number of observations : 23972
observation : households
country : Spain

References Journal of Applied Econometrics data archive : http://qed.econ.queensu.ca/jae/.

This dataset contains a series of indicators related to income and expenditure for Kiribati, Tuvalu and Vanuatu based on Household Income and Expenditure Surveys (HIES). Indicators included are the following: Number of households, Proportion of households, Number of persons, Proportion of persons, Income, Income per household, Income per person, Proportion of income, Expenditure, Expenditure per household, Expenditure per person, Proportion of expenditure. The table provides a breakdown by geography (1 sub-national level), sex, age and urbanization, poverty status (2 categories) and food security status (2 categories). This dataset has been compiled as a result of a collaborative project on food security between the Pacific Community (SPC) and the Food and Agriculture Organization of the United Nations (FAO).

Find more Pacific data on PDH.stat.

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.

The increase in current-dollar personal income in November primarily reflected an increase in compensation that was partly offset by decreases in personal income receipts on assets and personal current transfer receipts (table 2).

The $81.3 billion increase in current-dollar PCE in November reflected an increase of $48.3 billion in spending for goods and an increase of $33.0 billion in spending for services (table 2). Within goods, the largest contributors to the increase were motor vehicles and parts (led by new motor vehicles) and recreational goods and vehicles (led by video, audio, photographic and information processing equipment and media). Within services, the largest contributors to the increase were spending for financial services and insurance (led by financial service charges, fees, and commissions); recreation services (led membership clubs, sports centers, parks, theaters and museums as well as gambling); and health care (led by hospitals). Detailed information on monthly PCE spending can be found on Table 2.4.5U.

Personal outlays—the sum of PCE, personal interest payments, and personal current transfer payments—increased $78.2 billion in November (table 2). Personal saving was $968.1 billion in November and the personal saving rate—personal saving as a percentage of disposable personal income—was 4.4 percent (table 1).

Prices

From the preceding month, the PCE price index for November increased 0.1 percent (table 5). Prices for goods increased less than 0.1 percent and prices for services increased 0.2 percent. Food prices increased 0.2 percent and energy prices also increased 0.2 percent. Excluding food and energy, the PCE price index increased 0.1 percent. Detailed monthly PCE price indexes can be found on Table 2.4.4U.

From the same month one year ago, the PCE price index for November increased 2.4 percent (table 7). Prices for goods decreased 0.4 percent and prices for services increased 3.8 percent. Food prices increased 1.4 percent and energy prices decreased 4.0 percent. Excluding food and energy, the PCE price index increased 2.8 percent from one year ago.

Real PCE

The 0.3 percent increase in real PCE in November reflected an increase of 0.7 percent in spending on goods and an increase of 0.1 percent in spending on services (table 4). Within goods, the largest contributors to the increase were recreational goods and vehicles (led by video, audio, photographic and information processing equipment and media) and motor vehicles and parts (led by new motor vehicles). Within services, the largest contributors to the increase were recreation services (led by gambling as well as membership clubs, sports centers, parks, theaters and museums). Detailed information on monthly real PCE spending can be found on Table 2.4.6U.

Percent of Household Income Spent on Food as Forecasted for 2022

A collection of over 75 charts and maps presenting key statistics on the farm sector, food spending and prices, food security, rural communities, the interaction of agriculture and natural resources, and more. How much do you know about food and agriculture? What about rural America or conservation? ERS has assembled more than 75 charts and maps covering key information about the farm and food sectors, including agricultural markets and trade, farm income, food prices and consumption, food security, rural economies, and the interaction of agriculture and natural resources. How much, for example, do agriculture and related industries contribute to U.S. gross domestic product? Which commodities are the leading agricultural exports? How much of the food dollar goes to farmers? How do job earnings in rural areas compare with metro areas? How much of the Nation’s water is used by agriculture? These are among the statistics covered in this collection of charts and maps—with accompanying text—divided into the nine section titles.

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.

A structured data that describes how much of my 2021 annual salary was spent each month on; - Accomodating/Housing/Rent (how much I pay as rent every month) - Health/Fitness (how much I spent on gym subscriptions and dietary products and medicines) - Personal expenses (other social and personal expenses) - Utilities/Energy (how much I spent on gas and electricity and water and internet) - Automobiles/Transport (how much I spent on repairs of my car and fuel) - Food/Dining (cost of grocery and dinners I had with friends and family) I made sure I collected receipts and stamps for each item that was purchased and each bill that was paid and everything was totaled at the end of the month. For items that I could not get receipts or stamps, the expense was written in my handbook and captured during the data collection stage.

I categorised all the expenses under these 6 headings and created an excel table for its analysis and visualization purposes. I hope to find out if there's a way I can reduce my expenditure and increase savings and also understand how much of my incomes goes into what particular expenditure.

This dataset provides numbers and proportions of households involved in primary activities (livestock, fishing, handicraft), by geography (1 sub-national level), sex, age and urbanization, poverty status (2 categories) and food security status (2 categories) for Pacific island countries based on Household Income and Expenditure Surveys (HIES). The table has been compiled as a result of a collaborative project on food security between the Pacific Community (SPC) and the Food and Agriculture Organization of the United Nations (FAO).

Find more Pacific data on PDH.stat.

Survey of Household Spending (SHS), average household spending on detailed food categories.

This dataset provides insights into the cost of living and average monthly income across various countries and regions worldwide from 2000 to 2023. It includes critical economic indicators such as housing costs, taxes, healthcare, education, transportation expenses, and savings rates. The data is ideal for analyzing economic trends, regional comparisons, and financial planning.

Column Descriptions: Country: The name of the country where the data was recorded. Region: The geographical region to which the country belongs (e.g., Asia, Europe). Year: The year when the data was recorded. Average_Monthly_Income: The average monthly income of individuals in USD. Cost_of_Living: The average monthly cost of living in USD, including essentials like housing, food, and utilities. Housing_Cost_Percentage: The percentage of income spent on housing expenses. Tax_Rate: The average tax rate applied to individuals' income, expressed as a percentage. Savings_Percentage: The portion of income saved monthly, expressed as a percentage. Healthcare_Cost_Percentage: The percentage of income spent on healthcare services. Education_Cost_Percentage: The percentage of income allocated to educational expenses. Transportation_Cost_Percentage: The percentage of income spent on transportation costs.

Dataset Structure & Description

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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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The housing affordability measure illustrates the relationship between income and housing costs. A household that spends 30% or more of its collective monthly income to cover housing costs is considered to be “housing cost-burden[ed].”[1] Those spending between 30% and 49.9% of their monthly income are categorized as “moderately housing cost-burden[ed],” while those spending more than 50% are categorized as “severely housing cost-burden[ed].”[2]

How much a household spends on housing costs affects the household’s overall financial situation. More money spent on housing leaves less in the household budget for other needs, such as food, clothing, transportation, and medical care, as well as for incidental purchases and saving for the future.

The estimated housing costs as a percentage of household income are categorized by tenure: all households, those that own their housing unit, and those that rent their housing unit.

Throughout the period of analysis, the percentage of housing cost-burdened renter households in Champaign County was higher than the percentage of housing cost-burdened homeowner households in Champaign County. All three categories saw year-to-year fluctuations between 2005 and 2023, and none of the three show a consistent trend. However, all three categories were estimated to have a lower percentage of housing cost-burdened households in 2023 than in 2005.

Data on estimated housing costs as a percentage of monthly income was sourced from the U.S. Census Bureau’s American Community Survey (ACS) 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Housing Tenure.

[1] Schwarz, M. and E. Watson. (2008). Who can afford to live in a home?: A look at data from the 2006 American Community Survey. U.S. Census Bureau.

[2] Ibid.

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (17 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (22 September 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (30 September 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (10 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (10 June 2021).;U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (14 September 2017).; U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; 16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).

This dataset contains a series of indicators related to nutritional facts for Kiribati, Solomon Islands and Vanuatu based on Household Income and Expenditure Surveys (HIES). Indicators included are the following: Average edible quantity, Average Dietary Energy Consumption, Average expenditures, Percentage of HH who consumed at least one product of the group, Average quantity as acquired, Percentage of households who consumed more than the average number of products consumed in the group, Percentage of households who consumed less than the average number of products consumed in the group, Average number of products consumed by household by food group. The table provides a breakdown by type of food (21 FAO groups), geography (1 sub-national level), sex, age and urbanization, poverty status (2 categories) and food security status (2 categories). This dataset has been compiled as a result of a collaborative project on food security between the Pacific Community (SPC) and the Food and Agriculture Organization of the United Nations (FAO).

Find more Pacific data on PDH.stat.

This dataset provides numbers and proportions of households involved in primary activities (crop, livestock, fishing), by geography (1 sub-national level), sex, age and urbanization for the Pacific island countries, based on Household Income and Expenditure Surveys (HIES). The table has been compiled as a result of a collaborative project on food security between the Pacific Community (SPC) and the pacific island countries.

Find more Pacific data on PDH.stat.

Abstract

Household Income and Expenditure Surveys (HIES) are implemented to rebase consumer price indices and estimates of household contribution to national gross domestic product. More recently, HIES data have been used in poverty analyses and to conduct nutrition and food security oriented analyses.

The more recent applications of HIES data – poverty, nutrition and food security – require the use of edible-portion conversion factors to convert the reported acquisition of wholefoods into edible portions so estimates can be made of what people apparently ingest. These data then require the use of food composition tables (FCTs) to convert the edible portion into caloric and nutrient consumption values, so total energy and nutrient consumption can be estimated.

HIES data in the Pacific region are coded using the United Nations Statistics Division’s Classification of Individual Consumption According to Purpose (COICOP); however, there is no regionally standardised linkage between COICOP and the Pacific Islands Food Composition Tables Second Edition (PIFCT). Furthermore, the PIFCTs do not have edible-portion conversion factors and are insufficient to cover the full list of foods reported in the HIES.

To address this, the Pacific Nutrient Database (PNDB) was developed to provide the Pacific region with a standard set of conversion factors and food composition data that are mapped to COICOP (1999). To add more value to the database, each food item is also mapped to COICOP 2018, classified into FAO Commodity Groups and food groups to compute Household Dietary Diversity Scores (HDDS). The PNDB includes 26 components plus edible and inedible portions for a total of 822 foods.

Geographic coverage

Pacific Region.

Analysis unit

Commodities

Kind of data

Aggregate data [agg]

Mode of data collection

Other [oth]

Research instrument

Questionnaires used were those from Household Income and Expenditure Surveys (HIES) in the Pacific Region.

Cleaning operations

Data editing was done using the software Excel.

Data appraisal

The dataset was constructed in Excel and is a compilation of data from various food composition tables.

Abstract

The Household Income, Expenditure and Consumption Survey (HIECS) is of great importance among other household surveys conducted by statistical agencies in various countries around the world. This survey provides a large amount of data to rely on in measuring the living standards of households and individuals, as well as establishing databases that serve in measuring poverty, designing social assistance programs, and providing necessary weights to compile consumer price indices, considered to be an important indicator to assess inflation. The HIECS 2010/2011 is the tenth Household Income, Expenditure and Consumption Survey that was carried out in 2010/2011, among a long series of similar surveys that started back in 1955. The survey main objectives are:

• To identify expenditure levels and patterns of population as well as socio- economic and demographic differentials.

• To measure average household and per-capita expenditure for various expenditure items along with socio-economic correlates.

• To Measure the change in living standards and expenditure patterns and behavior for the individuals and households in the panel sample, previously surveyed in 2008/2009, for the first time during 12 months representing the survey period.

• To define percentage distribution of expenditure for various items used in compiling consumer price indices which is considered important indicator for measuring inflation.

• To estimate the quantities, values of commodities and services consumed by households during the survey period to determine the levels of consumption and estimate the current demand which is important to predict future demands.

• To define average household and per-capita income from different sources.

• To provide data necessary to measure standard of living for households and individuals. Poverty analysis and setting up a basis for social welfare assistance are highly dependent on the results of this survey.

• To provide essential data to measure elasticity which reflects the percentage change in expenditure for various commodity and service groups against the percentage change in total expenditure for the purpose of predicting the levels of expenditure and consumption for different commodity and service items in urban and rural areas.

• To provide data essential for comparing change in expenditure against change in income to measure income elasticity of expenditure.

• To study the relationships between demographic, geographical, housing characteristics of households and their income.

• To provide data necessary for national accounts especially in compiling inputs and outputs tables.

• To identify consumers behavior changes among socio-economic groups in urban and rural areas.

• To identify per capita food consumption and its main components of calories, proteins and fats according to its nutrition components and the levels of expenditure in both urban and rural areas.

• To identify the value of expenditure for food according to its sources, either from household production or not, in addition to household expenditure for non-food commodities and services.

• To identify distribution of households according to the possession of some appliances and equipments such as (cars, satellites, mobiles ,…etc) in urban and rural areas that enables measuring household wealth index.

• To identify the percentage distribution of income earners according to some background variables such as housing conditions, size of household and characteristics of head of household.

Compared to previous surveys, the current survey experienced certain peculiarities, among which :

1- The total sample of the current survey (26.5 thousand households) is divided into two sections:

a- A new sample of 16.5 thousand households. This sample was used to study the geographic differences between urban governorates, urban and rural areas, and frontier governorates as well as other discrepancies related to households characteristics and household size, head of the household's education status, etc.

b- A panel sample with 2008/2009 survey data of around 10 thousand households was selected to accurately study the changes that may have occurred in the households' living standards over the period between the two surveys and over time in the future since CAPMAS will continue to collect panel data for HIECS in the coming years.

2- The number of enumeration area segments is reduced from 2526 in the previous survey to 1000 segments for the new sample, with decreasing the number of households selected from each segment to be (16/18) households instead of (19/20) in the previous survey.

3- Some additional questions that showed to be important based on previous surveys results, were added, such as:

a- Collect the expenditure data on education and health on the person level and not on the household level to enable assessing the real level of average expenditure on those services based on the number of beneficiaries.

b- The extent of health services provided to monitor the level of services available in the Egyptian society.

c- Smoking patterns and behaviors (tobacco types- consumption level- quantities purchased and their values).

d- Counting the number of household members younger than 18 years of age registered in ration cards.

e- Add more details to social security pensions data (for adults, children, scholarships, families of civilian martyrs due to military actions) to match new systems of social security.

f- Duration of usage and current value of durable goods aiming at estimating the service cost of personal consumption, as in the case of imputed rents.

4- Quality control procedures especially for fieldwork, are increased, to ensure data accuracy and avoid any errors in suitable time, as well as taking all the necessary measures to guarantee that mistakes are not repeated, with the application of the principle of reward and punishment. 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 is a public good, in the interest of the region, and it is consistent with the Economic Research Forum's mandate to make micro data available, aiding regional research on this important topic.

Geographic coverage

National

Analysis unit

1- Household/family

2- Individual/Person

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 sample of HIECS, 2010-2011 is a self-weighted two-stage stratified cluster sample, of around 26500 households. The main elements of the sampling design are described in the following:

1- Sample Size It has been deemed important to collect a smaller sample size (around 26.5 thousand households) compared to previous rounds due to the convergence in the time period over which the survey is conducted to be every two years instead of five years because of its importance. The sample has been proportionally distributed on the governorate level between urban and rural areas, in order to make the sample representative even for small governorates. Thus, a sample of about 26500 households has been considered, and was distributed between urban and rural with the percentages of 47.1 % and 52.9, respectively. This sample is divided into two parts: a- A new sample of 16.5 thousand households selected from main enumeration areas. b- A panel sample with 2008/2009 survey data of around 10 thousand households.

2- Cluster size The cluster size in the previous survey has been decreased compared to older surveys since large cluster sizes previously used were found to be too large to yield accepted design effect estimates (DEFT). As a result, it has been decided to use a cluster size of only 16 households (that was increased to 18 households in urban governorates and Giza, in addition to urban areas in Helwan and 6th of October, to account for anticipated non-response in those governorates: in view of past experience indicating that non-response may almost be nil in rural governorates). While the cluster size for the panel sample was 4 households.

3- Core Sample The core sample is the master sample of any household sample required to be pulled for the purpose of studying the properties of individuals and families. It is a large sample and distributed on urban and rural areas of all governorates. It is a representative sample for the individual characteristics of the Egyptian society. This sample was implemented in January 2010 and its size reached more than 1 million household (1004800 household) selected from 5024 enumeration areas distributed on all governorates (urban/rural) proportionally with the sample size (the enumeration area

“Food Price Crowdsourcing Africa” (FPCA) is a research project led by the European Commission - Directorate Joint Research Centre (JRC), and implemented in collaboration with the International Institute of Tropical Agriculture (IITA), Nigeria and Wageningen University and Research (WUR), The Netherlands, to understand food price changes along the food chain while strengthening agricultural & market information systems through mobile phone technology and citizens' participation. This project is a component of a broader initiative themed "Support to the AgRi-Economic analysis & modelling of agriculture & development policies impact for Africa (AREA)", which is led and funded by the JRC and implemented in collaboration with WUR.

As society and economy rapidly transforms by the expansion of digital technologies, new data sources represent a unique opportunity to produce new and complementary statistics within collective collaborative frameworks. The goal of this project (~10 months) was to explore the potentiality of a “spontaneous crowdsourcing” (i.e. “Citizen Science”) approach by leveraging citizen involvement, smart phone technology and an adequate quality framework to produce reliable food price data with very high spatial details and temporal frequency, with the purpose to provide to consumers and farmers accurate and timely information on agricultural commodity markets, allowing them to make informed decisions about consumption and production and leading to a more efficient function of markets. Agricultural markets transparency is particularly relevant in many African countries where agriculture is still the main source of income for a big share of population as well as expenditure on food represents an important share of the total household income. At the same time market transparency can help to understand market dynamics and so to shape national and regional decisions and policies regarding food security and markets.

The dataset is continuously updated for the whole the duration of the project. The dataset is accompanied by an interactive dashboard updated twice a day that allows users to perform a wide exploration of the data. A set of graphs and charts are displayed according to states and regions, weeks, and products selected by the user.

This is considered a beta version: user feedback is welcome to further improve the dashboard.

Data extracted from the household income and expenditure surveys (HIES)

Find more Pacific data on PDH.stat.

The 2014 Global Nutrition Report Dataset contains data for all the indicators that were used in Global Nutrition Report 2014: Actions and Accountability to Accelerate the World's Progress on Nutrition . The data are compiled from secondary sources including United Nations Children's Fund (UNICEF), World Health Organization (WHO), and the World Bank among many others. The dataset broadly contains information on adult and child nutrition, economic demography, nutrition intervention coverage, and policy legislation in the nutrition sector. The data visualization based on a subset of this dataset can be accessed here.