required number of EDs/clusters from each stratum (urban and rural).

The sampling procedures are more fully described in "Multiple Indicator Cluster Survey 2012 - Final Report" pp.122-125.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires for the Generic MICS were structured questionnaires based on the MICS4 model questionnaire with some modifications and additions. Household questionnaires were administered in each household, which collected various information on household members including sex, age and relationship. The household questionnaire includes household listing form, education, water and sanitation, household characteristics, child labour, child discipline, hand washing and salt iodization.

In addition to a household questionnaire, questionnaires were administered in each household for women age 15-49 and children under age five. For children, the questionnaire was administered to the mother or primary caretaker of the child.

The women's questionnaire includes woman's background, access to mass media and use of information and communications technology, child mortality without birth history (abridged module used to calculate births in the last 2 years), desire for last birth, maternal and newborn health, post-natal health checks, contraception, unmet need for contraception, attitudes toward domestic violence, marriage/union, sexual behavior, HIV/AIDS, alcohol use.

The children's questionnaire includes child's age, birth registration, early childhood development, breastfeeding, care of illness, and anthropometry.

Cleaning operations

Data were entered on four desktop computers using the Census and Survey Processing System (CSPro) software by four data entry operators, one questionnaire administrator, one secondary editor and a data entry supervisor. In order to ensure quality control, all questionnaires were double entered (entered and verified) and internal consistency checks were performed. Procedures and standard programmes developed under the global MICS4 programme and adapted to the Saint Lucia questionnaire were used throughout. Data processing began simultaneously with data collection in April 2012 and was completed in June 2012. Data were analysed using the Statistical Package for Social Sciences (SPSS) software program, Version 18, and the model syntax and tabulation plans developed by UNICEF were used for this purpose.

Response rate

The 2,000 households selected were found to contain 2,009 households. All the households were visited and 1,800 were found to be occupied. Of these, 1,718 households were successfully interviewed, yielding a household response rate of 95 percent. In the interviewed households, 1,341 eligible women (aged 15-49 years) were identified. Of these, 1,253 women were successfully interviewed, yielding a response rate of 93 percent within interviewed households. There were 300 eligible children under age 5 listed in the household questionnaire, and questionnaires were completed for 291 of these children (a response rate of 97 percent). Overall response rates of 89 and 93 percent were calculated for the women's and under-5's interviews respectively. The response rates were similar for both the urban and rural areas, yielding rates of over 90 percent for the household, women and children under 5.

Sampling error estimates

Sampling errors are a measure of the variability between the estimates from all possible samples. The extent of variability is not known exactly but can be estimated statistically from the survey data.

The following sampling error measures are presented in this appendix for each of the selected indicators: - Standard error (se): Sampling errors are usually measured in terms of standard errors for particular indicators (means, proportions, etc). Standard error is the square root of the variance of the estimate. The Taylor linearization method is used for the estimation of standard errors. - Coefficient of variation (se/r) is the ratio of the standard error to the value of the indicator and is a measure of the relative sampling error. - Design effect (deff) is the ratio of the actual variance of an indicator, under the sampling method used in the survey, to the variance calculated under the assumption of simple random sampling. The square root of the design effect (deft) is used to show the efficiency of the sample design in relation to the precision. A deft value of 1.0 indicates that the sample design is as efficient as a simple random sample, while a deft value above 1.0 indicates an increase in the standard error due to the use of a more complex sample design. - Confidence limits are calculated to show the interval within which the true value for the population can be reasonably assumed to fall, with a specified level of confidence. For any given statistic calculated from the survey,

This raster dataset is a subdirectory containing the ArcGIS grid (world_admin1) of the countries/sub-countries. These are the standard Level 1 Administrative Boundaries. The data table (VAT) contains the cell areas in the field: [Area]. The units are square kilometers. The Value Field simply represents a row number for a specific Latitude. All cells on the same row have the same area. Cell areas are largest at the equator and smallest at the poles. Each year the models incorporate administrative boundary changes, refine the spatial precision of international and sub-national administrative boundaries, and reconcile temporal census information and administrative boundary inconsistencies. The administrative unit level by which the census data is distributed varies considerably in size and spatial precision from country to country. The number of administrative units per nation and spatial fidelity of the boundaries are considered in the model parameterization process. Nations with few, but very large administrative areas require different weights in the model parameters to allocate representative populations to their appropriate locations. Generally, smaller administrative boundaries lead to better population distribution – if the boundaries are spatially accurate. However, small administrative areas that are poorly geo-referenced or spatially characterized actually induce population distribution errors. To mitigate these errors, where possible, analysts will merge poor sub-province boundaries to the province level and distribute the entire province population according to the population likelihood locations determined by the model rather than constrict population distributions to incorrect locations. Very small administrative or enumeration areas equivalent to US census blocks or block groups have unintended consequences for modeling an ambient population. Since the populations associated with census tables are places of residence, commercial and industrial areas may have zero or very low populations associated with them. Thus the output would be reflective of a residential only population distribution instead of an ambient population distribution. This dataset is part of the LandScan 2012 Global Population Database.

Sri Lanka Population: 15 Years & Above: 30-39 yo data was reported at 2,821,713.000 Person in Dec 2017. This records a decrease from the previous number of 2,913,336.000 Person for Sep 2017. Sri Lanka Population: 15 Years & Above: 30-39 yo data is updated quarterly, averaging 2,899,739.000 Person from Mar 2016 (Median) to Dec 2017, with 8 observations. The data reached an all-time high of 2,979,075.000 Person in Jun 2017 and a record low of 2,821,713.000 Person in Dec 2017. Sri Lanka Population: 15 Years & Above: 30-39 yo data remains active status in CEIC and is reported by Department of Census and Statistics. The data is categorized under Global Database’s Sri Lanka – Table LK.G002: Population: MYPE 2012: Fifteen Years and Above.