This dataset analyzes expenditures on major consumption categories including food and different food subcategories across 114 countries. The dataset is created from USDA (United States Department of Agriculture)-Economic Research Service calculations using 2005 International Comparison Program (ICP) 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

This map shows the average amount spent on meals away from home at restaurants or other per household in the U.S. in 2020 in a multiscale map (by country, state, county, ZIP Code, tract, and block group).The pop-up is configured to include the following information for each geography level:Average annual spending on meals at restaurants per householdAverage annual spending on all food away from home per householdAverage annual spending on food by meal typeThis map shows Esri's 2020 U.S. Consumer Spending Data in Census 2010 geographies. The map adds increasing level of detail as you zoom in, from state, to county, to ZIP Code, to tract, to block group data.Esri's 2020 U.S. Consumer Spending database provides the details about which products and services consumers buy, including total dollars spent, average amount spent per household, and a Spending Potential Index. Esri's Consumer Spending database identifies hundreds of items in more than 15 categories, including apparel, food and beverage, financial, entertainment and recreation, and household goods and services. See Consumer Spending database to view the methodology statement and complete variable list.Additional Esri Resources:Esri DemographicsU.S. 2020/2025 Esri Updated DemographicsEssential demographic vocabularyPermitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

This data package contains information on adverse Food Events 2004 to 2018, ingredient database of dietary supplements, International Food Consumption Database and Nutrition Assistance Program Participation and Cost Database.

This database contains food demand elasticities estimates collected from a literature review carried out in 2015 as part of a contract funded by the International Food Policy Research Institute (IFPRI) (contract n° 2015X144.FEM). It served as a basis for the meta-analysis of price and income elasticities of food demand presented in Femenia (2019). Data collection: Two reports providing food demand elasticities published by the United States Department of Agriculture (USDA) (Seale et al. (2003) and Muhammad et al. (2011)) are frequently used to calibrate demand functions in global economic models. In these reports, price and income elasticities are estimated for eight broad food categories and for a large number of countries. This broad level of country coverage renders these elasticity data well-suited for calibrating large simulation models. Economists might however wish to use other source of elasticities for different reasons when, for instance, they consider food products at a higher disaggregation level or when they wish to compare results obtained with a calibration of demand parameters based on USDA estimates to those obtained with a calibration based on other estimates given in the literature. The USDA provides a literature review database (USDA, 2005), which contains this type of information. This database collects own price, cross price, expenditure and income demand elasticity estimates from papers that have been published and/or presented in the United States (US) between 1979 and 2005. While the database covers a large variety of products at various aggregation levels, few countries are included. These two sources of data, namely, the USDA’s estimates given in Seale et al. (2003) and Muhammad et al. (2011) and the USDA’s literature review database, were used as a basis to build the database presented here. We started with the structure of the USDA literature review database, which includes useful information on each elasticity estimate, such as the references of the papers from which the estimates have been collected; the countries, products and time periods concerned; the types of data used to conduct estimations; and the demand models estimated. The elasticities estimated by Seale et al. (2003) and Muhammad et al. (2011) were also included. We then reviewed the primary studies to check the information included in the USDA database and to ensure the consistency of the data. Of the 74 references present in these data, five PhD dissertations were not available to us, thus restricting our ability to verify the data and to collect new information, and we decided to exclude these references. In a second step, we searched for new references providing food demand elasticity estimates in the economic literature with a focus on pre-2005 studies dealing with countries other than the US and China and with a focus on post-2005 studies regardless of the country. The search was performed with Google Scholar in March 2015 using the following combinations of keywords: “price, elasticities, food, demand” and “income, elasticities, food, demand”. We did not limit our search to published papers; working papers, reports, and papers presented at conferences were also included. A total of 72 references were collected in this way. All price and income elasticity estimates of food demand reported in these references were collected. Among own price elasticities we distinguished uncompensated (Marshallian) price elasticities from compensated (Hicksian) elasticities. The final database contains 25,117 food demand elasticities estimates collected from 148 studies published between 1973 and 2014. Information included and data coding: In addition to the values of elasticity estimates and the references of the primary studies from which they have been collected, our database incorporate several variables aimed at providing detailed information on the estimated values. These descriptive variables contain information related to the type of data used to estimate the elasticities (time series, panel or cross section), to whether these data have been collected at the micro (household) or macro (country) level, to the decade in which they have been collected, which ranges from 1950 to 2010, and to the countries and products to which these data refer. To homogenize the information on food products, product names as they appear in the primary studies are mapped to the following eight product categories: beverages and tobacco, cereals, dairy products, fruits and vegetables, oils and fats, meat and fish, other food products and non-food products. Given that these categories are in some cases much broader than the product levels considered in primary studies, a variable representing the aggregation level of the primary data is also associated with each observation. The following four aggregation levels are considered: “global food aggregate”; “product category aggregate”, which corresponds to the aforementioned categories;...

The dataset contains information on the Food Consumption Score (FCS) and the Reduced Coping Strategy Index (rCSI) for various countries in Central America. The data include different columns according to the category, with three possibilities for FCS: Poor, Acceptable, Borderline. The categories for rCSI are Acceptable, Moderate, and Severe. Each value shows the percentage of the sample that falls under a certain category. The data have been collected since January 2023 and are categorized by country, department, and municipality.

For more information contact GIS4Tech: info@gis4tech.com. You can also visit the PREDISAN platform https://predisan.gis4tech.com/ca4 for detailed, accurate information.

Feed the Future Northern Kenya Interim Survey in the Zone of Influence: This dataset (n=53,070, vars=17) contains data from sub-Module E3: Non-Food Expenditures Over Past One Month. Each household with data for non-food expenditures over the past month has multiple records (for the 29 non-food items in sub-Module E3). (53,070 records divided by 29 non-food items = 1,830 Module E households with sub-Module E3 data.)

This table presents information on tourism spending of foreign visitors in Canada by country of residence, tourism region and spending category. Country of residence is organised into eleven major source of travellers to Canada including the United States, Australia, China, Japan, South Korea, India, United Kingdom, France, Germany, Mexico and other overseas countries. Spending categories include accommodation, food and beverage, transportation in Canada, recreation and entertainment, and clothes and gifts.

This table contains 85 series, with data for years 1981 - 1998 (not all combinations necessarily have data for all years), and was last released on 2009-01-21. This table contains data described by the following dimensions (Not all combinations are available): Geography (2 items: Canada; United States ...), Comparisons (5 items: Ratios of real consumption per person in the United States compared with Canada; Current expenditure; by category; Real expenditure; by category; Purchasing power parities ...), Expenditure categories (17 items: Gross domestic product (GDP); Food; beverages and tobacco; Clothing and footwear; Individual consumption by households ...).

This shows the market potential for an adult to regularly eat organic food in the U.S. in 2021 in a multiscale map (by country, state, county, ZIP Code, tract, and block group). The pop-up is configured to include the following information for each geography level:Market Potential Index and count of adults expected to regularly eat organic foodMarket Potential Index and count of adults expected to follow various dietary habitsEsri's 2021 Market Potential (MPI) data measures the likely demand for a product or service in an area. The database includes an expected number of consumers and a Market Potential Index (MPI) for each product or service. An MPI compares the demand for a specific product or service in an area with the national demand for that product or service. The MPI values at the US level are 100, representing average demand for the country. A value of more than 100 represents higher demand than the national average, and a value of less than 100 represents lower demand than the national average. For example, an index of 120 implies that demand in the area is 20 percent higher than the US average; an index of 80 implies that demand is 20 percent lower than the US average. See Market Potential database to view the methodology statement and complete variable list.Esri's Psychographics & Advertising Data Collection includes measurements of environmental concern, buying habits such as propensity to buy American products, likelihood to have healthy habits, and advertisement awareness. The database includes an expected number of consumers and a Market Potential Index (MPI) for each product or service. See the United States Data Browser to view complete variable lists for each Esri demographics collection.Additional Esri Resources:U.S. 2021/2026 Esri Updated DemographicsEssential demographic vocabularyEsri's arcgis.com demographic mapsPermitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

https://imgur.com/h4ObE3v.jpg" alt="World Trade">

TL;DR

• I'd recommend going through the Content to understand the Data.
• This data is Clean
• The data contains Exports made by Russia to the world i. 225 Partnering countries ii. ~**20** Years iii. ~**3000** unique commodities SITC Code description
• Starter Code to optimize performance

The world trade is going through a massive change after the COVID-19 Situation fueled by the recent Russia-Ukraine conflict, This is Russia's part in the World Trade for around 15years with 225 countries.
Reference to sanctions might help drawing conclusions

Context & Inspiration

The COVID-19 pandemic is likely to be known as that inflection point in the history which changed the nature of the post-World Trade Organization (WTO) global trade policy environment. The last time the world witnessed a similar situation was in 1995 when WTO was established, creating a rule-based global trading system.

The war in Ukraine is causing worldwide disruptions to trade and investment, affecting auto makers in Europe, hoteliers in Georgia and the Maldives, as well as impacting consumers of food and fuel globally . Although the world’s poor—who spend a large part of their incomes on life’s necessities—are the most vulnerable, no country, region, or industry is left untouched by these disruptions.

Content

Classification - SITC Version 4( Latest ) Code description | more
Year - Year when the trade was made 📆
Commodity Code - Code of the Commodity
Commodity - Name of the Commodity more
Qty Unit - Unit of the Item / Quantity
Qty - Quantity of Item Netweight (kg) - Item weight in kilograms ⚓️
Trade Value (US$) - Trade value in US Dollars 💵
Aggregate Level - 5 Levels ( 1 to 5 ) You can choose one / All ( Group is the sweet spot ) more | Aggregate Level | Level Name | Code Format | Number of Items | | --- | --- | --- | --- | | 1 | Section | 0 | 10 Items | | 2 | Division | 01 | 67 Items | | 3 | Group | 012 | 261 Items | | 4 | Subgroup | 012.1 | 1033 Items | | 5 | Item | 012.13 | 3121 Items |

Reporter Code	Reporter	Reporter ISO
Reporting Countries Code	Name of country reporting	Reporting countries ISO Code
644 (Constant)	Russia (Constant)	RUS (Constant)

Partner Code	Partner	Partner ISO
Partner ( Receiver ) Countries Code	Partner countries name	Partner ISO Code
3 Digit code	225 Unique countries	3 Digit code)

• Starter Code to optimize performance

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