30 datasets found
  1. f

    Comparative performance analysis of GFSI and its determinates scores/ranks...

    • plos.figshare.com
    xls
    Updated May 21, 2025
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    Xiuling Guo; Muhammad Islam (2025). Comparative performance analysis of GFSI and its determinates scores/ranks with population growth rate. [Dataset]. http://doi.org/10.1371/journal.pone.0324231.t002
    Explore at:
    xlsAvailable download formats
    Dataset updated
    May 21, 2025
    Dataset provided by
    PLOS ONE
    Authors
    Xiuling Guo; Muhammad Islam
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Comparative performance analysis of GFSI and its determinates scores/ranks with population growth rate.

  2. f

    Rule of Thumb for correlation coefficients.

    • plos.figshare.com
    xls
    Updated May 21, 2025
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    Xiuling Guo; Muhammad Islam (2025). Rule of Thumb for correlation coefficients. [Dataset]. http://doi.org/10.1371/journal.pone.0324231.t004
    Explore at:
    xlsAvailable download formats
    Dataset updated
    May 21, 2025
    Dataset provided by
    PLOS ONE
    Authors
    Xiuling Guo; Muhammad Islam
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Rising global food insecurity driven by population growth needs urgent measure for universal access to food. This research employs Comparative Performance Analysis (CPA) to evaluate the Global Food Security Index (GFSI), its components [Affordability (AF), Availability (AV), Quality & Safety (Q&S) and Sustainability & Adaptation (S&A)] in tandem with Annual Population Change (APC) for world’s five most populous countries (India, China, USA, Indonesia and Pakistan) using dataset spanning from 2012 to 2022. CPA is applied using descriptive analysis, correlation analysis, Rule of Thumb (RoT) and testing of hypothesis etc. RoT is used with a new analytical approach by applying the significance measures for correlation coefficients. The study suggests that India should enhance its GFSI rank by addressing AF and mitigating the adverse effects of APC on GFSI with a particular focus on Q&S and S&A. China needs to reduce the impact of APC on GFSI by prioritizing AV and S&A. The USA is managing its GFSI well, but focused efforts are still required to reduce APC’s impact on Q&S and S&A. Indonesia should improve across all sectors with a particular focus on APC reduction and mitigating its adverse effects on AF, AV, and S&A. Pakistan should intensify efforts to boost its rank and enhance all sectors with reducing APC. There is statistically significant and negative relation between GFSI and APC for China, Indonesia and found insignificant for others countries. This study holds promise for providing crucial policy recommendations to enhance food security by tackling its underlying factors.

  3. Data from: Country resolved combined emission and socio-economic pathways...

    • zenodo.org
    csv, pdf
    Updated Jul 22, 2024
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    Johannes Gütschow; Johannes Gütschow; M. Louise Jeffery; Annika Günther; Annika Günther; Malte Meinshausen; M. Louise Jeffery; Malte Meinshausen (2024). Country resolved combined emission and socio-economic pathways based on the RCP and SSP scenarios [Dataset]. http://doi.org/10.5281/zenodo.3638137
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    csv, pdfAvailable download formats
    Dataset updated
    Jul 22, 2024
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Johannes Gütschow; Johannes Gütschow; M. Louise Jeffery; Annika Günther; Annika Günther; Malte Meinshausen; M. Louise Jeffery; Malte Meinshausen
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Recommended citation

    Article citation will be added once the article is available.

    Content

    Use of the dataset and full description

    Before using the dataset, please read this document and the article describing the methodology, especially the "Discussion and limitations" section.

    The article will be referenced here as soon as it is published.

    Please notify us (johannes.guetschow@pik-potsdam.de) if you use the dataset so that we can keep track of how it is used and take that into consideration when updating and improving the dataset.

    When using this dataset or one of its updates, please cite the DOI of the precise version of the dataset used and also the data description article which this dataset is supplement to (see above). Please consider also citing the relevant original sources when using the RCP-SSP-dwn dataset. See the full citations in the References section further below.

    Support

    If you encounter possible errors or other things that should be noted or need support in using the dataset or have any other questions regarding the dataset, please contact johannes.guetschow@pik-potsdam.de.

    Abstract

    This dataset provides country scenarios, downscaled from the RCP (Representative Concentration Pathways) and SSP (Shared Socio-Economic Pathways) scenario databases, using results from the SSP GDP (Gross Domestic Product) country model results as drivers for the downscaling process harmonized to and combined with up to date historical data.

    Files included in the dataset

    The repository comprises several datasets. Each dataset comes in a csv file. The file name is constructed from dataset properties as follows:

    The "Source" flag indicates which input scenarios were used.

    • PMRCP: RCP scenarios downscaled using the SSPs: emissions and socio-economic data; scenarios are available both harmonized to historical data and non-harmonized.
    • PMSSP: Downscaled SSP IAM scenarios: emissions and socio-economic data; scenarios are available both harmonized to historical data and non-harmonized.

    the "Bunkers" flag indicates if the input emissions scenarios have been corrected for emissions from international shipping and aviation (bunkers) before downscaling to country level or not. The flag is "B" for scenarios where emissions from bunkers have been removed before downscaling and "" (no flag) where they have not been removed.

    The "Downscaling" flag indicates the downscaling technique used.

    • IE: Convergence downscaling with exponential convergence of emissions intensities and convergence before transition to negative emissions.
    • IC: Regional emission intensity growth rates for all countries.
    • CS: Constant emission shares as a reference case independent of the socio-economic scenario.

    All files contain data for all countries and variables. For detailed methodology descriptions we refer to the paper this dataset is a supplement to. A reference to the paper will be added as soon as it is published.

    Finally the data description including detailed references is included: RCP-SSP-dwn_v1.0_data_description.pdf.

    Notes

    If you encounter problems with the size of the csv files please let us know, so we can find solutions for future releases of the data.

    Data format description (columns)

    "source"

    For PMRCP files source values are

    • RCPSSP
    • PMRCP
    • PMRCPMISC

    For PMSSP files source values are

    • SSPIAM
    • PMSSP
    • PMSSPMISC

    For possible values of

    "scenario"

    For PMRCP files the scenarios have the format

    For PMSSP files the scenarios have the format

    Model codes in scenario names

    • AIMCGE: AIM-CGE
    • IMAGE: IMAGE
    • GCAM4: GCAM
    • MESGB: MESSAGE-GLOBIOM
    • REMMP: REMIND-MAGPIE
    • WITGB: WITCH-GLOBIOM

    "country"

    ISO 3166 three-letter country codes or custom codes for groups:

    Additional "country" codes for country groups.

    • EARTH: Aggregated emissions for all countries
    • ANNEXI: Annex I Parties to the UNFCCC
    • NONANNEXI: Non-Annex I Parties to the UNFCCC
    • AOSIS: Alliance of Small Island States
    • BASIC: BASIC countries (Brazil, South Africa, India and China)
    • EU28: European Union (still including the UK)
    • LDC: Least Developed Countries
    • UMBRELLA: Umbrella Group

    "category"

    Category descriptions.

    • IPCM0EL: Emissions: National Total excluding LULUCF
    • ECO: Economical data
    • DEMOGR: Demographical data

    "entity"

    Gases and gas baskets using global warming potentials (GWP) from either Second Assessment Report (SAR) or Fourth Assessment Report (AR4).

    Gases / gas baskets and underlying global warming potentials

    • CH4: Methane (CH4)
    • CO2: Carbon Dioxide (CO2)
    • N2O: Nitrous Oxide (N2O)
    • FGASES: Fluorinated Gases (SAR): HFCs, PFCs, SF6, NF3
    • FGASESAR4: Fluorinated Gases (AR4): HFCs, PFCs, SF6, NF3
    • KYOTOGHG: Kyoto greenhouse gases (SAR)
    • KYOTOGHGAR4: Kyoto greenhouse gases (AR4)

    "unit"

    The following units are used:

    • Million2011GKD: Million 2011 international dollars
    • ThousandPers: Thousand persons
    • kt: kilotonnes
    • Mt: Megatonnes
    • Gg: Gigagrams
    • MtCO2eq: Megatonnes of CO2 equivalents using the GWPs defined by "entity"
    • GgCO2eq: Gigagrams of CO2 equivalents using the GWPs defined by "entity"

    Remaining columns

    Years from 1850-2100.

    Data Sources

    The following data sources were used during the generation of this dataset:

    Scenario data

    Historical data

  4. Bitcoin historical price

    • kaggle.com
    Updated Nov 6, 2017
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    Ronny Kimathi kaimenyi (2017). Bitcoin historical price [Dataset]. https://www.kaggle.com/ronnykym/bitcoinprice/metadata
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Nov 6, 2017
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Ronny Kimathi kaimenyi
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    PART I: Distribution table: Interval Frequency Cumulative Frequency Percentage distribution Cumulative percentage distribution 10-12 2 2 13.33 13.33 12.1-14 5 7 33.33 46.66 14.1-16 8 15 53.33 99.99 16.1-18 0 15 0 99.99

    18.1 0 15 0 99.99

    Majority of the countries, eight, fall in the 14.1-16 category. Five countries fall in the 12.1-14 category and two countries in the 10-12 bin. The remaining categories have zero entries. This means the data does not follow a normal distribution since most of the countries are concentrated at the highest peak. This data could be better visualized in a histogram.

    Frequency distribution with revised interval: Interval Frequency Cumulative Frequency Percentage Frequency Cumulative percentage <12 2 2 13.33 13.33 12-12.9 1 3 6.67 20 13-13.9 4 7 26.67 46.67 14-14.9 4 11 26.67 73.34 15-15.9 3 14 20 93.34 16-16.9 1 15 6.67 100.01 17-17.9 0 15 0 100.01

    18 0 15 0 100.01 Eight countries have between 14% and 18% of their population above age 65. The number of countries with 14% - 18% of their population above 65 years remain the same even after revising the interval. The percentage of countries that have between 14-18 percent of their population above age 65 is 53.33%.

    PART II Q1. Time series chart for divorce rate in Netherlands

    Q2. Describe divorce rate in Netherlands before and after 1970. There is a decline in divorce rate between 1950 and 1960. There is a moderate rise in divorce rate between 1960 and 1970, the rate steadily rises between 1970 and 1980 and thereafter exhibits a slight decline between 1980 and 1990. The rate shifts to a declining trend after the year 2000. The decline does not indicate negative number of divorces, this could be attributed to increased population size and fewer number of divorce cases filed. Q3. A bar graph would best display the divorce rate for each year, hence easy comparison. Q4. Bar graph The highest number of divorce cases were recorded in the year 2000, while the least number was observed in 1960.

    Set 2: Show how different elements contributed to population change in 2018

    Immigration contributed 34 percent of the change in population; births, Emigration, and deaths contributed almost equal change in population.

    Q2. Elements of population growth

    Immigration contributed the largest change in population growth compared to birth.

    Q3. A time series to show changes in male and female population

    Both populations show an increasing trend over the 4 years. We could also conclude there are more females than males in the country’s population.

  5. Real GDP per capita

    • ec.europa.eu
    • db.nomics.world
    • +1more
    Updated May 13, 2018
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    Eurostat (2018). Real GDP per capita [Dataset]. http://doi.org/10.2908/SDG_08_10
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    tsv, json, application/vnd.sdmx.genericdata+xml;version=2.1, application/vnd.sdmx.data+csv;version=1.0.0, application/vnd.sdmx.data+xml;version=3.0.0, application/vnd.sdmx.data+csv;version=2.0.0Available download formats
    Dataset updated
    May 13, 2018
    Dataset authored and provided by
    Eurostathttps://ec.europa.eu/eurostat
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    2000 - 2024
    Area covered
    Bulgaria, Latvia, Netherlands, Malta, Romania, United Kingdom, Türkiye, Italy, Hungary, Switzerland
    Description

    The indicator is calculated as the ratio of real GDP (GDP adjusted for inflation) to the average population of a specific year, where GDP is expressed in millions and population is expressed in thousands. Real GDP is published without decimals. GDP measures the value of the total final output of goods and services produced by an economy within a certain period of time. It includes goods and services that have markets (or which could have markets) and products which are produced by general government and non-profit institutions. It is a measure of economic activity and is commonly used as a proxy for the development in a country’s material living standards. However, it is not a complete measure of economic welfare. For example, GDP does not include most unpaid household work. Neither does GDP take account of negative effects of economic activity, like environmental degradation.

  6. World Happiness Ranking

    • kaggle.com
    Updated May 23, 2020
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    Ana M. Villalpando (2020). World Happiness Ranking [Dataset]. https://www.kaggle.com/anamvillalpando/world-happiness-ranking
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    May 23, 2020
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Ana M. Villalpando
    License

    http://www.gnu.org/licenses/old-licenses/gpl-2.0.en.htmlhttp://www.gnu.org/licenses/old-licenses/gpl-2.0.en.html

    Area covered
    World
    Description

    Context

    The World Happiness Ranking focuses on the social, urban, and natural environment. Specifically, the ranking relies on self-reports from residents of how they weigh the quality of life they are currently experiencing which englobes three main points: current life evaluation, expected future life evaluation, positive and negative affect (emotion). Half of the underlying data comes from multiple Gallup world polls which asked people to give their assessment of the previously mentioned points, and the other half of the data is comprised of six variables that could be used to try to explain the individuals’ perception in their answers.

    Content

    The data sources’ datasets were obtained in two different formats. The World Happiness Ranking Dataset is a Comma-separated Values (CSV) file with multiple columns (for the different variables and the score) and a row for each of the analyzed countries.

    The rankings of national happiness are based on a Cantril ladder survey. Nationally representative samples of respondents are asked to think of a ladder, with the best possible life for them being a 10, and the worst possible life being a 0. They are then asked to rate their own current lives on that 0 to 10 scale. The report correlates the results with various life factors.

    1. GDP per capita is in terms of Purchasing Power Parity (PPP) adjusted to constant 2011 international dollars, taken from the World Development Indicators (WDI) released by the World Bank on November 28, 2019. See Statistical Appendix 1 for more details. GDP data for 2019 are not yet available, so we extend the GDP time series from 2018 to 2019 using country-specific forecasts of real GDP growth from the OECD Economic Outlook No. 106 (Edition November 2019) and the World Bank’s Global Economic Prospects (Last Updated: 06/04/2019), after adjustment for population growth. The equation uses the natural log of GDP per capita, as this form fits the data significantly better than GDP per capita.
    2. The time series of healthy life expectancy at birth are constructed based on data from the World Health Organization (WHO) Global Health Observatory data repository, with data available for 2005, 2010, 2015, and 2016. To match this report’s sample period, interpolation and extrapolation are used. See Statistical Appendix 1 for more details.
    3. Social support is the national average of the binary responses (0=no, 1=yes) to the Gallup World Poll (GWP) question, “If you were in trouble, do you have relatives or friends you can count on to help you whenever you need them, or not?”
    4. Freedom to make life choices is the national average of binary responses to the GWP question, “Are you satisfied or dissatisfied with your freedom to choose what you do with your life?”
    5. Generosity is the residual of regressing the national average of GWP responses to the question, “Have you donated money to a charity in the past month?” on GDP per capita.
    6. Perceptions of corruption are the average of binary answers to two GWP questions: “Is corruption widespread throughout the government or not?” and “Is corruption widespread within businesses or not?” Where data for government corruption are missing, the perception of business corruption is used as the overall corruption-perception measure.
    7. Positive affect is defined as the average of previous-day affect measures for happiness, laughter, and enjoyment for GWP waves 3-7 (years 2008 to 2012, and some in 2013). It is defined as the average of laughter and enjoyment for other waves where the happiness question was not asked. The general form for the affect questions is: Did you experience the following feelings during a lot of the day yesterday? See Statistical Appendix 1 for more details.
    8. Negative affect is defined as the average of previous-day affect measures for worry, sadness, and anger in all years.

    Acknowledgements

    The World Happiness Report is a publication of the Sustainable Development Solutions Network, powered by data from the Gallup World Poll, and supported by the Ernesto Illy Foundation, illycaffè, Davines Group, Blue Chip Foundation, the William, Jeff, and Jennifer Gross Family Foundation, and Unilever’s largest ice cream brand Wall’s.

    Inspiration

    Find the relationship between the ladder score and the other pieces of data.

  7. f

    System estimation by country equation.

    • figshare.com
    • plos.figshare.com
    xls
    Updated Jun 4, 2023
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    Goran Miladinov (2023). System estimation by country equation. [Dataset]. http://doi.org/10.1371/journal.pone.0259169.t003
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 4, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Goran Miladinov
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    System estimation by country equation.

  8. Countries with the most Facebook users 2024

    • statista.com
    • tokrwards.com
    • +4more
    + more versions
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    Stacy Jo Dixon, Countries with the most Facebook users 2024 [Dataset]. https://www.statista.com/topics/1164/social-networks/
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    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Stacy Jo Dixon
    Description

    Which county has the most Facebook users?

                  There are more than 378 million Facebook users in India alone, making it the leading country in terms of Facebook audience size. To put this into context, if India’s Facebook audience were a country then it would be ranked third in terms of largest population worldwide. Apart from India, there are several other markets with more than 100 million Facebook users each: The United States, Indonesia, and Brazil with 193.8 million, 119.05 million, and 112.55 million Facebook users respectively.
    
                  Facebook – the most used social media
    
                  Meta, the company that was previously called Facebook, owns four of the most popular social media platforms worldwide, WhatsApp, Facebook Messenger, Facebook, and Instagram. As of the third quarter of 2021, there were around 3,5 billion cumulative monthly users of the company’s products worldwide. With around 2.9 billion monthly active users, Facebook is the most popular social media worldwide. With an audience of this scale, it is no surprise that the vast majority of Facebook’s revenue is generated through advertising.
    
                  Facebook usage by device
                  As of July 2021, it was found that 98.5 percent of active users accessed their Facebook account from mobile devices. In fact, almost 81.8 percent of Facebook audiences worldwide access the platform only via mobile phone. Facebook is not only available through mobile browser as the company has published several mobile apps for users to access their products and services. As of the third quarter 2021, the four core Meta products were leading the ranking of most downloaded mobile apps worldwide, with WhatsApp amassing approximately six billion downloads.
    
  9. f

    S1 Data -

    • plos.figshare.com
    xlsx
    Updated Sep 1, 2023
    + more versions
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    Huafeng Zhai (2023). S1 Data - [Dataset]. http://doi.org/10.1371/journal.pone.0290897.s001
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    xlsxAvailable download formats
    Dataset updated
    Sep 1, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Huafeng Zhai
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    ObjectiveThe objective of this study was to identify factors influencing the development of China-ASEAN trade- from the total economic volume of both sides, distance, the population size of ASEAN countries, the construction of a free trade area, and the signing of the Belt and Road initiative, resource endowment per capita, the exchange rate between RMB and ASEAN countries, and the land area of ASEAN countries—to develop a conceptual framework for China-ASEAN trade potential.Study designThis study uses panel data from 2001 to 2021 that is evenly distributed among 10 ASEAN countries to serve as the dataset. Firstly, the unit roots are checked and the cointegration relationships are examined, focusing on the heterogeneity test. Based on the classical trade gravity model, the innovative trade gravity model with key influencing factors is constructed. On the basis of the classical trade gravity model, an innovative trade gravity model of key influencing factors is constructed. The trade potential model is used to calculate the direct trade potential coefficient between China and ASEAN countries, which points out the direction for the sustainability of bilateral trade.ResultsThis study finds that among the factors affecting China-ASEAN bilateral trade, the total economic output of both sides, distance, population size of ASEAN countries, the construction of the FTA, and the signing of the Belt and Road Initiative all have a positive impact on bilateral trade. Three influencing factors, namely per capita resource endowment, exchange rate between RMB and ASEAN countries, and the size of ASEAN countries, have a negative impact on bilateral trade, but to a lesser extent. The trade potential between China and Vietnam falls into the category of potential re-modelling, indicating that both sides are currently utilizing their trade potential to the greatest extent possible, that trade growth space is limited, and that new trade opportunities must be discovered. The trade potential index between China and nine ASEAN countries, excluding Vietnam, is in the potential-exploiting category, indicating that the potential has not been fully utilized by both sides and that there is still room for growth in the scale of trade between the two countries.ConclusionWith the shift of the world’s economic center of gravity in the direction of Asia following COVID-19, China and ASEAN countries should seize the opportunity to strengthen their comprehensive strength and economic aggregates and further develop China’s constructive role in the regional organization. The signing of the Belt and Road Initiative and the construction of a free trade zone has had a positive effect on the development of bilateral trade. Propose that: positive trade factors should continue to be strengthened, trade barriers should be removed, and new dynamics of bilateral trade growth should be enhanced.

  10. Enterprise survey 2006-2017, Panel data - Argentina

    • microdata.worldbank.org
    • datacatalog.ihsn.org
    • +1more
    Updated Jan 8, 2019
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    World Bank (2019). Enterprise survey 2006-2017, Panel data - Argentina [Dataset]. https://microdata.worldbank.org/index.php/catalog/3396
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    Dataset updated
    Jan 8, 2019
    Dataset provided by
    World Bank Grouphttp://www.worldbank.org/
    Authors
    World Bank
    Time period covered
    2006 - 2017
    Area covered
    Argentina
    Description

    Abstract

    The documented dataset covers Enterprise Survey (ES) panel data collected in Argentina in 2006, 2010 and 2017, as part of the Enterprise Survey initiative of the World Bank. An Indicator Survey is similar to an Enterprise Survey; it is implemented for smaller economies where the sampling strategies inherent in an Enterprise Survey are often not applicable due to the limited universe of firms.

    The objective of the 2006-2017 Enterprise Survey is to obtain feedback from enterprises in client countries on the state of the private sector as well as to build a panel of enterprise data that will make it possible to track changes in the business environment over time and allow, for example, impact assessments of reforms. Through interviews with firms in the manufacturing and services sectors, the Indicator Survey data provides information on the constraints to private sector growth and is used to create statistically significant business environment indicators that are comparable across countries.

    As part of its strategic goal of building a climate for investment, job creation, and sustainable growth, the World Bank has promoted improving the business environment as a key strategy for development, which has led to a systematic effort in collecting enterprise data across countries. The Enterprise Surveys (ES) are an ongoing World Bank project in collecting both objective data based on firms' experiences and enterprises' perception of the environment in which they operate.

    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 the universe, covered in the Enterprise Surveys is the non-agricultural 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.

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    The sample for the 2006-2017 Argentina Enterprise Survey (ES) was selected using stratified random sampling, following the methodology explained in the Sampling Manual. Stratified random sampling was preferred over simple random sampling for several reasons: - To obtain unbiased estimates for different subdivisions of the population with some known level of precision. - To obtain unbiased estimates for the whole population. The whole population, or universe of the study, is the non-agricultural economy. It comprises: all manufacturing sectors (group D), construction (group F), services (groups G and H), and transport, storage, and communications (group I). Groups are defined following ISIC revision 3.1. Note that this definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, excluding sub-sector 72, IT, which was added to the population under study), and all public or utilities-sectors. - To make sure that the final total sample includes establishments from all different sectors and that it is not concentrated in one or two of industries/sizes/regions. - To exploit the benefits of stratified sampling where population estimates, in most cases, will be more precise than using a simple random sampling method (i.e., lower standard errors, other things being equal.)

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

    Industry stratification was designed in the following way: In small economies the population was stratified into 3 manufacturing industries, one services industry - retail-, and one residual sector as defined in the sampling manual. Each industry had a target of 120 interviews. In middle size economies the population was stratified into 4 manufacturing industries, 2 services industries -retail and IT-, and one residual sector. For the manufacturing industries sample sizes were inflated by 25% to account for potential non-response in the financing data.

    For the Argentina ES, size stratification was defined following the standardized definition for the rollout: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposed, the number of employees was defined on the basis of reported permanent full-time workers. This resulted in some difficulties in certain countries where seasonal/casual/part-time labor is common.

    Mode of data collection

    Face-to-face [f2f]

    Research instrument

    The current survey instruments are available: - Core Questionnaire + Manufacturing Module [ISIC Rev.3.1: 15-37] - Core Questionnaire + Retail Module [ISIC Rev.3.1: 52] - Core Questionnaire [ISIC Rev.3.1: 45, 50, 51, 55, 60-64, 72] - Screener Questionnaire.

    The "Core Questionnaire" is the heart of the Enterprise Survey and contains the survey questions asked of all firms across the world. There are also two other survey instruments - the "Core Questionnaire + Manufacturing Module" and the "Core Questionnaire + Retail Module." The survey is fielded via three instruments in order to not 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.

    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.

    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 the 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. However, there were clear cases of low response. The following graph shows non-response rates for the sales variable, d2, by sector. Please, note that for this specific question, refusals were not separately identified from "Don't know" responses.

    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; whenever this was done, strict rules were followed to ensure replacements were randomly selected within the same stratum. Further research is needed on survey non-response in the Enterprise Surveys regarding potential introduction of bias.

  11. e

    Security and Defence Policy Opinions in Germany 1996 - Dataset - B2FIND

    • b2find.eudat.eu
    Updated Oct 21, 2023
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    (2023). Security and Defence Policy Opinions in Germany 1996 - Dataset - B2FIND [Dataset]. https://b2find.eudat.eu/dataset/327c71cd-302e-5202-b8dc-5ae7aeec291d
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    Dataset updated
    Oct 21, 2023
    Area covered
    Germany
    Description

    Since 1996, the Center for Military History and Social Sciences of the Bundeswehr (ZMSBw) has conducted a representative survey of the German population on defense and security policy issues on behalf of the Federal Ministry of Defense. In 1996, this study was continued. For this purpose, N = 2568 persons were interviewed on various issues. The present survey focused in particular on Security and threat perception, attitudes toward security policy, foreign deployments of the Federal Armed Forces, tasks of the Federal Armed Forces, the role of conscription, and military cooperation in Europe. Perception of security and threats: personal feeling of security; personal significance of various aspects of security (e.g. job security, military security, social security, security of income, ecological security, etc.) Interest in politics in general, in foreign policy, in security and defence policy as well as interest in the Federal Armed Forces; security policy interest at the beginning of the 1980s; security policy strategy of ´deterrence´ as a guarantee for peace in Europe, necessary Realpolitik or a threat to humanity; advocacy or rejection of military force; change in personal attitude towards military force; Reasons for change of attitude; reasons for not changing attitudes; personal relationship to the peace movement in the early 1980s and today; opinion on pacifism; opinion on the extent of public debate on security policy issues and on the Federal Armed Forces; future development of the number of international conflicts after the end of the Cold War; likelihood of a military threat to Germany; feeling threatened by: environmental destruction, violence, hatred, crime, unemployment, world wars, right-wing extremism, financial problems, new technologies, diseases and population growth; threat to world peace from various countries and regions (Islamic states, Third World, Russia, Central/Eastern Europe, USA, Western Europe, Germany, Middle East, China); current that will prevail worldwide in the future (national or nationalist thinking vs. voluntary cooperation and interdependence); assessment of nationalist thinking; assessment of voluntary cooperation; suitability of various institutions and instruments to protect Germany against military risks (NATO membership, other/ new treaties with neighbouring countries, United Nations (UN), European Union (EU), Federal Armed Forces, European Army, general disarmament, Organisation for Security and Cooperation in Europe (OSCE)). 2. Security policy attitudes, foreign missions of the Federal Armed Forces: Germany´s role in the world: preference for a rather active vs. rather passive international policy of Germany; approved or rejected measures for Germany´s international action (e.g. aid with food and medicine, aid of a financial and economic nature, technical aid by civil organisations, peacekeeping mission of the Federal Armed Forces within the framework of a UN mission, etc.); opinion on the peace-keeping mission of the Federal Armed Forces in various countries and regions (Eastern Europe, Russia, the Middle East, South-East Asia, Africa, NATO states, Western Europe; opinion on the future role of a state´s military power; opinion on the future staffing level of the Federal Armed Forces; assessment of Germany´s defence expenditure; general attitude towards the Federal Armed Forces. 3. Evaluation of public institutions: Institutional trust (Federal Constitutional Court, other courts, police, Bundesrat, state government, Federal Armed Forces, Bundestag, television, press, churches, trade unions, federal government, education, political parties); reliance on the Federal Armed Forces. 4. Attitude towards compulsory military service: Military service or alternative civilian service more important for society; decision for or against various community services (care of the sick, care of the elderly, military service/defence, care of the disabled, environmental protection/remedy of environmental damage, care of children in need of help, service with the police, border guards or fire brigade); community service which the interviewee would be most likely to opt for social service most likely to be refused; general attitude towards military service; opinion on the right to conscientious objection; frequency of different reasons for conscientious objection (religious reasons, military service as time lost, political reasons, military service not compatible with conscience, civilian service as a more convenient way, economic reasons, civilian service with greater benefit to society); general compulsory military service retained vs. conversion into a voluntary army; future of the Federal Armed Forces (Federal Armed Forces should be abolished, citizen´s army based on the Swiss model, purely voluntary army, current mix of conscripts, professional and temporary soldiers should be retained, fewer professional and temporary soldiers more military exercises for former soldiers); preference for the future of the Federal Armed Forces. 5. Tasks of the Federal Armed Forces: Preferences with regard to the tasks of the Federal Armed Forces (tasks of international arms control, fight against international terrorism, fight against international drug trafficking, border security against illegal immigrants, tasks in the field of environmental protection, international disaster relief, humanitarian aid and rescue services, reconstruction and development aid, international military advice, Combat operations on behalf of and under the control of the UN or other international organisations, peacekeeping operations on behalf of and under the control of the UN or international organisations, protection of the constitutional order in Germany, participation in celebrations and ceremonies, education and character building, defence of Germany, defence of allies, aid for threatened friendly nations); evaluation of the deployment of German soldiers in various UN missions with regard to: care of the suffering population, promotion of the international community, integration of Germany, strengthening of German national interests, stabilisation of world peace, strengthening of the reputation of the Federal Armed Forces, enforcement of human rights, establishment of democracy in the country of deployment, protection of the population in the country of deployment; assessment of the armament and equipment of the Federal Armed Forces; assessment of leadership training in the Federal Armed Forces; assessment of ´soldiering´ as a profession; personal acquaintance with a Federal Armed Forces soldier; personal advice to a relative or friend when considering volunteering for the Federal Armed Forces; importance of co-determination in civilian enterprises; importance of co-determination for soldiers in peacetime; preferences for voluntary service by women in the Federal Armed Forces (women do not belong in the Federal Armed Forces, only in unarmed service, all uses should be open to women); opinion on the complete withdrawal of US troops from Germany; opinion on the complete withdrawal of the Federal Armed Forces from the region; agreement on various possibilities for a new German security policy (extension of NATO security guarantees to Eastern Europe, common European foreign and security policy, restructuring of the military, return to national German interests, strengthening of political cooperation); the importance for Germany of a permanent seat on the UN Security Council; attitudes towards citizens of various neighbouring countries (Belgians, Danes, French, Dutch, Austrians, Poles, Swiss, Czechs and Luxemburgers); the most positive attitudes and the most negative attitudes towards neighbours; a feeling of belonging as West Germans, East Germans, Germans, Europeans or world citizens. 6. Military cooperation in Europe: familiarity of various associations with soldiers from different nations (e.g. German-French Brigade, Eurocorps, German-American Corps, German-Dutch Corps); opinion on military cooperation with various countries (USA, France, Netherlands, England, Belgium, Denmark, Italy); opinion on the creation of a European army; opinion on the political unification of Europe; opinion on the introduction of a common European currency, the Euro; evaluation of the performance of the Federal Armed Forces with regard to reunification in comparison to other institutions (trade unions, churches, political parties, employers´ associations, sports associations and media); opinion on the future NATO deployment of Federal Armed Forces combat troops. Demography: Sex; age (year of birth); education; additional vocational training; occupation; occupational group; net household income; marital status; denomination; residential environment (degree of urbanisation); city size; federal state; household size; number of persons in household aged 16 and over; Left-Right Self-Placement. Additionally coded: Respondent ID; age (categorised); West/East; weight.

  12. Enterprise Survey 2016 - Côte d'Ivoire

    • microdata.worldbank.org
    • catalog.ihsn.org
    Updated Mar 12, 2019
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    World Bank (2019). Enterprise Survey 2016 - Côte d'Ivoire [Dataset]. https://microdata.worldbank.org/index.php/catalog/2830
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    Dataset updated
    Mar 12, 2019
    Dataset provided by
    World Bank Grouphttp://www.worldbank.org/
    Authors
    World Bank
    Time period covered
    2016 - 2017
    Area covered
    Côte d'Ivoire
    Description

    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.

  13. Enterprise Survey 2009-2016, Panel Data - Lesotho

    • microdata.worldbank.org
    • catalog.ihsn.org
    Updated May 11, 2017
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    World Bank (2017). Enterprise Survey 2009-2016, Panel Data - Lesotho [Dataset]. https://microdata.worldbank.org/index.php/catalog/2835
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    Dataset updated
    May 11, 2017
    Dataset provided by
    World Bank Grouphttp://www.worldbank.org/
    Authors
    World Bank
    Time period covered
    2008 - 2016
    Area covered
    Lesotho
    Description

    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.

  14. Instagram: countries with the highest audience reach 2024

    • statista.com
    • es.statista.com
    • +4more
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    Stacy Jo Dixon, Instagram: countries with the highest audience reach 2024 [Dataset]. https://www.statista.com/topics/1164/social-networks/
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    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Stacy Jo Dixon
    Description

    As of April 2024, Bahrain was the country with the highest Instagram audience reach with 95.6 percent. Kazakhstan also had a high Instagram audience penetration rate, with 90.8 percent of the population using the social network. In the United Arab Emirates, Turkey, and Brunei, the photo-sharing platform was used by more than 85 percent of each country's population.

  15. Enterprise Survey 2013 - Uganda

    • microdata.worldbank.org
    • catalog.ihsn.org
    • +1more
    Updated Feb 21, 2014
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    World Bank (2014). Enterprise Survey 2013 - Uganda [Dataset]. https://microdata.worldbank.org/index.php/catalog/1965
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    Dataset updated
    Feb 21, 2014
    Dataset provided by
    World Bank Grouphttp://www.worldbank.org/
    Authors
    World Bank
    Time period covered
    2013
    Area covered
    Uganda
    Description

    Abstract

    The survey was conducted in Uganda between January 2013 and August 2013 as part of the Africa Enterprise Survey 2013 roll-out, an initiative of the World Bank. Data from 640 establishments was analyzed. Stratified random sampling was used to select the surveyed businesses.

    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.

    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. The mode of data collection is face-to-face interviews.

    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

    The sample for Uganda was selected using stratified random sampling. Three levels of stratification were used in this country: firm sector, firm size, and geographic region.

    Industry stratification was designed in the way that follows: the universe was stratified into three manufacturing industry (food, textiles and garments, other manufacturing) and two service sectors (retail and other services).

    Size stratification was defined following the standardized definition for the Enterprise Surveys: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposes, the number of employees was defined on the basis of reported permanent full-time workers. This seems to be an appropriate definition of the labor force since seasonal/casual/part-time employment is not a common practice, except in the sectors of construction and agriculture.

    Regional stratification for the Uganda ES was defined in six regions (city and the surrounding business area): Jinja, Kampala, Lira, Mbale, Mbarara, and Wakiso.

    Uganda Bureau of Statistics database was used as a sampling frame with the aim of obtaining interviews with 600 establishments.

    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 2% (36 out of 1,567 establishments).

    Mode of data collection

    Face-to-face [f2f]

    Research instrument

    The structure of the data base reflects the fact that 3 different versions of the survey instrument were used for all registered establishments. Questionnaires have common questions (Core module) and respectfully additional manufacturing and retail 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 Retail questionnaire (includes the Core module plus retail specific questions) and the residual eligible services have been covered using the Core module only. Each variation of the questionnaire is identified by the index variable, a0.

    All variables are named using, first, the letter of each section and, second, the number of the variable within the section, i.e. a1 denotes section A, question 1 (some exceptions apply due to comparability reasons). Variable names proceeded by a prefix "KEN" and "A2F" indicate questions specific to some countries in Africa, therefore, they may not be found in the implementation of the rollout in other countries. All other suffixed variables are global and are present in all country surveys over the world. All variables are numeric with the exception of those variables with an "x" at the end of their names. The suffix "x" denotes that the variable is alpha-numeric. In the implementation of the Africa roll out 2011 an experiment was carried in some of the countries to better estimate the effects of the use of show cards in data collection. In some of the sections (i.e. innovation) the enumerators were trained to alternatively implement the section using either show cards or asking only the questions without showing any cards, please see the variable "cards".

    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 contacted establishments per realized interview was 0.41. 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 0.20.

  16. Instagram: distribution of global audiences 2024, by age and gender

    • statista.com
    • es.statista.com
    • +4more
    + more versions
    Share
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    Stacy Jo Dixon, Instagram: distribution of global audiences 2024, by age and gender [Dataset]. https://www.statista.com/topics/1164/social-networks/
    Explore at:
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Stacy Jo Dixon
    Description

    As of April 2024, around 16.5 percent of global active Instagram users were men between the ages of 18 and 24 years. More than half of the global Instagram population worldwide was aged 34 years or younger.

                  Teens and social media
    
                  As one of the biggest social networks worldwide, Instagram is especially popular with teenagers. As of fall 2020, the photo-sharing app ranked third in terms of preferred social network among teenagers in the United States, second to Snapchat and TikTok. Instagram was one of the most influential advertising channels among female Gen Z users when making purchasing decisions. Teens report feeling more confident, popular, and better about themselves when using social media, and less lonely, depressed and anxious.
                  Social media can have negative effects on teens, which is also much more pronounced on those with low emotional well-being. It was found that 35 percent of teenagers with low social-emotional well-being reported to have experienced cyber bullying when using social media, while in comparison only five percent of teenagers with high social-emotional well-being stated the same. As such, social media can have a big impact on already fragile states of mind.
    
  17. Facebook users worldwide 2017-2027

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    Stacy Jo Dixon, Facebook users worldwide 2017-2027 [Dataset]. https://www.statista.com/topics/1164/social-networks/
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    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Stacy Jo Dixon
    Description

    The global number of Facebook users was forecast to continuously increase between 2023 and 2027 by in total 391 million users (+14.36 percent). After the fourth consecutive increasing year, the Facebook user base is estimated to reach 3.1 billion users and therefore a new peak in 2027. Notably, the number of Facebook users was continuously increasing over the past years. User figures, shown here regarding the platform Facebook, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

  18. Reddit: global paid subscription revenues 2018-2026

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    Statista Research Department, Reddit: global paid subscription revenues 2018-2026 [Dataset]. https://www.statista.com/topics/1164/social-networks/
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    Statistahttp://statista.com/
    Authors
    Statista Research Department
    Description

    In 2023, it was estimated that social forum and news aggregator Reddit saw over 26.5 million U.S. dollars in revenues from global paying users with an annual subscription. A premium Reddit subscription comes with an ad-free environment, as well as the possibility to join premium subreddits such as r/lounge. In 2022, Reddit counted approximately 530 thousand paying users. By 2026, Reddit annual subscription revenues are estimated to bring in 36.5 million U.S. dollars in revenues.

  19. Global social network penetration 2019-2028

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    Stacy Jo Dixon, Global social network penetration 2019-2028 [Dataset]. https://www.statista.com/topics/1164/social-networks/
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    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Stacy Jo Dixon
    Description

    The global social media penetration rate in was forecast to continuously increase between 2024 and 2028 by in total 11.6 (+18.19 percent). After the ninth consecutive increasing year, the penetration rate is estimated to reach 75.31 and therefore a new peak in 2028. Notably, the social media penetration rate of was continuously increasing over the past years.

  20. TikTok: account removed 2020-2024, by reason

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    Statista Research Department, TikTok: account removed 2020-2024, by reason [Dataset]. https://www.statista.com/topics/1164/social-networks/
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    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Statista Research Department
    Description

    During the fourth quarter 2024, approximately 20.6 million TikTok accounts were removed from the platform due to suspicion of being operated by users under the age of 13. During the last measured period, around 185 million fake accounts were removed from fake accounts removed from TikTok.

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Xiuling Guo; Muhammad Islam (2025). Comparative performance analysis of GFSI and its determinates scores/ranks with population growth rate. [Dataset]. http://doi.org/10.1371/journal.pone.0324231.t002

Comparative performance analysis of GFSI and its determinates scores/ranks with population growth rate.

Related Article
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xlsAvailable download formats
Dataset updated
May 21, 2025
Dataset provided by
PLOS ONE
Authors
Xiuling Guo; Muhammad Islam
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Description

Comparative performance analysis of GFSI and its determinates scores/ranks with population growth rate.

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