Cost of food in the United States increased 3.10 percent in September 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.

This dataset contains Food Prices data for India, sourced from the World Food Programme Price Database. The World Food Programme Price Database covers foods such as maize, rice, beans, fish, and sugar for 98 countries and some 3000 markets. The data goes back as far as 1992 for a few countries, although many countries started reporting from 2003 or thereafter.

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.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: Web page with links to Excel files For complete information, please visit https://data.gov.

This dataset contains Food Prices data for Benin, sourced from the World Food Programme Price Database. The World Food Programme Price Database covers foods such as maize, rice, beans, fish, and sugar for 98 countries and some 3000 markets. It is updated weekly but contains to a large extent monthly data. The data goes back as far as 1992 for a few countries, although many countries started reporting from 2003 or thereafter.

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 .

View monthly updates and historical trends for Food Price Index. Source: World Bank. Track economic data with YCharts analytics.

In May 2025, the Consumer Price Index (CPI) for food in Mexico reached 162.58. In the same month of the previous year, the value was of 155.04. The food inflation rate that month was of around four percent, and in July 2024, food was the category with the highest inflation rate in Mexico.

In July 2025, the 12-month inflation rate in the United States was at 2.7 percent. While some non-products continued to see increases in prices, such as hardware and housekeeping supplies, prices for other categories saw some decreases. In that month, the CPI for appliances fell by 0.3 percent in the U.S..

Access the Senegal Food Prices dataset, sourced from the World Food Programme Price Database. Covering staple foods like maize, rice, beans, fish, and sugar, this dataset provides comprehensive insights into food prices. Encompassing data from 98 countries and approximately 3000 markets, it serves as a valuable resource for researchers, policymakers, and humanitarian organizations keen on monitoring and analyzing food price dynamics in Senegal.

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

Food prices refer to the average price of particular food commodities.

Food prices can vary a lot, and changes over time can often give us insights into the underlying markets and agricultural production within countries.

The price of foods gives an important indicator of the balance between agricultural production and market demand. These prices matter to consumers and producers. They have obvious impacts on consumer affordability. But they also affect the income of farmers and producers.

In low-to-middle-income countries, a large share of the population is employed in agriculture. Producers typically benefit from higher food prices; consumers from lower prices. Food markets can therefore have a strong impact on food affordability, hunger and undernourishment, and dietary quality.

By Hannah Ritchie, Pablo Rosado and Max Roser (2023) - "Food Prices".

Slovakia Core and Net Inflation: YoY: Food Prices data was reported at 2.600 % in Oct 2018. This records a decrease from the previous number of 4.100 % for Sep 2018. Slovakia Core and Net Inflation: YoY: Food Prices data is updated monthly, averaging 2.450 % from Jan 1998 (Median) to Oct 2018, with 250 observations. The data reached an all-time high of 11.100 % in Jun 2008 and a record low of -6.400 % in Oct 2009. Slovakia Core and Net Inflation: YoY: Food Prices data remains active status in CEIC and is reported by Statistical Office of the Slovak Republic. The data is categorized under Global Database’s Slovakia – Table SK.I009: Core and Net Inflation.

Immerse yourself in this dataset, a vital resource within the Bangladesh Data Grid. Curated from the World Food Programme Price Database, it provides insights into food prices, including staples like maize, rice, beans, fish, and sugar.

Dive into market dynamics, reveal trends, and extract valuable insights for research, policymaking, and a nuanced understanding of Bangladesh's diverse culinary landscape.

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.

Unveil the economic dynamics of Uganda's food markets with this comprehensive dataset. A vital component of the Uganda Data Grid, this collection of Food Prices data is sourced from the World Food Programme Price Database.

Navigate through market trends, analyze pricing variations, and delve into essential commodities like maize, rice, beans, fish, and sugar. Whether for research, economic analysis, or policy-making, this dataset provides a valuable lens into Uganda's food economy.

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.

The Quarterly Food-at-Home Price Database provides food price data to support research on the economic determinants of food consumption, diet quality, and health outcomes.

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: 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, Market Average

Consumer Price Index for All Urban Consumers: Food at Home in U.S. City Average Based on the Federal Reserve Economic Data Research. Collected monthly

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: Likouala, Brazzaville, Point-Noire, Pool, Bouenza, Cuvette, Lekoumou, Market Average