This dataset shows the expenditure on food and drink by country. Information is given on expenditure as a percentage of total income and in dollars. what you can see from the data is areas where upto 50% of total household expenditure is devoted to food. These areas tend to be poorer developing nations in Africa. The developed countries spend less, as a percentage, on food, but obviously much more in terms of actual dollars. The data was sourced from the USDA Economic Research Service (http://www.ers.usda.gov/) and there is an interesting article here (http://www.vox.com/2014/7/6/5874499/map-heres-how-much-every-country-spends-on-food). The data was a flat excel document and has been linked to geographical boundaries in ArcGIS in order to display the data as map. GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2014-07-08 and migrated to Edinburgh DataShare on 2017-02-22.

Dataset Structure & Description

https://imgur.com/AYzsmYU.jpg" alt="Dataset Structure">

Context and Inspiration

I read an article yesterday which got my mind storming, A article by Worldbank on August 15th, 2022 better explains it, It has been quoted below,
I already have a project i'm working on since Feb 2021, trying to solving this problem, listed in my datasets

This dataset showcases the statistics over the past 6-7 decades which covers the production of 150+ unique crops, 50+ livestock elements, Land distribution by usage and population, As aspiring data scientists one can try to extract insights incentivizing the optimal use of natural resources and distribution of resources

August 15, 2022 - Worldbank

Record high food prices have triggered a global crisis that will drive millions more into extreme poverty, magnifying hunger and malnutrition, while threatening to erase hard-won gains in development. The war in Ukraine, supply chain disruptions, and the continued economic fallout of the COVID-19 pandemic are reversing years of development gains and pushing food prices to all-time highs. Rising food prices have a greater impact on people in low- and middle-income countries, since they spend a larger share of their income on food than people in high-income countries. This brief looks at rising food insecurity and World Bank responses to date.

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Hello 👋, If you are enjoying so far, Please checkout my other Datasets, I would love to hear your support & feedback on it, Thank you !

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Checkout my other Datasets & Notebooks

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

Domestic Food Prices After COVID-19

Analyzing Impact on Developing Countries' Food Security

By [source]

About this dataset

This dataset looks at the effect of the COVID-19 pandemic on food prices in both domestic and international markets, particularly in developing countries. It contains data on monthly changes in food prices, categorised by country, market, price type (domestic or international) and commodities. In particular, this dataset provides insight into how the pandemic has impacted food security for those living in poorer countries where price increases may be more acutely felt. This dataset gives us a greater understanding of these changing dynamics of global food systems to enable more efficient interventions and support for those who are most vulnerable

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How to use the dataset

This dataset is an excellent resource for anyone looking to analyze the impact of COVID-19 on domestic food prices in developing countries. With this dataset, you can get an up-to-date overview of changes in the costs of various commodities in a given market and by a given price type. Additionally, you can filter data by commodity, country and price type.

In order to use this dataset effectively, here are some steps: - Identify your research question(s) - Filter the dataset by selecting specific columns that best answer your research question (ex: month, country, commodity) - Analyze the data accordingly (for example: Sorting the results then calculating averages). - Interpret results into actionable insights or visualizations

Research Ideas

• Analyzing trends in the cost of food items across different countries to understand regional disparities in food insecurity.
• Comparing pre- and post-COVID international food prices to study how nations altered their trade policies in response to the pandemic, indicating a shift towards or away from trading with other nations for food procurement.
• Using sentiment analysis to study consumer sentiment towards purchasing certain items based on their market prices, allowing businesses and governments alike to better target interventions aimed at improving access and availability of food supplies

Acknowledgements

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

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: dom_clean_data.csv | Column name | Description | |:---------------|:---------------------------------------------------------------------------| | month | The month in which the data was collected. (Date) | | country | The country in which the data was collected. (String) | | price_type | The type of price (domestic or international) that was collected. (String) | | market | The market in which the data was collected. (String) | | commodity | The type of commodity that was collected. (String) |

File: int_clean_data.csv | Column name | Description | |:---------------|:---------------------------------------------------------------------------| | country | The country in which the data was collected. (String) | | commodity | The type of commodity that was collected. (String) | | price_type | The type of price (domestic or international) that was collected. (String) | | time | The month in which the data was collected. (String) |

Acknowledgements

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

This dataset is the basis for the International Food Security Assessment, 2016-2026 released in June 2016. This annual ERS report projects food availability and access for 76 low- and middle-income countries over a 10-year period. 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 record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Excel file listing For complete information, please visit https://data.gov.

Map and evaluate the effectiveness of food loss and waste reduction interventions in low- and middle-income countries, determine the reduction pathways related to waste prevention, re-use or recycling, and identify social, economic, environmental and nutritional co-benefits as they relate to the intervention. This will be done by: (i) systematically reviewing scientific and grey literature sources and (ii) conducting meta-analyses by food group where appropriate.

This replication folder describes the Stata v17 “do file” (code file) for statistical analysis for "Food inflation and child undernutrition in low and middle income countries " by Derek Headey & Marie Ruel. This do file can be used to replicate the analysis in the study mentioned above, published in Nature Communications. The study uses a combination of Demographic Health Survey (DHS) data for child, maternal, household level variables and national level indicators on real food price changes drawn from FAOSTAT, as well as conflict and climate variables. In summary, this is a large multi-country DHS dataset merged with FAO food and total consumer price indices (CPIs) and various other national level control variables. These are DHS surveys from 2000 onwards only.

The authors cannot publicly share the DHS data but can share it upon request, provided we can obtain approval from the DHS implementers. To make a request to access the data for this paper, please email Derek Headey at d.headey@cgiar.org. Alternatively researchers can access the raw DHS data from: https://dhsprogram.com/data/available-datasets.cfm and the country level indicators from the Food and Agriculture Organisation Consumer Prices data portal (https://www.fao.org/faostat/en/#data/CP) as well as The World Bank World Development Indicators (https://databank.worldbank.org/source/world-development-indicators) for obtaining data on various control variables.

Semi-quantitative dietary assessment methods are frequently used in low income countries, and the use of photographic series for portion size estimation is gaining popularity. However, when adequate data on commonly consumed foods and portion sizes are not available to design these tools, alternative data sources are needed. This study aimed to develop and test methods to: (i) identify foods likely to be consumed in a study population in rural Uganda, and; (ii) to derive distributions of portion sizes for common foods and dishes. A process was designed to derive detailed food and recipe lists using guided group interviews with women from the survey population, including a score for the likelihood of foods being consumed. A rapid recall method for portion size distribution estimation (PSDE) using direct weight by a representative sample of the survey population was designed and implemented. Results were compared to data from a 24 hour dietary recall (24HR). Of the 82 food items reported in the 24HR survey, 87% were among those scored with a high or medium likelihood of being consumed and accounted for 95% of kilocalories. Of the most frequently reported foods in the 24HR, portion sizes for many (15/25), but not all foods did not differ significantly (p<0.05) from those in the portion size estimation method. The percent of portion sizes reported in the 24 hour recall falling between the 5th and 95th percentiles as determined by the PSDE method ranged from 18% up to 100%. In conclusion, a simple food listing and scoring method effectively identified foods most likely to occur in a dietary survey. A novel PSDE method produced similar estimates as for the 24HR, while the approach for others should be further considered and validated. These methods are an improvement on those in current use.

BackgroundRelative to plant-based foods, animal source foods (ASFs) are richer in accessible protein, iron, zinc, calcium, vitamin B-12 and other nutrients. Because of their nutritional value, particularly for childhood growth and nutrition, it is important to identify factors influencing ASF consumption, especially for poorer households that generally consume less ASFs.ObjectiveTo estimate differential responsiveness of ASF consumption to changes in total household expenditures for households with different expenditures in a middle-income country with substantial recent income increases.MethodsThe Peruvian Young Lives household panel (n = 1750) from 2002, 2006 and 2009 was used to characterize patterns of ASF expenditures. Multivariate models with controls for unobserved household fixed effects and common secular trends were used to examine nonlinear relationships between changes in household expenditures and in ASF expenditures.ResultsHouseholds with lower total expenditures dedicated greater percentages of expenditures to food (58.4% vs.17.9% in 2002 and 24.2% vs. 21.5% in 2009 for lowest and highest quintiles respectively) and lower percentages of food expenditures to ASF (22.8% vs. 33.9% in 2002 and 30.3% vs. 37.6% in 2009 for lowest and highest quintiles respectively). Average percentages of overall expenditures spent on food dropped from 47% to 23.2% between 2002 and 2009. Households in the lowest quintiles of expenditures showed greater increases in ASF expenditures relative to total consumption than households in the highest quintiles. Among ASF components, meat and poultry expenditures increased more than proportionately for households in the lowest quintiles, and eggs and fish expenditures increased less than proportionately for all households.ConclusionsIncreases in household expenditures were associated with substantial increases in consumption of ASFs for households, particularly households with lower total expenditures. Increases in ASF expenditures for all but the top quintile of households were proportionately greater than increases in total food expenditures, and proportionately less than overall expenditures.

This dataset contains key characteristics about the data described in the Data Descriptor Physical activity, time use, and food intakes of rural households in Ghana, India, and Nepal. Contents:

    1. human readable metadata summary table in CSV format


    2. machine readable metadata file in JSON format 
     Versioning Note:Version 2 was generated when the metadata format was updated from JSON to JSON-LD. This was an automatic process that changed only the format, not the contents, of the metadata.

This layer gives the overview of Food Accessibility in the State of Texas. Food accessLimited access to supermarkets, supercenters, grocery stores, or other sources of healthy and affordable food may make it harder for some people to eat a healthy diet in this country. There are many ways to measure food store access for individuals and for neighborhoods, and many ways to define which areas are low-income and low access—neighborhoods that lack healthy food sources. Most measures and definitions consider at least some of the following indicators of access:Accessibility to sources of healthy food, as measured by distance to a store or by the number of stores in an area;Individual-level resources that may affect accessibility, such as family income or vehicle availability; andNeighborhood-level indicators of resources, such as the average income of the neighborhood and the availability of public transportation.In the Food Access Research Atlas, several indicators are available to measure food access along these dimensions. For example, users can choose alternative distance markers to measure low access in a neighborhood, such as the number and share of people more than one-half mile to a supermarket or 1 mile to a supermarket. Users can also view other census-tract-level characteristics that provide context on food access in neighborhoods, such as whether the tract has a high percentage of households far from supermarkets and without vehicles, individuals with low income, or people residing in group quarters.Specialized Stores - The Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) is the USDA’s third-largest food assistance program, supporting low-income, nutritionally at-risk women, infants, and children. In 2019, it served 6.4 million participants with Federal spending of $5.2 billion. Participants primarily redeem benefits at WIC-authorized retailers, which range from large supermarkets to smaller stores like convenience stores or specialized “above-50-percent” (A50) stores, where more than 50% of food sales come from WIC transactions. A50 stores can reduce travel distances and improve access to WIC-approved foods, especially in urban areas, but they face stricter pricing regulations to prevent cost inflation. WIC food packages are tailored to participants' nutritional needs and can include fixed quantities of milk, eggs, fruits, vegetables, and infant-specific foods.

IntroductionFeeding infants a sub-optimal diet deprives them of critical nutrients for their physical and cognitive development. The objective of this study is to describe the intake of foods of low nutritional value (junk foods) and identify the association with growth and developmental outcomes in infants up to 18 months in low-resource settings.MethodsThis is a secondary analysis of data from an iron-rich complementary foods (meat versus fortified cereal) randomized clinical trial on nutrition conducted in low-resource settings in four low- and middle-income countries (Democratic Republic of the Congo, Guatemala, Pakistan, and Zambia). Mothers in both study arms received nutritional messages on the importance of exclusive breastfeeding up to 6 months with continued breastfeeding up to at least 12 months. This study was designed to identify the socio-demographic predictors of feeding infants’ complementary foods of low nutritional value (junk foods) and to assess the associations between prevalence of junk food use with neurodevelopment (assessed with the Bayley Scales of Infant Development II) and growth at 18 months.Results1,231 infants were enrolled, and 1,062 (86%) completed the study. Junk food feeding was more common in Guatemala, Pakistan, and Zambia than in the Democratic Republic of Congo. 7% of the infants were fed junk foods at 6 months which increased to 70% at 12 months. Non-exclusive breastfeeding at 6 months, higher maternal body mass index, more years of maternal and paternal education, and higher socioeconomic status were associated with feeding junk food. Prevalence of junk foods use was not associated with adverse neurodevelopmental or growth outcomes.ConclusionThe frequency of consumption of junk food was high in these low-resource settings but was not associated with adverse neurodevelopment or growth over the study period.

Abstract

The main objective of the HEIS survey is to obtain detailed data on household expenditure and income, linked to various demographic and socio-economic variables, to enable computation of poverty indices and determine the characteristics of the poor and prepare poverty maps. Therefore, to achieve these goals, the sample had to be representative on the sub-district level. The raw survey data provided by the Statistical Office was cleaned and harmonized by the Economic Research Forum, in the context of a major research project to develop and expand knowledge on equity and inequality in the Arab region. The main focus of the project is to measure the magnitude and direction of change in inequality and to understand the complex contributing social, political and economic forces influencing its levels. However, the measurement and analysis of the magnitude and direction of change in this inequality cannot be consistently carried out without harmonized and comparable micro-level data on income and expenditures. Therefore, one important component of this research project is securing and harmonizing household surveys from as many countries in the region as possible, adhering to international statistics on household living standards distribution. Once the dataset has been compiled, the Economic Research Forum makes it available, subject to confidentiality agreements, to all researchers and institutions concerned with data collection and issues of inequality.

Data collected through the survey helped in achieving the following objectives: 1. Provide data weights that reflect the relative importance of consumer expenditure items used in the preparation of the consumer price index 2. Study the consumer expenditure pattern prevailing in the society and the impact of demograohic and socio-economic variables on those patterns 3. Calculate the average annual income of the household and the individual, and assess the relationship between income and different economic and social factors, such as profession and educational level of the head of the household and other indicators 4. Study the distribution of individuals and households by income and expenditure categories and analyze the factors associated with it 5. Provide the necessary data for the national accounts related to overall consumption and income of the household sector 6. Provide the necessary income data to serve in calculating poverty indices and identifying the poor chracteristics as well as drawing poverty maps 7. Provide the data necessary for the formulation, follow-up and evaluation of economic and social development programs, including those addressed to eradicate poverty

Geographic coverage

National

Analysis unit

• Household/families
• Individuals

Universe

The survey covered a national sample of households and all individuals permanently residing in surveyed households.

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2008 Household Expenditure and Income Survey sample was designed using two-stage cluster stratified sampling method. In the first stage, the primary sampling units (PSUs), the blocks, were drawn using probability proportionate to the size, through considering the number of households in each block to be the block size. The second stage included drawing the household sample (8 households from each PSU) using the systematic sampling method. Fourth substitute households from each PSU were drawn, using the systematic sampling method, to be used on the first visit to the block in case that any of the main sample households was not visited for any reason.

To estimate the sample size, the coefficient of variation and design effect in each subdistrict were calculated for the expenditure variable from data of the 2006 Household Expenditure and Income Survey. This results was used to estimate the sample size at sub-district level, provided that the coefficient of variation of the expenditure variable at the sub-district level did not exceed 10%, with a minimum number of clusters that should not be less than 6 at the district level, that is to ensure good clusters representation in the administrative areas to enable drawing poverty pockets.

It is worth mentioning that the expected non-response in addition to areas where poor families are concentrated in the major cities were taken into consideration in designing the sample. Therefore, a larger sample size was taken from these areas compared to other ones, in order to help in reaching the poverty pockets and covering them.

Mode of data collection

Face-to-face [f2f]

Research instrument

List of survey questionnaires: (1) General Form (2) Expenditure on food commodities Form (3) Expenditure on non-food commodities Form

Cleaning operations

Raw Data The design and implementation of this survey procedures were: 1. Sample design and selection 2. Design of forms/questionnaires, guidelines to assist in filling out the questionnaires, and preparing instruction manuals 3. Design the tables template to be used for the dissemination of the survey results 4. Preparation of the fieldwork phase including printing forms/questionnaires, instruction manuals, data collection instructions, data checking instructions and codebooks 5. Selection and training of survey staff to collect data and run required data checkings 6. Preparation and implementation of the pretest phase for the survey designed to test and develop forms/questionnaires, instructions and software programs required for data processing and production of survey results 7. Data collection 8. Data checking and coding 9. Data entry 10. Data cleaning using data validation programs 11. Data accuracy and consistency checks 12. Data tabulation and preliminary results 13. Preparation of the final report and dissemination of final results

Harmonized Data - The Statistical Package for Social Science (SPSS) was used to clean and harmonize the datasets - The harmonization process started with cleaning all raw data files received from the Statistical Office - Cleaned data files were then all merged to produce one data file on the individual level containing all variables subject to harmonization - A country-specific program was generated for each dataset to generate/compute/recode/rename/format/label harmonized variables - A post-harmonization cleaning process was run on the data - Harmonized data was saved on the household as well as the individual level, in SPSS and converted to STATA format

Livestock production may improve nutritional outcomes of pregnant women and children by increasing household income, availability of nutrient-dense foods, and women's empowerment. Nevertheless, the relationship is complex, and the nutritional status of children may be impaired by presence of or proximity to livestock and their pathogens. In this paper, we review the benefits and risks of livestock production on child nutrition. Evidence supports the nutritional benefits of livestock farming through income, production, and women's empowerment. Increasing animal source food consumption requires a combination of efforts, including improved animal management so that herd size is adequate to meet household income needs and consumption and addressing sociocultural and gendered norms. Evidence supports the inclusion of behavior change communication strategies into livestock production interventions to facilitate the sustainability of nutritional benefits over time, particularly interventions that engage women and foster dimensions of women's empowerment. In evaluating the risks of livestock production, evidence indicates that a broad range of enteric pathogens may chronically infect the intestines of children and, in combination with dietary deficits, may cause environmental enteric dysfunction (EED), a chronic inflammation of the gut. Some of the most important pathogens associated with EED are zoonotic in nature with livestock as their main reservoir. Very few studies have aimed to understand which livestock species contribute most to colonization with these pathogens, or how to reduce transmission. Control at the point of exposure has been investigated in a few studies, but much less effort has been spent on improving animal husbandry practices, which may have additional benefits. There is an urgent need for dedicated and long-term research to understand which livestock species contribute most to exposure of young children to zoonotic enteric pathogens, to test the potential of a wide range of intervention methods, to assess their effectiveness in randomized trials, and to assure their broad adaptation and sustainability. This review highlights the benefits and risks of livestock production on child nutrition. In addition to identifying research gaps, findings support inclusion of poor gut health as an immediate determinant of child undernutrition, expanding the established UNICEF framework which includes only inadequate diet and disease.

Total factor productivity (TFP) is the ratio of total output (crop and livestock products) to total production inputs (land, labor, capital and materials). An increase in TFP implies that more output is being produced from a constant amount of resources used in the production process. In the long run, TFP is the main driver of growth in agriculture and can be affected by policies and investment. Partial factor productivity (PFP) measures, such as labor and land productivity, are often used to measure agricultural prodcution performance because they are easy to estimate. These measures of productivity normally show higher rates of growth than TFP because growth in land and labor productivity could result from more intensive use of inputs, including fertilizer and machinery, rather than TFP increase. If productivity increases without the addition of more inputs, then the only source of growth is TFP. The data file provides estimates of IFPRI's TFP and PFP measures for developing countries for three-sub-periods between 1990 and 2011(1991-2000,2001-2007 and 2008-2013). These TFP and PFP estimates were generated using data from the Food and Agriculture Organization of the United Nations (FAO) on outputs and inputs. The output values are the FAO-constructed gross agricultural outputs, measured in constant 2004-2006 US dollars and smoothed using the Hodrick-Prescott filter. Each output v alue is a composite of 190 crop and livestock commodities aggregated using a constant set of global average prices from 2004-2006. Inputs include agricultural land, measured by the sum, in hectares, of cropland and permanent pasture; labor, measured by the number of animals in cattle equivalents; machinery, measured by the total amount of horsepower available from four-wheel tractors, pedestrian-operated tractors, and combine-threshers in use; and fertilizer, measured by tons of fertilizer nutrients used. The dataset of outputs and inputs was checked and cleaned using different statistical techniques. TFP estimates were obtained using Data Envelopment Analysis (DEA) techniques. These techniques have been extensively used because they make TFPs easy to compute, do not involve restrictive assumptions regarding economic behavior, such as cost minimization or profit maximization. On the other hand, DEA productivity estimates are sensitive to data noise and outliers and can suffer from the probel of ""unusual"" weights that are higher or lower than expected when aggregating inputs to meas ure TFP. Given these limitations, outlier detection methods were used to determine influential observations in the dataset and input weights were allowed to vary only within a certain range of expected values because specific lower and upper bounds were imposed for each input in different regions. Results are also afected by data characteristics and quality issues. In particular, the data series on fertilizer and machinery show high volatility and could result in high variablity of TFP estimates for some countries.

Background During the years 2019-2023, Tony’s Chocolonely commissioned five annual household surveys among farmers in all of their partner cooperatives in Ghana and Côte d’Ivoire. In both countries, a research company was tasked with conducting the face-to-face interviews. The main aim of these surveys was to assess the multidimensional poverty rates, using the global Multidimensional Poverty Index (MPI). Besides the MPI-relevant items on health, education and standard of living, questionnaires also included a range of other questions. These covered various topics such as the use of external labour, livestock ownership or the ratio of cocoa Vs. non-cocoa income. The surveys were set up as a panel. In as much as possible, enumerators returned to the same respondents each year. Out of the initial 2019 sample of farmers, 45% participated in all five rounds of the panel. Sampling Simple random samples were drawn in each coop, using cooperative membership lists as sample frames. As new cooperatives in Côte d’Ivoire joined Tony’s Open Chain in 2021, 2022 and 2023, those were added to the panel. During the first two survey rounds, sample sizes were determined in proportion to the total number of farmers in the coop: in each coop, a sample of about 10% of total coop size was drawn. As Tony’s Chocolonely data needs changed in 2021, it was decided to switch to non-proportionate sampling: during the last three rounds of the panel, about 150 farmers per coop were interviewed, irrespective of coop size. For these rounds, a weight variable per country is included in the dataset for country-level analyses. MPI and HFIAS scales The provided dataset includes the ten MPI deprivation indicators, the sum of weighted MPI deprivations, as well as a binary variable to distinguish MPI-poor from MPI-non-poor households. Furthermore, the dataset includes a variable to categorize households as ‘food secure’, ‘mildly food insecure’, ‘moderately food insecure’ or ‘severely food insecure’, based on the nine items of the Household Food Insecurity Access Scale (HFIAS). Data anonymization All data have been anonymized by removing personally identifiable information, such as names, phone numbers, GPS data, detailed household composition and responses to open questions. Each respondent has been assigned a unique ID to be identifiable across rounds (famer_hash). To further increase confidentiality, coop names have been replaced by pseudonyms. Across time consistency of farmer codes Users of the dataset should note that some farmer codes may have different demographic information associated with them across different years. In most cases, this will mean that a different household member has been interviewed by the enumerator. In some cases it may be that the farmer code has been reassigned to a different household (likely due to farmers leaving the cooperative). This should not affect the representativeness of the sample per year but will introduce some noise when running time series analyses. Using the data Tony’s Open Chain encourage open use of this data and are excited to learn from how others use and analyse the data – if you carry out an analysis of this data, please do share it with us by contacting emmavandam@tonyschocolonely.com.

Context

Of the three major crops – rice, wheat and maize – rice is by far the most important food crop for people in low- and lower-middle-income countries. Although rich and poor people alike eat rice in low-income countries, the poorest people consume relatively little wheat and are therefore deeply affected by the cost and availability of rice.

In many Asian countries, rice is the fundamental and generally irreplaceable staple, especially of the poor. For the extreme poor in Asia, who live on less than $1.25 a day, rice accounts for nearly half of their food expenditures and a fifth of total household expenditures, on average. This group alone annually spends the equivalent of $62 billion (purchasing power parity) on rice. Rice is critical to food security for many of the world’s poor people.

~ Quote from ricepedia.org

Content

This dataset contains 120 jpg images of disease infected rice leaves. The images are grouped into 3 classes based on the type of disease. There are 40 images in each class.

Classes

• Leaf smut
• Brown spot
• Bacterial leaf blight

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1086574%2F440c2d8d39025fc8be9929836686cbc1%2Frice_leaves.png?generation=1582347404740337&alt=media" alt="">

This dataset is associated with the following paper: Detection and Classification of Rice Plant Diseases

The authors gathered these leaves from a rice field in a village called Shertha in Gujarat, India.

Citation

Prajapati HB, Shah JP, Dabhi VK. Detection and classification of rice plant diseases. Intelligent Decision Technologies. 2017 Jan 1;11(3):357-73, doi: 10.3233/IDT-170301.

UCI Machine Learning Repository https://archive.ics.uci.edu/ml/datasets/Rice+Leaf+Diseases#

Acknowledgements

Many thanks to the research team at the Department of Information Technology, Dharmsinh Desai University for making this dataset publicly available.

Inspiration

• Build a dataset like this that includes more types of rice leaf diseases. Collect samples of both healthy and disease infected rice leaves from a farming community. Label the dataset using information from local farmers or from plant pathologists.
• Build a model to automatically classify rice leaf diseases.
• Deploy your model as a Tensorflow.js web app so it can be accessed from anywhere in the world.
• Plantix is an excellent example of an impactful agtech mobile app. This video has more info.

Header image by HoangTuan_photography on Pixabay.

Low-income cut-offs, after tax (LICO-AT) - The Low-income cut-offs, after tax refers to an income threshold, defined using 1992 expenditure data, below which economic families or persons not in economic families would likely have devoted a larger share of their after-tax income than average to the necessities of food, shelter and clothing. More specifically, the thresholds represented income levels at which these families or persons were expected to spend 20 percentage points or more of their after-tax income than average on food, shelter and clothing. These thresholds have been adjusted to current dollars using the all-items Consumer Price Index (CPI).The LICO-AT has 35 cut-offs varying by seven family sizes and five different sizes of area of residence to account for economies of scale and potential differences in cost of living in communities of different sizes. These thresholds are presented in Table 4.3 Low-income cut-offs, after tax (LICO-AT - 1992 base) for economic families and persons not in economic families, 2015, Dictionary, Census of Population, 2016.When the after-tax income of an economic family member or a person not in an economic family falls below the threshold applicable to the person, the person is considered to be in low income according to LICO-AT. Since the LICO-AT threshold and family income are unique within each economic family, low-income status based on LICO-AT can also be reported for economic families.Return to footnote1referrerFootnote 2Users should be aware that the estimates associated with this variable are more affected than most by the incomplete enumeration of certain Indian reserves and Indian settlements in the Census of Population.For more information on Aboriginal variables, including information on their classifications, the questions from which they are derived, data quality and their comparability with other sources of data, please refer to the Aboriginal Peoples Reference Guide, Census of Population, 2016 and the Aboriginal Peoples Technical Report, Census of Population, 2016.Return to footnote2referrerFootnote 3Low-income status - The income situation of the statistical unit in relation to a specific low-income line in a reference year. Statistical units with income that is below the low-income line are considered to be in low income.For the 2016 Census, the reference period is the calendar year 2015 for all income variables.Return to footnote3referrerFootnote 4The low-income concepts are not applied in the territories and in certain areas based on census subdivision type (such as Indian reserves). The existence of substantial in-kind transfers (such as subsidized housing and First Nations band housing) and sizeable barter economies or consumption from own production (such as product from hunting, farming or fishing) could make the interpretation of low-income statistics more difficult in these situations.Return to footnote4referrerFootnote 5Prevalence of low income - The proportion or percentage of units whose income falls below a specified low-income line.Return to footnote5referrerFootnote 6Users should be aware that the estimates associated with this variable are more affected than most by the incomplete enumeration of certain Indian reserves and Indian settlements in the 2016 Census of Population. For more information on Aboriginal variables, including information on their classifications, the questions from which they are derived, data quality and their comparability with other sources of data, refer to the Aboriginal Peoples Reference Guide, Census of Population, 2016 and the Aboriginal Peoples Technical Report, Census of Population, 2016.Return to footnote6referrerFootnote 7'Aboriginal identity' includes persons who are First Nations (North American Indian), Métis or Inuk (Inuit) and/or those who are Registered or Treaty Indians (that is, registered under the Indian Act of Canada) and/or those who have membership in a First Nation or Indian band. Aboriginal peoples of Canada are defined in the Constitution Act, 1982, section 35 (2) as including the Indian, Inuit and Métis peoples of Canada.Return to footnote7referrerFootnote 8'Single Aboriginal responses' includes persons who are in only one Aboriginal group, that is First Nations (North American Indian), Métis or Inuk (Inuit).Return to footnote8referrerFootnote 9Users should be aware that the estimates associated with this variable are more affected than most by the incomplete enumeration of certain Indian reserves and Indian settlements in the 2016 Census of Population. For additional information, refer to the Aboriginal Peoples Reference Guide, Census of Population, 2016.Return to footnote9referrerFootnote 10'Multiple Aboriginal responses' includes persons who are any two or all three of the following: First Nations (North American Indian), Métis or Inuk (Inuit).Return to footnote10referrerFootnote 11'Aboriginal responses not included elsewhere' includes persons who are not First Nations (North American Indian), Métis or Inuk (Inuit), but who have Registered or Treaty Indian status and/or Membership in a First Nation or Indian band.

Abstract

Access to up-to-date socio-economic data is a widespread challenge in Vanuatu and other Pacific Island Countries. To increase data availability and promote evidence-based policymaking, the Pacific Observatory provides innovative solutions and data sources to complement existing survey data and analysis. One of these data sources is a series of High Frequency Phone Surveys (HFPS), which began in 2020 to monitor the socio-economic impacts of the COVID-19 Pandemic, and since 2023 has grown into a series of continuous surveys for socio-economic monitoring. See https://www.worldbank.org/en/country/pacificislands/brief/the-pacific-observatory for further details.

For Vanuatu, data for December 2023 – January 2025 was collected with each month having approximately 1000 households in the sample and is representative of urban and rural areas but is not representative at the province level. This dataset contains combined monthly survey data for all months of the continuous HFPS in Vanuatu. There is one date file for household level data with a unique household ID. And a separate file for individual level data within each household data, that can be matched to the household file using the household ID, and which also has a unique individual ID within the household data which can be used to track individuals over time within households, where the data is panel data.

Geographic coverage

National, urban and rural. Six provinces were covered by this survey: Sanma, Shefa, Torba, Penama, Malampa and Tafea.

Analysis unit

Household and individuals.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Vanuatu High Frequency Phone Survey (HFPS) sample is drawn from the list of customer phone numbers (MSIDNS) provided by Digicel Vanuatu, one of the country’s two main mobile providers. Digicel’s customer base spans all regions of Vanuatu. For the initial data collection, Digicel filtered their MSIDNS database to ensure a representative distribution across regions. Recognizing the challenge of reaching low-income respondents, Digicel also included low-income areas and customers with a low-income profile (defined by monthly spending between 50 and 150 VT), as well as those with only incoming calls or using the IOU service without repayment. These filtered lists were then randomized, and enumerators began calling the numbers.

This approach was used to complete the first round of 1,000 interviews. The respondents from this first round formed a panel to be surveyed monthly. Each month, phone numbers from the panel are contacted until all have been interviewed, at which point new phone numbers (fresh MSIDNS from Digicel’s database) are used to replace those that have been exhausted. These new respondents are then added to the panel for future surveys.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire was developed in both English and Bislama. Sections of the Questionnaire:

-Interview Information -Household Roster (separate modules for new households and returning households) -Labor (separate modules for new households and returning households) -Food Security
-Household Income -Agriculture
-Social Protection
-Access to Services -Assets -Perceptions -Follow-up

Cleaning operations

At the end of data collection, the raw dataset was cleaned by the survey firm and the World Bank team. Data cleaning mainly included formatting, relabeling, and excluding survey monitoring variables (e.g., interview start and end times). Data was edited using the software STATA.

The data are presented in two datasets: a household dataset and an individual dataset. The total number of observations is 13,779 in the household dataset and 77,501 in the individual dataset. The individual dataset contains information on individual demographics and labor market outcomes of all household members aged 15 and above, and the household data set contains information about household demographics, education, food security, household income, agriculture activities, social protection, access to services, and durable asset ownership. The household identifier (hhid) is available in both the household dataset and the individual dataset. The individual identifier (hhid_mem) can be found in the individual dataset.

Response rate

In November 2024, a total of 7,874 calls were made. Of these, 2,251 calls were successfully connected, and 1,000 respondents completed the survey. By February 2024, the sample was fully comprised of returning respondents, with a re-contact rate of 99.9 percent.

IntroductionNon-communicable diseases (NCDs) disproportionately affect low- and lower-middle income countries (LLMICs) where 80% of global NCD related deaths occur. LLMICs are the primary focus of interventions to address development and poverty indicators. We aimed to synthesise the evidence of these interventions' impact on the four primary NCDs (cardiovascular disease, diabetes, chronic respiratory disease and cancer) and their common behavioural risk factors (unhealthy diets, physical inactivity, tobacco and alcohol use).MethodsWe systematically searched four online databases (Medline, Embase, Web of Science and Global Health) for primary research conducted in LLMICS, published between January 1st 1990 and February 15th 2016. Studies involved development or poverty interventions which reported on outcomes relating to NCDs. We extracted summary level data on study design, population, health outcomes and potential confounders.ResultsFrom 6383 search results, 29 studies from 24 LLMICs published between 1999 and 2015 met our inclusion criteria. The quality of included studies was limited and heterogeneity of outcome measures required narrative synthesis. One study measured impact on NCD prevalence, one physical activity and 27 dietary components. The majority of papers (23), involved agricultural interventions. Primary outcome measures tended to focus on undernutrition. Intensive agricultural interventions were associated with improved calorie, vitamin, fruit and vegetable intake. However, positive impacts were reliant on participant's land ownership, infection status and limited in generalisability. Just three studies measured adult obesity; two indicated increased income and consequential food affordability had the potential to increase obesity. Overall, there was poor alignment between included studies outcome measures and the key policy options and objectives of the Global Action Plan on NCDs.ConclusionsThough many interventions addressing poverty and development have great potential to impact on NCD prevalence and risk, most fail to measure or report these outcomes. Current evidence is limited to behavioural risk factors, namely diet and suggests a positive impact of agricultural-based food security programmes on dietary indicators. However, studies investigating the impact of improved income on obesity tend to show an increased risk. Embedding NCD impact evaluation into development programmes is crucial in the context of the Sustainable Development Goals and the rapid epidemiological transitions facing LLMICs.