The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly. While it was estimated at ***** zettabytes in 2025, the forecast for 2029 stands at ***** zettabytes. Thus, global data generation will triple between 2025 and 2029. Data creation has been expanding continuously over the past decade. In 2020, the growth was higher than previously expected, caused by the increased demand due to the coronavirus (COVID-19) pandemic, as more people worked and learned from home and used home entertainment options more often.

All the details in https://www.kaggle.com/gemartin/world-bank-data-1960-to-2016/ also apply to this new dataset.

The preprocessed data has been generated using the data preparatory notebook https://www.kaggle.com/neomatrix369/chaieda-world-bank-data-1960-2016-data-prep.

Context

Extending the current dataset to enable better analysis and reasonings

Content

A number of economic, geographic, country and region-specific data and indicators from different datasets have been aggregated.

Acknowledgements

A number of Kaggle users have been helpful in the process of creation of this dataset, they have been mentioned in the data preparatory notebook https://www.kaggle.com/neomatrix369/chaieda-world-bank-data-1960-2016-data-prep.

Inspiration

Other similar aggregated datasets and competition data and notebooks.

Abstract

The documented dataset covers Enterprise Survey (ES) panel data collected in Lesotho in 2009 and 2016, as part of Africa Enterprise Surveys rollout, an initiative of the World Bank. The objective of the Enterprise Survey is to obtain feedback from enterprises on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms.

Enterprise Surveys target a sample consisting of longitudinal (panel) observations and new cross-sectional data. Panel firms are prioritized in the sample selection, comprising up to 50% of the sample in the current wave. For all panel firms, regardless of the sample, current eligibility or operating status is determined and included in panel datasets.

Lesotho ES 2009 was conducted from September 2008 to February 2009, Lesotho ES 2016 was carried out in June - August 2016. Stratified random sampling was used to select the surveyed businesses. Data was collected using face-to-face interviews.

Data from 301 establishments was analyzed: 90 businesses were from 2009 only, 89 - from 2016 only, and 122 firms were from 2009 and 2016.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs and labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90 percent of the questions objectively measure characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance.

Geographic coverage

National

Analysis unit

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

Universe

The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural private economy. It comprises: all manufacturing sectors according to the ISIC Revision 3.1 group classification (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this population definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities sectors. Companies with 100% government ownership are not eligible to participate in the Enterprise Surveys.

Kind of data

Sample survey data [ssd]

Sampling procedure

Two levels of stratification were used in this country: industry and establishment size.

Industry stratification was designed as follows: the universe was stratified as into manufacturing and services industries - Manufacturing (ISIC Rev. 3.1 codes 15 - 37), and Services (ISIC codes 45, 50-52, 55, 60-64, and 72).

For the Lesotho ES, size stratification was defined as follows: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees). Regional stratification did not take place for the Lesotho ES.

In 2009, it was not possible to obtain a single usable frame for Lesotho. Instead frames were obtained from two government branches: the Chamber of Commerce and the Ministry of Trade, Industry, Cooperatives and Marketing. Those frames were merged and duplicates removed to provide the frame used for the survey.

In 2016 ES, the sample frame consisted of listings of firms from two sources: for panel firms the list of 151 firms from the Lesotho 2009 ES was used and for fresh firms (i.e., firms not covered in 2009) firm data from Lesotho Bureau of Statistics Business Register, published in August 2015, was used.

Mode of data collection

Face-to-face [f2f]

Research instrument

The following survey instruments were used for Lesotho ES: - Manufacturing Module Questionnaire - Services Module Questionnaire

The survey is fielded via manufacturing or services questionnaires in order not to ask questions that are irrelevant to specific types of firms, e.g. a question that relates to production and nonproduction workers should not be asked of a retail firm. In addition to questions that are asked across countries, all surveys are customized and contain country-specific questions. An example of customization would be including tourism-related questions that are asked in certain countries when tourism is an existing or potential sector of economic growth. There is a skip pattern in the Service Module Questionnaire for questions that apply only to retail firms.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect "Refusal to respond" (-8) as a different option from "Don't know" (-9). b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary.

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

Abstract

The Future of Business Survey is a new source of information on small and medium-sized enterprises (SMEs). Launched in February 2016, the monthly survey - a partnership between Facebook, OECD, and The World Bank - provides a timely pulse on the economic environment in which businesses operate and who those businesses are to help inform decision-making at all levels and to deliver insights that can help businesses grow. The Future of Business Survey provides a perspective from newer and long-standing digitalized businesses and provides a unique window into a new mobilized economy.

Policymakers, researchers and businesses share a common interest in the environment in which SMEs operate, as well their outlook on the future, not least because young and innovative SMEs in particular are often an important source of considerable economic and employment growth. Better insights and timely information about SMEs improve our understanding of economic trends, and can provide new insights that can further stimulate and help these businesses grow.

To help provide these insights, Facebook, OECD and The World Bank have collaborated to develop a monthly survey that attempts to improve our understanding of SMEs in a timely and forward-looking manner. The three organizations share a desire to create new ways to hear from businesses and help them succeed in the emerging digitally-connected economy. The shared goal is to help policymakers, researchers, and businesses better understand business sentiment, and to leverage a digital platform to provide a unique source of information to complement existing indicators.

With more businesses leveraging online tools each day, the survey provides a lens into a new mobilized, digital economy and, in particular, insights on the actors: a relatively unmeasured community worthy of deeper consideration and considerable policy interest.

Geographic coverage

When the survey was initially launched in February 2016, it included 22 countries. When the survey was initially launched in February 2016, it included 22 countries. The Future of Business Survey is now conducted in over 90 countries in every region of the world.

Analysis unit

The study describes small and medium-sized enterprises.

Universe

The target population consists of SMEs that have an active Facebook business Page and include both newer and longer-standing businesses, spanning across a variety of sectors. With more businesses leveraging online tools each day, the survey provides a lens into a new mobilized, digital economy and, in particular, insights on the actors: a relatively unmeasured community worthy of deeper consideration and considerable policy interest.

Kind of data

Sample survey data [ssd]

Sampling procedure

Twice a year in over 97 countries, the Facebook Survey Team sends the Future of Business to admins and owners of Facebook-designated small business pages. When we share data from this survey, we anonymize responses to all survey questions and only share country-level data publicly. To achieve better representation of the broader small business population, we also weight our results based on known characteristics of the Facebook Page admin population.

A random sample of firms, representing the target population in each country, is selected to respond to the Future of Business Survey each month.

Mode of data collection

Internet [int]

Research instrument

The survey includes questions about perceptions of current and future economic activity, challenges, business characteristics and strategy. Custom modules include questions related to regulation, access to finance, digital payments, and digital skills. The full questionnaire is available for download.

The questionnaire was pretested by the target audience, as well as experts from the area of research interest. Additionally, steps were taken to translate the survey in order to reduce sensitivities to cultural response bias: - Respondents were given the option to respond to the survey in any of fifteen languages native to the countries in which it was conducted. - Translations were done only by native speakers, with two rounds of additional online checks in the context of the survey environment. - Translators were provided with context material for this survey (e.g., the Facebook for Business website) in order to understand the context of the survey. They were also instructed to take the English survey at least two times before starting with the translations. - Translations were discussed in a group in order to ensure a common understanding of questions and items. - The tone (formal vs. informal) of the survey was based on cultural conventions, e.g., Facebook usually uses an informal tone, while in cultures such as the Japanese this is very uncommon and thus a formal tone was used there.

Response rate

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.

Note: Response rates are calculated as the number of respondents who completed the survey divided by the total number of SMEs invited.

Sampling error estimates

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 (sampling frame of page owners does not include all relevant businesses but also may include individuals that don't represent businesses), and nonresponse error.

Note that the sample is meant to reflect the population of businesses on Facebook, not the population of small businesses in general. This group of digitized SMEs is itself a community worthy of deeper consideration and of considerable policy interest. However, care should be taken when extrapolating to the population of SMEs in general. Moreover, future work should evaluate the external validity of the sample. Particularly, respondents should be compared to the broader population of SMEs on Facebook, and the economy as a whole.

United States US: GDP: % of GDP: Gross Value Added: Industry: Manufacturing data was reported at 11.601 % in 2016. This records a decrease from the previous number of 11.919 % for 2015. United States US: GDP: % of GDP: Gross Value Added: Industry: Manufacturing data is updated yearly, averaging 12.807 % from Dec 1997 (Median) to 2016, with 20 observations. The data reached an all-time high of 16.022 % in 1997 and a record low of 11.601 % in 2016. United States US: GDP: % of GDP: Gross Value Added: Industry: Manufacturing data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Gross Domestic Product: Share of GDP. Manufacturing refers to industries belonging to ISIC divisions 15-37. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3. Note: For VAB countries, gross value added at factor cost is used as the denominator.; ; World Bank national accounts data, and OECD National Accounts data files.; Weighted average; Note: Data for OECD countries are based on ISIC, revision 4.

In 2025, smartphones across the globe used an average of 21.11 gigabytes of mobile data per month, up from 19.14 gigabytes the previous year. This figure is expected to reach 36.51 gigabytes by 2030.

As of 2019, forecasts suggest that the predictive analytics market will reach over *********** U.S. dollars in total revenue. By 2022 the market is expected to reach nearly ** billion dollars in annual revenue as an increasingly large number of businesses make use of predictive analytics techniques for everything from fraud detection to medical diagnosis. Predictive analytics The field of predictive analytics involves the use of various statistical methods and models within businesses to make predictions about a wide range of future outcomes. Predictive analytical analysis is already one of the most widely adopted intelligent automation technologies in the world, with over ** percent of major enterprises deploying smart analytics that include predictive analytics. As business interactions around the world become increasingly digitalized, massive amounts of data are created which can be evaluated through predictive analytics tools in order to give users a better understanding of market dynamics and underlying trends. Considering this, it is no surprise that predictive models rank as the one of the top big data technology trends around the world.

Nepal NP: Import Value Index data was reported at 549.815 2000=100 in 2016. This records an increase from the previous number of 422.860 2000=100 for 2015. Nepal NP: Import Value Index data is updated yearly, averaging 90.210 2000=100 from Dec 1980 (Median) to 2016, with 37 observations. The data reached an all-time high of 549.815 2000=100 in 2016 and a record low of 21.755 2000=100 in 1980. Nepal NP: Import Value Index data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Nepal – Table NP.World Bank.WDI: Trade Index. Import value indexes are the current value of imports (c.i.f.) converted to U.S. dollars and expressed as a percentage of the average for the base period (2000). UNCTAD's import value indexes are reported for most economies. For selected economies for which UNCTAD does not publish data, the import value indexes are derived from import volume indexes (line 73) and corresponding unit value indexes of imports (line 75) in the IMF's International Financial Statistics.; ; United Nations Conference on Trade and Development, Handbook of Statistics and data files, and International Monetary Fund, International Financial Statistics.; ;

Catholic Carbon Footprint Story Map Map:DataBurhans, Molly A., Cheney, David M., Gerlt, R.. . “PerCapita_CO2_Footprint_InDioceses_FULL”. Scale not given. Version 1.0. MO and CT, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2019.Map Development: Molly BurhansMethodologyThis is the first global Carbon footprint of the Catholic population. We will continue to improve and develop these data with our research partners over the coming years. While it is helpful, it should also be viewed and used as a "beta" prototype that we and our research partners will build from and improve. The years of carbon data are (2010) and (2015 - SHOWN). The year of Catholic data is 2018. The year of population data is 2016. Care should be taken during future developments to harmonize the years used for catholic, population, and CO2 data.1. Zonal Statistics: Esri Population Data and Dioceses --> Population per dioceses, non Vatican based numbers2. Zonal Statistics: FFDAS and Dioceses and Population dataset --> Mean CO2 per Diocese3. Field Calculation: Population per Diocese and Mean CO2 per diocese --> CO2 per Capita4. Field Calculation: CO2 per Capita * Catholic Population --> Catholic Carbon FootprintAssumption: PerCapita CO2Deriving per-capita CO2 from mean CO2 in a geography assumes that people's footprint accounts for their personal lifestyle and involvement in local business and industries that are contribute CO2. Catholic CO2Assumes that Catholics and non-Catholic have similar CO2 footprints from their lifestyles.Derived from:A multiyear, global gridded fossil fuel CO2 emission data product: Evaluation and analysis of resultshttp://ffdas.rc.nau.edu/About.htmlRayner et al., JGR, 2010 - The is the first FFDAS paper describing the version 1.0 methods and results published in the Journal of Geophysical Research.Asefi et al., 2014 - This is the paper describing the methods and results of the FFDAS version 2.0 published in the Journal of Geophysical Research.Readme version 2.2 - A simple readme file to assist in using the 10 km x 10 km, hourly gridded Vulcan version 2.2 results.Liu et al., 2017 - A paper exploring the carbon cycle response to the 2015-2016 El Nino through the use of carbon cycle data assimilation with FFDAS as the boundary condition for FFCO2."S. Asefi‐Najafabady P. J. Rayner K. R. Gurney A. McRobert Y. Song K. Coltin J. Huang C. Elvidge K. BaughFirst published: 10 September 2014 https://doi.org/10.1002/2013JD021296 Cited by: 30Link to FFDAS data retrieval and visualization: http://hpcg.purdue.edu/FFDAS/index.phpAbstractHigh‐resolution, global quantification of fossil fuel CO2 emissions is emerging as a critical need in carbon cycle science and climate policy. We build upon a previously developed fossil fuel data assimilation system (FFDAS) for estimating global high‐resolution fossil fuel CO2 emissions. We have improved the underlying observationally based data sources, expanded the approach through treatment of separate emitting sectors including a new pointwise database of global power plants, and extended the results to cover a 1997 to 2010 time series at a spatial resolution of 0.1°. Long‐term trend analysis of the resulting global emissions shows subnational spatial structure in large active economies such as the United States, China, and India. These three countries, in particular, show different long‐term trends and exploration of the trends in nighttime lights, and population reveal a decoupling of population and emissions at the subnational level. Analysis of shorter‐term variations reveals the impact of the 2008–2009 global financial crisis with widespread negative emission anomalies across the U.S. and Europe. We have used a center of mass (CM) calculation as a compact metric to express the time evolution of spatial patterns in fossil fuel CO2 emissions. The global emission CM has moved toward the east and somewhat south between 1997 and 2010, driven by the increase in emissions in China and South Asia over this time period. Analysis at the level of individual countries reveals per capita CO2 emission migration in both Russia and India. The per capita emission CM holds potential as a way to succinctly analyze subnational shifts in carbon intensity over time. Uncertainties are generally lower than the previous version of FFDAS due mainly to an improved nightlight data set."Global Diocesan Boundaries:Burhans, M., Bell, J., Burhans, D., Carmichael, R., Cheney, D., Deaton, M., Emge, T. Gerlt, B., Grayson, J., Herries, J., Keegan, H., Skinner, A., Smith, M., Sousa, C., Trubetskoy, S. “Diocesean Boundaries of the Catholic Church” [Feature Layer]. Scale not given. Version 1.2. Redlands, CA, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2016.Using: ArcGIS. 10.4. Version 10.0. Redlands, CA: Environmental Systems Research Institute, Inc., 2016.Boundary ProvenanceStatistics and Leadership DataCheney, D.M. “Catholic Hierarchy of the World” [Database]. Date Updated: August 2019. Catholic Hierarchy. Using: Paradox. Retrieved from Original Source.Catholic HierarchyAnnuario Pontificio per l’Anno .. Città del Vaticano :Tipografia Poliglotta Vaticana, Multiple Years.The data for these maps was extracted from the gold standard of Church data, the Annuario Pontificio, published yearly by the Vatican. The collection and data development of the Vatican Statistics Office are unknown. GoodLands is not responsible for errors within this data. We encourage people to document and report errant information to us at data@good-lands.org or directly to the Vatican.Additional information about regular changes in bishops and sees comes from a variety of public diocesan and news announcements.GoodLands’ polygon data layers, version 2.0 for global ecclesiastical boundaries of the Roman Catholic Church:Although care has been taken to ensure the accuracy, completeness and reliability of the information provided, due to this being the first developed dataset of global ecclesiastical boundaries curated from many sources it may have a higher margin of error than established geopolitical administrative boundary maps. Boundaries need to be verified with appropriate Ecclesiastical Leadership. The current information is subject to change without notice. No parties involved with the creation of this data are liable for indirect, special or incidental damage resulting from, arising out of or in connection with the use of the information. We referenced 1960 sources to build our global datasets of ecclesiastical jurisdictions. Often, they were isolated images of dioceses, historical documents and information about parishes that were cross checked. These sources can be viewed here:https://docs.google.com/spreadsheets/d/11ANlH1S_aYJOyz4TtG0HHgz0OLxnOvXLHMt4FVOS85Q/edit#gid=0To learn more or contact us please visit: https://good-lands.org/Esri Gridded Population Data 2016DescriptionThis layer is a global estimate of human population for 2016. Esri created this estimate by modeling a footprint of where people live as a dasymetric settlement likelihood surface, and then assigned 2016 population estimates stored on polygons of the finest level of geography available onto the settlement surface. Where people live means where their homes are, as in where people sleep most of the time, and this is opposed to where they work. Another way to think of this estimate is a night-time estimate, as opposed to a day-time estimate.Knowledge of population distribution helps us understand how humans affect the natural world and how natural events such as storms and earthquakes, and other phenomena affect humans. This layer represents the footprint of where people live, and how many people live there.Dataset SummaryEach cell in this layer has an integer value with the estimated number of people likely to live in the geographic region represented by that cell. Esri additionally produced several additional layers World Population Estimate Confidence 2016: the confidence level (1-5) per cell for the probability of people being located and estimated correctly. World Population Density Estimate 2016: this layer is represented as population density in units of persons per square kilometer.World Settlement Score 2016: the dasymetric likelihood surface used to create this layer by apportioning population from census polygons to the settlement score raster.To use this layer in analysis, there are several properties or geoprocessing environment settings that should be used:Coordinate system: WGS_1984. This service and its underlying data are WGS_1984. We do this because projecting population count data actually will change the populations due to resampling and either collapsing or splitting cells to fit into another coordinate system. Cell Size: 0.0013474728 degrees (approximately 150-meters) at the equator. No Data: -1Bit Depth: 32-bit signedThis layer has query, identify, pixel, and export image functions enabled, and is restricted to a maximum analysis size of 30,000 x 30,000 pixels - an area about the size of Africa.Frye, C. et al., (2018). Using Classified and Unclassified Land Cover Data to Estimate the Footprint of Human Settlement. Data Science Journal. 17, p.20. DOI: http://doi.org/10.5334/dsj-2018-020.What can you do with this layer?This layer is unsuitable for mapping or cartographic use, and thus it does not include a convenient legend. Instead, this layer is useful for analysis, particularly for estimating counts of people living within watersheds, coastal areas, and other areas that do not have standard boundaries. Esri recommends using the Zonal Statistics tool or the Zonal Statistics to Table tool where you provide input zones as either polygons, or raster data, and the tool will summarize the count of population within those zones. https://www.esri.com/arcgis-blog/products/arcgis-living-atlas/data-management/2016-world-population-estimate-services-are-now-available/

This service is available to all ArcGIS Online users with organizational accounts. For more information on this service, including the terms of use, visit us at http://goto.arcgisonline.com/landscape7/World_Population_Density_Estimate_2016.This layer is a global estimate of human population density for 2016. The advantage population density affords over raw counts is the ability to compare levels of persons per square kilometer anywhere in the world. Esri calculated density by converting the the World Population Estimate 2016 layer to polygons, then added an attribute for geodesic area, which allowed density to be derived, and that was converted back to raster. A population density raster is better to use for mapping and visualization than a raster of raw population counts because raster cells are square and do not account for area. For instance, compare a cell with 185 people in northern Quito, Ecuador, on the equator to a cell with 185 people in Edmonton, Canada at 53.5 degrees north latitude. This is difficult because the area of the cell in Edmonton is only 35.5% of the area of a cell in Quito. The cell in Edmonton represents a density of 9,810 persons per square kilometer, while the cell in Quito only represents a density of 3,485 persons per square kilometer. Dataset SummaryEach cell in this layer has an integer value with the estimated number of people per square kilometer likely to live in the geographic region represented by that cell. Esri additionally produced several additional layers: World Population Estimate 2016: this layer contains estimates of the count of people living within the the area represented by the cell. World Population Estimate Confidence 2016: the confidence level (1-5) per cell for the probability of people being located and estimated correctly. World Settlement Score 2016: the dasymetric likelihood surface used to create this layer by apportioning population from census polygons to the settlement score raster.To use this layer in analysis, there are several properties or geoprocessing environment settings that should be used:Coordinate system: WGS_1984. This service and its underlying data are WGS_1984. We do this because projecting population count data actually will change the populations due to resampling and either collapsing or splitting cells to fit into another coordinate system. Cell Size: 0.0013474728 degrees (approximately 150-meters) at the equator. No Data: -1Bit Depth: 32-bit signedThis layer has query, identify, pixel, and export image functions enabled, and is restricted to a maximum analysis size of 30,000 x 30,000 pixels - an area about the size of Africa.What can you do with this layer?This layer is primarily intended for cartography and visualization, but may also be useful for analysis, particularly for estimating where people living above specified densities. There are two processing templates defined for this layer: the default, "World Population Estimated 2016 Density Classes" uses a classification, described above, to show locations of levels of rural and urban populations, and should be used for cartography and visualization; and "None," which provides access to the unclassified density values, and should be used for analysis. The breaks for the classes are at the following levels of persons per square kilometer:100 - Rural (3.2% [0.7%] of all people live at this density or lower) 400 - Settled (13.3% [4.1%] of all people live at this density or lower)1,908 - Urban (59.4% [81.1%] of all people live at this density or higher)16,978 - Heavy Urban (13.0% [24.2%] of all people live at this density or higher)26,331 - Extreme Urban (7.8% [15.4%] of all people live at this density or higher) Values over 50,000 are likely to be erroneous due to spatial inaccuracies in source boundary dataNote the above class breaks were derived from Esri's 2015 estimate, which have been maintained for the sake of comparison. The 2015 percentages are in gray brackets []. The differences are mostly due to improvements in the model and source data. While improvements in the source data will continue, it is hoped the 2017 estimate will produce percentages that shift less.For analysis, Esri recommends using the Zonal Statistics tool or the Zonal Statistics to Table tool where you provide input zones as either polygons, or raster data, and the tool will summarize the average, highest, or lowest density within those zones.

Abstract

The documented dataset covers Enterprise Survey (ES) panel data collected in Dominican Republic in 2010 and 2016, as part of Latin America and the Caribbean Enterprise Surveys rollout, an initiative of the World Bank. The objective of the Enterprise Survey is to obtain feedback from enterprises on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms.

Enterprise Surveys target a sample consisting of longitudinal (panel) observations and new cross-sectional data. Panel firms are prioritized in the sample selection, comprising up to 50% of the sample. For all panel firms, regardless of the sample, current eligibility or operating status is determined and included in panel datasets.

Dominican Republic ES 2010 was conducted in March - September 2011, ES 2016 was carried out in August 2016 - April 2017. Stratified random sampling was used to select the surveyed businesses. Data was collected using face-to-face interviews.

Data from 719 establishments was analyzed: 257 businesses were from 2010 ES only, 256 - from 2016 only, and 206 firms were from 2010 and 2016.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs and labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90 percent of the questions objectively measure characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance.

Geographic coverage

National

Analysis unit

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

Universe

The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural private economy. It comprises: all manufacturing sectors according to the ISIC Revision 3.1 group classification (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this population definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities sectors. Companies with 100% government ownership are not eligible to participate in the Enterprise Surveys.

Kind of data

Sample survey data [ssd]

Sampling procedure

Three levels of stratification were used in this country: industry, establishment size and region.

Industry stratification was designed as follows: the universe was stratified as into manufacturing and services industries - Manufacturing (ISIC Rev. 3.1 codes 15 - 37), and Services (ISIC codes 45, 50-52, 55, 60-64, and 72).

Size stratification was defined as follows: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees).

In 2016, regional stratification was done across three regions: Santo Domingo, Santiago-Puerto Plata-Espaillat and the Rest of the country.

The sample frame consisted of listings of firms from three sources: for panel firms the list of 360 firms from the Dominican Republic 2010 ES was used and for fresh firms (i.e., firms not covered in 2010) a listing of firms obtained from El Directorio de Empresas y Establecimientos (DEE) 2015 and Oficina Nacional de Estadística (ONE), were used.

In 2010, regional stratification was defined in two locations: Santo Domingo and the rest of the country (constituted by urban centers around Santiago and Higuey). For the purposes of sampling, the rest of the country was treated as one area.

The sample frame for 2010 ES was provided by the Oficina Nacional de Estadistica (ONE), dated 2009.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect "Refusal to respond" (-8) as a different option from "Don't know" (-9). b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary.

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

NBA Rookies Performance Statistics and Minutes Played: 1980-2016

Tracking Basketball Prodigies' Growth and Achievements

By Gabe Salzer [source]

About this dataset

This dataset contains essential performance statistics for NBA rookies from 1980-2016. Here you can find minute per game stats, points scored, field goals made and attempted, three-pointers made and attempted, free throws made and attempted (with the respective percentages for each), offensive rebounds, defensive rebounds, assists, steals blocks turnovers efficiency rating and Hall of Fame induction year. It is organized in descending order by minutes played per game as well as draft year. This Kaggle dataset is an excellent resource for basketball analysts to gain a better understanding of how rookies have evolved over the years—from their stats to how they were inducted into the Hall of Fame. With its great detail on individual players' performance data this dataset allows you to compare their performances against different eras in NBA history along with overall trends in rookie statistics. Compare rookies drafted far apart or those that played together- whatever your goal may be!

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How to use the dataset

This dataset is perfect for providing insight into the performance of NBA rookies over an extended period of time. The data covers rookie stats from 1980 to 2016 and includes statistics such as points scored, field goals made, free throw percentage, offensive rebounds, defensive rebounds and assists. It also provides the name of each rookie along with the year they were drafted and their Hall of Fame class.

This data set is useful for researching how rookies’ stats have changed over time in order to compare different eras or identify trends in player performance. It can also be used to evaluate players by comparing their stats against those of other players or previous years’ stats.

In order to use this dataset effectively, a few tips are helpful:

• Consider using Field Goal Percentage (FG%), Three Point Percentage (3P%) and Free Throw Percentage (FT%) to measure a player’s efficiency beyond just points scored or field goals made/attempted (FGM/FGA).

• Lookout for anomalies such as low efficiency ratings despite high minutes played as this could indicate that either a player has not had enough playing time in order for their statistics to reach what would be per game average when playing more minutes or that they simply did not play well over that short period with limited opportunities.

• Try different visualizations with the data such as histograms, line graphs and scatter plots because each may offer different insights into varied aspects of the data set like comparison between individual years vs aggregate trends over multiple years etc.

  Lastly it is important keep in mind whether you're dealing with cumulative totals over multiple seasons versus looking at individual season averages or per game numbers when attempting analysis on these sets!

Research Ideas

• Evaluating the performance of historical NBA rookies over time and how this can help inform future draft picks in the NBA.
• Analysing the relative importance of certain performance stats, such as three-point percentage, to overall success and Hall of Fame induction from 1980-2016.
• Comparing rookie seasons across different years to identify common trends in terms of statistical contributions and development over time

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: NBA Rookies by Year_Hall of Fame Class.csv | Column name | Description | |:-----------------------|:------------------------------------------------------------------| | Name | The name of...

US Tobacco Use Trends by Age and State

Age and State-wise Trends in US Tobacco Use 2011-2016

By Throwback Thursday [source]

About this dataset

The US Tobacco Use 2011-2016 dataset provides comprehensive information on tobacco use trends in the United States from 2011 to 2016. The data is derived from the CDC Behavioral Risk Factor Survey, which collects data on tobacco use across different age groups and states. The dataset includes variables such as age group, year of data collection, type of tobacco product used, state abbreviation where the data was collected, and the corresponding percentage or number representing the tobacco use data. Additionally, it specifies the unit of measurement for the data value (e.g., percentage or number). This dataset aims to offer valuable insights into patterns of tobacco use in different demographic segments and geographical locations within the United States over a six-year period

How to use the dataset

Step 1: Familiarize yourself with the columns: - Year: Represents the year in which the data was collected. - State Abbreviation: Indicates the abbreviation of the state where the data was collected. - Tobacco Type: Specifies the type of tobacco product used. - Data Value: Represents either a percentage or a number that represents tobacco use data. - Data Value Unit: Indicates whether the measurement is a percentage or a number. - Age Group: Specifies which age group corresponds to each piece of tobacco use data.

Step 2: Identify your area of interest: Consider what specific information you are looking for within this dataset. For example, if you want to examine trends in cigarette smoking among young adults (age group), select relevant columns like Year, State Abbreviation, Data Value (percentage/number), etc. By narrowing down your focus, you can analyze specific trends efficiently.

Step 3: Filter and sort your data: Use filtering features provided by spreadsheet software or coding languages (e.g., Python) to extract only relevant information based on your area of interest. You can filter by year(s), state(s), age group(s), or type(s) of tobacco product used using logical operators such as equal (=) and not equal (!=). This way, you can obtain a subset of data that meets your criteria for analysis conveniently.

Step 4: Analyze trends over time: Utilize line charts or bar graphs to visualize changes in tobacco use percentages or numbers over the years. This will allow you to identify any significant patterns or fluctuations, observing whether there are any consistent trends across different states or age groups.

Step 5: Compare tobacco use between states: To assess the differences in tobacco use across various states, aggregate and compare the data using statistical measures such as averages, medians, and standard deviations. By identifying states with higher or lower tobacco use rates, you can gain insights into potential factors affecting these patterns (e.g., state-specific regulations, cultural norms).

Step 6: Explore variations by age group: Investigate how tobacco use varies among different age groups. Compare percentages/

Research Ideas

• Analyzing trends in tobacco use by age and state: This dataset provides information on tobacco use in the United States from 2011 to 2016, allowing for the analysis of trends over time and differences between states. Researchers or policymakers can use this information to examine changes in tobacco consumption rates and identify patterns or factors influencing tobacco use across different age groups and states.
• Comparing the effectiveness of tobacco control measures: With this dataset, it is possible to assess how different tobacco control measures implemented by states have impacted tobacco consumption rates. By comparing data on tobacco use with specific policies, such as smoke-free laws or increased taxation, researchers can evaluate the effectiveness of these interventions and guide future public health initiatives.
• Investigating disparities in tobacco use: By examining data on age, state, and type of tobacco product used, it is possible to explore disparities in smoking prevalence across different demographic groups and geographic areas. This dataset can be used to identify populations that are more susceptible to smoking or are experiencing higher rates of cigarette usage compared to other groups. This information can inform targeted interventions aimed at reducing these disparities and promoting healthier behaviors among vulnerable populations

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

...

Abstract

The survey was conducted in Côte d'Ivoire between July 2016 and February 2017 as part of Enterprise Surveys project, an initiative of the World Bank. The objective of the survey is to obtain feedback from enterprises on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms. Through interviews with firms in the manufacturing and services sectors, the survey assesses the constraints to private sector growth and creates statistically significant business environment indicators that are comparable across countries. Only registered businesses are surveyed in the Enterprise Survey.

Data from 361 establishments was analyzed. Stratified random sampling was used to select the surveyed businesses. The data was collected using face-to-face interviews.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs and labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90 percent of the questions objectively ascertain characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance.

Geographic coverage

National

Analysis unit

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

Universe

The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural private economy. It comprises: all manufacturing sectors according to the ISIC Revision 3.1 group classification (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this population definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities sectors. Companies with 100% government ownership are not eligible to participate in the Enterprise Surveys.

Kind of data

Sample survey data [ssd]

Sampling procedure

Three levels of stratification were used in this country: industry, establishment size, and region.

Industry stratification was designed in the way that follows: the universe was stratified into Manufacturing industries (ISIC Rev. 3.1 codes 15 - 37), Retail Industries (ISIC code 52) and Other Services industries (ISIC codes 45, 50-51, 55, 60-64, and 72).

For the Côte d'Ivoire ES, size stratification was defined as follows: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees).

Regional stratification was done across two regions: Abidjan and the rest of the country. The rest of the country includes Bas-Sassandra, Sassandra-Marahoué, Gôh-Djiboua, Lagunes, and Yamoussoukro.

The sample frame consisted of listings of firms from two sources: for panel firms the list of 526 firms from the Côte d'Ivoire 2009 ES was used, and for fresh firms (i.e., firms not covered in 2009) lists obtained from the Central des Bilans database, INS 2012 was used.

Given the impact that non-eligible units included in the sample universe may have on the results, adjustments may be needed when computing the appropriate weights for individual observations. The percentage of confirmed non-eligible units as a proportion of the total number of sampled establishments contacted for the survey was 0.4% (3 out of 849 establishments).

Mode of data collection

Face-to-face [f2f]

Research instrument

The following survey instruments are available: - Manufacturing Module Questionnaire - Services Module Questionnaire

Questionnaires have common questions (core module) and respectfully additional manufacturing and services specific questions.

The eligible manufacturing industries have been surveyed using the Manufacturing questionnaire (includes the core module, plus manufacturing specific questions). Retail firms have been interviewed using the Services questionnaire (includes the core module plus retail specific questions) and the residual eligible services have been covered using the Services questionnaire (includes the core module). Each variation of the questionnaire is identified by the index variable, a0.

The survey is fielded via manufacturing or services questionnaires in order not to ask questions that are irrelevant to specific types of firms, e.g. a question that relates to production and nonproduction workers should not be asked of a retail firm. In addition to questions that are asked across countries, all surveys are customized and contain country-specific questions. An example of customization would be including tourism-related questions that are asked in certain countries when tourism is an existing or potential sector of economic growth.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect "Refusal to respond" (-8) as a different option from "Don't know" (-9). b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary.

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

The share of interviews per contacted establishments was 0.42. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The share of rejections per contact was 0.51.

Abstract

The Household Income and Expenditure Survey (HIES) is conducted by National Bureau of Statistics (NBS) with the most recent HIES conducted in 2016. In HIES 2016, 330 enumeration blocks were randomly selected from all 20 administrative Atolls and Male' with a sample of 4,985 households. HIES 2016 is the first such survey where the sample was designed in such a way that the results are representative at the level of each Atoll in addition to Male'. The survey was conducted in 172 administrative islands (excluding Male') in the country at the time. The high coverage of the islands and the resulting travel costs increased the total cost.

The first nationwide HIES conducted in 2002-2003 covered 834 households from the capital Male' and 40 islands randomly selected from all the Atolls. And the second national wide HIES was conducted in 2009-2010 covered 600 households from the capital Male' and 1,460 households from the islands randomly selected from all the Atolls.

NBS plans to conduct a nationwide HIES every 5 years in the future. Due to extensive revisions in the design of the survey instrument, results on poverty are not comparable to previous years.

Geographic coverage

The geographic domains of analysis for the HIES are the 21 atolls of the Maldives, as well as the national level. There is also interest in obtaining HIES results at the national level for the following administrative island size groups: (1) less than 500 population; (2) 501 to 1000 population; (3) 1001 to 2000 population; and (4) greater than 2000 population. Data were not collected in resort and industrial islands.

Analysis unit

household and individual

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame for the HIES 2016 is based on the summary data and cartography from the 2014 Maldives Population and Housing Census. The survey covers all of the household-based population in the administrative islands of each atoll of the Maldives, but excluded the institutional population (for example, persons in prisons, hospitals, military barracks and school dormitories).

A stratified two-stage sample design is used for the HIES. The primary sampling units (PSUs) selected at the first stage for the administrative islands are the enumeration blocks (EBs), which are small operational areas defined on maps for the 2014 Census enumeration. The average number of households per EB is 65.

Sampling deviation

Data were not collected in resort and industrial islands

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was developed by the National Bureau of Statistics (NBS) in consultation with the World Bank (WB), International Labour Organization (ILO) and United National Economic and Social Commission for Asia and the Pacific (UNESCAP). Several meetings were conducted to discuss the HIES questionnaire during 2015, beginning with a data users workshop held on 22 April 2015. After conducting several pretests (K.Gulhu, K. Dhiffushi, K.Himmafushi, and Male') during the period June 2015 to January 2016, the questionnaire was finalized in January 2016.

In order to accommodate important data requirements of other government agencies, meetings were held with relevant personnel. In this regard focused discussions were held with Ministry of Tourism to incorporate the domes??c tourism into the HIES Questionnaire. Similarly, meetings were held with Ministry of Health to formulate the questions to capture details of health expenditure required to compile National Health Accounts.

During the HIES questionnaire design, International Labour Organization (ILO) provided the technical guidance in the development of Labour Force module, which was newly introduced in HIES 2016 according to the most recent ILO guidelines. World Bank (WB) provided the technical guidance to improve the methodology to better capture the poverty aspects, with a special focus on including questions relevant to capture the ownership of durable goods and their user value, capture food consumption and food away by a newly introduction food consumption module, and to better capturing the rental value of owner occupied housing. Technical experts from World Bank were involved in some of the pretests and during the questionnaire finalization process. United Nations Economic and Social Commission for Asia and the Pacific (UNESCAP), Statistics advisor provided overall technical guidance in development of the questionnaire, during the data users workshop and participated in initial pretests. This work was led by the technical team of NBS.

Cleaning operations

As the survey was on hold during the Ramadan period, the manual editing and coding of the 3 batch of the forms was carried out during Ramadan period. The coding of data started during June 2016 and was able to complete by the end of July 2016 using 10 coders who also worked as data collection officers in the survey. In order to reduce the coding errors and also to maintain consistency, 4 staff from the NBS was assigned as supervisors during the coding operation.

Coding of the second batch of the questionnaires started during December 2016 using 6 coders and additional staff from NBS were actively involved in the coding.

The classification used to code industry was International Standard Industrial Classification of all Economic Activities (ISIC) Rev. 4 and to code occupation, International Standard Classification of Occupation (ISCO) 08 was used. Classification of Individual Consumption According to purpose (COICOP), 2003 was used to give code for food and non-food items in the forms. COICOP codes were given at 7-digit level for food items and non-food items. Most of the COICOP was already pre-coded in the questionnaire and only few needed to be coded. Revision of the international Standard.

During the manual editing, all the questionnaires by household level were stamped together and assigned a serial number to the household which was provided by the data entry team. Form 4(Individual form) and Form 3 (Expenditure Unit form) information was verified with Form 2 (member listing form) information. Coders verified if all the members in Form 2 was recorded in Form 4. If the and sex was not filled in Form 4 (Individual form) than coders transferred this information from Form 2 to Form 4. In form 3 (expenditure unit form) if the expenditure unit number was missing this information also was transferred from form 2 to form 3. These checks were necessary to done before sending to data entry as Form 2 (member listing form) was decided not to enter. Classification of Education (ISCED) 39c/19, resolution 20 was used to identified the field of education. ISCED code was given at 4-digit level code with first two digits was from ISCED and last two digits was localized one code produced by the NBS to detail out the field of education. Atoll Island codes were the codes used in Census 2014. ISIC, ISCO and Atoll Island codes were in four-digit level.

Response rate

98.5% response rates for the number of sampled households

Abstract

This survey was conducted in Cambodia between February - June 2016, as part of the Enterprise Survey project, an initiative of the World Bank. The objective of the survey is to obtain feedback from enterprises on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms. Through interviews with firms in the manufacturing and services sectors, the survey assesses the constraints to private sector growth and creates statistically significant business environment indicators that are comparable across countries. Only registered businesses are surveyed in the Enterprise Survey.

Data from 373 establishments was analyzed. Stratified random sampling was used to select the surveyed businesses. The data was collected using face-to-face interviews.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90% of the questions objectively ascertain characteristics of a country's business environment. The remaining questions assess the survey respondents' opinions on what are the obstacles to firm growth and performance.

Geographic coverage

Phnom Penh, Plains, Mountains, Coastal and Tonle Sap

Analysis unit

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 must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population, or universe of the study, is the non-agricultural economy. It comprises: all manufacturing sectors according to the group classification of ISIC Revision 3.1: (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities-sectors.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample was selected using stratified random sampling. Three levels of stratification were used in this country: industry, establishment size, and region.

Industry stratification was designed in the way that follows: the universe was stratified into one manufacturing industry and two services industries- Manufacturing (ISIC 3.1 codes 15 - 37), Retail (ISIC code 52), and Other Services (ISIC codes 45, 50, 51, 55, 60-64, and 72).

For the Cambodia ES, size stratification was defined as follows: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees).

Regional stratification for the Cambodia ES was done across five regions: Phnom Penh, Plains, Mountains, Coastal and Tonle Sap.

The sample frame consisted of listings of firms from two sources: First, for panel firms the list of 472 firms from the Cambodia 2013 ES was used. Second, for fresh firms (i.e., firms not covered in 2013), data from the National Institute of Statistics (NIS) was used.

The quality of the frame was enhanced by the verification process conducted by Mekong Economics. However, the sample frame was not immune from the typical problems found in establishment surveys: positive rates of non-eligibility, repetition, non-existent units, etc.

Given the impact that non-eligible units included in the sample universe may have on the results, adjustments may be needed when computing the appropriate weights for individual observations. The percentage of confirmed non-eligible units as a proportion of the total number of sampled establishments contacted for the survey was 0% (0 out of 984 establishments).

Mode of data collection

Face-to-face [f2f]

Research instrument

The structure of the data base reflects the fact that two different versions of the survey instrument were used for all registered establishments. Questionnaires have common questions (core module) and respectfully additional manufacturing- and services-specific questions. The eligible manufacturing industries have been surveyed using the Manufacturing questionnaire (includes the core module, plus manufacturing specific questions). Retail firms have been interviewed using the Services questionnaire (includes the core module plus retail specific questions) and the residual eligible services have been covered using the Services questionnaire (includes the core module). Each variation of the questionnaire is identified by the index variable, a0.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect "Refusal to respond" (-8) as a different option from "Don't know" (-9). b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary.

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

The number of interviews per contacted establishments was 0.38. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The number of rejections per contact was 0.08.

Abstract

This survey was conducted in Thailand between November 2015 and June 2016, as part of the Enterprise Survey project, an initiative of the World Bank. The objective of the survey is to obtain feedback from enterprises on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms. Through interviews with firms in the manufacturing and services sectors, the survey assesses the constraints to private sector growth and creates statistically significant business environment indicators that are comparable across countries. Only registered businesses are surveyed in the Enterprise Survey.

Data from 1,000 establishments was analyzed. Stratified random sampling was used to select the surveyed businesses. The data was collected using face-to-face interviews.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90% of the questions objectively ascertain characteristics of a country's business environment. The remaining questions assess the survey respondents' opinions on what are the obstacles to firm growth and performance.

Geographic coverage

National

Analysis unit

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 must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population, or universe of the study, is the non-agricultural economy. It comprises: all manufacturing sectors according to the group classification of ISIC Revision 3.1: (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities-sectors.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample was selected using stratified random sampling. Three levels of stratification were used in this country: industry, establishment size, and region.

Industry stratification was designed in the way that follows: the universe was stratified into five manufacturing industries and two services industries- Food and Beverages (ISIC Rev. 3.1 code 15), Garments (ISIC code 18), Rubber and Plastics (ISIC code 25), Electronic Products (ISIC codes 31 and 32), Other Manufacturing (ISIC codes 16,17, 19-24, 26-29, 30, 33-37), Retail (ISIC code 52) and Other Services (ISIC codes 45, 50, 51, 55, 60-64, and 72).

For the Thailand ES, size stratification was defined as follows: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees).

Regional stratification for the Thailand ES was done across five regions: Bangkok, Central, North, Northeast, and South.

The sample frame consisted of a listings of firms from the National Statistical Office of Thailand; no panel firms were included in the sample frame for Thailand ES 2016.

The quality of the frame was enhanced by the verification process conducted Mekong Economics. However, the sample frame was not immune from the typical problems found in establishment surveys: positive rates of non-eligibility, repetition, non-existent units, etc.

Given the impact that non-eligible units included in the sample universe may have on the results, adjustments may be needed when computing the appropriate weights for individual observations. The percentage of confirmed non-eligible units as a proportion of the total number of sampled establishments contacted for the survey was 1.5% (73 out of 4,866 establishments).

Mode of data collection

Face-to-face [f2f]

Research instrument

The structure of the data base reflects the fact that two different versions of the survey instrument were used for all registered establishments. Questionnaires have common questions (core module) and respectfully additional manufacturing- and services-specific questions. The eligible manufacturing industries have been surveyed using the Manufacturing questionnaire (includes the core module, plus manufacturing specific questions). Retail firms have been interviewed using the Services questionnaire (includes the core module plus retail specific questions) and the residual eligible services have been covered using the Services questionnaire (includes the core module). Each variation of the questionnaire is identified by the index variable, a0.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect "Refusal to respond" (-8) as a different option from "Don't know" (-9). b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary.

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

The number of interviews per contacted establishments was 0.21. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The number of rejections per contact was 0.12.

Israel IL: GDP: % of GDP: Gross Value Added: Services data was reported at 77.858 % in 2016. This records an increase from the previous number of 77.696 % for 2015. Israel IL: GDP: % of GDP: Gross Value Added: Services data is updated yearly, averaging 75.411 % from Dec 1995 (Median) to 2016, with 22 observations. The data reached an all-time high of 77.858 % in 2016 and a record low of 71.883 % in 1995. Israel IL: GDP: % of GDP: Gross Value Added: Services data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Israel – Table IL.World Bank: Gross Domestic Product: Share of GDP. Services correspond to ISIC divisions 50-99 and they include value added in wholesale and retail trade (including hotels and restaurants), transport, and government, financial, professional, and personal services such as education, health care, and real estate services. Also included are imputed bank service charges, import duties, and any statistical discrepancies noted by national compilers as well as discrepancies arising from rescaling. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The industrial origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3. Note: For VAB countries, gross value added at factor cost is used as the denominator.; ; World Bank national accounts data, and OECD National Accounts data files.; Weighted Average; Note: Data for OECD countries are based on ISIC, revision 4.

The Gross Domestic Product (GDP) in the United States was worth 29184.89 billion US dollars in 2024, according to official data from the World Bank. The GDP value of the United States represents 27.49 percent of the world economy. This dataset provides - United States GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Vietnam VN: International Tourism: Number of Arrivals data was reported at 10,013,000.000 Person in 2016. This records an increase from the previous number of 7,944,000.000 Person for 2015. Vietnam VN: International Tourism: Number of Arrivals data is updated yearly, averaging 3,530,000.000 Person from Dec 1995 (Median) to 2016, with 22 observations. The data reached an all-time high of 10,013,000.000 Person in 2016 and a record low of 1,351,000.000 Person in 1995. Vietnam VN: International Tourism: Number of Arrivals data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Vietnam – Table VN.World Bank.WDI: Tourism Statistics. International inbound tourists (overnight visitors) are the number of tourists who travel to a country other than that in which they have their usual residence, but outside their usual environment, for a period not exceeding 12 months and whose main purpose in visiting is other than an activity remunerated from within the country visited. When data on number of tourists are not available, the number of visitors, which includes tourists, same-day visitors, cruise passengers, and crew members, is shown instead. Sources and collection methods for arrivals differ across countries. In some cases data are from border statistics (police, immigration, and the like) and supplemented by border surveys. In other cases data are from tourism accommodation establishments. For some countries number of arrivals is limited to arrivals by air and for others to arrivals staying in hotels. Some countries include arrivals of nationals residing abroad while others do not. Caution should thus be used in comparing arrivals across countries. The data on inbound tourists refer to the number of arrivals, not to the number of people traveling. Thus a person who makes several trips to a country during a given period is counted each time as a new arrival.; ; World Tourism Organization, Yearbook of Tourism Statistics, Compendium of Tourism Statistics and data files.; Gap-filled total;