18 datasets found
  1. H

    2014 Global Hunger Index Data

    • dataverse.harvard.edu
    Updated Mar 31, 2017
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    Welthungerhilfe (WHH) (2017). 2014 Global Hunger Index Data [Dataset]. http://doi.org/10.7910/DVN/27557
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Mar 31, 2017
    Dataset provided by
    Harvard Dataverse
    Authors
    Welthungerhilfe (WHH)
    License

    https://dataverse.harvard.edu/api/datasets/:persistentId/versions/1.1/customlicense?persistentId=doi:10.7910/DVN/27557https://dataverse.harvard.edu/api/datasets/:persistentId/versions/1.1/customlicense?persistentId=doi:10.7910/DVN/27557

    Time period covered
    1990 - 2012
    Area covered
    CARIBBEAN; Commonwealth of Independent States; LATIN AMERICA; MIDDLE EAST; NORTH AFRICA; EAST AFRICA; EAST ASIA; SOUTH ASIA; EASTERN EUROPE; SOUTHERN AFRICA; AFRICA SOUTH OF SAHARA; AFRICA; ASIA;
    Description

    The Global Hunger Index (GHI) is a tool designed to comprehensively measure and track hunger globally and by region and country. Calculated each year by the International Food Policy Research Institute (IFPRI), the GHI highlights successes and failures in hunger reduction and provide insights into the drivers of hunger, and food and nutrition security. The 2014 GHI has been calculated for 120 countries for which data on the three component indicators are available and for which measuring hung er is considered most relevant. The GHI calculation excludes some higher income countries because the prevalence of hunger there is very low. The GHI is only as current as the data for its three component indicators. This year's GHI reflects the most recent available country level data for the three component indicators spanning the period 2009 to 2013. Besides the most recent GHI scores, this dataset also contains the GHI scores for four other reference periods- 1990, 1995, 2000, and 2005. A country's GHI score is calculated by averaging the percentage of the population that is undernourished, the percentage of children youn ger than five years old who are underweight, and the percentage of children dying before the age of five. This calculation results in a 100 point scale on which zero is the best score (no hunger) and 100 the worst, although neither of these extremes is reached in practice. The three component indicators used to calculate the GHI scores draw upon data from the following sources: 1. Undernourishment: Updated data from the Food and Agriculture Organization of the United Nations (FAO) were used for the 1990, 1995, 2000, 2005, and 2014GHI scores. Undernourishment data for the 2014 GHI are for 2011-2013. 2. Child underweight: The "child underweight" component indicator of the GHI scores includes the latest additions to the World Health Organization's (WHO) Global Database on Child Growth and Malnutrition, and additional data from the joint data base by the United Nations Children's Fund (UNICEF), WHO and the World Bank; the most recent Demographic and Health Survey (DHS) and Multiple Indicator Cluster Survey reports; and statistical tables from UNICEF. For the 2014 GHI, data on child underweight are for the latest year for which data are available in the period 2009-2014. 3. Child mortality: Updated data from the UN Inter-agency Group for Child Mortality Estimation were used for the 1990, 1995, 2000, and 2005, and 2014 GHI scores. For the 2014 GHI, data on child mortality are for 2012. Resources related to 2014 Global Hunger Index

  2. d

    Capital Area Food Bank Hunger Estimates

    • catalog.data.gov
    • opendata.dc.gov
    • +2more
    Updated Feb 5, 2025
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    D.C. Office of the Chief Technology Officer (2025). Capital Area Food Bank Hunger Estimates [Dataset]. https://catalog.data.gov/dataset/capital-area-food-bank-hunger-estimates
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    Dataset updated
    Feb 5, 2025
    Dataset provided by
    D.C. Office of the Chief Technology Officer
    Description

    Polygons in this layer represent Census Tracts in the DMV (DC, Maryland, and Virginia). Data are included for each tract which estimate hunger and food insecurity. Data were compiled by the CAFB through internal tracking, and the layer was shared with the DC government as a courtesy. Fields include (all available for 2015 and 2014):15_FI_Rate: The estimated portion of the population in the census tract experiencing food insecurity (by CAFB standards). 15/14 indicates year measured.15_FI_Pop: The estimated number of people in the census tract experiencing food insecurity (by CAFB standards). 15/14 indicates year measured.15_LB_Need: The estimated pounds of food needed by the food insecure population in the census tract. 15/14 indicates year measured.15_Distrib: The number of pounds of food distributed by CAFB and partners in the census tract. 15/14 indicates year in which the distribution took place.15_LB_Unme: The difference between the estimated pounds of food needed and the real pounds of food distributed by CAFB and partners, representing the unmet need for food assistance in the census tract. 15/14 indicates year.The layer was shared with the DC government in May 2016 and is based on 2015 and 2014 data.

  3. International Food Security

    • agdatacommons.nal.usda.gov
    txt
    Updated Feb 8, 2024
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    US Department of Agriculture, Economic Research Service (2024). International Food Security [Dataset]. http://doi.org/10.15482/USDA.ADC/1299294
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    txtAvailable download formats
    Dataset updated
    Feb 8, 2024
    Dataset provided by
    Economic Research Servicehttp://www.ers.usda.gov/
    United States Department of Agriculturehttp://usda.gov/
    Authors
    US Department of Agriculture, Economic Research Service
    License

    U.S. Government Workshttps://www.usa.gov/government-works
    License information was derived automatically

    Description

    This dataset measures food availability and access for 76 low- and middle-income countries. The dataset includes annual country-level data on area, yield, production, nonfood use, trade, and consumption for grains and root and tuber crops (combined as R&T in the documentation tables), food aid, total value of imports and exports, gross domestic product, and population compiled from a variety of sources. This dataset is the basis for the International Food Security Assessment 2015-2025 released in June 2015. This annual ERS report projects food availability and access for 76 low- and middle-income countries over a 10-year period. Countries (Spatial Description, continued): Democratic Republic of the Congo, Ecuador, Egypt, El Salvador, Eritrea, Ethiopia, Gambia, Georgia, Ghana, Guatemala, Guinea, Guinea-Bissau, Haiti, Honduras, India, Indonesia, Jamaica, Kenya, Kyrgyzstan, Laos, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritania, Moldova, Mongolia, Morocco, Mozambique, Namibia, Nepal, Nicaragua, Niger, Nigeria, North Korea, Pakistan, Peru, Philippines, Rwanda, Senegal, Sierra Leone, Somalia, Sri Lanka, Sudan, Swaziland, Tajikistan, Tanzania, Togo, Tunisia, Turkmenistan, Uganda, Uzbekistan, Vietnam, Yemen, Zambia, and Zimbabwe. Resources in this dataset:Resource Title: CSV File for all years and all countries. File Name: gfa25.csvResource Title: International Food Security country data. File Name: GrainDemandProduction.xlsxResource Description: Excel files of individual country data. Please note that these files provide the data in a different layout from the CSV file. This version of the data files was updated 9-2-2021

    More up-to-date files may be found at: https://www.ers.usda.gov/data-products/international-food-security.aspx

  4. M

    China Hunger Statistics

    • macrotrends.net
    csv
    Updated May 31, 2025
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    MACROTRENDS (2025). China Hunger Statistics [Dataset]. https://www.macrotrends.net/global-metrics/countries/chn/china/hunger-statistics
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    csvAvailable download formats
    Dataset updated
    May 31, 2025
    Dataset authored and provided by
    MACROTRENDS
    License

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

    Time period covered
    Jan 1, 2001 - Dec 31, 2022
    Area covered
    China
    Description

    Historical chart and dataset showing China hunger statistics by year from 2001 to 2022.

  5. Food Insecurity Experience Scale 2021 - Lao PDR

    • microdata.worldbank.org
    • catalog.ihsn.org
    Updated Jan 17, 2023
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    FAO Statistics Division (2023). Food Insecurity Experience Scale 2021 - Lao PDR [Dataset]. https://microdata.worldbank.org/index.php/catalog/5472
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    Dataset updated
    Jan 17, 2023
    Dataset provided by
    Food and Agriculture Organizationhttp://fao.org/
    Authors
    FAO Statistics Division
    Time period covered
    2021
    Area covered
    Laos
    Description

    Abstract

    Sustainable Development Goal (SDG) target 2.1 commits countries to end hunger, ensure access by all people to safe, nutritious and sufficient food all year around. Indicator 2.1.2, “Prevalence of moderate or severe food insecurity based on the Food Insecurity Experience Scale (FIES)”, provides internationally-comparable estimates of the proportion of the population facing difficulties in accessing food. More detailed background information is available at http://www.fao.org/in-action/voices-of-the-hungry/fies/en/.

    The FIES-based indicators are compiled using the FIES survey module, containing 8 questions. Two indicators can be computed:
    1. The proportion of the population experiencing moderate or severe food insecurity (SDG indicator 2.1.2). 2. The proportion of the population experiencing severe food insecurity.

    These data were collected by FAO through GeoPoll. National institutions can also collect FIES data by including the FIES survey module in nationally representative surveys.

    Microdata can be used to calculate the indicator 2.1.2 at national level. Instructions for computing this indicator are described in the methodological document available in the documentations tab. Disaggregating results at sub-national level is not encouraged because estimates will suffer from substantial sampling and measurement error.

    Geographic coverage

    National coverage

    Analysis unit

    Individuals

    Universe

    Individuals of 15 years or older.

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    A sampling quota of at least 200 observations per each Administrative 1 areas is set Exclusions: NA Design effect: NA

    Mode of data collection

    Computer Assisted Telephone Interview [cati]

    Cleaning operations

    Statistical validation assesses the quality of the FIES data collected by testing their consistency with the assumptions of the Rasch model. This analysis involves the interpretation of several statistics that reveal 1) items that do not perform well in a given context, 2) cases with highly erratic response patterns, 3) pairs of items that may be redundant, and 4) the proportion of total variance in the population that is accounted for by the measurement model.

    Sampling error estimates

    The margin of error is estimated as NA. This is calculated around a proportion at the 95% confidence level. The maximum margin of error was calculated assuming a reported percentage of 50% and takes into account the design effect.

    Data appraisal

    Since the population with access to mobile telephones is likely to differ from the rest of the population with respect to their access to food, post-hoc adjustments were made to control for the potential resulting bias. Post-stratification weights were built to adjust the sample distribution by gender and education of the respondent at admin-1 level, to match the same distribution in the total population. However, an additional step was needed to try to ascertain the food insecurity condition of those with access to phones compared to that of the total population.

    Using FIES data collected by FAO through the GWP between 2014 and 2019, and a variable on access to mobile telephones that was also in the dataset, it was possible to compare the prevalence of food insecurity at moderate or severe level, and severe level only, of respondents with access to a mobile phone to that of the total population at national level.

  6. M

    Singapore Hunger Statistics

    • macrotrends.net
    csv
    Updated May 31, 2025
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    MACROTRENDS (2025). Singapore Hunger Statistics [Dataset]. https://www.macrotrends.net/global-metrics/countries/sgp/singapore/hunger-statistics
    Explore at:
    csvAvailable download formats
    Dataset updated
    May 31, 2025
    Dataset authored and provided by
    MACROTRENDS
    License

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

    Area covered
    Singapore
    Description

    Historical chart and dataset showing Singapore hunger statistics by year from N/A to N/A.

  7. T

    SDG Indicator 2.1.3 Zero Hunger - Block Group

    • opendata.sandag.org
    Updated Aug 24, 2022
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    U.S. Department of Agriculture (2022). SDG Indicator 2.1.3 Zero Hunger - Block Group [Dataset]. https://opendata.sandag.org/w/g46x-6ivp/default?cur=e3XbJRfjSL5
    Explore at:
    tsv, csv, application/rdfxml, kml, xml, application/rssxml, application/geo+json, kmzAvailable download formats
    Dataset updated
    Aug 24, 2022
    Dataset authored and provided by
    U.S. Department of Agriculture
    License

    U.S. Government Workshttps://www.usa.gov/government-works
    License information was derived automatically

    Description

    "Food deserts" are defined as areas where residents do not live near supermarkets or other food retailers that carry affordable and nutritious food.

    This dataset describes the total and percentage of people in relation to their relative distance to a major grocery store and their poverty level within block groups of the San Diego County. The dataset is curated from multiple sources, such as the Census ACS and the California Economic Development Department, using methodology from the Economic Research Service (ERS) in the U.S. Department of Agriculture.

  8. T

    SDG Indicator 2.1.3 Zero Hunger - Region

    • opendata.sandag.org
    application/rdfxml +5
    Updated Aug 24, 2022
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    U.S. Department of Agriculture (2022). SDG Indicator 2.1.3 Zero Hunger - Region [Dataset]. https://opendata.sandag.org/w/ee9j-djs6/default?cur=EQiNhDIMfnx
    Explore at:
    csv, json, application/rssxml, application/rdfxml, tsv, xmlAvailable download formats
    Dataset updated
    Aug 24, 2022
    Dataset authored and provided by
    U.S. Department of Agriculture
    License

    U.S. Government Workshttps://www.usa.gov/government-works
    License information was derived automatically

    Description

    "Food deserts" are defined as areas where residents do not live near supermarkets or other food retailers that carry affordable and nutritious food.

    This dataset describes the total and percentage of people in relation to their relative distance to a major grocery store and their poverty level in the San Diego County. The dataset is curated from multiple sources, such as the Census ACS and the California Economic Development Department, using methodology from the Economic Research Service (ERS) in the U.S. Department of Agriculture.

  9. a

    Zero Hunger

    • senegal2-sdg.hub.arcgis.com
    • rwanda-sdg.hub.arcgis.com
    • +13more
    Updated Jul 1, 2022
    + more versions
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    arobby1971 (2022). Zero Hunger [Dataset]. https://senegal2-sdg.hub.arcgis.com/items/c9ae8f87e05a47b99791e1982d623cc5
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    Dataset updated
    Jul 1, 2022
    Dataset authored and provided by
    arobby1971
    Area covered
    Description

    Goal 2End hunger, achieve food security and improved nutrition and promote sustainable agricultureTarget 2.1: By 2030, end hunger and ensure access by all people, in particular the poor and people in vulnerable situations, including infants, to safe, nutritious and sufficient food all year roundIndicator 2.1.1: Prevalence of undernourishmentSN_ITK_DEFC: Prevalence of undernourishment (%)SN_ITK_DEFCN: Number of undernourish people (millions)Indicator 2.1.2: Prevalence of moderate or severe food insecurity in the population, based on the Food Insecurity Experience Scale (FIES)AG_PRD_FIESMS: Prevalence of moderate or severe food insecurity in the adult population (%)AG_PRD_FIESMSN: Total population in moderate or severe food insecurity (thousands of people)AG_PRD_FIESS: Prevalence of severe food insecurity in the adult population (%)AG_PRD_FIESSN: Total population in severe food insecurity (thousands of people)Target 2.2: By 2030, end all forms of malnutrition, including achieving, by 2025, the internationally agreed targets on stunting and wasting in children under 5 years of age, and address the nutritional needs of adolescent girls, pregnant and lactating women and older personsIndicator 2.2.1: Prevalence of stunting (height for age SH_STA_STNT: Proportion of children moderately or severely stunted (%)SH_STA_STNTN: Children moderately or severely stunted (thousands)+2 or SH_STA_WAST: Proportion of children moderately or severely wasted (%)SH_STA_WASTN: Children moderately or severely wasted (thousands)SN_STA_OVWGT: Proportion of children moderately or severely overweight (%)SN_STA_OVWGTN: Children moderately or severely overweight (thousands)Indicator 2.2.3: Prevalence of anaemia in women aged 15 to 49 years, by pregnancy status (percentage)SH_STA_ANEM: Proportion of women aged 15-49 years with anaemia (%)SH_STA_ANEM_PREG: Proportion of women aged 15-49 years with anaemia, pregnant (%)SH_STA_ANEM_NPRG: Proportion of women aged 15-49 years with anaemia, non-pregnant (%)Target 2.3: By 2030, double the agricultural productivity and incomes of small-scale food producers, in particular women, indigenous peoples, family farmers, pastoralists and fishers, including through secure and equal access to land, other productive resources and inputs, knowledge, financial services, markets and opportunities for value addition and non-farm employmentIndicator 2.3.1: Volume of production per labour unit by classes of farming/pastoral/forestry enterprise sizePD_AGR_SSFP: Productivity of small-scale food producers (agricultural output per labour day, PPP) (constant 2011 international $)PD_AGR_LSFP: Productivity of large-scale food producers (agricultural output per labour day, PPP) (constant 2011 international $)Indicator 2.3.2: Average income of small-scale food producers, by sex and indigenous statusSI_AGR_SSFP: Average income of small-scale food producers, PPP (constant 2011 international $)SI_AGR_LSFP: Average income of large-scale food producers, PPP (constant 2011 international $)Target 2.4: By 2030, ensure sustainable food production systems and implement resilient agricultural practices that increase productivity and production, that help maintain ecosystems, that strengthen capacity for adaptation to climate change, extreme weather, drought, flooding and other disasters and that progressively improve land and soil qualityIndicator 2.4.1: Proportion of agricultural area under productive and sustainable agricultureTarget 2.5: By 2020, maintain the genetic diversity of seeds, cultivated plants and farmed and domesticated animals and their related wild species, including through soundly managed and diversified seed and plant banks at the national, regional and international levels, and promote access to and fair and equitable sharing of benefits arising from the utilization of genetic resources and associated traditional knowledge, as internationally agreedIndicator 2.5.1: Number of plant and animal genetic resources for food and agriculture secured in either medium- or long-term conservation facilitiesER_GRF_ANIMRCNTN: Number of local breeds for which sufficient genetic resources are stored for reconstitutionER_GRF_PLNTSTOR: Plant breeds for which sufficient genetic resources are stored (number)Indicator 2.5.2: Proportion of local breeds classified as being at risk of extinctionER_RSK_LBREDS: Proportion of local breeds classified as being at risk as a share of local breeds with known level of extinction risk (%)Target 2.a: Increase investment, including through enhanced international cooperation, in rural infrastructure, agricultural research and extension services, technology development and plant and livestock gene banks in order to enhance agricultural productive capacity in developing countries, in particular least developed countriesIndicator 2.a.1: The agriculture orientation index for government expendituresAG_PRD_ORTIND: Agriculture orientation index for government expendituresAG_PRD_AGVAS: Agriculture value added share of GDP (%)AG_XPD_AGSGB: Agriculture share of Government Expenditure (%)Indicator 2.a.2: Total official flows (official development assistance plus other official flows) to the agriculture sectorDC_TOF_AGRL: Total official flows (disbursements) for agriculture, by recipient countries (millions of constant 2018 United States dollars)Target 2.b: Correct and prevent trade restrictions and distortions in world agricultural markets, including through the parallel elimination of all forms of agricultural export subsidies and all export measures with equivalent effect, in accordance with the mandate of the Doha Development RoundIndicator 2.b.1: Agricultural export subsidiesAG_PRD_XSUBDY: Agricultural export subsidies (millions of current United States dollars)Target 2.c: Adopt measures to ensure the proper functioning of food commodity markets and their derivatives and facilitate timely access to market information, including on food reserves, in order to help limit extreme food price volatilityIndicator 2.c.1: Indicator of food price anomaliesAG_FPA_COMM: Indicator of Food Price Anomalies (IFPA), by type of productAG_FPA_CFPI: Consumer Food Price IndexAG_FPA_HMFP: Proportion of countries recording abnormally high or moderately high food prices, according to the Indicator of Food Price Anomalies (%)

  10. Food Insecurity Experience Scale 2021 - Madagascar

    • microdata.worldbank.org
    • catalog.ihsn.org
    Updated Jan 13, 2023
    + more versions
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    FAO Statistics Division (2023). Food Insecurity Experience Scale 2021 - Madagascar [Dataset]. https://microdata.worldbank.org/index.php/catalog/5438
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    Dataset updated
    Jan 13, 2023
    Dataset provided by
    Food and Agriculture Organizationhttp://fao.org/
    Authors
    FAO Statistics Division
    Time period covered
    2021
    Area covered
    Madagascar
    Description

    Abstract

    Sustainable Development Goal (SDG) target 2.1 commits countries to end hunger, ensure access by all people to safe, nutritious and sufficient food all year around. Indicator 2.1.2, “Prevalence of moderate or severe food insecurity based on the Food Insecurity Experience Scale (FIES)”, provides internationally-comparable estimates of the proportion of the population facing difficulties in accessing food. More detailed background information is available at http://www.fao.org/in-action/voices-of-the-hungry/fies/en/.

    The FIES-based indicators are compiled using the FIES survey module, containing 8 questions. Two indicators can be computed:
    1. The proportion of the population experiencing moderate or severe food insecurity (SDG indicator 2.1.2). 2. The proportion of the population experiencing severe food insecurity.

    These data were collected by FAO through GeoPoll. National institutions can also collect FIES data by including the FIES survey module in nationally representative surveys.

    Microdata can be used to calculate the indicator 2.1.2 at national level. Instructions for computing this indicator are described in the methodological document available in the documentations tab. Disaggregating results at sub-national level is not encouraged because estimates will suffer from substantial sampling and measurement error.

    Geographic coverage

    National coverage

    Analysis unit

    Individuals

    Universe

    Individuals of 15 years or older.

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    A sampling quota of at least 200 observations per each Administrative 1 areas is set Exclusions: NA Design effect: NA

    Mode of data collection

    Computer Assisted Personal Interview [capi]

    Cleaning operations

    Statistical validation assesses the quality of the FIES data collected by testing their consistency with the assumptions of the Rasch model. This analysis involves the interpretation of several statistics that reveal 1) items that do not perform well in a given context, 2) cases with highly erratic response patterns, 3) pairs of items that may be redundant, and 4) the proportion of total variance in the population that is accounted for by the measurement model.

    Sampling error estimates

    The margin of error is estimated as NA. This is calculated around a proportion at the 95% confidence level. The maximum margin of error was calculated assuming a reported percentage of 50% and takes into account the design effect.

    Data appraisal

    Since the population with access to mobile telephones is likely to differ from the rest of the population with respect to their access to food, post-hoc adjustments were made to control for the potential resulting bias. Post-stratification weights were built to adjust the sample distribution by gender and education of the respondent at admin-1 level, to match the same distribution in the total population. However, an additional step was needed to try to ascertain the food insecurity condition of those with access to phones compared to that of the total population.

    Using FIES data collected by FAO through the GWP between 2014 and 2019, and a variable on access to mobile telephones that was also in the dataset, it was possible to compare the prevalence of food insecurity at moderate or severe level, and severe level only, of respondents with access to a mobile phone to that of the total population at national level.

  11. Food Insecurity Experience Scale (FIES) - Maldives

    • microdata.fao.org
    Updated Jun 29, 2022
    + more versions
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    FAO Statistics Division (2022). Food Insecurity Experience Scale (FIES) - Maldives [Dataset]. https://microdata.fao.org/index.php/catalog/2270
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    Dataset updated
    Jun 29, 2022
    Dataset provided by
    Food and Agriculture Organizationhttp://fao.org/
    Authors
    FAO Statistics Division
    Time period covered
    2021
    Area covered
    Maldives
    Description

    Abstract

    Sustainable Development Goal (SDG) target 2.1 commits countries to end hunger, ensure access by all people to safe, nutritious and sufficient food all year around. Indicator 2.1.2, “Prevalence of moderate or severe food insecurity based on the Food Insecurity Experience Scale (FIES)”, provides internationally-comparable estimates of the proportion of the population facing difficulties in accessing food. More detailed background information is available at http://www.fao.org/in-action/voices-of-the-hungry/fies/en/.

    The FIES-based indicators are compiled using the FIES survey module, containing 8 questions. Two indicators can be computed:
    1. The proportion of the population experiencing moderate or severe food insecurity (SDG indicator 2.1.2). 2. The proportion of the population experiencing severe food insecurity.

    These data were collected by FAO through GeoPoll. National institutions can also collect FIES data by including the FIES survey module in nationally representative surveys.

    Microdata can be used to calculate the indicator 2.1.2 at national level. Instructions for computing this indicator are described in the methodological document available in the documentations tab. Disaggregating results at sub-national level is not encouraged because estimates will suffer from substantial sampling and measurement error.

    Geographic coverage

    National

    Analysis unit

    Individuals

    Universe

    Individuals of 15 years or older.

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    A sampling quota of at least 200 observations per each Administrative 1 areas is set Exclusions: NA Design effect: NA

    Mode of data collection

    Computer Assisted Telephone Interview [CATI]

    Cleaning operations

    Statistical validation assesses the quality of the FIES data collected by testing their consistency with the assumptions of the Rasch model. This analysis involves the interpretation of several statistics that reveal 1) items that do not perform well in a given context, 2) cases with highly erratic response patterns, 3) pairs of items that may be redundant, and 4) the proportion of total variance in the population that is accounted for by the measurement model.

    Sampling error estimates

    The margin of error is estimated as NA. This is calculated around a proportion at the 95% confidence level. The maximum margin of error was calculated assuming a reported percentage of 50% and takes into account the design effect.

    Data appraisal

    Since the population with access to mobile telephones is likely to differ from the rest of the population with respect to their access to food, post-hoc adjustments were made to control for the potential resulting bias. Post-stratification weights were built to adjust the sample distribution by gender and education of the respondent at admin-1 level, to match the same distribution in the total population. However, an additional step was needed to try to ascertain the food insecurity condition of those with access to phones compared to that of the total population.

    Using FIES data collected by FAO through the GWP between 2014 and 2019, and a variable on access to mobile telephones that was also in the dataset, it was possible to compare the prevalence of food insecurity at moderate or severe level, and severe level only, of respondents with access to a mobile phone to that of the total population at national level.

  12. Food Insecurity Experience Scale 2020 - Nigeria

    • microdata.worldbank.org
    • catalog.ihsn.org
    • +1more
    Updated Jan 11, 2023
    + more versions
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    FAO Statistics Division (2023). Food Insecurity Experience Scale 2020 - Nigeria [Dataset]. https://microdata.worldbank.org/index.php/catalog/5408
    Explore at:
    Dataset updated
    Jan 11, 2023
    Dataset provided by
    Food and Agriculture Organizationhttp://fao.org/
    Authors
    FAO Statistics Division
    Time period covered
    2020
    Area covered
    Nigeria
    Description

    Abstract

    Sustainable Development Goal (SDG) target 2.1 commits countries to end hunger, ensure access by all people to safe, nutritious and sufficient food all year around. Indicator 2.1.2, “Prevalence of moderate or severe food insecurity based on the Food Insecurity Experience Scale (FIES)”, provides internationally-comparable estimates of the proportion of the population facing difficulties in accessing food. More detailed background information is available at http://www.fao.org/in-action/voices-of-the-hungry/fies/en/.

    The FIES-based indicators are compiled using the FIES survey module, containing 8 questions. Two indicators can be computed:
    1. The proportion of the population experiencing moderate or severe food insecurity (SDG indicator 2.1.2). 2. The proportion of the population experiencing severe food insecurity.

    These data were collected by FAO through GeoPoll. National institutions can also collect FIES data by including the FIES survey module in nationally representative surveys.

    Microdata can be used to calculate the indicator 2.1.2 at national level. Instructions for computing this indicator are described in the methodological document available in the documentations tab. Disaggregating results at sub-national level is not encouraged because estimates will suffer from substantial sampling and measurement error.

    Geographic coverage

    National coverage

    Analysis unit

    Individuals

    Universe

    Individuals of 15 years or older.

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    A Random Digit Dialling (RDD) approach was used to form a random sample of telephone numbers. Stratified phone numbers made available from telephone service providers or administrative registers were also used to integrate RDD when needed. Socio-demographic characteristics collected in the survey were then compared with the available information from recent national surveys to verify the extent to which the sample mirrored the total population structure. In case of discrepancies, post-stratification sampling weights were computed to adjust for the under-represented populations, typically using sex and education level. Exclusions: NA Design effect: NA

    Mode of data collection

    Computer Assisted Telephone Interview [cati]

    Cleaning operations

    Statistical validation assesses the quality of the FIES data collected by testing their consistency with the assumptions of the Rasch model. This analysis involves the interpretation of several statistics that reveal 1) items that do not perform well in a given context, 2) cases with highly erratic response patterns, 3) pairs of items that may be redundant, and 4) the proportion of total variance in the population that is accounted for by the measurement model.

    Sampling error estimates

    Not Available.

    Data appraisal

    Since the population with access to mobile telephones is likely to differ from the rest of the population with respect to their access to food, post-hoc adjustments were made to control for the potential resulting bias. Post-stratification weights were built to adjust the sample distribution by gender and education of the respondent at admin-1 level, to match the same distribution in the total population. However, an additional step was needed to try to ascertain the food insecurity condition of those with access to phones compared to that of the total population.

    Using FIES data collected by FAO through the GWP between 2014 and 2019, and a variable on access to mobile telephones that was also in the dataset, it was possible to compare the prevalence of food insecurity at moderate or severe level, and severe level only, of respondents with access to a mobile phone to that of the total population at national level.

  13. M

    Bangladesh Hunger Statistics

    • macrotrends.net
    csv
    Updated May 31, 2025
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    MACROTRENDS (2025). Bangladesh Hunger Statistics [Dataset]. https://www.macrotrends.net/global-metrics/countries/bgd/bangladesh/hunger-statistics
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    csvAvailable download formats
    Dataset updated
    May 31, 2025
    Dataset authored and provided by
    MACROTRENDS
    License

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

    Time period covered
    Jan 1, 2001 - Dec 31, 2022
    Area covered
    Bangladesh
    Description

    Historical chart and dataset showing Bangladesh hunger statistics by year from 2001 to 2022.

  14. Food Insecurity Experience Scale (FIES) - Somalia

    • microdata.fao.org
    Updated Jul 12, 2021
    + more versions
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    FAO Statistics Division (2021). Food Insecurity Experience Scale (FIES) - Somalia [Dataset]. https://microdata.fao.org/index.php/catalog/1996
    Explore at:
    Dataset updated
    Jul 12, 2021
    Dataset provided by
    Food and Agriculture Organizationhttp://fao.org/
    Authors
    FAO Statistics Division
    Time period covered
    2020
    Area covered
    Somalia
    Description

    Abstract

    Sustainable Development Goal (SDG) target 2.1 commits countries to end hunger, ensure access by all people to safe, nutritious and sufficient food all year around. Indicator 2.1.2, “Prevalence of moderate or severe food insecurity based on the Food Insecurity Experience Scale (FIES)”, provides internationally-comparable estimates of the proportion of the population facing difficulties in accessing food. More detailed background information is available at http://www.fao.org/in-action/voices-of-the-hungry/fies/en/.

    The FIES-based indicators are compiled using the FIES survey module, containing 8 questions. Two indicators can be computed:
    1. The proportion of the population experiencing moderate or severe food insecurity (SDG indicator 2.1.2). 2. The proportion of the population experiencing severe food insecurity.

    These data were collected by FAO through GeoPoll. National institutions can also collect FIES data by including the FIES survey module in nationally representative surveys.

    Microdata can be used to calculate the indicator 2.1.2 at national level. Instructions for computing this indicator are described in the methodological document available in the documentations tab. Disaggregating results at sub-national level is not encouraged because estimates will suffer from substantial sampling and measurement error.

    Geographic coverage

    National

    Analysis unit

    Individuals

    Universe

    Individuals of 15 years or older.

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    A Random Digit Dialling (RDD) approach was used to form a random sample of telephone numbers. Stratified phone numbers made available from telephone service providers or administrative registers were also used to integrate RDD when needed. Socio-demographic characteristics collected in the survey were then compared with the available information from recent national surveys to verify the extent to which the sample mirrored the total population structure. In case of discrepancies, post-stratification sampling weights were computed to adjust for the under-represented populations, typically using sex and education level. Exclusions: None Design effect: NA

    Mode of data collection

    Computer Assisted Telephone Interview [CATI]

    Cleaning operations

    Statistical validation assesses the quality of the FIES data collected by testing their consistency with the assumptions of the Rasch model. This analysis involves the interpretation of several statistics that reveal 1) items that do not perform well in a given context, 2) cases with highly erratic response patterns, 3) pairs of items that may be redundant, and 4) the proportion of total variance in the population that is accounted for by the measurement model.

    Sampling error estimates

    The margin of error is estimated as NA. This is calculated around a proportion at the 95% confidence level. The maximum margin of error was calculated assuming a reported percentage of 50% and takes into account the design effect.

    Data appraisal

    Since the population with access to mobile telephones is likely to differ from the rest of the population with respect to their access to food, post-hoc adjustments were made to control for the potential resulting bias. Post-stratification weights were built to adjust the sample distribution by gender and education of the respondent at admin-1 level, to match the same distribution in the total population. However, an additional step was needed to try to ascertain the food insecurity condition of those with access to phones compared to that of the total population.

    Using FIES data collected by FAO through the GWP between 2014 and 2019, and a variable on access to mobile telephones that was also in the dataset, it was possible to compare the prevalence of food insecurity at moderate or severe level, and severe level only, of respondents with access to a mobile phone to that of the total population at national level. The variable HEALTHY was not considered in the computation of the published FAO food insecurity indicator based on FIES due to the results of the validation process.

  15. o

    Free WiFi to monitor flow in Hanoian traditional markets

    • explore.openaire.eu
    Updated Jan 1, 2021
    + more versions
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    Louis Reymondin; Hieu Le Trung; Thibaud Vantalon; Huong Pham; Trong Phan; Kien Nguyen (2021). Free WiFi to monitor flow in Hanoian traditional markets [Dataset]. http://doi.org/10.5281/zenodo.5707312
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    Dataset updated
    Jan 1, 2021
    Authors
    Louis Reymondin; Hieu Le Trung; Thibaud Vantalon; Huong Pham; Trong Phan; Kien Nguyen
    Description

    Despite being the main source of fresh, convenient, and affordable food for 80% of Hanoi���s population, food flows within traditional markets remain largely invisible due to a lack of tracing systems and environmental conditions which make traditional tracking approaches challenging. By providing free internet to a series of wholesalers and markets in the Cau Giay and Dong Anh districts of Hanoi, Vietnam, this project will put in place the first pieces of tracking system that will characterize and monitor food flows between traders, retailers, and consumers. Research has found that 10-40% of traditional market food is contaminated with microbes or parasites which cause foodborne illnesses. As shoppers become increasingly concerned about food safety and large-scale retailers that can offer food safety certification expand rapidly, this project aims to equip traditional market actors with data that could prevent their marginalization through urban policy decisions that may favor organized retailers, as well as improve the safety of traditional market goods. The collected food flow data will allow for improved linkages among key traditional market actors and help identify better policy and planning options for improving distribution channels in ways that benefits under-resourced communities. To implement the project, the Alliance of Bioversity International and CIAT and the General Statistics Office (GSO) of Vietnam survey actors and track space and time data points on all devices within the range of the WiFi routers and signal amplifiers, whether connected to the internet or not. The pilot system ran on three layers of data: Layer One Every smartphone has a unique media access control (MAC) address that the WiFi routers installed in the markers use to identify how many MAC addresses visit the markets over time, how many return to the market and how often, and how markets differ on these metrics. This data is collected even if the smartphone is not connected to the WiFi network. Layer Two When a smartphone user connects to the free WiFi, they are prompted to answer a series of questions depending on their user type (vendor, customer, etc.). For example, a user that identifies as a vendor is asked questions regarding sales of specific commodities which will allow for sales to be characterized across time and space. Layer Three To validate findings in Layer One and Two, in-person surveys were conducted with vegetable, pork and rice sellers in five traditional markets in Hanoi mac: An anonymized version of the MAC. All the MAC address were anonymized through a SHA-3 256 hashing function. The hashed mac ensure anonymity while is consistent across all markets and during the whole period of the analysis. We can therefore ensure that a given mac found in two different dataset will correspond to the same phone. market: The name of the market where the phone was seen role: Self-identified role if the user connected to the wifi and filled-out the layer 2 form gender: Self-identified role if the user connected to the wifi and filled-out the layer 2 form median_first_seen: The median time when the user is first seen in the markets (in minutes starting at 0 from midnight) (e.g. the time the user entered the market) median_last_seen: The median time when the user is last seen in the markets (in minutes starting at 0 from midnight) (e.g. the time the user left the market) average_time_day: The average number of time the user visited the market. A period of time of at least 2 hours between two consecutive observation of the user in the market is needed to be counted as a different visit. average_duration_day: The average duration spent on the market daily. average_day_week: The average number of visits per week. average_total_day_seen: The total number of days a user was seen on the market. total_durantion: Total duration spent by a single user on the market. https://bigdata.cgiar.org/inspire/inspire-challenge-2018/revealing-informal-food-flows-through-free-wifi/#news-resources

  16. d

    Global Coral Bleaching Database (NCEI Accession 0228498)

    • catalog.data.gov
    • datasets.ai
    Updated Jun 1, 2025
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    (Point of Contact) (2025). Global Coral Bleaching Database (NCEI Accession 0228498) [Dataset]. https://catalog.data.gov/dataset/global-coral-bleaching-database-ncei-accession-0228498
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    Dataset updated
    Jun 1, 2025
    Dataset provided by
    (Point of Contact)
    Description

    Coral reefs are one of the most important ecosystems in the world, supporting hundreds of thousands of species and providing food, coastal protection, and income to hundreds of millions of people. However, coral reefs are experiencing more frequent and intense stress events leading to coral bleaching. The process of coral bleaching involves a breakdown of the symbiosis between the coral hosts and their endosymbiotic algae, which can lead to starvation and mortality. Here we compile a global coral bleaching database of 33,244 records of the presence or absence of coral bleaching from 1963 - 2021.

  17. Hunger Safety Net Programme Survey 2016 - Kenya

    • microdata.fao.org
    Updated Nov 8, 2022
    + more versions
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    Oxford Policy Management Limited (2022). Hunger Safety Net Programme Survey 2016 - Kenya [Dataset]. https://microdata.fao.org/index.php/catalog/1520
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    Dataset updated
    Nov 8, 2022
    Dataset provided by
    Oxford Policy Managementhttp://www.opml.co.uk/
    Authors
    Oxford Policy Management Limited
    Time period covered
    2016
    Area covered
    Kenya
    Description

    Abstract

    The Hunger Safety Net Programme (HSNP) is a social protection project being conducted in the Arid and Semi-Arid Lands (ASALs) of northern Kenya. The ASALs are extremely food-insecure areas highly prone to drought, which have experienced recurrent food crises and food aid responses for decades. The HSNP is intended to reduce dependency on emergency food aid by sustainably strengthening livelihoods through cash transfers. The pilot phase ran from 2009 to 2013. The second phase has been launched in July 2013 and contracted to run until March 2018. Oxford Policy Management (OPM) was responsible for the monitoring and evaluation (M&E) of the programme under the pilot phase, as well as the second phase of implementation. Within the impact evaluation component for Phase 2, OPM used a range of analytical methods within an overarching mixed-method approach. The quantitative impact evaluation of HSNP Phase 2 compares the situation of HSNP2 beneficiaries and control households, relying on the Regression Discontinuity approach, integrated by a targeted Propensity Score Matching approach. In addition to the analysis at the household level, a Local Economy-Wide Impact Evaluation (LEWIE) was conducted to investigate the impact of the HSNP2 on the local economy, including on the production activities of both beneficiary and non-beneficiary households. A single round of data collection based on a household and business survey underpins the household quantitative impact evaluation and the LEWIE study. The objective of the survey is to collect household and business data to provide an assessment of the programme's impact on the local economy, as well as beneficiary households. The household survey is a survey of 5,979 people, carried out between 13 February and 29 June 2016 in 187 sub-locations across the four counties of Mandera, Marsabit, Turkana and Wajir. The survey covered modules on household demographic characteristics, livestock, assets, land, transfers, food and non-food consumption, food security, saving and borrowing, jobs, business, livestock trading and subjective poverty. In addition to the household survey, a business questionnaire was conducted in the three main commercial hubs of each county. Overall, 282 business questionnaires were administered in the four counties. The purpose of the survey was to learn more about local economic activities and livelihoods in the HSNP counties, and the data was used for the LEWIE analysis. The aim was to capture information on three main sectors of the local economy:

    1. Retailing - shops that sell retail goods on which a price mark-up is applied
    2. Services
    3. Producers - businesses that transform inputs into outputs

    Lastly, since livestock trading is a very important activity in the HSNP counties, livestock traders have been interviewed to understand better how the market works. In each county, three main livestock markets were targeted for interviews.

    Geographic coverage

    Regional

    Analysis unit

    Households

    Universe

    (a) At the household level, the study population consists of all the households in the four HSNP counties (i.e. Mandera, Marsabit, Turkana and Wajir). Within a household, the survey covered all de jure household members (usual residents).

    (b) At the market level, the survey covered a random sample of businesses in the three main commercial hubs of each county. The following categories of businesses were excluded from the listing:

    • Temporary stalls or mobile sellers located outside permanent kiosks
    • Banks
    • Education institutions (schools, universities etc.)
    • Health facilities

    (c) The livestock trader survey was conducted in the three main livestock markets of each county. To the extent possible, livestock traders have been sampled in order to achieve a balance between those trading large animals, those trading small or medium value animals, those trading only within the HSNP counties and those who also trade outside the HSNP counties.

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    (a) HOUSEHOLD SURVEY The household survey used a two-stage sampling approach, for which the sample frame was defined by sub-locations and households in the HSNP Management Information System (MIS) data. The MIS data are data from a census of nearly all households in the four HSNP counties. The census contains the information that was gathered in respect of these households during the registration for the HSNP programme, their Proxy Means Test (PMT) score and their assignment to the HSNP cash transfers, as well as information about all payments received by all households since the start of Phase 2. The HSNP acknowledges that a small number of the population was recognised to be missed and was registered at a later date. The sampling procedure was intended to cover the different sample requirements of the impact evaluation approaches, including the Local Economy-Wide Impact Evaluation (LEWIE), the quantitative impact evaluation based on the Regression Discontinuity (RD) approach, and the Propensity Score Matching (PSM) back-up.

    Drawing the sample consisted of two stages: 1. First stage: sampling of sub-locations 2. Second stage: sampling of households within a sub-location.

    The sampling process yielded a sample of 187 sub-locations, including the 24 that were sampled with certainty. 11 sub-locations were sampled twice, and one sub-location was sampled three times. 44 sub-locations were selected in Mandera, 46 in Wajir, 48 in Marsabit and 49 in Turkana. In each sub-location 32 households were sampled. In a few sub-locations there were insufficient households to select the desired LEWIE sample, resulting in fewer than 32 households sampled. Overall, 6,384 households were sampled.

    (b) BUSINESS SURVEY A business questionnaire was conducted in the three main commercial hubs of each county. The purpose of the survey was to learn more about local economic activities and livelihoods in the HSNP counties, and the data was used for the LEWIE analysis. In each sub-location, a sample of at least seven businesses from each category was targeted. Since no sampling frame for local businesses was available, the survey research teams in each county undertook a listing exercise of all businesses on the main commercial centre of the selected sub-locations. Once the listing was completed, the team leader sampled the required number of businesses using a step sampling approach. Overall, 282 business questionnaires were administered in the four counties. The business survey is not representative of any commercial hubs.

    (c) LIVESTOCK TRADER SURVEY Since livestock trading is a very important activity in the HSNP counties, a number of livestock traders have been interviewed to understand better how the market works. In each county, three main livestock markets were targeted for interviews. Each enumerator team was asked to interview four traders in each of the sub-locations, leading to a total sample size of 12 livestock trader interviews per county. Sampling of livestock traders was mostly done purposively. To the extent possible, team leaders sampled livestock traders in order to achieve a balance between those trading large animals, those trading small or medium value animals, those trading only within the HSNP counties and those who also trade outside the HSNP counties. The livestock trader survey is not representative of any livestock markets.

    Mode of data collection

    Computer Assisted Personal Interview [capi]

    Cleaning operations

    (a) QUALITY CHECKS

    Given the data was electronically collected, it was continually checked, edited and processed throughout the survey cycle. A first stage of data checking was done by the survey team which involved: (i) checking of all IDs (ii) checking for missing observations (iii) checking for missing item responses where none should be missing (iv) first round of checks for inadmissible/out of range and inconsistent values.

    (b) DATA PROCESSING Additional data processing activities were performed at the end of data collection in order to transform the collected cleaned data into a format that is ready for analysis. The aim of these activities was to produce reliable, consistent and fully-documented datasets that can be analysed throughout the survey and archived at the end in such a way that they can be used by other data users well into the future. Data processing activities involved:

    • Computing and merging in the sampling weights
    • Reshaping datasets in order to produce data files for each unit of observation (households, household members, and businesses)
    • Anonymising data by removing all variables that identify respondents such as names, address, GPS coordinates, etc.
    • Classifying non-response and coding them using a pre-determined classification scheme
    • Properly naming and labelling the variables in each dataset

    Response rate

    Household survey response rate was 88.9 percent. For business survey and livestock trader survey, the response rate was 100 percent.

    Data appraisal

    The datasets were then sent to the analysis team where they were subjected to a second set of checking and cleaning activities. This included checking for out of range responses and inadmissible values not captured by the filters built into the CAPI software or the initial data checking process by the survey team. A comprehensive data checking and analysis system was created including a logical folder structure, the development of template syntax files (in Stata), to ensure data checking and cleaning activities were recorded, that all analysts used the same file and variable naming conventions, variable definitions,

  18. Food Insecurity Experience Scale 2020 - Ethiopia

    • microdata.worldbank.org
    • catalog.ihsn.org
    Updated Jan 20, 2023
    + more versions
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    FAO Statistics Division (2023). Food Insecurity Experience Scale 2020 - Ethiopia [Dataset]. https://microdata.worldbank.org/index.php/catalog/5525
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    Dataset updated
    Jan 20, 2023
    Dataset provided by
    Food and Agriculture Organizationhttp://fao.org/
    Authors
    FAO Statistics Division
    Time period covered
    2020
    Area covered
    Ethiopia
    Description

    Abstract

    Sustainable Development Goal (SDG) target 2.1 commits countries to end hunger, ensure access by all people to safe, nutritious and sufficient food all year around. Indicator 2.1.2, “Prevalence of moderate or severe food insecurity based on the Food Insecurity Experience Scale (FIES)”, provides internationally-comparable estimates of the proportion of the population facing difficulties in accessing food. More detailed background information is available at http://www.fao.org/in-action/voices-of-the-hungry/fies/en/.

    The FIES-based indicators are compiled using the FIES survey module, containing 8 questions. Two indicators can be computed:
    1. The proportion of the population experiencing moderate or severe food insecurity (SDG indicator 2.1.2). 2. The proportion of the population experiencing severe food insecurity.

    These data were collected by FAO through GeoPoll. National institutions can also collect FIES data by including the FIES survey module in nationally representative surveys.

    Microdata can be used to calculate the indicator 2.1.2 at national level. Instructions for computing this indicator are described in the methodological document available in the documentations tab. Disaggregating results at sub-national level is not encouraged because estimates will suffer from substantial sampling and measurement error.

    Geographic coverage

    National coverage

    Analysis unit

    Individuals

    Universe

    Individuals of 15 years or older.

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    A Random Digit Dialling (RDD) approach was used to form a random sample of telephone numbers. Stratified phone numbers made available from telephone service providers or administrative registers were also used to integrate RDD when needed. Socio-demographic characteristics collected in the survey were then compared with the available information from recent national surveys to verify the extent to which the sample mirrored the total population structure. In case of discrepancies, post-stratification sampling weights were computed to adjust for the under-represented populations, typically using sex and education level. Exclusions: NA Design effect: NA

    Mode of data collection

    Computer Assisted Telephone Interview [cati]

    Cleaning operations

    Statistical validation assesses the quality of the FIES data collected by testing their consistency with the assumptions of the Rasch model. This analysis involves the interpretation of several statistics that reveal 1) items that do not perform well in a given context, 2) cases with highly erratic response patterns, 3) pairs of items that may be redundant, and 4) the proportion of total variance in the population that is accounted for by the measurement model.

    Sampling error estimates

    Not Available.

    Data appraisal

    Since the population with access to mobile telephones is likely to differ from the rest of the population with respect to their access to food, post-hoc adjustments were made to control for the potential resulting bias. Post-stratification weights were built to adjust the sample distribution by gender and education of the respondent at admin-1 level, to match the same distribution in the total population. However, an additional step was needed to try to ascertain the food insecurity condition of those with access to phones compared to that of the total population.

    Using FIES data collected by FAO through the GWP between 2014 and 2019, and a variable on access to mobile telephones that was also in the dataset, it was possible to compare the prevalence of food insecurity at moderate or severe level, and severe level only, of respondents with access to a mobile phone to that of the total population at national level.

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

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Welthungerhilfe (WHH) (2017). 2014 Global Hunger Index Data [Dataset]. http://doi.org/10.7910/DVN/27557

2014 Global Hunger Index Data

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13 scholarly articles cite this dataset (View in Google Scholar)
CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
Dataset updated
Mar 31, 2017
Dataset provided by
Harvard Dataverse
Authors
Welthungerhilfe (WHH)
License

https://dataverse.harvard.edu/api/datasets/:persistentId/versions/1.1/customlicense?persistentId=doi:10.7910/DVN/27557https://dataverse.harvard.edu/api/datasets/:persistentId/versions/1.1/customlicense?persistentId=doi:10.7910/DVN/27557

Time period covered
1990 - 2012
Area covered
CARIBBEAN; Commonwealth of Independent States; LATIN AMERICA; MIDDLE EAST; NORTH AFRICA; EAST AFRICA; EAST ASIA; SOUTH ASIA; EASTERN EUROPE; SOUTHERN AFRICA; AFRICA SOUTH OF SAHARA; AFRICA; ASIA;
Description

The Global Hunger Index (GHI) is a tool designed to comprehensively measure and track hunger globally and by region and country. Calculated each year by the International Food Policy Research Institute (IFPRI), the GHI highlights successes and failures in hunger reduction and provide insights into the drivers of hunger, and food and nutrition security. The 2014 GHI has been calculated for 120 countries for which data on the three component indicators are available and for which measuring hung er is considered most relevant. The GHI calculation excludes some higher income countries because the prevalence of hunger there is very low. The GHI is only as current as the data for its three component indicators. This year's GHI reflects the most recent available country level data for the three component indicators spanning the period 2009 to 2013. Besides the most recent GHI scores, this dataset also contains the GHI scores for four other reference periods- 1990, 1995, 2000, and 2005. A country's GHI score is calculated by averaging the percentage of the population that is undernourished, the percentage of children youn ger than five years old who are underweight, and the percentage of children dying before the age of five. This calculation results in a 100 point scale on which zero is the best score (no hunger) and 100 the worst, although neither of these extremes is reached in practice. The three component indicators used to calculate the GHI scores draw upon data from the following sources: 1. Undernourishment: Updated data from the Food and Agriculture Organization of the United Nations (FAO) were used for the 1990, 1995, 2000, 2005, and 2014GHI scores. Undernourishment data for the 2014 GHI are for 2011-2013. 2. Child underweight: The "child underweight" component indicator of the GHI scores includes the latest additions to the World Health Organization's (WHO) Global Database on Child Growth and Malnutrition, and additional data from the joint data base by the United Nations Children's Fund (UNICEF), WHO and the World Bank; the most recent Demographic and Health Survey (DHS) and Multiple Indicator Cluster Survey reports; and statistical tables from UNICEF. For the 2014 GHI, data on child underweight are for the latest year for which data are available in the period 2009-2014. 3. Child mortality: Updated data from the UN Inter-agency Group for Child Mortality Estimation were used for the 1990, 1995, 2000, and 2005, and 2014 GHI scores. For the 2014 GHI, data on child mortality are for 2012. Resources related to 2014 Global Hunger Index

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