8 datasets found
  1. f

    Wealth distribution in villages - Datasets

    • figshare.com
    zip
    Updated Dec 3, 2021
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    Istvan Gere; Szabolcs Kelemen; Zoltan Neda; Tamás S. Biró (2021). Wealth distribution in villages - Datasets [Dataset]. http://doi.org/10.6084/m9.figshare.17013467.v2
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    zipAvailable download formats
    Dataset updated
    Dec 3, 2021
    Dataset provided by
    figshare
    Authors
    Istvan Gere; Szabolcs Kelemen; Zoltan Neda; Tamás S. Biró
    License

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

    Description

    Socio-economic inequalities derived from an exhaustive wealth distribution is studied in a closed geographical region from Transylvania (Romania). Exhaustive wealth data is computed from the agricultural records of the Sancraiu commune for three different economic situations. The gathered data is spanning two different periods from the communist economy and the present situation after 31 years of free market economy in Romania. The local growth and reset model based on an analytically solvable master equation is used to describe the observed data. The model with realistically chosen growth and reset rates is successful in describing both the experimentally observed distributions and the inequality indexes (Lorenz curve, Gini coefficient and Pareto point) derived from this data. The observed changes in these inequality measures are discussed in the context of the relevant socio-economic conditions.

  2. f

    Multicollinearity test of independent variables.

    • plos.figshare.com
    xls
    Updated Oct 31, 2023
    + more versions
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    Woldemariam Erkalo Gobena; Teramaj Wongel Wotale; Mesfin Esayas Lelisho; Wubishet Gezimu (2023). Multicollinearity test of independent variables. [Dataset]. http://doi.org/10.1371/journal.pone.0293364.t002
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    xlsAvailable download formats
    Dataset updated
    Oct 31, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Woldemariam Erkalo Gobena; Teramaj Wongel Wotale; Mesfin Esayas Lelisho; Wubishet Gezimu
    License

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

    Description

    BackgroundStunting, short for age, affects the overall growth and development of the children. It occurs due to chronic under nutrition. Stunting vastly occurs in impoverished regions of the world, including Ethiopia.ObjectiveThis study aimed to investigate the prevalence and correlates of stunting among under-five children in Ethiopia using marginal models.MethodsData were taken from the 2016 Ethiopian Demographic Health Survey, which is a nationally representative survey of children in the 0–59 month age group. For marginal models, generalized estimating equations and alternating logistic regression models were used for the analysis.ResultsThe prevalence of stunting among the under-five children was 34.91% in the area. The proportion was slightly higher among male (36.01%) than female (33.76%) child. The Alternating Logistic Regression model analysis revealed that the child’s age, the mother’s education level, the mother’s body mass index, the place of residence, the wealth index, and the previous birth interval were found to be significant determinants of childhood stunting, and the result shows that children born with a lower previous birth interval (less than 24 months) were more likely to be stunted than those born within a higher birth interval. Children in rural Ethiopia were more likely to be stunted than children in urban Ethiopia.ConclusionThis study found that more than one third of children were stunted in the area. The study also determined that child’s age, the mother’s education, the mother’s body mass index, the place of residence, the wealth index, and birth interval influence stunting. Therefore, it is better enhancing the nutritional intervention programs.

  3. 500 Richest People 2021

    • kaggle.com
    Updated May 13, 2021
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    Firat Gonen (2021). 500 Richest People 2021 [Dataset]. https://www.kaggle.com/frtgnn/500-richest-people-2021/discussion
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    May 13, 2021
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Firat Gonen
    License

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

    Description

    Based on Bloomberg's Billionaires index...

    The Bloomberg Billionaires Index is a daily ranking of the world's richest people. In calculating net worth, Bloomberg News strives to provide the most transparent calculations available, and each individual billionaire profile contains a detailed analysis of how that person's fortune is tallied.

    The index is a dynamic measure of personal wealth based on changes in markets, the economy and Bloomberg reporting. Each net worth figure is updated every business day after the close of trading in New York. Stakes in publicly traded companies are valued using the share's most recent closing price. Valuations are converted to U.S. dollars at current exchange rates.

    Closely held companies are valued in several ways, such as by comparing the enterprise value-to-Ebitda or price-to-earnings ratios of similar public companies or by using comparable transactions. Calculations of closely held company debt -- if net debt cannot be determined -- are based on the net debt-to-Ebitda ratios of comparable peers. The value of closely held companies adjusts daily based on market moves for peer companies or by applying the market movement of a relevant industry index. The criteria used to choose peer companies is based on the closely held asset's industry and size.

    When ownership of closely held assets cannot be verified, they aren't included in the calculations. The specific valuation methodology for each closely held company is included in the net worth analysis section of a billionaire's profile. Additional details included in the valuation notes for each asset are available to subscribers of the Bloomberg Professional Service.

    A standard liquidity discount of 5 percent is applied to most closely held companies where assets may be hard to sell. When a different percentage is used an explanation is given. No liquidity discounts are applied to the values of public stakes. In some instances, a country risk discount is also applied based on a person's concentration of assets and ease of selling them in a given geography. A country's risk is assessed based on Standard & Poor's sovereign debt ratings.

    If a billionaire has pledged as collateral shares he or she holds in a public company, the value of those shares or the value of a loan taken against them is removed from the net worth calculation. If reliable information can be obtained about the ultimate use of those borrowed funds, that value is added back into the calculation.

    Hedge fund businesses are valued using the average market capitalization-to-assets under management ratios of the most comparable publicly traded funds. Fee income is not considered because it cannot be uniformly verified. Personal funds invested along with outside capital are not included in the calculation. A "key man" risk discount of 25 percent is applied to funds whose performance is tied to a single individual. Assets under management are updated using ADV forms filed with the federal government and news reports, and returns are factored when sourced to reports from credible news outfits, the HFRI Index and industry analysts.

    Net worth calculations include dividend income paid and proceeds from the sale of public and closely held shares. Taxes are deducted based on prevailing income, dividend and capital gains tax rates in a billionaire's country of residence. Taxes are applied at the highest rate unless there is evidence to support a lower percentage, in which case an explanation is given in the net worth summary. For calculations of cash and other investable assets, a hybrid return based on holdings in cash, government bonds, equities and commodities is applied.

    No assumptions are made about personal debt. Family members often hold a portion of a billionaire's assets. Such transfers don't change the nature of who ultimately controls the fortune. As a result, Bloomberg News operates under the rule that all billionaire fortunes are inherently family fortunes and credit family fortunes to the founders or ranking family members who are determined to have direct control over the assets. When individual stakes can be verified and adult family members have an active role in a business, the value is credited to each individual.

    Each billionaire -- or a representative -- is given an opportunity to respond to questions regarding the net worth calculation, including assets and liabilities.

    Bloomberg News editorial policy is to not cover Bloomberg L.P. As a result, Michael Bloomberg, the founder and majority owner of Bloomberg L.P., isn't considered for this ranking.

    Because calculating net worth requires a degree of estimation, bull and bear case scenarios that would make a person's fortune higher or lower than the Bloomberg Billionaires Index valuation are included on the Bloomberg Professional Service. A confidence rating also is included on each profile:

  4. f

    Additional file 7: of On the estimation of population cause-specific...

    • springernature.figshare.com
    xlsx
    Updated Jun 1, 2023
    + more versions
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    Gail Williams; Ian Riley; Riley Hazard; Hafizur Chowhury; Nurul Alam; Peter Streafield; Veronica Tallo; Diozele Sanvictores; Marilla Lucero; Tim Adair; Alan Lopez (2023). Additional file 7: of On the estimation of population cause-specific mortality fractions from in-hospital deaths [Dataset]. http://doi.org/10.6084/m9.figshare.7693253.v1
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    xlsxAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    figshare
    Authors
    Gail Williams; Ian Riley; Riley Hazard; Hafizur Chowhury; Nurul Alam; Peter Streafield; Veronica Tallo; Diozele Sanvictores; Marilla Lucero; Tim Adair; Alan Lopez
    License

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

    Description

    Bangladesh and Philippines wealth index calculation. Demographic covariates used to calculate a wealth index using principal component analysis (PCA) for Bangladesh and the Philippines. (XLSX 13 kb)

  5. f

    Household-specific inflation rates for Italy 2015-2023

    • figshare.com
    csv
    Updated Dec 13, 2024
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    Leonardo Ciambezi; Alessandro Pietropaoli (2024). Household-specific inflation rates for Italy 2015-2023 [Dataset]. http://doi.org/10.6084/m9.figshare.26105755.v2
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    csvAvailable download formats
    Dataset updated
    Dec 13, 2024
    Dataset provided by
    figshare
    Authors
    Leonardo Ciambezi; Alessandro Pietropaoli
    License

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

    Area covered
    Italy
    Description

    The file "dataset_regression_income.csv" contains a dataset developed in the analysis of inflation heterogeneity for Italian Households in the period 2015-2023.The dataset is the outcome of merging the yearly Household Budget Surveys (HBS) conducted by the Italian National Institute of Statistics (Istat), the Harmonised Index of Consumer Prices (HICP) which is calculated monthly by Istat, according to EU regulations, and the Survey on Households Income and Wealth (SHIW) conducted by Bank of Italy.Mapping price information into consumption decisions and aggregating an individual price index for each household according to a Laspeyres Formula leads to the computation of household-level inflation rates.Furthermore, we compute non-durable equivalent expenditure for each household as a proxy of living standards. The variable is obtained by subtracting durable expenditure from total aggregate expenditure and scaling down by an household equivalent scale (in the benchmark specification, the square root of the household size). The decile distribution of the variable is also computed.Finally, we apply a statistical matching procedure to integrate income information from SHIW data sources. The output is a synthetic dataset containing both expenditure and income information that preserves the joint distribution and correlation structures of the original datasets.The file "ISTAT_MFR_HBS_EUR.csv" is a conversion table that maps ECOICOP items to HBS expenditure voices.

  6. f

    Sociodemographic Characteristics of Mixed Milk Feeding Practices Among...

    • plos.figshare.com
    xls
    Updated Mar 5, 2025
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    Mekuriaw Nibret Aweke; Muluken Chanie Agimas; Moges Tadesse Abebe; Tigabu Kidie Tesfie; Meron Asmamaw Alemayehu; Werkneh Melkie Tilahun; Gebrie Getu Alemu; Worku Necho Asferie (2025). Sociodemographic Characteristics of Mixed Milk Feeding Practices Among Infants Aged 0–5 Months in Ethiopia, EDHS 2019 (n =  524). [Dataset]. http://doi.org/10.1371/journal.pone.0317089.t001
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    xlsAvailable download formats
    Dataset updated
    Mar 5, 2025
    Dataset provided by
    PLOS ONE
    Authors
    Mekuriaw Nibret Aweke; Muluken Chanie Agimas; Moges Tadesse Abebe; Tigabu Kidie Tesfie; Meron Asmamaw Alemayehu; Werkneh Melkie Tilahun; Gebrie Getu Alemu; Worku Necho Asferie
    License

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

    Area covered
    Ethiopia
    Description

    Sociodemographic Characteristics of Mixed Milk Feeding Practices Among Infants Aged 0–5 Months in Ethiopia, EDHS 2019 (n =  524).

  7. f

    Geographic weighted regression (GWR) model for mixed milk practices in...

    • plos.figshare.com
    xls
    Updated Mar 5, 2025
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    Mekuriaw Nibret Aweke; Muluken Chanie Agimas; Moges Tadesse Abebe; Tigabu Kidie Tesfie; Meron Asmamaw Alemayehu; Werkneh Melkie Tilahun; Gebrie Getu Alemu; Worku Necho Asferie (2025). Geographic weighted regression (GWR) model for mixed milk practices in Ethiopia, EDHS 2019. [Dataset]. http://doi.org/10.1371/journal.pone.0317089.t003
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Mar 5, 2025
    Dataset provided by
    PLOS ONE
    Authors
    Mekuriaw Nibret Aweke; Muluken Chanie Agimas; Moges Tadesse Abebe; Tigabu Kidie Tesfie; Meron Asmamaw Alemayehu; Werkneh Melkie Tilahun; Gebrie Getu Alemu; Worku Necho Asferie
    License

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

    Area covered
    Ethiopia
    Description

    Geographic weighted regression (GWR) model for mixed milk practices in Ethiopia, EDHS 2019.

  8. f

    Summary of OLS results mixed milk feeding practice in Ethiopia, EDHS 2019.

    • plos.figshare.com
    xls
    Updated Mar 5, 2025
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    Mekuriaw Nibret Aweke; Muluken Chanie Agimas; Moges Tadesse Abebe; Tigabu Kidie Tesfie; Meron Asmamaw Alemayehu; Werkneh Melkie Tilahun; Gebrie Getu Alemu; Worku Necho Asferie (2025). Summary of OLS results mixed milk feeding practice in Ethiopia, EDHS 2019. [Dataset]. http://doi.org/10.1371/journal.pone.0317089.t002
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Mar 5, 2025
    Dataset provided by
    PLOS ONE
    Authors
    Mekuriaw Nibret Aweke; Muluken Chanie Agimas; Moges Tadesse Abebe; Tigabu Kidie Tesfie; Meron Asmamaw Alemayehu; Werkneh Melkie Tilahun; Gebrie Getu Alemu; Worku Necho Asferie
    License

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

    Area covered
    Ethiopia
    Description

    Summary of OLS results mixed milk feeding practice in Ethiopia, EDHS 2019.

  9. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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Istvan Gere; Szabolcs Kelemen; Zoltan Neda; Tamás S. Biró (2021). Wealth distribution in villages - Datasets [Dataset]. http://doi.org/10.6084/m9.figshare.17013467.v2

Wealth distribution in villages - Datasets

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15 scholarly articles cite this dataset (View in Google Scholar)
zipAvailable download formats
Dataset updated
Dec 3, 2021
Dataset provided by
figshare
Authors
Istvan Gere; Szabolcs Kelemen; Zoltan Neda; Tamás S. Biró
License

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

Description

Socio-economic inequalities derived from an exhaustive wealth distribution is studied in a closed geographical region from Transylvania (Romania). Exhaustive wealth data is computed from the agricultural records of the Sancraiu commune for three different economic situations. The gathered data is spanning two different periods from the communist economy and the present situation after 31 years of free market economy in Romania. The local growth and reset model based on an analytically solvable master equation is used to describe the observed data. The model with realistically chosen growth and reset rates is successful in describing both the experimentally observed distributions and the inequality indexes (Lorenz curve, Gini coefficient and Pareto point) derived from this data. The observed changes in these inequality measures are discussed in the context of the relevant socio-economic conditions.

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