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

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.

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

DESCRIPTION Enclosed are data from CIESIN's Global subnational rates of child underweight status database. Further documentation for these data is available in the enclosed catalog and on the CIESIN Poverty Mapping web site at: http://www.ciesin.columbia.edu/povmap This is the beta release of this product. See the Poverty Mapping home page for additional information on the product. CITATION We recommend the following for citing the database: Center for International Earth Science Information Network (CIESIN), Columbia University; 2005 Global subnational rates of child underweight status [dataset]. CIESIN, Palisades, NY, USA. Available at: http://www.ciesin.columbia.edu/povmap/ds_global.html

Abstract copyright UK Data Service and data collection copyright owner. The Trussell Trust has commissioned 'Hunger in the UK', a multi-year large-scale quantitative and qualitative research project to help support their strategic vision of ending the need for food banks. The Trussell Trust has appointed Ipsos Mori to deliver this research. The project focuses on three elements, each intended to build on existing evidence from research that the Trussell Trust had previously commissioned:1. Exploring the life experiences and socio-demographics of people referred to food banks in the Trussell Trust network through quantitative research. This study includes a survey of people referred to food banks in the Trussell Trust network. The survey collected a broad range of demographic and socioeconomic status information at both the individual and household level.2. A survey of the general population of the United Kingdom to establish benchmarks of, and track over time, the level of destitution, food-aid use, and food insecurity amongst this population. This survey mirrors the survey of people referred to food banks, thereby allowing for a comparative analysis of both populations. 3. Qualitative research with people experiencing food insecurity and destitution to understand their lived experience and enrich understanding of the drivers of food bank use and the impact on individuals and families.**Currently, this study includes only the survey data from elements 1. and 2. of the project.The research aims to contribute to the Trussell Trust’s goal of ending the need for food banks across the UK by providing evidence on the drivers of food insecurity and the need to receive support from a food bank. It allows exploration of the groups of people who are more likely to need support, how these experiences differ across the countries of the United Kingdom and what factors may allow people to escape food insecurity.Further information may be found on The Trussell Trust's Hunger in the UK webpage. Main Topics: The survey data collected includesHousehold composition, activities and employment Attitudinal statements Health and personal support Life events and housing Finance Sources of support and cost of living Food insecurityDemographics Food Bank Survey: Questionnaires were distributed in food parcels by 99 food banks. These food banks were selected at random. General Population Survey: A random probability unclustered address-based sampling method. This means that every household in the UK has a known chance of being selected to join the panel. Self-completion 2022 AGE BASIC NEEDS CHARITABLE ORGANIZA... CHILDREN CONDITIONS OF EMPLO... COST OF LIVING COSTS Consumption and con... DEBILITATIVE ILLNESS DEBTS DISABLED PERSONS ECONOMIC ACTIVITY EDUCATIONAL BACKGROUND EMPLOYMENT EMPLOYMENT CONTRACTS ETHNIC GROUPS EXPENDITURE Equality FINANCIAL DIFFICULTIES FINANCIAL RESOURCES FINANCIAL SUPPORT FOOD AID FOOD AND NUTRITION FOOD RESOURCES FOSTER CARE FREE SCHOOL MEALS GENDER IDENTITY HOMELESSNESS HOUSEHOLD BUDGETS HOUSEHOLD INCOME HOUSEHOLDS HOUSING BENEFITS HOUSING TENURE HUNGER ILL HEALTH INFORMAL CARE INTERNET ACCESS LIFE EVENTS MARITAL STATUS MENTAL HEALTH PERSONAL DEBT REPAY... POVERTY RELIGIOUS AFFILIATION RESIDENTIAL CHILD CARE SAVINGS SEXUAL ORIENTATION SHOPPING SOCIAL ATTITUDES SOCIAL PARTICIPATION SOCIAL SECURITY BEN... SOCIAL SUPPORT SOCIAL WELFARE Social welfare policy Society and culture UNEMPLOYMENT United Kingdom WAGES WELL BEING HEALTH inequality and soci... Identifier

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.

"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.

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.

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.

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.

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.

"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.

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.

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.

This data collection contains data primarily from both the 1841 and 1851 Census of Ireland used in Fernihough and Ó Gráda (2022). Also contained, where available, are population counts from the 1821 and 1831 censuses. The data collection also includes an amended version of the Civil Parish Shapefile from townlands.ie (OpenStreetMap Ireland, 2020). Both data sources were adjusted to ensure concordance. The towlands.ie data is open data is open data, licensed under the Open Data Commons Open Database License (ODbL). Please contact Alan Fernihough for further details or queries. The “shapefile” files are the GIS files one needs to load the spatial boundaries. The census data is included in the “data.csv” file and one must merge this to the shapefiles to work with these data. However, this is a simple process. The file “load and join.R” is an example of how this could be performed using the R statistical software package.Was early 19th century Ireland overpopulated and fertility at an unsustainable level, or did other factors cause the Great Irish Famine? Did the famine-induced migration to Britain spread infectious diseases and have a substantial impact on British mortality rates? Similarly, what impact did the famine have on the British labour force and economy generally? This research project will answer these questions. The Great Famine was a watershed in global history. It was the last major famine to occur in a Western economy, and had long-run impacts. The enduring legacy of the famine has sparked the interest of numerous novelists and playwrights. Earlier this year, news that media group Channel 4 was considering commissioning a Great Famine-based sitcom stoked an intense public debate. Many felt that this would trivialise the tragedy. The length and breadth of this debate underlined the immense interest that still surrounds the famine. However, the spectrum of opinions as to the causes and consequences of the famine also highlighted the need for further historical research. Let the Data Speak Joel Mokyr's influential 1983 book Why Ireland Starved redefined famine research. Before, famine-related research was largely based on qualitative assessments that left ample room for both conjecture and, rhetoric, and errors. Unlike previous researchers, Mokyr, wanted to let the data decide whether or not it was Ireland's overpopulation that caused the famine. To do this he gathered data on the population density of Irish regions and found that it was Ireland's least densely populated regions that were the ones that suffered worse during the famine. Mokyr's test did not support the overpopulation theory (captured by what is known as the Malthusian model). I hasten to add that the Malthusian model cannot be considered to have been refuted by this finding. For one thing, the possibility that more sophisticated econometric techniques and improved data will reverse the finding cannot be ruled out. (Mokyr, 1983). Whilst striking, Mokyr's analysis was based on variation between relatively few data points (Ireland's 32 counties), as the quote above testifies. This study is motivated by the above quote. Better data (from over 3,000 civil parishes) and more sophisticated econometric techniques exist, and therefore Mokyr's findings can at last be re-evaluated, something this project will do. Mokyr's philosophy of letting the data speak, can also be applied to help uncover some of the Great Famine's consequences. Specifically, this project will quantify the impact that famine-induced migration had on Britain. The famine caused a mass movement of the Irish population to Britain. Before the famine, there were around 430,000 Irish born in Britain. By 1851, the Irish-born population had grown to 730,000. This crisis-driven mass-migration echoes Europe's migration crisis today, as people flea from war-torn and economically desolate nations in Africa and Asia. In this sense, the Great Irish Famine provides a form of historical natural experiment from which we can learn from and gain a greater understanding of the consequences of mass migrations. What effect did the Irish famine have on Britain? This research will use newly available census data (released as part of the ESRC-funded ICeM project) to uncover how the Irish famine influenced the British economy and labour force. For example, did the influx of Irish in certain cities such as Liverpool and Manchester boost demand and help to speed up economic growth, or did this migration depress the wages of locals and therefore stifle economic advancement? In addition, this project will also use newly available records of regional mortality to calculate what impact, if any, the Great Famine had on mortality in England and Wales. If the Irish famine caused elevated levels of mortality, this implies that the ultimate death toll of the Irish famine is underestimated. Parish-level data transcribed from published sources, the official census returns for 1841 and 1851. In addition, to 1821 and 1831 were partially transcribed where they could be satisfactorily matched. Spatial data (shapefiles) were downloaded from townlands.ie. Both the transcribed census returns and the townlands.ie shapefile were amended to ensure concordance between all of the sources. For example, in instances where a civil parish straddled two or more baronies the individual returns for the parish were aggregated to a single data observation, which was then matched to the corresponding townlands.ie spatial polygon. Variables from the 1841 and 1851 censuses are split according to total and rural portions of each parish. The census reports reported the non-rural (towns, villages, etc.) share of each parish separately. In parishes with no non-rural portions (villages, small towns, etc.) the totals for the complete and rural variables will be equal. 58 per cent of parishes fit this criteria.

Since late 2020, one of the worst wars has been raging in Tigray, Ethiopia's northernmost region. A humanitarian tragedy has been caused by the fighting (Dedefo Bedaso 2021; Annys et al. 2021). Intense fighting occurred throughout the whole region, and looting and damage were rampant. Farmers were harvesting their crops in the middle of a desert locust infestation when the conflict began in late 2020. To record war impacts, commonly, direct expenses or losses at a particular period are quantified (Lindgren 2004). Post-conflict damage assessments typically concentrate on losses to businesses, services, infrastructure, and facilities in cities, even though the primary source of income in developing countries is the agricultural sector. Even when agricultural evaluations are done, they mostly focus on crop losses and ignore how wars affect land management. In Tigray's small-scale family farms, which use a permanent farming system based on cereals, oxen are utilized for traction (Westphal 1975). Crop agriculture has been practiced in Tigray for at least three thousand years (D'Andrea 2008; Blond et al. 2018), allowing for the gradual improvement of the agricultural system, including considerable farmers' understanding of the procedures involved in seed selection and of land suitability (Fetien Abay et al. 2008). The indigenous knowledge (sensu Bruchac 2018) also includes a broad vocabulary for different soil types (Nyssen et al. 2019), and the capacity to interpret the rainy season when selecting the crop to be sown (Frankl et al. 2013). A significant degree of equality in the extent of landholdings has resulted from the strengthening of the egalitarian land tenure system during the 1980s (Hendrie 1999). In the study region, a typical household uses two or three farmland pieces totalling less than a hectare. The ownership and management of grasslands, rangelands, and woodlands are communal (Nyssen et al. 2008). In the first half of 2021, armed forces of the Ethiopian government and from Eritrea as well as from the neighboring Amhara region were engaged in warfare against the forces of Tigray's regional government; in the second half of the year, warfare was essentially outside of Tigray, more to the south, while Tigray itself was submitted to a blockade with all telecommunication and lifelines to the outside world cut (Pellet 2021; Gayim 2021; Ramos 2021), a blockade that continued into 2022. The near-absence of economic activities, combined with limited food stocks and restricted humanitarian access resulted in 70% of the population experiencing starvation (sensu Stratton et al. 2003), i.e. high levels of acute food insecurity and excess mortality (Plaut 2021; Istratii 2021; Teklehaymanot G Weldemichel 2021; Oxford Analytica 2021; Devi 2021; Müller and Read 2021). The famine was worst from September to December 2021, as it took up to December before the years' poor harvest could be consumed REF; the lean period (also called “lean season”, “hunger season”) has been very severe. The lean season is the time in between finishing the last food that people had at hand and starting to consume the new harvest (Hirvonen et al. 2016). Farmers' main goals in these dire circumstances were to attempt to generate a better harvest in 2022 and, despite everything, to try and survive another year. We offer field data obtained by the end of August 2022, which were evaluated to determine the percentage of Tigray's land that was seeded on schedule, the types of crops sown, and the condition of these crops. Despite difficult living and travel conditions, the agricultural status in some of Tigray's reachable districts was examined for the 2022 growing season. A team of geographers visited 262 agricultural plots in an area indicative of the region's diverse bio-physical circumstances, including elevation (plots ranged from 1931 to 2600 meters above sea level), lithology, soil type, rainfall patterns, and hence cropping strategies (Alemtsehay Tsegay et al. 2019; Nyssen et al. 2019). Other land uses, such as irrigated land, grassland, barren land, bushland, and forest, were left out of the analysis. We visited ecoregions with different biophysical and agro-ecological characteristics along main roads in six districts between 24 and 29 August 2022: Tsa'ida Imba, Kilte Awula'ilo (especially croplands on the outskirts of Wukro's urban district), Dogu'a Tembien (surroundings of Hagere Selam), Samre, Hintalo (particularly Addi Gudom), and Inderta (Aynalem and Didiba). The investigations typically took place in the wider surroundings of small towns, as transect walks, observing and talking to farmers present on the land. Participatory monitoring was used to collect data for each cropland, which included recording the crop type, a group assessment of the crop's status according to local standards (good, medium, bad, failed; taking into account growth features such as plant height, greenness and density, ear length, homogeneity in crop stand), observations of whether or not neighboring farmers cropped in block, and a semi-structured interview with the farmer or a group discussion, addressing among others the use of fertilizer (Van De Fliert et al. 2000; Nyumba et al. 2018; Young and Hinton 1996). Aside from the usual crop evaluation, emphasis was paid to block wise cropping with adjacent farmers since, like three-field systems, this practice is an indicative of an internally well-organized community, and hints to a superior yield forecast as it prevents disruptions (Nyssen et al. 2008; Hopcroft 1994; Ruthenberg 1980). Data have been collected in such a way that homogeneous areas of at least 30 m x 30 m are represented, so that they can serve as calibration and validation points in remote sensing analysis. According to descriptive statistics from the dataset, at the end of August, 15% of the monitored farm parcels had been left fallow, meaning no crops had been planted (40 plots out of 262). During a similar monitoring in 2021 (Tesfaalem Ghebreyohannes et al. 2022a), 21% of the monitored lands were fallowed. However, 7 percent of the fallow plots had no weeds, indicating that the ground had been ploughed but not seeded. A further 4% of the plots were planted with flax or niger seed, which is often used to improve fallow soil quality rather than crop output. Among the cultivated parcels, 104 plots (40%) were planted with wheat, barley, or a mixture of both (hanfets) (49% in 2021), while 84 plots (32%) were planted with tef (26% in 2021). Only 1% of the land was planted with maize, and another 1% with sorghum (6% and 4% in 2021). In the plots containing crops that were examined, 46% had been seeded in block, in collaboration with the owners of surrounding lands (40% in 2021). Wheat and barley (54%) as well as tef (52%) were seeded in blocks. Three quarter (76 percent) of the wheat and barley fields were in good or medium condition. Seventy-one percent of the tef lands were in poor condition (67% in 2021). Overall, the crop stands improved slightly over those of the very bad year 2021 (Tesfaalem Ghebreyohannes et al. 2022b), and there was less fallowing. Fertilizer was used on only 56 of the 222 sampled plots with crop: on these lands, at least some mineral fertilizer was administered at sowing, after crop emergence, or manure was applied. Due to a shortage of fertilizers, farmers frequently applied insufficient amounts. Mineral fertilizer was used exclusively for cereal production. A significant issue was the farmers' inappropriate use of potassium fertilizer, which led to crop burn, particularly in Tsa'ida Imba and Samre. Overall, and adopting a very low threshold, 34% of the analyzed lands were fallowed or are expected to provide a very poor crop harvest, while 66% of the sampled fields are promising and would yield medium or excellent crops.

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.

Rural areas of Madagascar face a tense food and nutrition security situation. The country reports some of the highest rates of undernourishment and malnutrition worldwide. Evidence is scant, however, about how the rural people’s diets vary over the course of the agricultural year, and how different household types deal with fluctuating food availability. This lack of detailed understanding of the food and nutrition security situation across time and social strata currently limits development stakeholders’ ability to tailor interventions to local needs. Using randomly sampled survey data from Farafangana District in south-eastern Madagascar, this study analyzes rural households’ diets across three time points within one year (minor lean period, major lean period, main post-harvest period). In addition, anthropometric data on children aged 6–59  months were collected during the major lean period to determine levels of chronic and acute child malnutrition. Overall, food insecurity stood at high levels at all times, but with substantial variation across the year. Shortly before the main rice harvest, the prevalence of moderate and severe food insecurity was 78%, twice as high as shortly after harvest. With 57% of children stunted and a 17% prevalence of wasting, the observed levels of chronic and acute child malnutrition exceed levels reported previously. By studying what households eat and how they source it (own production vs. purchases), we found distinct patterns of food acquisition between the three periods. Diminishing food security is reflected by substituting rice by cassava, unripe jackfruit (minor lean period), and local tuber tavolo (major lean period), as well as by lower diversity of side dishes. Our results underline the need for long-term agricultural development strategies that contribute to greater household food self-sufficiency especially during the lean periods. But the alarming level of acute child malnutrition also calls for more immediate humanitarian aid and public health interventions.

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 (%)

BackgroundMalnutrition and inadequate nutrient intake are associated with functional decline, frailty, and bad clinical outcomes among community-dwelling older adults. Despite the growing proportion of the elderly population in Indonesia, data on the prevalence of malnutrition and adequacy of macronutrient and micronutrient intakes among Indonesian older adults are scattered and vary between studies. Therefore, our study aims to obtain data on malnutrition prevalence, level and distribution of nutrient intakes, and prevalence of macronutrient and micronutrient inadequacies in Indonesian community-dwelling older adults.MethodsWe carried out a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement and registered in PROSPERO. A systematic electronic database search of MEDLINE, CENTRAL, EMBASE, ProQuest, HINARI, IMSEAR, GARUDA, and Indonesian Publication Index was undertaken. Additional searches were conducted in gray literature sources, hand-searching, retrospective searching, and personal communication with authors of the relevant publication. Observational studies presenting the malnutrition prevalence of habitual dietary intakes of older adults (60 years or older) were included. The risk of bias of studies was assessed using the Joanna Briggs Institute critical appraisal form. Sex-specific mean (and standard deviation) habitual macronutrient and a selection of micronutrients (calcium, vitamin D, and vitamin B12) intakes were extracted from each article to calculate the percentage of older people who were at risk for inadequate micronutrient intakes using a proxy of estimated average requirement (EAR) cut-point method, which is calculated from the national guideline of recommended dietary allowance (RDA). Prevalence of malnutrition, based on body mass index (BMI) categories and mini-nutritional assessment (MNA) criteria. and the population at risk of malnutrition were presented descriptively.ResultsNine studies retrieved from electronic databases and gray literature were included in the pooled systematic analysis. According to BMI criteria, the underweight prevalence ranged from 8.0 to 26.6%. According to the MNA, the prevalence of malnutrition ranged from 2.1 to 14.6%, whereby the prevalence of at risk of malnutrition amounted to 18–78%. Our systematic review identified a high prevalence of nutrient inadequacies, most markedly for protein, calcium, vitamin D, and vitamin B12.ConclusionWe signal a high risk of malnutrition along with poor macronutrients and micronutrients intakes among Indonesian community-dwelling older adults. These findings provide important and robust evidence on the magnitude of malnutrition and nutrient inadequacy concerns that call for appropriate nutrition, as well as public health policies and prompt intervention.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42018102268.