This layer shows particulate matter in the air sized 2.5 micrometers of smaller (PM 2.5). The data is aggregated from NASA Socioeconomic Data and Applications Center (SEDAC) gridded data into country boundaries, administrative 1 boundaries, and 50 km hex bins. The unit of measurement is micrograms per cubic meter.The layer shows the annual average PM 2.5 from 1998 to 2016, highlighting if the overall mean for an area meets the World Health Organization guideline of 10 micrograms per cubic meter annually. Areas that don't meet the guideline and are above the threshold are shown in red, and areas that are lower than the guideline are in grey.The data is averaged for each year and over the the 19 years to provide an overall picture of air quality globally. Some of the things we can learn from this layer:What is the average annual PM 2.5 value over 19 years? (1998-2016)What is the annual average PM 2.5 value for each year from 1998 to 2016?What is the statistical trend for PM 2.5 over the 19 years? (downward or upward)Are there hot spots (or cold spots) of PM 2.5 over the 19 years?How many people are impacted by the air quality in an area?What is the death rate caused by the joint effects of air pollution?Choose a different attribute to symbolize in order to reveal any of the patterns above.A space time cube was performed on a multidimensional mosaic version of the data in order to derive an emerging hot spot analysis, trends, and a 19-year average. The country and administrative 1 layers provide a population-weighted PM 2.5 value to emphasize which areas have a higher human impact. Citations:van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2018. Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR, 1998-2016. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H4ZK5DQS. Accessed 1 April 2020van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2016. Global Estimates of Fine Particulate Matter Using a Combined Geophysical-Statistical Method with Information from Satellites. Environmental Science & Technology 50 (7): 3762-3772. https://doi.org/10.1021/acs.est.5b05833.Boundaries and population figures:Antarctica is excluded from all maps because it was not included in the original NASA grids.50km hex bins generated using the Generate Tessellation tool - projected to Behrmann Equal Area projection for analysesPopulation figures generated using Zonal Statistics from the World Population Estimate 2016 layer from ArcGIS Living Atlas.Administrative boundaries from World Administrative Divisions layer from ArcGIS Living Atlas - projected to Behrmann Equal Area projection for analyses and hosted in Web MercatorSources: Garmin, CIA World FactbookPopulation figures generated using Zonal Statistics from the World Population Estimate 2016 layer from ArcGIS Living Atlas.Country boundaries from Esri 2019 10.8 Data and Maps - projected to Behrmann Equal Area projection for analyses and hosted in Web Mercator. Sources: Garmin, Factbook, CIAPopulation figures attached to the country boundaries come from the World Population Estimate 2016 Sources Living Atlas layer Data processing notes:NASA's GeoTIFF files for 19 years (1998-2016) were first brought into ArcGIS Pro 2.5.0 and put into a multidimensional mosaic dataset.For each geography level, the following was performed: Zonal Statistics were run against the mosaic as a multidimensional layer.A Space Time Cube was created to compare the 19 years of PM 2.5 values and detect hot/cold spot patterns. To learn more about Space Time Cubes, visit this page.The Space Time Cube is processed for Emerging Hot Spots where we gain the trends and hot spot results.The layers are hosted in Web Mercator Auxillary Sphere projection, but were processed using an equal area projection: Behrmann. If using this layer for analysis, it is recommended to start by projecting the data back to Behrmann.The country and administrative layer were dissolved and joined with population figures in order to visualize human impact.The dissolve tool ensures that each geographic area is only symbolized once within the map.Country boundaries were generalized post-analysis for visualization purposes. The tolerance used was 700m. If performing analysis with this layer, find detailed country boundaries in ArcGIS Living Atlas. To create the population-weighted attributes on the country and Admin 1 layers, the hex value population values were used to create the weighting. Within each hex bin, the total population figure and average PM 2.5 were multiplied.The hex bins were converted into centroids and the PM2.5 and population figures were summarized within the country and Admin 1 boundaries.The summation of the PM 2.5 values were then divided by the total population of each geography. This population value was determined by summarizing the population values from the hex bins within each geography.Some artifacts in the hex bin layer as a result of the input NASA rasters. Because the gridded surface is created from multiple satellites, there are strips within some areas that are a result of satellite paths. Some areas also have more of a continuous pattern between hex bins as a result of the input rasters.Within the country layer, an air pollution attributable death rate is included. 2016 figures are offered by the World Health Organization (WHO). Values are offered as a mean, upper value, lower value, and also offered as age standardized. Values are for deaths caused by all possible air pollution related diseases, for both sexes, and all age groups. For more information visit this page, and here for methodology. According to WHO, the world average was 95 deaths per 100,000 people.To learn the techniques used in this analysis, visit the Learn ArcGIS lesson Investigate Pollution Patterns with Space-Time Analysis by Esri's Kevin Bulter and Lynne Buie.
This layer shows particulate matter in the air sized 2.5 micrometers of smaller (PM 2.5). The data is aggregated from NASA Socioeconomic Data and Applications Center (SEDAC) gridded data into country boundaries, administrative 1 boundaries, and 50 km hex bins. The unit of measurement is micrograms per cubic meter.The layer shows the annual average PM 2.5 from 1998 to 2016, highlighting if the overall mean for an area meets the World Health Organization guideline of 10 micrograms per cubic meter annually. Areas that don't meet the guideline and are above the threshold are shown in red, and areas that are lower than the guideline are in grey.The data is averaged for each year and over the the 19 years to provide an overall picture of air quality globally. Some of the things we can learn from this layer:What is the average annual PM 2.5 value over 19 years? (1998-2016)What is the annual average PM 2.5 value for each year from 1998 to 2016?What is the statistical trend for PM 2.5 over the 19 years? (downward or upward)Are there hot spots (or cold spots) of PM 2.5 over the 19 years?How many people are impacted by the air quality in an area?What is the death rate caused by the joint effects of air pollution?Choose a different attribute to symbolize in order to reveal any of the patterns above.A space time cube was performed on a multidimensional mosaic version of the data in order to derive an emerging hot spot analysis, trends, and a 19-year average. The country and administrative 1 layers provide a population-weighted PM 2.5 value to emphasize which areas have a higher human impact. Citations:van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2018. Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR, 1998-2016. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H4ZK5DQS. Accessed 1 April 2020van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2016. Global Estimates of Fine Particulate Matter Using a Combined Geophysical-Statistical Method with Information from Satellites. Environmental Science & Technology 50 (7): 3762-3772. https://doi.org/10.1021/acs.est.5b05833.Boundaries and population figures:Antarctica is excluded from all maps because it was not included in the original NASA grids.50km hex bins generated using the Generate Tessellation tool - projected to Behrmann Equal Area projection for analysesPopulation figures generated using Zonal Statistics from the World Population Estimate 2016 layer from ArcGIS Living Atlas.Administrative boundaries from World Administrative Divisions layer from ArcGIS Living Atlas - projected to Behrmann Equal Area projection for analyses and hosted in Web MercatorSources: Garmin, CIA World FactbookPopulation figures generated using Zonal Statistics from the World Population Estimate 2016 layer from ArcGIS Living Atlas.Country boundaries from Esri 2019 10.8 Data and Maps - projected to Behrmann Equal Area projection for analyses and hosted in Web Mercator. Sources: Garmin, Factbook, CIAPopulation figures attached to the country boundaries come from the World Population Estimate 2016 Sources Living Atlas layer Data processing notes:NASA's GeoTIFF files for 19 years (1998-2016) were first brought into ArcGIS Pro 2.5.0 and put into a multidimensional mosaic dataset.For each geography level, the following was performed: Zonal Statistics were run against the mosaic as a multidimensional layer.A Space Time Cube was created to compare the 19 years of PM 2.5 values and detect hot/cold spot patterns. To learn more about Space Time Cubes, visit this page.The Space Time Cube is processed for Emerging Hot Spots where we gain the trends and hot spot results.The layers are hosted in Web Mercator Auxillary Sphere projection, but were processed using an equal area projection: Behrmann. If using this layer for analysis, it is recommended to start by projecting the data back to Behrmann.The country and administrative layer were dissolved and joined with population figures in order to visualize human impact.The dissolve tool ensures that each geographic area is only symbolized once within the map.Country boundaries were generalized post-analysis for visualization purposes. The tolerance used was 700m. If performing analysis with this layer, find detailed country boundaries in ArcGIS Living Atlas. To create the population-weighted attributes on the country and Admin 1 layers, the hex value population values were used to create the weighting. Within each hex bin, the total population figure and average PM 2.5 were multiplied.The hex bins were converted into centroids and the PM2.5 and population figures were summarized within the country and Admin 1 boundaries.The summation of the PM 2.5 values were then divided by the total population of each geography. This population value was determined by summarizing the population values from the hex bins within each geography.Some artifacts in the hex bin layer as a result of the input NASA rasters. Because the gridded surface is created from multiple satellites, there are strips within some areas that are a result of satellite paths. Some areas also have more of a continuous pattern between hex bins as a result of the input rasters.Within the country layer, an air pollution attributable death rate is included. 2016 figures are offered by the World Health Organization (WHO). Values are offered as a mean, upper value, lower value, and also offered as age standardized. Values are for deaths caused by all possible air pollution related diseases, for both sexes, and all age groups. For more information visit this page, and here for methodology. According to WHO, the world average was 95 deaths per 100,000 people.To learn the techniques used in this analysis, visit the Learn ArcGIS lesson Investigate Pollution Patterns with Space-Time Analysis by Esri's Kevin Bulter and Lynne Buie.
This layer shows particulate matter in the air sized 2.5 micrometers of smaller (PM 2.5). The data is aggregated from NASA Socioeconomic Data and Applications Center (SEDAC) gridded data into country boundaries, administrative 1 boundaries, and 50 km hex bins. The unit of measurement is micrograms per cubic meter.The layer shows the annual average PM 2.5 from 1998 to 2016, highlighting if the overall mean for an area meets the World Health Organization guideline of 10 micrograms per cubic meter annually. Areas that don't meet the guideline and are above the threshold are shown in red, and areas that are lower than the guideline are in grey.The data is averaged for each year and over the the 19 years to provide an overall picture of air quality globally. Some of the things we can learn from this layer:What is the average annual PM 2.5 value over 19 years? (1998-2016)What is the annual average PM 2.5 value for each year from 1998 to 2016?What is the statistical trend for PM 2.5 over the 19 years? (downward or upward)Are there hot spots (or cold spots) of PM 2.5 over the 19 years?How many people are impacted by the air quality in an area?What is the death rate caused by the joint effects of air pollution?Choose a different attribute to symbolize in order to reveal any of the patterns above.A space time cube was performed on a multidimensional mosaic version of the data in order to derive an emerging hot spot analysis, trends, and a 19-year average. The country and administrative 1 layers provide a population-weighted PM 2.5 value to emphasize which areas have a higher human impact. Citations:van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2018. Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR, 1998-2016. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H4ZK5DQS. Accessed 1 April 2020van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2016. Global Estimates of Fine Particulate Matter Using a Combined Geophysical-Statistical Method with Information from Satellites. Environmental Science & Technology 50 (7): 3762-3772. https://doi.org/10.1021/acs.est.5b05833.Boundaries and population figures:Antarctica is excluded from all maps because it was not included in the original NASA grids.50km hex bins generated using the Generate Tessellation tool - projected to Behrmann Equal Area projection for analysesPopulation figures generated using Zonal Statistics from the World Population Estimate 2016 layer from ArcGIS Living Atlas.Administrative boundaries from World Administrative Divisions layer from ArcGIS Living Atlas - projected to Behrmann Equal Area projection for analyses and hosted in Web MercatorSources: Garmin, CIA World FactbookPopulation figures generated using Zonal Statistics from the World Population Estimate 2016 layer from ArcGIS Living Atlas.Country boundaries from Esri 2019 10.8 Data and Maps - projected to Behrmann Equal Area projection for analyses and hosted in Web Mercator. Sources: Garmin, Factbook, CIAPopulation figures attached to the country boundaries come from the World Population Estimate 2016 Sources Living Atlas layer Data processing notes:NASA's GeoTIFF files for 19 years (1998-2016) were first brought into ArcGIS Pro 2.5.0 and put into a multidimensional mosaic dataset.For each geography level, the following was performed: Zonal Statistics were run against the mosaic as a multidimensional layer.A Space Time Cube was created to compare the 19 years of PM 2.5 values and detect hot/cold spot patterns. To learn more about Space Time Cubes, visit this page.The Space Time Cube is processed for Emerging Hot Spots where we gain the trends and hot spot results.The layers are hosted in Web Mercator Auxillary Sphere projection, but were processed using an equal area projection: Behrmann. If using this layer for analysis, it is recommended to start by projecting the data back to Behrmann.The country and administrative layer were dissolved and joined with population figures in order to visualize human impact.The dissolve tool ensures that each geographic area is only symbolized once within the map.Country boundaries were generalized post-analysis for visualization purposes. The tolerance used was 700m. If performing analysis with this layer, find detailed country boundaries in ArcGIS Living Atlas. To create the population-weighted attributes on the country and Admin 1 layers, the hex value population values were used to create the weighting. Within each hex bin, the total population figure and average PM 2.5 were multiplied.The hex bins were converted into centroids and the PM2.5 and population figures were summarized within the country and Admin 1 boundaries.The summation of the PM 2.5 values were then divided by the total population of each geography. This population value was determined by summarizing the population values from the hex bins within each geography.Some artifacts in the hex bin layer as a result of the input NASA rasters. Because the gridded surface is created from multiple satellites, there are strips within some areas that are a result of satellite paths. Some areas also have more of a continuous pattern between hex bins as a result of the input rasters.Within the country layer, an air pollution attributable death rate is included. 2016 figures are offered by the World Health Organization (WHO). Values are offered as a mean, upper value, lower value, and also offered as age standardized. Values are for deaths caused by all possible air pollution related diseases, for both sexes, and all age groups. For more information visit this page, and here for methodology. According to WHO, the world average was 95 deaths per 100,000 people.To learn the techniques used in this analysis, visit the Learn ArcGIS lesson Investigate Pollution Patterns with Space-Time Analysis by Esri's Kevin Bulter and Lynne Buie.
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License information was derived automatically
In the last century, the global population has increased by billions of people. And it is still growing. Job opportunities in large cities have caused an influx of people to these already packed locations. This has resulted in an increase in population density for these cities, which are now forced to expand in order to accommodate the growing population. Population density is the average number of people per unit, usually miles or kilometers, of land area. Understanding and mapping population density is important. Experts can use this information to inform decisions around resource allocation, natural disaster relief, and new infrastructure projects. Infectious disease scientists use these maps to understand the spread of infectious disease, a topic that has become critical after the COVID-19 global pandemic.While a useful tool for decision and policymakers, it is important to understand the limitations of population density. Population density is most effective in small scale places—cities or neighborhoods—where people are evenly distributed. Whereas at a larger scale, such as the state, region, or province level, population density could vary widely as it includes a mix of urban, suburban, and rural places. All of these areas have a vastly different population density, but they are averaged together. This means urban areas could appear to have fewer people than they really do, while rural areas would seem to have more. Use this map to explore the estimated global population density (people per square kilometer) in 2020. Where do people tend to live? Why might they choose those places? Do you live in a place with a high population density or a low one?
Population Health Management Market Size 2025-2029
The population health management market size is forecast to increase by USD 19.40 billion at a CAGR of 10.7% between 2024 and 2029.
The Population Health Management Market is experiencing significant growth, driven by the increasing adoption of healthcare IT solutions and the rising focus on personalized medicine. The implementation of electronic health records (EHRs) and other digital health technologies has enabled healthcare providers to collect and analyze large amounts of patient data, facilitating proactive care and population health management. Moreover, the trend towards personalized medicine, which aims to tailor healthcare treatments to individual patients based on their unique genetic makeup and health history, is further fueling the demand for PHM solutions. However, the high cost of installing and implementing these platforms poses a significant challenge for market growth.
Despite this, the potential benefits of PHM, including improved patient outcomes, reduced healthcare costs, and enhanced population health, make it an attractive area for investment and innovation. Companies seeking to capitalize on these opportunities must navigate the challenges of data privacy and security, interoperability, and integration with existing healthcare systems. By addressing these challenges and focusing on delivering actionable insights from patient data, PHM solution providers can help healthcare organizations optimize their resources, improve patient care, and ultimately, improve population health.
What will be the Size of the Population Health Management Market during the forecast period?
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The market is experiencing significant growth, driven by the increasing focus on accountable care organizations (ACOs) and payer organizations to improve health outcomes and reduce costs. Healthcare professionals are leveraging big data, data analytics services, and clinical data integration to develop personalized care plans and implement intervention strategies for various populations. Telehealth services have become essential in population health management, enabling care coordination, health promotion, and health navigation for patients. Health equity is a critical factor in population health management, with a growing emphasis on addressing disparities and ensuring equal access to care.
Data security and interoperability standards are essential in population health management, as healthcare providers exchange sensitive patient data for risk adjustment, care pathways, and quality reporting. Data mining and data visualization tools are used to identify health behavior changes and lifestyle modifications, leading to better health outcomes. Consumer health technology, such as patient engagement tools and wearable technology, are playing an increasingly important role in population health management. Health coaching and evidence-based medicine are intervention strategies used to prevent diseases and improve health outcomes. In summary, the market in the US is characterized by the adoption of precision medicine, health literacy, clinical guidelines, and personalized care plans.
The market is driven by the need for care coordination, data analytics, and patient engagement to improve health outcomes and reduce costs. The use of data security, data mining, and interoperability standards ensures the effective exchange and utilization of health data.
How is this Population Health Management Industry segmented?
The population health management industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.
Component
Software
Services
End-user
Large enterprises
SMEs
Delivery Mode
On-Premise
Cloud-Based
Web-Based
On-Premise
Cloud-Based
End-Use
Providers
Payers
Employer Groups
Government Bodies
Providers
Payers
Employer Groups
Geography
North America
US
Canada
Europe
France
Germany
Italy
UK
APAC
China
India
Japan
South Korea
Rest of World
By Component Insights
The software segment is estimated to witness significant growth during the forecast period.
The market's software segment is experiencing significant growth and innovation. Healthcare organizations are utilizing these solutions to effectively manage and enhance the health outcomes of diverse populations. The software component incorporates various tools that collect, analyze, and utilize health data for informed decision-making. Population health management platforms gather data from multiple sources, such as electronic health records, claims data, and patient-generated data. These platforms employ advanced analytics to generate valuable insi
Facebook’s Survey on Gender Equality at Home generates a global snapshot of women and men’s access to resources, their time spent on unpaid care work, and their attitudes about equality. This survey covers topics about gender dynamics and norms, unpaid caregiving, and life during the COVID-19 pandemic. Aggregated data is available publicly on Humanitarian Data Exchange (HDX). De-identified microdata is also available to eligible nonprofits and universities through Facebook’s Data for Good (DFG) program. For more information, please email dataforgood@fb.com.
This survey is fielded once a year in over 200 countries and 60 languages. The data can help researchers track trends in gender equality and progress on the Sustainable Development Goals.
The survey was fielded to active Facebook users.
Sample survey data [ssd]
Respondents were sampled across seven regions: - East Asia and Pacific; Europe and Central Asia - Latin America and Caribbean - Middle East and North Africa - North America - Sub-Saharan Africa - South Asia
For the purposes of this report, responses have been aggregated up to the regional level; these regional estimates form the basis of this report and its associated products (Regional Briefs). In order to ensure respondent confidentiality, these estimates are based on responses where a sufficient number of people responded to each question and thus where confidentiality can be assured. This results in a sample of 461,748 respondents.
The sampling frame for this survey is the global database of Facebook users who were active on the platform at least once over the past 28 days, which offers a number of advantages: It allows for the design, implementation, and launch of a survey in a timely manner. Large sample sizes allow for more questions to be asked through random assignment of modules, avoiding respondent fatigue. Samples may be drawn from diverse segments of the online population. Knowledge of the overall sampling frame allowed for more rigorous probabilistic sampling techniques and non-response adjustments than is typical for online and phone surveys
Internet [int]
The survey includes a total of 75 questions, split across into the following sections: - Basic demographics and gender norms - Decision making and resource allocation across household members - Unpaid caregiving - Additional household demographics and COVID-19 impact - Optional questions for special groups (e.g. students, business owners, the employed, and the unemployed)
Questions were developed collaboratively by a team of economists and gender experts from the World Bank, UN Women, Equal Measures 2030, and Ladysmith. Some of the questions have been borrowed from other surveys that employ alternative modes of administration (e.g., face-to-face, telephone surveys, etc.); this allows for comparability and identification of potential gaps and biases inherent to Facebook and other online survey platforms. As such, the survey also generates methodological insights that are useful to researchers undertaking alternative modes of data collection during the COVID-19 era.
In order to avoid “survey fatigue,” wherein respondents begin to disengage from the survey content and responses become less reliable, each respondent was only asked to answer a subset of questions. Specifically, each respondent saw a maximum of 30 questions, comprising demographics (asked of all respondents) and a set of additional questions randomly and purposely allocated to them.
Response rates to online surveys vary widely depending on a number of factors including survey length, region, strength of the relationship with invitees, incentive mechanisms, invite copy, interest of respondents in the topic and survey design.
Any survey data is prone to several forms of error and biases that need to be considered to understand how closely the results reflect the intended population. In particular, the following components of the total survey error are noteworthy:
Sampling error is a natural characteristic of every survey based on samples and reflects the uncertainty in any survey result that is attributable to the fact that not the whole population is surveyed.
Other factors beyond sampling error that contribute to such potential differences are frame or coverage error and nonresponse error.
Survey Limitations The survey only captures respondents who: (1) have access to the Internet (2) are Facebook users (3) opt to take this survey through the Facebook platform. Knowledge of the overall demographics of the online population in each region allows for calibration such that estimates are representative at this level. However, this means the results only tell us something about the online population in each region, not the overall population. As such, the survey cannot generate global estimates or meaningful comparisons across countries and regions, given the heterogeneity in internet connectivity across countries. Estimates have only been generated for respondents who gave their gender as male or female. The survey included an “other” option but very few respondents selected it, making it impossible to generate meaningful estimates for non-binary populations. It is important to note that the survey was not designed to paint a comprehensive picture of household dynamics but rather to shed light on respondents’ reported experiences and roles within households
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.
India
Household Individual
National Population, Both sexes,18 and more years
Sample survey data [ssd]
Sample size: 2002
As part of the India component of the World Values Survey, it was decided to conduct 2000 face-toface interviews. A rigorous scientific method was employed to generate the target sample for the study. The survey was conducted in 18 states of India, which covered nearly 97 % of the nations population.
40 districts in the country were identified for the purpose of the survey (a little less than 1/10 of the districts in the country: 466 districts as per 1991 census). The 40 districts were spread across the 18 states, in which the survey was conducted keeping in mind the population of the states, even while ensuring that the survey was conducted in at least one district in each of the sampled states.
Within each state, the district/s in which the survey was to be conducted was selected by circular sampling (PPS: Probability Proportion to Size). Once all the 40 districts were selected, the Lok Sabha (Lower House of the Indian Parliament)constituency that covered the district was identified. If the sampled district had more than one Lok Sabha constituency, the one, which had a larger proportion of the districts electorate, was selected.
The next stage in the sampling process was the selection of 2 State Assembly (Lower House of the State Legislature) constituencies in each of the sampled 40 Lok Sabha constituencies. Circular Sampling (PPS: Probability Proportion to Size) was once again employed. Thus, 80 Assembly Constituencies in 40 Lok Sabha constituencies (in 40 districts) were selected. Subsequently, a polling booth area in each of the 80 sampled Assembly constituencies was selected by simple circular sampling method.
The number of respondents to be interviewed in each state was determined on the basis of the proportion of the states share in the national population. This was equally divided among the polling booth areas that were sampled in a state. The number of respondents in the polling booth area was the same within a state, but varied from state to state. In a polling booth area, the respondents were selected from the electoral rolls (voters list) by circular sampling with a random first number.
While drawing up the random list of respondents to be interviewed in every sampled polling booth area, the number of target respondents was increased by nearly 20 %. This was done in view of the fact that the field investigators were required to interview only those respondents whose names were included in the sample list. No replacements or alteration in the list of sampled respondents was permitted. Previous survey experience has shown that it has never been possible for the investigator to interview all those included in the list of sampled respondents. A wide range of factors is responsible for the same. The investigators were told to make every effort to interview all those included in the list of respondents. In the event of the investigator not being able to complete an interview, they were asked to record the reason for the same. Such a rigorous method of sampling was followed in order to obtain as representative a national sample as possible. The analysis of the sample profile clearly indicates that the detailed and objective criteria employed has eminently served its purpose as the sample mirrors the nations social, economic, political, cultural and religious diversity.
Remarks about sampling: - Final numbers of clusters or sampling points: No clusters - Sample unit from office sampling: Named individual
Face-to-face [f2f]
The questionnaire was translated into ten Indian languages by a specialist translator. A few modifications were undertaken in response categories for the scale answer questions. It was then back-translated to English. For each of the 10 languages the pre test was done on a sample of 5 each. There were several concepts and questions difficult to translate: more specifically v75/76/v103/v175/v208/v212/v229/. These problems were solved by developing new phrases close to the original statement or using it in the context of social reality The sample was designed to be representative of the entire adult population, i.e. 18 years and older, of your country. The lower age cut-off for the sample was 18 and there was not any upper age cut-off for the sample.
The following table presents completion rate results: - Total number of starting names/addresses 2354 - Addresses which could not be traced at all 56 - Addresses established as empty, demolished or containing no private dwellings 39 - Selected respondent too sick/incapacitated to participate 29 - Selected respondent away during survey period 62 - Selected respondent had inadequate understanding of language of survey 27 - No contact at selected address 76 - No contact with selected person 31 - Refusal at selected address 34 - Full productive interviews 2002
Estimated Error: 2,2
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The global dental explorers market size was valued at approximately USD 1.3 billion in 2023 and is poised to reach an estimated USD 2.1 billion by 2032, reflecting a compound annual growth rate (CAGR) of 5.6% from 2024 to 2032. The market growth is mainly driven by the increasing prevalence of dental disorders and the rising awareness about oral health globally. The surge in dental visits, coupled with advancements in dental diagnostic tools, are significant contributors to this market's robust expansion.
One of the primary growth factors for the dental explorers market is the growing aging population, which is more susceptible to dental problems such as tooth decay, gum disease, and oral cancer. The World Health Organization (WHO) predicts that by 2050, the global population aged 60 years and older will total 2 billion, up from 900 million in 2015. This demographic shift significantly increases the demand for dental services and preventive care, thereby driving the need for dental explorers. Moreover, the increased focus on preventive dental care to avoid complex procedures is further accelerating market growth.
Technological advancements in dental diagnostic tools are another major growth driver. Innovations such as digital dental explorers, which provide more accurate and detailed assessments of dental health, are becoming more prevalent. These tools help in early detection of dental issues, thereby improving patient outcomes. The integration of such advanced diagnostic tools in dental practices is not only enhancing the precision of dental examinations but also reducing the time required for diagnosis, which is highly beneficial for both patients and dental professionals.
Additionally, the rising dental tourism industry is contributing to the growth of the dental explorers market. Countries like India, Thailand, and Mexico have become popular destinations for dental procedures due to their cost-effective services and high-quality care. This trend is not only boosting the market in those regions but also prompting technological advancements and investments in dental equipment, including explorers, in order to cater to the influx of international patients. Moreover, dental insurance coverage has been expanding, allowing more people to afford regular dental check-ups and treatments, thus driving the demand for dental explorers.
From a regional perspective, North America holds a dominant position in the dental explorers market, driven by high healthcare expenditure, advanced healthcare infrastructure, and a significant focus on oral health. Europe follows as the second-largest market due to similar healthcare standards and an increasing geriatric population. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by rising healthcare investments, increasing awareness about dental health, and growing medical tourism. Countries like China and India are anticipated to be key contributors to this regional growth.
The dental explorers market is segmented into double-ended explorers and single-ended explorers. Double-ended explorers are widely used in dental practices due to their versatility and efficiency. These tools feature different types of explorer tips on either end, allowing dentists to perform multiple diagnostic procedures without the need to switch instruments. The convenience offered by double-ended explorers makes them a popular choice in both diagnostic and surgical applications. This segment is expected to maintain a significant market share over the forecast period, driven by its broad usage and the ongoing need for efficient dental diagnostic tools.
Single-ended explorers, on the other hand, have a more specialized application but are equally important in dental diagnostics. These tools are typically used for precise examination of specific areas in the mouth. They are particularly useful in detecting areas of decay, calculus, and other abnormalities. The single-ended explorers market is anticipated to grow steadily, supported by increasing demand for specialized dental diagnostic tools that offer precision. Innovations in single-ended explorers, such as ergonomic designs and advanced materials for better durability and comfort, are also contributing to the market's growth.
The choice between double-ended and single-ended
The Multiple Indicator Cluster Survey (MICS) is a household survey programme developed by UNICEF to assist countries in filling data gaps for monitoring human development in general and the situation of children and women in particular. MICS is capable of producing statistically sound, internationally comparable estimates of social indicators. The current round of MICS is focused on providing a monitoring tool for the Millennium Development Goals (MDGs), the World Fit for Children (WFFC), as well as for other major international commitments, such as the United Nations General Assembly Special Session (UNGASS) on HIV/AIDS and the Abuja targets for malaria.
Survey Objectives The 2005 Georgia Multiple Indicator Cluster Survey has as its primary objectives: - To provide up-to-date information for assessing the situation of children and women in Georgia; - To furnish data needed for monitoring progress toward goals established in the Millennium Declaration, the goals of A World Fit For Children (WFFC), and other internationally agreed upon goals, as a basis for future action; - To contribute to the improvement of data and monitoring systems in Georgia and to strengthen technical expertise in the design, implementation, and analysis of such systems.
Survey Content MICS questionnaires are designed in a modular fashion that can be easily customized to the needs of a country. They consist of a household questionnaire, a questionnaire for women aged 15-49 and a questionnaire for children under the age of five (to be administered to the mother or caretaker). Other than a set of core modules, countries can select which modules they want to include in each questionnaire.
Survey Implementation The survey was carried out by the State Department of Statistics of Georgia and the National Centre for Disease Control of Georgia, with the support and assistance of UNICEF.
Technical assistance and training for the MICS surveys is provided through a series of regional workshops, covering questionnaire content, sampling and survey implementation; data processing; data quality and data analysis; report writing and dissemination.
The survey is nationally representative and covers the whole of Georgia.
Households (defined as a group of persons who usually live and eat together)
De jure household members (defined as memers of the household who usually live in the household, which may include people who did not sleep in the household the previous night, but does not include visitors who slept in the household the previous night but do not usually live in the household)
Women aged 15-49
Children aged 0-4
The survey covered all de jure household members (usual residents), all women aged 15-49 years resident in the household, and all children aged 0-4 years (under age 5) resident in the household.
Sample survey data [ssd]
The principal objective of the sample design was to provide current and reliable estimates on a set of indicators covering the four major areas of the World Fit for Children declaration, including promoting healthy lives; providing quality education; protecting against abuse, exploitation and violence; and combating HIV/AIDS. The population covered by the 2005 MICS is defined as the universe of all women aged 15-49 and all children aged under 5. A sample of households was selected and all women aged 15-49 identified as usual residents of these households were interviewed. In addition, the mother or the caretaker of all children aged under 5 who were usual residents of the household were also interviewed about the child.
The 2005 MICS collected data from a nationally representative sample of households, women and children. The primary focus of the 2005 MICS was to prodvide estimates of key population and health, education, child protection and HIV related indicators for the country as a whole, and for urban and rural areas separately. In additon, the sample was designed to provide estimates for each of the 11 regions for key indicators. Georgia is devided into 11 regions: Tbilisi, Kakheti, Mtskheta - Mtianeti, Shida Kartli, Kvemo Kartli, Samtskhe - Javakheti, Racha - Lechkhumi and Kvemo, Svaneti, Imereti, Guria, Samegrelo and Zemo Svaneti, Adjara. The sample frame for this survey was based on the list of enumeration areas developed from the 2002 population census.
The primary sampling unit (PSU), the cluster for the 2005 MICS, is defined on the basis of the enumeration areas from the census frame. The minimum PSU size in Georgia is 11 households and the maximum PSU size is 188 households. The average PSU size is 70.8 households. While constructing the sampling frame the PSUs that are smaller then 30 households is merged with the neighbouring PSUs to achieve the minimum size of PSU equalling to 30 households. Although the original sample design for the Georgia MICS 2005 called for approximately 14000 households with an equal number of clusters (42) of households in each of the 11 regions, stratified into urban and rural areas, this sample design was changed to use a more complicated stratification design, with unequal numbers of clusters in each stratum. The rationale for this was for the selection to more closely follow the population distribution of the population.
The sample was selected in four stages and in the first two stages, sample design was stratified according to 11 regions, 3 settlement types (Large town, Small town, and Village), and 4 geographic strata (Valley, Foothills, Mountain, and High mountain). This stratification was applied in all regions, except the city of Tbilisi where the region is stratified according to 10 districts. In total 49 separate strata were identified. The last two stages of the sample design were for the selection of clusters and households.
First stage of sampling: The number of clusters based on sample size calculations was 467 and these were allocated to regions based on the cube root of the number of households in the region. Because the number of clusters for the Racha-Lechkumi region was small (12 clusters), it was decided to increase the number of clusters in that region by 8 for a total of 20 clusters in that region for a total of 475 clusters nationwide.
Second stage of sampling: Within each region, another level of stratification was on a combination of the following: size of settlement (large town, small town, and village) and topography (valley, foothills, mountain, and mountain). The allocation of the number of clusters for a settlement/topography stratum was based on the square root of the number of households in each stratum. Some regions did not have each of the different size settlements or topography. Also, in Tbilisi, the Rayons (districts) were used for stratification.
Third stage of sampling: Within each stratum, clusters were selected with probability proportional to population size (PPS).
Fourth stage of sampling: Within each cluster, 30 households were systematically selected, resulting with 14,250 households.
The Georgia Multiple Indicator Cluster Survey sample is not self-weighted. The basic weighting of the data has been done using the inverse of the probability of selection of each household.
Following standard MICS data collection rules, if a household was actually more than one household when visited, then a) if the selected household contained two households, both were interviewed, or b) if the selected household contained 3 or more households, then only the household of the person named as the head was interviewd.
No replacement of households was permitted in case of non-response or non-contactable households. Adjustments were made to the sampling weights to correct for non-response, according to MICS standard procedures.
The sampling procedures are more fully described in the sampling design document and the sampling appendix of the final report.
No major deviations from the original sample design were made. All sample enumeration areas were accessed and successfully interviewed with good response rates.
Face-to-face [f2f]
The questionnaires for the Georgia MICS were structured questionnaires based on the MICS3 Model Questionnaire with some modifications and additions. A household questionnaire was administered in each household, which collected various information on household members including sex, age, relationship, and orphanhood status. The household questionnaire includes household listing, education, water and sanitation, household characteristics, child labour, child discipline, disability, 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 caretaker of the child.
The women's questionnaire includes child mortality, maternal and newborn health, marriage and union, contraception, attitudes towards domestic violence, HIV knowledge, cigarette smoking, and hemoglobin test.
The children's questionnaire includes birth registration and early learning, child development, breastfeeding, care of illness, immunization*, and anthropometry.
The questionnaires are based on the MICS3 model questionnaire. From the MICS3 model English and Russian versions, the questionnaires were translated into Georgian and were pre-tested in Tbilisi and in Mtskheta-Mtianeti during September 2005. Based on the results of the pre-test, modifications were made to the wording and translation of the
The National Family Health Survey 2019-21 (NFHS-5), the fifth in the NFHS series, provides information on population, health, and nutrition for India, each state/union territory (UT), and for 707 districts.
The primary objective of the 2019-21 round of National Family Health Surveys is to provide essential data on health and family welfare, as well as data on emerging issues in these areas, such as levels of fertility, infant and child mortality, maternal and child health, and other health and family welfare indicators by background characteristics at the national and state levels. Similar to NFHS-4, NFHS-5 also provides information on several emerging issues including perinatal mortality, high-risk sexual behaviour, safe injections, tuberculosis, noncommunicable diseases, and the use of emergency contraception.
The information collected through NFHS-5 is intended to assist policymakers and programme managers in setting benchmarks and examining progress over time in India’s health sector. Besides providing evidence on the effectiveness of ongoing programmes, NFHS-5 data will help to identify the need for new programmes in specific health areas.
The clinical, anthropometric, and biochemical (CAB) component of NFHS-5 is designed to provide vital estimates of the prevalence of malnutrition, anaemia, hypertension, high blood glucose levels, and waist and hip circumference, Vitamin D3, HbA1c, and malaria parasites through a series of biomarker tests and measurements.
National coverage
The survey covered all de jure household members (usual residents), all women aged 15-49, all men age 15-54, and all children aged 0-5 resident in the household.
Sample survey data [ssd]
A uniform sample design, which is representative at the national, state/union territory, and district level, was adopted in each round of the survey. Each district is stratified into urban and rural areas. Each rural stratum is sub-stratified into smaller substrata which are created considering the village population and the percentage of the population belonging to scheduled castes and scheduled tribes (SC/ST). Within each explicit rural sampling stratum, a sample of villages was selected as Primary Sampling Units (PSUs); before the PSU selection, PSUs were sorted according to the literacy rate of women age 6+ years. Within each urban sampling stratum, a sample of Census Enumeration Blocks (CEBs) was selected as PSUs. Before the PSU selection, PSUs were sorted according to the percentage of SC/ST population. In the second stage of selection, a fixed number of 22 households per cluster was selected with an equal probability systematic selection from a newly created list of households in the selected PSUs. The list of households was created as a result of the mapping and household listing operation conducted in each selected PSU before the household selection in the second stage. In all, 30,456 Primary Sampling Units (PSUs) were selected across the country in NFHS-5 drawn from 707 districts as on March 31st 2017, of which fieldwork was completed in 30,198 PSUs.
For further details on sample design, see Section 1.2 of the final report.
Computer Assisted Personal Interview [capi]
Four survey schedules/questionnaires: Household, Woman, Man, and Biomarker were canvassed in 18 local languages using Computer Assisted Personal Interviewing (CAPI).
Electronic data collected in the 2019-21 National Family Health Survey were received on a daily basis via the SyncCloud system at the International Institute for Population Sciences, where the data were stored on a password-protected computer. Secondary editing of the data, which required resolution of computer-identified inconsistencies and coding of open-ended questions, was conducted in the field by the Field Agencies and at the Field Agencies central office, and IIPS checked the secondary edits before the dataset was finalized.
Field-check tables were produced by IIPS and the Field Agencies on a regular basis to identify certain types of errors that might have occurred in eliciting information and recording question responses. Information from the field-check tables on the performance of each fieldwork team and individual investigator was promptly shared with the Field Agencies during the fieldwork so that the performance of the teams could be improved, if required.
A total of 664,972 households were selected for the sample, of which 653,144 were occupied. Among the occupied households, 636,699 were successfully interviewed, for a response rate of 98 percent.
In the interviewed households, 747,176 eligible women age 15-49 were identified for individual women’s interviews. Interviews were completed with 724,115 women, for a response rate of 97 percent. In all, there were 111,179 eligible men age 15-54 in households selected for the state module. Interviews were completed with 101,839 men, for a response rate of 92 percent.
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.
The survey covers Georgia.
The WVS for Georgia covers national population aged 18 years and over, for both sexes.
Sample survey data [ssd]
The sampling universe included the adult population of Georgia residing in both rural and urban areas, excluding the conflict zones of Abkhazia and Ossetia. Military bases and prisons were also not included. In addition, some villages near the regional city of Gori and Zugdidi that are still under occupation by Russian troops were not included in the sampling. The sample design involved a fivestage random cluster sampling. The sampling frame design is based on the 2002 census information.
In this sampling design the sampling units were:
1) Regions and individual cities (Tbilisi and other principal cities) 2) Towns and villages (primary sampling units, PSUs) 3) Districts in cities, towns, and villages in rural areas (sampling points, SPs) 4) Household (by household we mean a group of individuals who live under the same roof and use the same kitchen for cooking) 5) Randomly selected adult members of households At the first stage, the number of respondents was allocated by probability-proportional-to-size (PPS) method. Likewise, at the second and third stages PSUs and SPs were selected by the probability proportional to the unit size (PPS) method. Households were selected via a random route technique and respondents at the household level were selected via the next-birthday technique:
Stage 1 - Primary sampling units At the first stage of the sampling design Georgia was divided into 11 regions; the division being based on the official administrative and geographic regions of the country.
1 Tbilisi
2 Kakheti
3 Shida Kartli
4 Kvemo Kartli
5 Samtskhe Javakheti
6 Ajara
7 Guria
8 Samegrelo
9 Imereti & Svaneti
10 Mtskheta Mtianeti
11 Racha
Each region was stratified according to three criteria:
a) Large cities over 45,000 individuals - There are seven large cities in Georgia including the capital. All of them will be included in the sampling frame and are regarded as having been selfrepresentative cities or PSUs.
b) Other cities and towns with populations of less than 45,000
c) Rural settlements The number of interviews in all 10 regions was allocated proportional to the size of the adult population in each region.
Stage 2 - Selection of PSUs In this stage the PSUs are equivalent to rayons- there are a total of 59 rayons (PSUs) in Georgia (excluding Abkhazia and Ossetia). The final sample covered 24 PSUs; this included seven self-representative PSUs were also included in this number. Due to the security reasons, areas close to Ossetian (town of Akhalgori, which was and continues to be under by Russian troops and the buffer zone areas), as well as the town Zugdidi (villages and small towns surrounding town of Zugdidi) were excluded from the sampling framework. Stage 3 - Selection of sampling points (SPs) In urban areas the SPs were census districts whereas in rural areas an entire village was considered as an SP. There are total of 16,582 registered census districts in Georgia and for each one, information existed as to its location/address and the size of the adult population. In the pre-selected PSUs (according to PPS), the number of SPs were determined and per each selected SP around 10 interviews were completed. Rural areas villages are considered as a separate SP and from the list of villages, (this list contains information on the number of adult population per village), and the SPs was selected by PPS. The achieved sampling framework consisted of 188 randomly selected (via PPS) SPs Stage 4 - Selection of households Selection of households was conducted by the application of a random route technique. For each one, SP starting points were identified and given to supervisors who then instructed interviewers as to how sampling mechanism was to be completed. Interviewers were then instructed to make up to two call backs if the original respondent was not available at the time of the initial contact.
Remarks about sampling:
The interviewer was given a route map in which a starting point for each sample point was accurately indicated. Every interviewer was then expected to have conducted no less than 10 interviews for urban SP and 5 among rural sampling points. The choice of starting points for all SP was made by the project manager or supervisor and was not left to the interviewers discretion. The STARTING POINT may be any point along the route, including day care establishments, schools, hospitals, administration buildings, or the beginning or end of a street (the starting point was indicated on the route map beforehand). If the starting point was the beginning of a street, it is necessary to keep to one side of the street (right or left). If a crossroad is met during the route, it is necessary to turn at this juncture and stay to the same side of the route until an appropriate respondent was chosen (i.e. if the left side is chosen, it was necessary to keep to the left side of the crossroad). If the required number of appropriate respondents was not found and the street ended, the interviewer should than have turned to the other side of the street and continued to the left handed side of the street. If the starting point had been a multi-storied building, the interviewer should have proceeded to the top floor and knocked at the door of the apartment on the side of which he followed during the route. It was not possible skipped any apartment until the appropriate respondent was found. After the interview with the appropriate respondent was completed the interviewer was to have followed the route and selected every fifth apartment. The interviewer followed this method after a successful interview was completed. In other cases s/he should have visited the next apartment until an interview was completed. If the interviewer were meeting private houses/plots on the sampling route, he should follow the instructions as indicated above and to have visited every fifth household. Interviews were held only in buildings that contained residences. Exceptions were those buildings (private hospitals, shops, restaurants, etc.), where one or more families permanently resided. The interviewer must allowed the supervisor to have been informed of and coordinated with him any changes that were concerned with the route that occurred during the fieldwork.
The sample size for Georgia is N=1500 and includes the national population aged 18 years and over for both sexes.
Face-to-face [f2f]
Length of interviews - Report each instrument separately - Report quartiles and interquartile range as well as median and mean Issues with survey instrument - Problems with particular questions - -for each question why was this problematic - Problems with length No serious problem that could cause the quality of the interviewing process was encountered either during the interviewing or due to the length of the surveys.
Reason Cases No one at home 2146 Refusal from the family member 343 Refusal from the respondent 243 Respondents could not communicate (health related problems, language related problems, etc) 31 Respondent was not at home 311 Respondent is out of home during duration of the fieldwork 48 Termination of interview 0 Completed interview 1500
+/- 2,6%
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This dataset contains information on how to fairly distribute the mitigation efforts that countries need to undertake to together achieve certain climate goals. There is no single answer to this question, but we explore this topic by looking at various global emissions pathways, and subsequently allocate these emissions to countries using different effort-sharing rules. Some of these rules can be considered fair and can be used as information in the debate on just transitions. Beyond this dataset, we also published the Carbon Budget Explorer: an online interactive tool that allows users to navigate through these results, without having to download and plot the data themselves. It is free and publicly available at www.carbonbudgetexplorer.eu.
Currently, the Carbon Budget Explorer relies on a previous version of this dataset (version 0.1, unpublished, but available upon request). The Explorer will updated with new data during summer 2024 (i.e., with the version presented in this data repository).
The research behind this dataset is still under development and therefore this dataset is not final. A preprint of a scientific publication is being drafted and will be published on a preprint server in summer 2024, along with potential updates of this dataset. Subsequently, the data is subject to potential changes upon peer review of this publication. Nevertheless, because (a version of) this data is already used in the Carbon Budget Explorer and in scientific projects, we feel it should be available and versioned. Hence this release of a preliminary version.
For many users, these are the main datafiles. Per country and region, allocations and reduction targets are shown for two trajectories, which are associated with 1.5 (with slight overshoot: peak temperature 1.6) and 2.0 degree pathways, and default settings across all other dimensions. The exact parameters used in these precooked pathways are shown in Table 1 (see "Dimensions"). The reductions_default_*.csv files show data along the same structure, also using the default pathways, but contain the emission reductions with respect to 2015 rather than absolute allocations.
Allocating emissions to countries starts with determining global emissions pathways. The files in GlobalPathways.zip contain projected global emissions on GHG, CO2 and non-CO2 levels, constrained by various global settings (see below) such as temperature targets and derived CO2 budgets. The pathway shapes are informed by mitigation scenarios from the IPCC AR6 database. The starting values are all harmonized with 2021 historical datapoints. For convenience, the emissionspathways_default.csv datafile provides the pathways with default settings (see Table 1, column 'Default'). The complete dataset can be found in emissionspathways_all.csv.
The emissions from the global pathways can be divided among countries according to different allocation rules (see 'Allocation rules' for more information). Files of the format allocations_region.nc indicate allocations according to all allocation rules, parameters and global choices, for a single region. Because of the high number of parameters and dimensions, these files are shared in NetCDF (.nc) format. NetCDF files are commonly used for storing multidimensional scientific data and can be displayed, analyzed and read/written using GIS systems (such as ArcGIS, QGIS), MATLAB funcions (such as nccreate, ncread), R (e.g. using the ncdf4 package) and Python (e.g. using the xarray package).
Additional input data coming from third parties, such as population and GDP data, is stored in Inputdata.zip. We prepared these input data sources in the exact same format as the rest for convenience of the user, but we would like to emphasize that the appropriate references should be cited. For further information, please check 'Input data sources'.
A file has been added in the most recent version, including cumulative CO2 budget allocations. That is, allocated parts of the global remaining CO2 budget, which is defined as cumulative CO2 emissions up to CO2 net zero. The file contains allocations on a Per Capita (PC), an Ability to Pay (AP) and an Equal Cumulative Per Capita (ECPC) basis.
Below you can find a summarized description of all allocation rules. More detailed information can be found in Van den Berg et al. (2020), as well as in a scientific paper (preprint) expected in summer 2024. The rules have a variety of parameters, each included as dimensions in the data. See Table 1, in "Dimensions", for details.
Table 1 - Data dimensions
Name | Unit | Range | Default | Description |
General | ||||
Time | Year |
Past: 1850-2021 Future: 2021-2100 (yearly or 5-year increments) | All | The historic data reported here ends in 2021, and we start our analysis in 2021. Intentionally, to be able to exactly match historic and future data. The year 2021 is chosen because of limited availability of more recent data sources. |
Region | ISO3 code |
Country-level (ISO3) Country groups (e.g., G20 and Umbrella) World ('EARTH') | All | |
Global | ||||
Temperature | Degrees temperature rise with respect to pre-industrial times |
1.5 - 2.4 degrees | 1.6 and 2.0 | Peak temperature without overshoot |
Risk | Probability of reaching a temperature target (i.e., percentile of climate sensitivity) |
17%, 33%, 50%, 67%, 83% |
50% (for 1.6 degrees) and 33% (for 2.0 degrees) |
This governs the uncertainty in climate sensitivity. Because there is still uncertainty about the exact numerical response of temperature to CO2, we have to include this. Low-risk (e.g., 0.17) indicates that we assume a high climate sensitivity: for a given amount of greenhouse gas emissions, temperature rises higher. This means that carbon budgets at a given temperature level have to be lower. Vice-versa for high-risk (e.g., 0.83). |
NegEmis | Quantiles of 2100 GHG emissions among AR6 scenarios with a similar temperature target |
20%, 40%, 60%, 80% |
50% |
Even though negative emissions (predominantly in the second-half of the century) are not very relevant for achieving a certain peak temperature, they do alter the second half of global emissions pathways. |
NonCO2red | Quantiles of non-CO2 reductions in 2040 with respect to 2020 among AR6 scenarios with a similar temperature target |
10%, 33%, 50%, 67%, 90% | 50% | Non-CO2 reduction varies greatly among mitigation scenarios, but |
The Project for Statistics on Living standards and Development was a coutrywide World Bank Living Standards Measurement Survey. It covered approximately 9000 households, drawn from a representative sample of South African households. The fieldwork was undertaken during the nine months leading up to the country's first democratic elections at the end of April 1994. The purpose of the survey was to collect statistical information about the conditions under which South Africans live in order to provide policymakers with the data necessary for planning strategies. This data would aid the implementation of goals such as those outlined in the Government of National Unity's Reconstruction and Development Programme.
National coverage
Sample survey data [ssd]
Sample size is 9,000 households
The sample design adopted for the study was a two-stage self-weightingdesign in which the first stage units were Census Enumerator Subdistricts (ESDs, or their equivalent) and the second stage were households.
The advantage of using such a design is that it provides a representative sample that need not be based on accurate census population distribution.in the case of South Africa, the sample will automatically include many poor people, without the need to go beyond this and oversample the poor. Proportionate sampling as in such a self-weighting sample design offers the simplest possible data files for further analysis, as weights do not have to be added. However, in the end this advantage could not be retained and weights had to be added.
The sampling frame was drawn up on the basis of small, clearly demarcated area units, each with a population estimate. The nature of the self-weighting procedure adopted ensured that this population estimate was not important for determining the final sample, however. For most of the country, census ESDs were used. Where some ESDs comprised relatively large populations as for instance in some black townships such as Soweto, aerial photographs were used to divide the areas into blocks of approximately equal population size. In other instances, particularly in some of the former homelands, the area units were not ESDs but villages or village groups.
In the sample design chosen, the area stage units (generally ESDs) were selected with probability proportional to size, based on the census population. Systematic sampling was used throughout that is, sampling at fixed interval in a list of ESDs, starting at a randomly selected starting point. Given that sampling was self-weighting, the impact of stratification was expected to be modest. The main objective was to ensure that the racial and geographic breakdown approximated the national population distribution. This was done by listing the area stage units (ESDs) by statistical region and then within the statistical region by urban or rural. Within these sub-statistical regions, the ESDs were then listed in order of percentage African. The sampling interval for the selection of the ESDs was obtained by dividing the 1991 census population of 38,120,853 by the 300 clusters to be selected. This yielded 105,800. Starting at a randomly selected point, every 105,800th person down the cluster list was selected. This ensured both geographic and racial diversity (ESDs were ordered by statistical sub-region and proportion of the population African). In three or four instances, the ESD chosen was judged inaccessible and replaced with a similar one.
In the second sampling stage the unit of analysis was the household. In each selected ESD a listing or enumeration of households was carried out by means of a field operation. From the households listed in an ESD a sample of households was selected by systematic sampling. Even though the ultimate enumeration unit was the household, in most cases "stands" were used as enumeration units. However, when a stand was chosen as the enumeration unit all households on that stand had to be interviewed.
Census population data, however, was available only for 1991. An assumption on population growth was thus made to obtain an approximation of the population size for 1993, the year of the survey. The sampling interval at the level of the household was determined in the following way: Based on the decision to have a take of 125 individuals on average per cluster (i.e. assuming 5 members per household to give an average cluster size of 25 households), the interval of households to be selected was determined as the census population divided by 118.1, i.e. allowing for population growth since the census. It was subsequently discovered that population growth was slightly over-estimated but this had little effect on the findings of the survey.
Individuals in hospitals, old age homes, hotels and hostels of educational institutions were not included in the sample. Migrant labour hostels were included. In addition to those that turned up in the selected ESDs, a sample of three hostels was chosen from a national list provided by the Human Sciences Research Council and within each of these hostels a representative sample was drawn on a similar basis as described abovefor the households in ESDs.
Face-to-face [f2f]
The main instrument used in the survey was a comprehensive household questionnaire. This questionnaire covered a wide range of topics but was not intended to provide exhaustive coverage of any single subject. In other words, it was an integrated questionnaire aimed at capturing different aspects of living standards. The topics covered included demography, household services, household expenditure, educational status and expenditure, remittances and marital maintenance, land access and use, employment and income, health status and expenditure and anthropometry (children under the age of six were weighed and their heights measured). This questionnaire was available to households in two languages, namely English and Afrikaans. In addition, interviewers had in their possession a translation in the dominant African language/s of the region.
In addition to the detailed household questionnaire referred to above, a community questionnaire was administered in each cluster of the sample. The purpose of this questionnaire was to elicit information on the facilities available to the community in each cluster. Questions related primarily to the provision of education, health and recreational facilities. Furthermore there was a detailed section for the prices of a range of commodities from two retail sources in or near the cluster: a formal source such as a supermarket and a less formal one such as the "corner cafe" or a "spaza". The purpose of this latter section was to obtain a measure of regional price variation both by region and by retail source. These prices were obtained by the interviewer. For the questions relating to the provision of facilities, respondents were "prominent" members of the community such as school principals, priests and chiefs.
All the questionnaires were checked when received. Where information was incomplete or appeared contradictory, the questionnaire was sent back to the relevant survey organization. As soon as the data was available, it was captured using local development platform ADE. This was completed in February 1994. Following this, a series of exploratory programs were written to highlight inconsistencies and outlier. For example, all person level files were linked together to ensure that the same person code reported in different sections of the questionnaire corresponded to the same person. The error reports from these programs were compared to the questionnaires and the necessary alterations made. This was a lengthy process, as several files were checked more than once, and completed at the beginning of August 1994. In some cases questionnaires would contain missing values, or comments that the respondent did not know, or refused to answer a question.
These responses are coded in the data files with the following values: VALUE MEANING -1 : The data was not available on the questionnaire or form -2 : The field is not applicable -3 : Respondent refused to answer -4 : Respondent did not know answer to question
The data collected in clusters 217 and 218 should be viewed as highly unreliable and therefore removed from the data set. The data currently available on the web site has been revised to remove the data from these clusters. Researchers who have downloaded the data in the past should revise their data sets. For information on the data in those clusters, contact SALDRU http://www.saldru.uct.ac.za/.
Living Standards Measurement Study surveys have been developed by the World Bank to collect the information necessary to measure living standards and evaluate government interventions in the areas of poverty alleviation and social services. The Azerbaijan Survey of Living Conditions (ASLC) applies many of the features of LSMS surveys to provide data for the World Bank Poverty Assessment.
National
Sample survey data [ssd]
Design
The methodology that was chosen reflects the purpose of the survey. To balance a desire for a large, representative sample with the expense of a detailed survey instrument, a sample size of 2,016 households was selected. Three separate populations were covered: households in Baku, households outside of Baku and households of Displaced Persons. Within each of those populations, the sample was chosen in such a manner that each household had an equal probability of being selected. At the same time, the logistics of locating the households and conducting the interviews within a specific time frame required that the households be grouped into "work loads" of 12 households each. The size of the workload was determined by the number of interviews that could be carried out in one day by one team of three interviewers and a supervisor.
The Azerbaijan Survey of Living Conditions sample design included 408 households in the eleven raions that make up the city of Baku, 1200 households in the population outside of Baku, and 408 households among the registered Internally Displaced Persons residing throughout the country. This results in an oversampling of the Internally Displaced Persons population and an undersampling of the urban population of Baku. In order to use all data to provide nationally representative estimates, weighting factors must be applied to the data to account for the difference between the population and sample distributions.
Outside of Baku
The most recent data on population came from the 1989 census, the most recent data on number of households was reported in 1994 by the National Statistical Committee. The country is divided into towns, villages of the town type, and villages. Every household is located in one of those three types of population points. A list prepared by the National Statistical Committee contains just over 4,250 of these population points. To choose the sample outside of Baku, Baku was excluded from this list as were all the population points located in raions of the country currently occupied (Agdam, Xankendi, Xodjali, Xodjvendi, Susha, Kubatli, Zangelan, Kelbadjar, Lachin, Fizuli and Djebrali). The remainder of the country included 3453 population points. Information on the number of households was not available for all population points, specifically, "villages of the town type" and cities did not have this information. Average household size was calculated for those points that had both population and the number of households and this number was used to impute the number of households for those population points where it was missing. Average household size was 4.25 which is smaller than expected but reflects the fact that numerator is a 1989 statistic and the denominator is from 1994. First stage of sampling: Using the list of actual and estimated number of households for each population point, 100 workloads were spread across the population points in the following manner: 1. the sampling interval, i, was calculated to be the total number of households outside of Baku divided by 100, 2. the random start, s, was calculated by taking the integer portion of [random number * i + 1], 3. the population point containing the sth household, the (s+i)th household, the (s+2i)th household, etc. were then selected. 4. in the event that more than one interval landed on the same population point, multiple workloads of 12 households were surveyed in that population point. In this manner 100 workloads were distributed in 91 population points. Second stage of sampling: In order to select the households within the selected population points, household lists maintained by the administrative office of each Selsoviet were used. Selsoviets are administrative units that cover from one to ten population points. In the population points covered by a single group of 12 households, 16 dwellings were selected--12 to be interviewed and 4 to be used as replacements if necessary. The sampling interval used was the total number of households on the list divided by 16. Each population point had been assigned a randomly generated number with which to calculate a starting point. In population points with more that one group of 12 households, 16 households were selected for each workload and the sampling interval was number of households divided by 16 multiplied by the number of workloads. It is possible that a second household with separate finances could occupy a dwelling that was only listed once in the Selsoviet’s list. If an interviewer discovered more than one family living in a single dwelling, separate questionnaires were to be filled out for both, and a household randomly selected from among the households not yet interviewed on the list for that population point was taken off the list. This replacement of households, opposed to adding households, was adopted because the schedule did not allow time for more than 12 interviews per workload.
Baku
In February of 1995, SORGU was commissioned to do a random sampling survey in Baku. At that time a list was compiled of 2000 households in Baku. The 2000 households were distributed across the 11 raions of Baku according to each raion’s proportion of the total population. In each raion, the passport office lists were consulted to select the required number of addresses. In each office, the depth of each drawer full of cards was measured, the total length was divided by the number of households to be selected from that raion and cards were then pulled out at those intervals. From each card a specific address in Baku was noted. There is one passport for each dwelling in that raion regardless of the number of separate household/family units occupied the dwelling. The passport lists are, in principle, continuously updated with information from the housing maintenance offices. However, dwellings that are used for business, unoccupied, abandoned or rented to foreigners may remain listed. Furthermore, it is not clear how new privately built housing units would be listed.The 408 households and 92 replacements for this survey were selected by choosing a random number between 1 and 4, starting with that number and then selecting every fifth address from the existing list.
Internally Displaced Population
The National Statistical Committee prepared a listing of population and number of households of internally displaced persons by raion in July 1995. From that list, 34 workloads of 12 households each were selected from 26 raions and 11 Baku Administrative Regions using with a sampling interval and a random start similar to the method used outside of Baku. Ten workloads were selected in Baku and 24 were selected in 17 raions. As before, some raions received more than one workload. In each raion, the administrative offices for the Ministry of Refugees was consulted to locate the internally displaced persons. Each office should have a list of internally displaced persons by households. An additional level of sampling took place to choose three places and four interviews will be conducted in each place. These places were buildings, towns, or tent camps depending on how the households were listed.
Sampling as Implemented
In the course of the field work, it was discovered that population lists are not maintained in major urban areas. In Kuba, Xachmas, Devichi, Qaxi, Sheki, Ali Bairamli, Gojai and Agdash, supervisors had to improvise. In some cases passport registration lists were used, as was done in Baku. In other cases electric users lists, gas office books and butter/meat coupon distribution lists were used in order to capture a sample that was as representative as possible. During field work, one population point, Xandar, was not accessible due to security concerns and its proximity to the occupied region. A second population point, Sofukent, was not accessible because of the weather. In both cases, it was not practicable to replace the population points with two other population points randomly selected from the national list. Instead, field teams were instructed to visit the nearest population point of approximately the same size to the chosen population point. The only major disruption to fieldwork occurred in Naxicevan where interviewers were shot at by terrorists, fortunately none was hurt.
Face-to-face [f2f]
DEVELOPMENT OF QUESTIONNAIRES
A questionnaire based on the Living Standards Measurement Study surveys was adapted for use in Azerbaijan. Significant reductions in the number of questions reflected the need to conduct the survey in a short period of time and the more limited scope of a poverty assessment as compared to a full-blown government policy analysis. Questionnaire development was done using the Russian language version. The finalized versions were translated into Azeri by SORGU personnel. A special version of the questionnaire with both Russian and English was prepared for use by data analysts.
DESCRIPTION OF QUESTIONNAIRES
The survey includes questionnaires at both the household and population point (community) levels. Population point is an administrative designation that can be a village, a "village of the town type" or a
The Cox’s Bazar Panel Survey (CBPS) was completed in August 2019, through a partnership between the Yale Macmillan Center Program on Refugees, Forced Displacement, and Humanitarian Responses (Yale Macmillan PRFDHR), the Gender & Adolescence: Global Evidence (GAGE) program, the Poverty and Equity Global Practice of the World Bank and the State and Peacebuilding Fund (SPF) administered by the World Bank. It is a representative survey of the post-2017 population of displaced Rohingya and households in host communities in the Cox’s Bazar district in Bangladesh.
The high-frequency phone tracking (HFT) surveys were built to maintain communication with baseline respondents while collecting rapid data on key welfare indicators on labor, basic needs and education. Three rounds of the HFT have been completed between 2020-2021, which have been used to produce welfare updates on the host and Rohingya population residing in Cox's Bazar, Bangladesh, particularly amidst the COVID-19 crisis.
The tracking surveys collected information across three broad welfare dimensions: labor, access to basic needs and education status of school-aged children. Round 1 collected information on labor and access to basic needs only; the module on education was added Round 2 onwards.
Cox's Bazar district and some parts of Bandarban district.
Households and individuals
a) Rohingya population living in camps and b) host population within Cox's Bazar and Bandarban district.
Sample survey data [ssd]
The CBPS study has a total sample size of 5,020 households (HHs), divided among three strata covering Rohingya refugees in camps and host communities in Cox’s Bazar district and some adjacent regions of Bandarban district. The CBPS HFT attempted to follow the full baseline sample of 5,020 household in each round, with no alterations or additions made to the sampling design. The baseline sampling strategy is detailed below.
The three strata are defined as:
i. Rohingya refugees in camps
ii. High exposure hosts: hosts within 15 km (3-hour walking distance) of camps
iii. Low exposure hosts: hosts at more than 15 km (3-hour walking distance) from camps
(In the datasets, the 'settlement_type' and 'stratum' variables identify the different levels at which the sample is representative)
Defining the camp strata: A two-step data collection on Rohingya refugee prevalence within host communities (i.e., outside of camps) confirmed that prevalence in host communities was low, and that this was the case not only for newer Rohingya displaced, but for the older cohort of displaced, as well. This pattern of refugee prevalence supported having one stratum for the Rohingya displaced living in camps. The sampling strategy for the CBPS therefore focused on generating representative estimates for the camp based Rohingya population in Cox’s Bazar district.
Defining the host strata: For hosts, the sampling strategy was designed to account for the differential implications of a camp-based concentration of close to a million Rohingya displaced for different areas of Cox’s Bazar. To distinguish between host communities that are differentially affected by the arrival of the Rohingya, the CBPS sampling strategy used a threshold of three hours’ walking time from a campsite to define two survey strata: (i) host communities with potentially high exposure (HE) to the displaced Rohingya, and (ii) host communities with potentially low exposure (LE).
Sampling frame: The camp sample uses the Needs and Population Monitoring Round 12 (NPM12) data from the International Organization for Migration as the sampling frame. For the host sample, a combination of the 2011 population census, Admin 4 shapefiles from the Bureau of Statistics and publicly available Google Earth imagery and OpenStreetMaps were used to develop a sampling frame.
Stages of sample selection: For camps, NPM12 divided all camps into 1,954 majhee blocks.1 200 blocks were randomly selected using a probability proportional to the size of the camp. A full listing was carried out in each selected camp block.
For hosts, a two-stage sampling strategy was followed. The first stage of selection was done at the mauza level by strata. A random sample of 66 mauzas was drawn from a frame of 286 mauzas using probability proportional to size. Based on census population size, each mauza was divided into segments of roughly 100-150 households. The second stage selected three segments from each selected mauza with equal probability of selection.
Listing and replacements: Within each selected PSU in camps (blocks) and hosts (mauza-segments), all households (100-150 on average) were listed. Of listed households, 13 households were selected at random for interview, with an additional replacement list of 5 households. More information on the sampling strategy and process can be found on the published working paper titled “Data Triangulation Strategies to Design a Representative Household Survey of Hosts and Rohingya Displaced in Cox’s Bazar, Bangladesh”.
While the original sampling strategy was designed to be representative of all camp-based Rohingya displaced, campsites with older Rohingya displaced refused to participate in the listing due to other political sensitivities. This refusal was maintained despite many attempts. Since the older Rohingya displaced were not a separate stratum, a decision was made to drop these households from the survey. Therefore, the attained sample does not contain registered refugees from the two camps – Kutupalong RC and Nayapara RC.
The host sample covers six out of eight upazilas in Cox’s Bazar District (Chakaria, Cox’s Bazar Sadar, Pekua, Ramu, Teknaf, and Ukhia upazilas) and one upazila in Bandarban District (Naikhongchhori upazila). The two upazilas not covered within the sample are the islands of Kutubdia and Maheshkhali.
Computer Assisted Personal Interview [capi]
The R1 tracking questionnaire was developed as a lean version of the questionnaire implemented during the CBPS baseline. The R2 and R3 questionnaires retained certain aspects of the R1 questionnaire, but also added more detailed questions on aspects such as food security (in consultation with UN-WFP) and credit-seeking and coping behavior based on findings observed in previous rounds and dynamic research needs within the COVID-19 crisis.
One questionnaire was developed per round of data collection with modules containing household level questions on access to basic needs, credit-seeking behavior, access to health services, vaccinations and individual level questions on labor market status. Any adult, knowledgeable member of the confirmed sample household were eligible to answer the household modules. The labor module was only permitted if the respondent reached was any one of the 2-3 selected adults within the household who had completed the baseline adult questionnaires.
Questionnaires were developed in English and translated into Bengali. The translations to Bengali were thoroughly reviewed by the World Bank team’s local consultants to ensure quality. Pretesting and piloting were done using the Bengali questionnaires.
All questionnaires and modules in English are provided as external resources.
Data was collected through computer-assisted telephone interviews via SurveyCTO, an ODK-based platform. Maintenance of correct questionnaire flow was ensured through in-built skips and logic checks within the programmed questionnaire.
No manual data corrections were made on submitted interviews by the data processing team. Interviews flagged as needing field corrections due to mistaken entries were re-submitted by enumerators upon strict evaluation by the project team upon close review of the concerns raised and filtered by the program automatically before closing of data collection in each round.
In addition to logic checks within the survey program itself, extensive data consistency checks and quality indicators were developed by the WB team to monitor data quality during survey implementation. Field debriefs were held frequently during the piloting phase and first week of data collection, and once a week in latter weeks to provide feedback to enumerators and gain clarity on data quality concerns.
Post data collection, structural and consistency checks have been conducted on each round dataset and in-between datasets from different rounds.
The response rates at household level for each round of the CBPS HFT, based on the baseline sample of 5,020 and disaggregated at stratum-level are: Round 1: Overall - 67%; Camps - 54%; High exposure: 71%; Low exposure: 72% Round 2: Overall - 72%; Camps - 63%; High exposure: 81%; Low exposure: 80% Round 3: Overall - 68%; Camps - 55%; High exposure: 81%; Low exposure: 80%
*Note that the Round 1 tracking exercise was a joint-effort between the Yale Y-Rise team and the WB team. The Yale team contacted and surveyed a randomly selected 25% of baseline households, while the WB team completed the remaining 75%. The Round 1 dataset contains data on this segment of the sample only as the welfare surveys implemented by the teams were different.
The study was conducted in Belarus between October 2008 and February 2009 as part of the first round of The Management, Organization and Innovation Survey. Data from 102 manufacturing companies with 50 to 5,000 full-time employees was analyzed.
The survey topics include detailed information about a company and its management practices - production performance indicators, production target, ways employees are promoted/dealt with when underperforming. The study also focuses on organizational matters, innovation, spending on research and development, production outsourcing to other countries, competition, and workforce composition.
National
The primary sampling unit of the study is the 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 is defined as a separate production unit, regardless of whether or not it has its own financial statements separate from those of the firm, and whether it has it own management and control over payroll. So the bottling plant of a brewery would be counted as an establishment.
The survey universe was defined as manufacturing establishments with at least fifty, but less than 5,000, full-time employees.
Sample survey data [ssd]
Random sampling was used in the study. For all MOI countries, except Russia, there was a requirement that all regions must be covered and that the percentage of the sample in each region was required to be equal to at least one half of the percentage of the sample frame population in each region.
In most countries the sample frame used was an extract from the Orbis database of Bureau van Dijk, which was provided to the Consultant by the EBRD. The sample frame contained details of company names, location, company size (number of employees), company performance measures and contact details. The sample frame downloaded from Orbis was cleaned by the EBRD through the addition of regional variables, updating addresses and phone numbers of companies.
Examination of the Orbis sample frames showed their geographic distributions to be wide with many locations, a large number of which had only a small number of records. Each establishment was selected with two substitutes that can be used if it proves impossible to conduct an interview at the first establishment. In practice selection was confined to locations with the most records in the sample frame, so the sample frame was filtered to just the cities with the most establishments.
The quality of the frame was assessed at the onset of the project. The frame proved to be useful though it showed positive rates of non-eligibility, repetition, non-existent units, etc. These problems are typical of establishment surveys. For Belarus, the percentage of confirmed non-eligible units as a proportion of the total number of contacts to complete the survey was 30.6% (83 out of 271 establishments).
Face-to-face [f2f]
Two different versions of the questionnaire were used. Questionnaire A was used when interviewing establishments that are part of multiestablishment firms, while Questionnaire B was used when interviewing single-establishment firms. Questionnaire A incorporates all questions from Questionnaire B, the only difference is in the reference point, which is the so-called national firm in the first part of Questionnaire A and firm in Questionnaire B. Second part of the questionnaire refers to the interviewed establishment only in both Questionnaire A and Questionnaire B. Each variation of the questionnaire is identified by the index variable, a0.
Item non-response was addressed by two strategies: - For sensitive questions that may generate negative reactions from the respondent, such as ownership information, enumerators were instructed to collect the refusal to respond as (-8). - Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary. However, there were clear cases of low response.
Survey non-response was addressed by maximising efforts to contact establishments that were initially selected for interviews. Up to 15 attempts (but at least 4 attempts) were made to contact an establishment for interview at different times/days of the week before a replacement establishment (with similar characteristics) was suggested for interview. Survey non-response did occur, but substitutions were made in order to potentially achieve the goals.
Additional information about sampling, response rates and survey implementation can be found in "MOI Survey Report on Methodology and Observations 2009" in "Technical Documents" folder.
The Ghana Living Standards Survey (GLSS) is a nationwide survey carried out by the Government of Ghana (Ghana Statistical Service) with the support of the World Bank (Social Dimensions of Adjustment Project Unit). The objective of the survey is to provide data to the government for measuring the living standards of the population and the progress made in raising them. The survey data will permit a more effective formulation and implementation of policies designed to improve the welfare of the population.
The GLSS was launched in September 1987 and is currently planned to be undertaken over a five-year period. The five interval ensures that a steady stream of data becomes available to monitor the impact of the Government's Economic Recovery Program, including the Program of Actions to Mitigate the Social Costs of Adjustment (PAMSCAD). GLSS provides data on various aspects of the Ghanaian household economic and social activities and the interactions between these activities. Data are collected at three levels: the individual level, the household level and community level. The household questionnaire was administered to 1525 households over a six month period from september 1987 to march 1988.
National
Sample survey data [ssd]
The methodology that was used reflects the purpose of the survey. To balance the desire for a large, representative sample with the expense of a long, detailed survey instrument, a sample size of 3,200 households was selected. The households were to be chosen in such a manner that each household had an equal probability of being selected. At the same time, the logistics of locating the households and conducting all interviews within a specific time frame required that the households be grouped into "workloads" of 16 households each. A final concern was that all three of the country's ecological zones (coastal, forest and savannah), and each of urban, semi-urban and rural areas (population greater than 5000, 1500 to 5000, and less than 1500, respectively) form the same proportion in the sample as they do in the national population.
To achieve the three objectives simultaneously, a stratified selection process was used. For the 1984 Census, all of Ghana was divided into approximately 13,000 enumeration areas (EAs). From this list it was determined what proportion of the 200 GLSS workloads should be selected from each of the nine zone/urban categories. Two hundred sampling areas were then selected from the enumeration areas in the sub-divided list. For each enumeration area, the probability of being selected was proportional to the number of households contained in that area.
After the 200 sampling areas were selected, households in those areas were enumerated in 1987. Therefore it was possible to take into account changes in the number of households and preserve the self-weighting nature of the sample. The 200 workloads were assigned among the 200 sampling areas with probability equal to the number of households in that area in 1987 divided by the number of households in that area in 1984 and multiplied by the total number of households in 1984 divided by the total number of households in 1987. That is, sampling areas that had greater than average increases in size had a greater than one chance of being selected. Thus, each sampling area was assigned zero, one, two, or even three workloads of sixteen households. The households (sixteen selected and four replacement for each workload) were then chosen randomly from the household list for each sampling area. The resulting list is 3200 households and 800 replacement households in something less than 200 sampling areas (specifically 178 in 1987-88 and 170 in 1988-89). Each group of 16, 32 or 48 households within a sampling area is referred to as a cluster in the GLSS data sets and in this document.
Face-to-face [f2f]
The household survey contains modules (sections) to collect data on household demographic structure, housing conditions, schooling, health, employment, migration, expenditure and income, household non-agricultural businesses, agricultural activities, fertility and contraceptive use, savings and credit, and anthropometric (height and weight) measures.
The community questionnaire collected data on the population of the community, a list of principal ethnic groups and religions, the length of time the community has existed and whether or not it has grown, principal economic activities, access to a motorable road, electricity, pipe-borne water, restaurant or food stall, post office, bank, daily market and public transport, employment, migration for jobs, existence of community development projects, schools and how far from the community, information is obtained on whether it is public or private, data on distance and travel time to the nearest of each of several types of health post, dispensary, pharmacy, maternity home, family planning clinic, type of crops grown in the community, how often and when they are planted and harvested, and how the harvest is generally sold.
Price questionnaire collected information on prices from up to three vendors i.e. food, pharmaceutical and other non-food items.
The quality control of the data collection occured at three instances. First, on the field, the supervisor randormly visited 25% of the households already surveyed to verify the answers to some key questions. In addition the supervisor periodically attended interviews conducted by each interviewer. Second, in the regional office, the data entry computer package used performed consistency checks, so that inconsistencies and errors in data collected during the first round were immediately reported to the interviewers for verification during the second round. Finally, daily supervisory checks of the data entry process were performed.
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.
China
Household Individual
National Population, Both sexes,18 and more years
Sample survey data [ssd]
Sample size: 1000
The sample is a representative national sample of China containing 40 county/city sample units to collect individual level data of, from a political cultural perspective, the values and attitudes currently held by Chinese citizens. With considerations of representativeness, feasibility, and budgetary constrains, it was decided this project would draw a subsidiary probability sample out of a master sample that RCCC created based on its previous national survey on environmental awareness of the general public in China conducted in 1998. The Environmental Awareness Survey, which was used as a master sample, was a national survey conducted through out the entire country. The target population was the same as the one defined for this survey. Through the stratification, the proportionally allocated multi-stage PPS (probability proportional to size) technique was employed in order to obtain the self-weighted household samples. There were different stages in the sampling procedure: Counties and county-level cities are taken as primary sampling units (PSUs). Family households are the basic sampling unit. Demographic data at all levels was obtained from The Demographic Data for Chinese Cities and Counties, 1997, published by the State Bureau of Statistics.
Nation wide, there were 2,860 county-level units for the first stage sampling (including 1,689 counties, 436 county-level cities, and 735 urban district--with administrative rank equivalent to county--in large cities). The total households were 337,659,447. This was the base for establishing the sampling frames. Some readjustments: Taking into account of cost and accessibility, only the provincial capitals (Lhasa and Urumchi) and their surrounding areas in Tibet and Sinkiang were included in the sampling frame; in other remote western provinces, a few areas that are extremely hard to access were left out as well. After such readjustment the sampling frame then includes 2,708 county-level units, of which the total households are 322,002,173. Compared to the target population, there was a 5.3% reduction (152 units) in the first stage sampling units. However, since the population density in the remote areas of the western provinces is very low, the reduction counts merely 1.4% of the total households in the sampling frame. Geographical administrative divisions of China were regarded as the primary labels of stratification, that is, each province was treated as an independent stratum. Allocation of target sampling units among the sampling stages was designed as following: 135 PSUs out of the first sampling (county-level) units; 2 secondary sampling (townshiplevel) units in each of the PSUs; then 2 third sampling (village-level) units in each of the SSUs; 25 households in each of the third sampling units, on average. Based on the proportional stratification principle, sample allocation to strata was proportional to the size of each stratum, by an equal probability of f = .0042%. Within each stratum (province), sample sizes were calculated and allocated proportionally to each of the sampling stages. A self-weighted national sample thus was obtained.
Multi-stage PPS: -The first stage: equidistance PPS was employed to draw the county sample. -The second stage: in each of the chosen county-level units, a sampling frame was created based on the data of townships/ward and size measurement; then the equidistance PPS is employed to choose the township/streets sample. -The third stage: a third sampling frame was obtained from each of the chosen township-level units (neighbourhoods, villages and size measurement), and, again, the equidistance PPS is employed to choose the village/neighbourhood sample. -The fourth stage: in each of the chosen village/neighbourhood units, the official list of households registration was obtained; using the size measurement of this unit and the desired number of households to count the sampling distance, then households were selected according to the sampling interval. Since the household registration also listed all family members of each of the household, respondents were drawn randomly immediately after the household drawing. The WVS-China sample was drawn out of the above described master sample.
Some readjustments: Primarily because of the budgetary constrains of the WVS project, six remote provinces in the master sample were excluded. They were: Hainan, Tibet, Gansu, Qinghai, Ningxia, and Sinkiang. These provinces are all with very low population density, and all together they count 5.1% of the total population and 4.6% of total households of the country. After the adjustments, seven of the 139 county-level units of the master sample were removed. Therefore, the target 40 PSUs were to be drawn out of the remaining 132 units.
Sampling Stages: -The first stage: 40 units were drawn from 132 county-level units of the master sample were removed. Therefore, the 40 PSUs were to be drawn out of the remaining 132 units. -The second stage: one unit was chosen randomly out of the 2 original township-level units (SSUs) in each of the 40 selected PSUs. -The third stage: one unit was chosen randomly out of the 2 original village-level units in each of the selected SSUs. -The fourth stage: from each of the chosen village-level units, 35 households were drawn out of the household registration list with equidistance, along with one respondent in each selected household.
Remarks about sampling: -Sample unit from office sampling: Housing
Face-to-face [f2f]
As a participating country-team of the World Values Survey (WVS), the Research Center of Contemporary China (RCCC) at Peking University implemented the WVS-China survey in 2001. The target population covers those who are between 18 and 65 of age (born between July 2, 1935 and July 1, 1982), formally registered and actually reside in dowelings within the households in China when the survey is conducted.
The sample size was determined to be approximately 1,000 -- eligible individuals are to be drawn out of the above defined target population in China. Based on previous experience of response rate, it was decided to increase the target sample to 1,400 in order to reach a satisfied response rate. The final results are summarized as follows: - Target sample size: 1,400 - Sample drawn in the field: 1,385 - Completed, valid interviews: 1,000 - Response rate: 72.2% Summary of Non-Responses Types of Non-Responses (missing cases) % - Be away/not seen for several times: 145-37.7% - Be away for long time/be on a business trip/go abroad/travel:138-35.8% - The interviewer didnt write the reason: 23-6.0% - Rejection: 19-4.9% - Move/investigation reveals no this person: 15-3.9% - Impediments in body or language/at variance with qualification: 12-3.1% - Useless: 11-2.9% - Address is nor clear/cant find the address: 10-2.6% - A vacant house: 6-1.6% - Tenant: 6-1.6% - Total: 385-100%
Estimated Error: 3,2
Over the past decade, Albania has been undergoing a transition toward a market economy and a more open society. It has faced severe internal and external challenges, such as lack of basic infrastructure, rapid collapse of output and inflation rise after the collapse of the communist regime, turmoil during the 1997 pyramid crisis, and social and economic instability because of the 1999 Kosovo crisis. Despite these shocks, Albanian economy has recovered from a very low income level through a sustained growth during the past few years, even though it remains one of the poorest countries in Europe, with GDP per capita at around 1,300$.
Based on the Living Standard Measurement Study (LSMS) 2002 survey data (wave 1, henceforth), for the first time in Albania INSTAT has computed an absolute poverty line on a nationally representative poverty survey at household level. Based on this welfare measure, one quarter (25.4 percent) of the Albanian population, or close to 790,000 individuals, were defined as poor in 2002. The distribution of poverty is also disproportionately rural, as 68 percent of the poor are in rural areas, against 32 percent in urban areas (as compared to a total urban population well over 40 percent). These estimates are quite sensitive to the choice of the poverty line, as there are a large number of households clustered around the poverty line. Income related poverty is compounded by the severe lack of access to basic infrastructure, education and health services, clean water, etc., and the ability of the Government to address these issues is complicated by high levels of internal and external migration that are not well understood.
The availability of a nationally representative survey is crucial as the paucity of household-level information has been a constraining factor in the design, implementation and evaluation of economic and social programs in Albania. Two recent surveys carried out by the Albanian Institute of Statistics (INSTAT) –the 1998 Living Conditions Survey (LCS) and the 2000 Household Budget Survey (HBS)– drew attention, once again, to the need for accurately measuring household welfare according to well-accepted standards, and for monitoring these trends on a regular basis. This target is well-achieved by drawing information over time on a panel component of LSMS 2002 households, namely the Albanian Panel Survey (APS), conducted in 2003 and 2004.
An increasing attention to the policies aimed at achieving the Millennium Development Goals (MDGs) is paid by the National Parliament of Albania, recently witnessed by the resolution approved in July 2003, where it pushes “[...] the total commitment of both state structures and civil society to achieve the MDGs in Albania by 2015”. The path towards a sustained growth is constantly monitored through the National Reports on Progress toward Achieving the MDGs, which involves a close collaboration of the UN with the national institutions, led by the National Strategy for Social and Economic Development (NSSED) Department of the Ministry of Finance. Also, in the process leading to the Poverty Reduction Strategy Paper (PRSP; also known in Albania as Growth and Poverty Reduction Strategy, GPRS), the Government of Albania reinforced its commitment to strengthening its own capacity to collect and analyze on a regular basis information it needs to inform policy-makers.
In its first phase (2001-2006), this monitoring system will include the following data collection instruments: (i) Population and Housing Census; (ii) Living Standards Measurement Surveys every 3 years, and (iii) annual panel surveys. The focus during this first phase of the monitoring system is on a periodic LSMS (in 2002 and 2005), followed by panel surveys on a sub-sample of LSMS households (APS 2003, 2004 and 2006), drawing heavily on the 2001 census information. Here our target is to illustrate the main characteristics of the APS 2004 data with reference to the LSMS.
The survey work was undertaken by the Living Standards Unit of INSTAT, with the technical assistance of the World Bank.
National coverage. Domains: Tirana, other urban,rura
Sample survey data [ssd]
Panel sample, with LSMS 2002 and 2004
The APS 2004 collects information on 1,797 valid observations at household level and 7,476 at individual level. The sample of the second and third waves of the panel (APS) has been selected from the LSMS 2002 in order to be representative of Albanian households and individuals at national level. The LSMS 2002 differs from the APS 2003 and 2004 in that the former is designed to be representative at regional level (Mountain, Central, Coastal and Tirana) as well as for urban and rural domains, while the latter are for last domains only (urban and rural)
LSMS 2002 sample design
The LSMS is based on a probability sample of housing units (HUs) within the 16 strata of the sampling frame. It is divided in three regions: Coastal, Central, and Mountain Area. In addition, urban areas of Tirana are also considered as a separate region/stratum. The three regions are further stratified in major cities (the most important cities in the region), other urban (other cities in the region), and rural. The city of Tirana and its suburbs have been implicitly stratified to improve the efficiency of the sample design. Each stratum has been divided in Enumeration Area (EA), in accordance with the 2001 Census data, and each Primary Sampling Unit (PSU) selected with probabilities proportional to the number of occupied HUs in the EA. Every EA includes occupied and unoccupied HUs. Occupied rather than total units have been used because of the large amount of empty dwellings registered in the Census data.
The Housing Unit, defined as the space occupied by one household, is taken as sampling unit because is more permanent and easy to identify compared to the household. 10 EAs for each major city (75 for Tirana) and 65 EAs for each rural region -with the exception of the mountain area which is over-represented (75 EAs)- are selected. 8 households, plus 4 eventual substitutes, have been systematically selected in each EAs. As the LSMS consists of 450 EAs, total sample size is 3,600 households.
The sample is not self-weighted, hence to obtain correct estimates data need to be weighted. The weights, at household level, are included in the dataset ("weights" file). When working at individual level, household weights must be multiplied by household size.
APS 2003-2004 sample design
The panel component selected from the LSMS is designed to provide a nationally representative sample of households and individuals within Albania. It consists of roughly half of the households in the 2002 LSMS, interviewed both in 2003 and 2004. Contrarily to what done for the LSMS, no over-sampling in the Mountain Area has been performed for the panel survey.
The sample is designed to minimize the variability in households' selection probabilities. It insures national representativeness by matching the sample distribution across strata with the population distribution drawn from 2001 Census data. In Table 3 the ex-ante sampling scheme of the 2003-2004 APS is shown.
Compared to the LSMS design, statistical precision has improved. Under equal stratum population variances hypothesis, sample design effects are expected to be around 1.02, compared to the 1.28 of the LSMS sample. Moreover, further precision is obtained by keeping all 450 EAs of LSMS in the panel sample, thus reducing the eventual bias due to clustering because of new design.
Finally, the panel survey has a number of peculiar features that should be considered when using the data. The sample is designed to focus on individuals, who have been also traced when moving from the original household to a new one. This possibility represents the only way a household can enter the panel sample if it has not been already interviewed in the wave 1 (or in wave 2 for the APS 2004). If an original survey member (OSM) moves to a new household, his/her old and new household -and their members- are both included in the panel sample. Though a moved OSM will be present in the roster of both sampled households, he/she is a valid member only in the new one. In the old household he/she is taken into account as "moved away", hence not a valid member. This might generate some confusion.
Three modalities exist to classify an individual in the third wave. First, when he/she is an OSM, that is a respondent interviewed both in wave 1 and 2. Second, when he is a rejoiner from 2002, that is an OSM not interviewed in 2003 (i.e. because temporarily absent) who returns in 2004. Third, when he/she is a new member, whenever he/she is a newborn of an original household, a member joined by an OSM or a person who co-resides with an original survey household. So the APS is an indefinite life panel study, without replacement by drawing new sample units.
From wave 2, only individuals aged 15 years and over are considered valid members, hence eligible for the interview. Individuals moved out of Albania are not accounted as valid for this survey year, though they are still eligible for future waves.
Face-to-face [f2f]
A single questionnaire on households has been used to collect information in the APS 2004. Contrary to the LSMS 2002 survey (see Basic Information Document, 2003), both in 2003 and 2004 the Diary for Household Consumption (the “booklet”), the Community questionnaire and the Price questionnaire were not repeated. The target is to collect a similar set of information (only data comparable across time is
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This layer shows particulate matter in the air sized 2.5 micrometers of smaller (PM 2.5). The data is aggregated from NASA Socioeconomic Data and Applications Center (SEDAC) gridded data into country boundaries, administrative 1 boundaries, and 50 km hex bins. The unit of measurement is micrograms per cubic meter.The layer shows the annual average PM 2.5 from 1998 to 2016, highlighting if the overall mean for an area meets the World Health Organization guideline of 10 micrograms per cubic meter annually. Areas that don't meet the guideline and are above the threshold are shown in red, and areas that are lower than the guideline are in grey.The data is averaged for each year and over the the 19 years to provide an overall picture of air quality globally. Some of the things we can learn from this layer:What is the average annual PM 2.5 value over 19 years? (1998-2016)What is the annual average PM 2.5 value for each year from 1998 to 2016?What is the statistical trend for PM 2.5 over the 19 years? (downward or upward)Are there hot spots (or cold spots) of PM 2.5 over the 19 years?How many people are impacted by the air quality in an area?What is the death rate caused by the joint effects of air pollution?Choose a different attribute to symbolize in order to reveal any of the patterns above.A space time cube was performed on a multidimensional mosaic version of the data in order to derive an emerging hot spot analysis, trends, and a 19-year average. The country and administrative 1 layers provide a population-weighted PM 2.5 value to emphasize which areas have a higher human impact. Citations:van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2018. Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR, 1998-2016. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H4ZK5DQS. Accessed 1 April 2020van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2016. Global Estimates of Fine Particulate Matter Using a Combined Geophysical-Statistical Method with Information from Satellites. Environmental Science & Technology 50 (7): 3762-3772. https://doi.org/10.1021/acs.est.5b05833.Boundaries and population figures:Antarctica is excluded from all maps because it was not included in the original NASA grids.50km hex bins generated using the Generate Tessellation tool - projected to Behrmann Equal Area projection for analysesPopulation figures generated using Zonal Statistics from the World Population Estimate 2016 layer from ArcGIS Living Atlas.Administrative boundaries from World Administrative Divisions layer from ArcGIS Living Atlas - projected to Behrmann Equal Area projection for analyses and hosted in Web MercatorSources: Garmin, CIA World FactbookPopulation figures generated using Zonal Statistics from the World Population Estimate 2016 layer from ArcGIS Living Atlas.Country boundaries from Esri 2019 10.8 Data and Maps - projected to Behrmann Equal Area projection for analyses and hosted in Web Mercator. Sources: Garmin, Factbook, CIAPopulation figures attached to the country boundaries come from the World Population Estimate 2016 Sources Living Atlas layer Data processing notes:NASA's GeoTIFF files for 19 years (1998-2016) were first brought into ArcGIS Pro 2.5.0 and put into a multidimensional mosaic dataset.For each geography level, the following was performed: Zonal Statistics were run against the mosaic as a multidimensional layer.A Space Time Cube was created to compare the 19 years of PM 2.5 values and detect hot/cold spot patterns. To learn more about Space Time Cubes, visit this page.The Space Time Cube is processed for Emerging Hot Spots where we gain the trends and hot spot results.The layers are hosted in Web Mercator Auxillary Sphere projection, but were processed using an equal area projection: Behrmann. If using this layer for analysis, it is recommended to start by projecting the data back to Behrmann.The country and administrative layer were dissolved and joined with population figures in order to visualize human impact.The dissolve tool ensures that each geographic area is only symbolized once within the map.Country boundaries were generalized post-analysis for visualization purposes. The tolerance used was 700m. If performing analysis with this layer, find detailed country boundaries in ArcGIS Living Atlas. To create the population-weighted attributes on the country and Admin 1 layers, the hex value population values were used to create the weighting. Within each hex bin, the total population figure and average PM 2.5 were multiplied.The hex bins were converted into centroids and the PM2.5 and population figures were summarized within the country and Admin 1 boundaries.The summation of the PM 2.5 values were then divided by the total population of each geography. This population value was determined by summarizing the population values from the hex bins within each geography.Some artifacts in the hex bin layer as a result of the input NASA rasters. Because the gridded surface is created from multiple satellites, there are strips within some areas that are a result of satellite paths. Some areas also have more of a continuous pattern between hex bins as a result of the input rasters.Within the country layer, an air pollution attributable death rate is included. 2016 figures are offered by the World Health Organization (WHO). Values are offered as a mean, upper value, lower value, and also offered as age standardized. Values are for deaths caused by all possible air pollution related diseases, for both sexes, and all age groups. For more information visit this page, and here for methodology. According to WHO, the world average was 95 deaths per 100,000 people.To learn the techniques used in this analysis, visit the Learn ArcGIS lesson Investigate Pollution Patterns with Space-Time Analysis by Esri's Kevin Bulter and Lynne Buie.