Cost of food in the United States increased 2.90 percent in May of 2025 over the same month in the previous year. This dataset provides the latest reported value for - United States Food Inflation - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Value chain disruptions are expected to trigger sudden price changes and increase in price volatility. This is data from the FAO Daily Prices pages which monitors consumer prices of 14 main food products in all countries and compiles the average price change for each product since 14 February 2020.

Cost of food in Canada increased 3.40 percent in May of 2025 over the same month in the previous year. This dataset provides the latest reported value for - Canada Food Inflation - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Cost of food in India increased 0.99 percent in May of 2025 over the same month in the previous year. This dataset provides - India Food Inflation - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Context:

Global food price fluctuations can cause famine and large population shifts. Price changes are increasingly critical to policymakers as global warming threatens to destabilize the food supply.

Content:

Over 740k rows of prices obtained in developing world markets for various goods. Data includes information on country, market, price of good in local currency, quantity of good, and month recorded.

Acknowledgements:

Compiled by the World Food Program and distributed by HDX.

Inspiration:

This data would be particularly interesting to pair with currency fluctuations, weather patterns, and/or refugee movements--do any price changes in certain staples predict population upheaval? Do certain weather conditions influence market prices?

License:

Released under CC BY-IGO.

Abstract

Food price inflation is an important metric to inform economic policy but traditional sources of consumer prices are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual price trends in rural or poverty-stricken areas, where large populations reside in fragile situations. This data set includes food price estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.

        A dataset of monthly food price inflation estimates (aggregated for all food products available in the data) is also available for all countries covered by this modeling exercise.

Geographic coverage notes

The data cover the following sub-national areas: North, South, Artibonite, Centre, South-East, Grande'Anse, North-East, West, North-West, Market Average

Abstract

Food price inflation is an important metric to inform economic policy but traditional sources of consumer prices are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual price trends in rural or poverty-stricken areas, where large populations reside in fragile situations. This data set includes food price estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.

        A dataset of monthly food price inflation estimates (aggregated for all food products available in the data) is also available for all countries covered by this modeling exercise.

Geographic coverage notes

The data cover the following sub-national areas: Badakhshan, Badghis, Baghlan, Balkh, Bamyan, Daykundi, Farah, Faryab, Paktya, Ghazni, Ghor, Hilmand, Hirat, Nangarhar, Jawzjan, Kabul, Kandahar, Kapisa, Khost, Kunar, Kunduz, Laghman, Logar, Wardak, Nimroz, Nuristan, Paktika, Panjsher, Parwan, Samangan, Sar-e-pul, Takhar, Uruzgan, Zabul, Market Average, Armavir, Ararat, Aragatsotn, Tavush, Gegharkunik, Shirak, Kotayk, Syunik, Lori, Vayotz Dzor, Yerevan, Kayanza, Ruyigi, Bubanza, Karuzi, Bujumbura Mairie, Muramvya, Gitega, Rumonge, Bururi, Kirundo, Cankuzo, Cibitoke, Muyinga, Rutana, Bujumbura Rural, Makamba, Ngozi, Mwaro, SAHEL, CASCADES, SUD-OUEST, EST, BOUCLE DU MOUHOUN, CENTRE-NORD, PLATEAU-CENTRAL, HAUTS-BASSINS, CENTRE, NORD, CENTRE-SUD, CENTRE-OUEST, CENTRE-EST, Khulna, Chittagong, Barisal, Rajshahi, Dhaka, Rangpur, Sylhet, Mymensingh, Ouaka, Mbomou, Bangui, Nana-Mambéré, Ouham, Sangha-Mbaéré, Ombella M'Poko, Mambéré-Kadéï, Vakaga, Ouham Pendé, Lobaye, Haute-Kotto, Kémo, Nana-Gribizi, Bamingui-Bangoran, Haut-Mbomou, Nord, Extrême-Nord, Ouest, Nord-Ouest, Adamaoua, Sud-Ouest, Est, Littoral, Centre, Haut-Uele, Nord-Kivu, Ituri, Tshopo, Kwilu, Kasai, Sud-Kivu, Kongo-Central, Nord-Ubangi, Sud-Ubangi, Kasai-Central, Bas-Uele, Tanganyika, Lualaba, Kasai-Oriental, Kwango, Haut-Lomami, Haut-Katanga, Maniema, Kinshasa, Equateur, Lomami, Likouala, Brazzaville, Point-Noire, Pool, Bouenza, Cuvette, Lekoumou, Nzerekore, Boke, Kindia, Kankan, Faranah, Mamou, Labe, Kanifing Municipal Council, Central River, Upper River, West Coast, North Bank, Lower River, Bafata, Tombali, Cacheu, Sector Autonomo De Bissau, Biombo, Oio, Gabu, Bolama, Quinara, North, South, Artibonite, South-East, Grande'Anse, North-East, West, North-West, SULAWESI UTARA, SUMATERA UTARA, KALIMANTAN UTARA, JAWA BARAT, NUSA TENGGARA BARAT, NUSA TENGGARA TIMUR, SULAWESI SELATAN, JAMBI, JAWA TIMUR, KALIMANTAN SELATAN, BALI, BANTEN, JAWA TENGAH, RIAU, SUMATERA BARAT, KEPULAUAN RIAU, PAPUA, SULAWESI BARAT, BENGKULU, MALUKU UTARA, DAERAH ISTIMEWA YOGYAKARTA, KALIMANTAN BARAT, KALIMANTAN TENGAH, PAPUA BARAT, SUMATERA SELATAN, MALUKU, KEPULAUAN BANGKA BELITUNG, ACEH, DKI JAKARTA, SULAWESI TENGGARA, KALIMANTAN TIMUR, LAMPUNG, GORONTALO, SULAWESI TENGAH, Anbar, Babil, Baghdad, Basrah, Diyala, Dahuk, Erbil, Ninewa, Kerbala, Kirkuk, Missan, Muthanna, Najaf, Qadissiya, Salah al-Din, Sulaymaniyah, Thi-Qar, Wassit, Coast, North Eastern, Nairobi, Rift Valley, , Eastern, Central, Nyanza, Attapeu, Bokeo, Bolikhamxai, Champasack, Houaphan, Khammouan, Louangphabang, Louangnamtha, Oudomxai, Phongsaly, Salavan, Savannakhet, Sekong, Vientiane Capital, Vientiane, Xaignabouly, Xiengkhouang, Akkar, Mount Lebanon, Baalbek-El Hermel, Beirut, Bekaa, El Nabatieh, Nimba, Grand Kru, Grand Cape Mount, Gbarpolu, Grand Bassa, Rivercess, Montserrado, River Gee, Lofa, Bomi, Bong, Sinoe, Maryland, Margibi, Grand Gedeh, East, North Central, Uva, Western, Sabaragamuwa, Southern, Northern, North Western, Kidal, Gao, Tombouctou, Bamako, Kayes, Koulikoro, Mopti, Segou, Sikasso, Yangon, Rakhine, Shan (North), Kayin, Kachin, Shan (South), Mon, Tanintharyi, Mandalay, Kayah, Shan (East), Chin, Magway, Sagaing, Zambezia, Cabo_Delgado, Tete, Manica, Sofala, Maputo, Gaza, Niassa, Inhambane, Maputo City, Nampula, Hodh Ech Chargi, Hodh El Gharbi, Brakna, Adrar, Assaba, Guidimakha, Gorgol, Trarza, Tagant, Dakhlet-Nouadhibou, Nouakchott, Tiris-Zemmour, Central Region, Southern Region, Northern Region, Tillaberi, Tahoua, Agadez, Zinder, Dosso, Niamey, Maradi, Diffa, Abia, Borno, Yobe, Katsina, Kano, Kaduna, Gombe, Jigawa, Kebbi, Oyo, Sokoto, Zamfara, Lagos, Adamawa, Cordillera Administrative region, Region XIII, Region VI, Region V, Region III, Autonomous region in Muslim Mindanao, Region IV-A, Region VIII, Region VII, Region X, Region II, Region IV-B, Region XII, Region XI, Region I, National Capital region, Region IX, North Darfur, Blue Nile, Nile, Eastern Darfur, West Kordofan, Gedaref, West Darfur, North Kordofan, South Kordofan, Kassala, Khartoum, White Nile, South Darfur, Red Sea, Sennar, Al Gezira, Central Darfur, Tambacounda, Diourbel, Ziguinchor, Kaffrine, Dakar, Saint Louis, Fatick, Kolda, Louga, Kaolack, Kedougou, Matam, Thies, Sedhiou, Shabelle Hoose, Juba Hoose, Bay, Banadir, Shabelle Dhexe, Gedo, Hiraan, Woqooyi Galbeed, Awdal, Bari, Juba Dhexe, Togdheer, Nugaal, Galgaduud, Bakool, Sanaag, Mudug, Sool, Warrap, Unity, Jonglei, Northern Bahr el Ghazal, Upper Nile, Central Equatoria, Western Bahr el Ghazal, Eastern Equatoria, Western Equatoria, Lakes, Aleppo, Dar'a, Quneitra, Homs, Deir-ez-Zor, Damascus, Ar-Raqqa, Al-Hasakeh, Hama, As-Sweida, Rural Damascus, Tartous, Idleb, Lattakia, Ouaddai, Salamat, Wadi Fira, Sila, Ennedi Est, Batha, Tibesti, Logone Oriental, Logone Occidental, Guera, Hadjer Lamis, Lac, Mayo Kebbi Est, Chari Baguirmi, Ennedi Ouest, Borkou, Tandjile, Mandoul, Moyen Chari, Mayo Kebbi Ouest, Kanem, Barh El Gazal, Ndjaména, Al Dhale'e, Aden, Al Bayda, Al Maharah, Lahj, Al Jawf, Raymah, Al Hudaydah, Hajjah, Amran, Shabwah, Dhamar, Ibb, Sana'a, Al Mahwit, Marib, Hadramaut, Sa'ada, Amanat Al Asimah, Socotra, Taizz, Abyan

Cost of food in Jamaica increased 6.46 percent in May of 2025 over the same month in the previous year. This dataset provides - Jamaica Food Inflation- actual values, historical data, forecast, chart, statistics, economic calendar and news.

ABSTRACT Food prices play a major role in setting inflation rates, and in recent years’ global climatic conditions has worsened a lot while global demand is increasing due to the growth of the middle class in countries such as China and India. Rising food prices remains a key concern for the government of Saudi Arabia. Saudi Arabia remains vulnerable to increases in food prices due to its high dependence on imports. The Saudi economy is an open-market based economy which is reflected by data of foreign trade with trading partners of the Kingdom. High degree of economic openness of a country causes the domestic inflation rate to be affected by change in the prices of goods in the country of origin. Saudi government is facing the challenge of limiting inflation amid a spike in global food prices. Another major challenge to the effectiveness of the Saudi monetary policy is the lack of autonomy due to the pegged exchange rate system with the US dollar. This paper attempts to study the market dynamics of the kingdom of Saudi Arabia, drivers responsible for inflation and measures that has been taken by the government to deal with the situation.

The global food prices have surged to historical highs, and there is no consensus on the reasons behind this round of price increases in academia. Based on theoretical analysis, this study uses monthly data from January 2000 to May 2022 and machine learning models to examine the root causes of that period’s global food price surge and global food security situation. The results show that: Firstly, the increase in the supply of US dollars and the rise in oil prices during pandemic are the two most important variables affecting food prices. The unlimited quantitative easing monetary policy of the US dollar is the primary factor driving the global food price surge, and the alternating impact of oil prices and excessive US dollar liquidity are key features of the surge. Secondly, in the context of the global food shortage, the impact of food production reduction and demand growth expectations on food prices will further increase. Thirdly, attention should be paid to potential agricultural import supply chain risks arising from international uncertainty factors such as the ongoing Russia-Ukraine conflict. The Russian-Ukrainian conflict has profoundly impacted the global agricultural supply chain, and crude oil and fertilizers have gradually become the main driving force behind the rise in food prices.

About this Dataset

The Consumer Price Index (CPI) for food is a component of the all-items CPI. The CPI measures the average change over time in the prices paid by urban consumers for a representative market basket of consumer goods and services. While the all-items CPI measures the price changes for all consumer goods and services, including food, the CPI for food measures the changes in the retail prices of food items only. ERS's monthly update is usually released on the 25th of the month; however, if the 25th falls on a weekend or a holiday, the monthly update will be published on either the 23rd or 24th. This report provides a detailed outline of ERS's forecasting methodology, along with measures to test the precision of the estimates (May 2015). At ERS, work on the CPI for food consists of several activities. ERS reports the current index level for food, examines changes in the CPI for food, and constructs forecasts of the CPI for food for the next 12-18 months. Forecasting the CPI for food has become increasingly important due to the changing structure of food and agricultural economies and the important signals the forecasts provide to farmers, processors, wholesalers, consumers, and policymakers. As a natural extension of ERS's work with the CPI for food, ERS also analyzes and models forecasts for the Producer Price Index (PPI). The PPI is similar to the CPI in that it measures price changes over time; however, instead of measuring changes in retail prices, the PPI measures the average change in prices paid to domestic producers for their output. The PPI collects data for nearly every industry in the goods-producing sector of the economy. Changes in farm-level and wholesale-level PPIs are of particular interest in forecasting food CPIs. cpi

Category

Economic

Keywords

cpi,restaurant,wholesale-food-prices

Row Count

68

Price

Free

Food Price Index in World decreased to 127.70 Index Points in May from 128.70 Index Points in April of 2025. This dataset includes a chart with historical data for World Food Price Index.

Overview

The report presents updated estimates of household food expenditure trends and examines further issues relating to Australia's household food expenditure. The analysis builds on a June 2017 ABARES report that examined recent trends in food demand in Australia and a range of food security issues.

Key Issues

Between 2009-10 and 2016-17, the key drivers of Australia's household food demand growth were, in order of importance, population growth, changes in tastes and preferences (including lifestyle choices), lower real food prices and real income growth. While population growth is important, increasing the number of people seeking to meet their energy and nutrition requirements, there has also been a broadly-based shift toward spending on meals out and fast foods, with the share of meals out and fast foods in household food expenditure in Australia increasing from 31 per cent in 2009-10 to 34 per cent in 2015-16. This increases food expenditure per person, all else constant.

Domestic household consumption is still the most important market for food producers (based on value), but food exports have recovered strongly in recent years, from $25 billion in 2009-10 to $39 billion in 2016-17 (in 2015-16 prices); the share of exports in Australia's indicative food production increased from a recent low of 25 per cent in 2009-10 to 33 per cent in 2016-17.

Two key questions posed in the report relate to food security across population sub-groups and economic opportunities for farmers and other food product and service providers. • Food security-based on average outcomes in population sub-groups in 2015-16 using HES data, the Australian Government's transfer system is important in ensuring a high level of food security across households in Australia; some households, such as those highly reliant on family support payments, may require complementary support, for example, from non-government organisations.

• Economic opportunities in the domestic food supply chain-future food demand growth in Australia will be underpinned by population and income growth. For people living in higher income and/or net worth households, there is a demonstrated willingness to pay a premium for quality attributes of food products and services, including convenience factors. Food labelling is a key approach to inform consumers about quality attributes that may earn a price premium.

A key challenge in the long-term trend toward increased demand for meals out and fast foods is to ensure people have information about food attributes such as nutrition content. Reliable and well understood food product and service labelling may enhance nutrition security in Australia, and allow consumers to make food choices that are more closely aligned with their tastes and preferences (including in relation to nutrition and health), and wider circumstances, as well as contributing to reducing food waste.

Monthly average retail prices for selected products, for Canada and provinces. Prices are presented for the current month and the previous four months. Prices are based on transaction data from Canadian retailers, and are presented in Canadian current dollars.

Cost of food in European Union increased 3.60 percent in May of 2025 over the same month in the previous year. This dataset provides - European Union Food Inflation - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Summary of Commodity Price Relationships Across Data Sources.

Abstract

Food price inflation is an important metric to inform economic policy but traditional sources of consumer prices are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual price trends in rural or poverty-stricken areas, where large populations reside in fragile situations. This data set includes food price estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.

        A dataset of monthly food price inflation estimates (aggregated for all food products available in the data) is also available for all countries covered by this modeling exercise.

Geographic coverage notes

The data cover the following sub-national areas: Khulna, Chittagong, Barisal, Rajshahi, Dhaka, Rangpur, Sylhet, Mymensingh, Market Average

A food delivery service is facing challenges in achieving profitability across its operations. With a dataset of 1,000 food orders, the service seeks to understand the dynamics of its cost structure and profitability to identify strategic opportunities for improvement.

The dataset contains comprehensive details on food orders, including Order ID, Customer ID, Restaurant ID, Order and Delivery Date and Time, Order Value, Delivery Fee, Payment Method, Discounts and Offers, Commission Fee, Payment Processing Fee, and Refunds/Chargebacks. This data provides a foundation for analyzing the cost structure and profitability of the food delivery service.

Your task is to conduct:

1. Detailed Cost Analysis: Identifying the major cost components associated with delivering food orders, including direct costs like delivery fees and indirect costs like discounts and payment processing fees.
2. Profitability Evaluation: Calculating the profitability of individual orders and aggregating this data to assess overall profitability. This involves examining how revenue generated from commission fees measures against the total costs.
3. Strategic Recommendations for Improvement: Based on the cost and profitability analysis, identifying actionable strategies to reduce costs, adjust pricing, commission fees, and discount strategies to improve profitability. This includes finding a “sweet spot” for commission and discount percentages that ensures profitability across orders.
4. Impact Simulation of Proposed Strategies: Simulating the financial impact of the recommended strategies on profitability, using the dataset to forecast how adjustments in commission rates and discount strategies could potentially transform current losses into profits.

Cost of food in Iceland increased 6 percent in June of 2025 over the same month in the previous year. This dataset provides the latest reported value for - Iceland Food Inflation - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

A transition to healthy diets like the EAT-Lancet Planetary Health Diet could considerably reduce GHG emissions. However, the specific contributions of dietary shifts for the feasibility of 1.5°C pathways remain unclear. Here, we use the open-source Integrated Assessment Modeling (IAM) framework REMIND-MAgPIE to compare 1.5°C pathways with and without dietary shifts. We find that a flexitarian diet increases the feasibility of the Paris Agreement climate goals in different ways: The reduction of GHG emissions related to dietary shifts, especially methane from ruminant enteric fermentation, increases the 1.5°C-compatible carbon budget. Therefore, dietary shifts allow us to achieve the same climate outcome with less carbon dioxide removal (CDR) and less stringent CO2 emission reductions in the energy system, which reduces pressure on GHG prices, energy prices and food expenditures. This dataset provides raw data for all figures shown in the paper. It includes data for three scenarios: SSP2-NDC, SSP2-1.5°C and SSP2-1.5°C-DietShift. All data is at global level and for the period 2020-2100. Raw data for each figure panel is provided on individual data sheets in a single .xlsx file. The structure of the data is identical on each sheet, organized in columns for model, scenario, region, variable, unit, year and value. Methods This dataset has been produced with the open-source Integrated Assessment Modeling (IAM) framework REMIND-MAgPIE. The source code for MAgPIE 4.6.7 is openly available at https://github.com/magpiemodel and http://doi.org/10.5281/zenodo.7923602. The model documentation can be found at https://rse.pik-potsdam.de/doc/magpie/4.6.7/. Instructions for software installation and running the model are available at https://github.com/magpiemodel/magpie. The source code for REMIND 3.2.0 is openly available at https://github.com/remindmodel and http://doi.org/10.5281/zenodo.7852740. The model documentation can be found at https://rse.pik-potsdam.de/doc/remind/3.2.0. Instructions for software installation, running the model and coupling to MAgPIE (tutorials subfolder) are available at https://github.com/remindmodel/remind.