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.

Food Prices Year By Year

Findings and Implications

About this dataset

In 2022, the world may face a global food crisis. This dataset includes information on food prices, meat prices, dairy prices, cereal prices, oil prices, and sugar prices. This data is of utmost importance to researchers as it will help inform their work on finding solutions to this potential crisis. With this data, we can better understand the factors that may contribute to the crisis and work towards finding solutions that could help prevent or mitigate its effects

How to use the dataset

This dataset contains information on food prices, meat prices, dairy prices, cereal prices, oil prices, and sugar prices. This data is of utmost importance to researchers as it will help inform their work on finding solutions to this potential crisis.

To use this dataset effectively, researchers should focus on the trends in food prices over time. Additionally, they should look at the relationships between different types of food prices. For example, does an increase in meat price lead to a corresponding increase in dairy price? Finally, researchers should also consider how other factors such as oil price or sugar price may impact food prices

Research Ideas

1. Identifying choke points in the global food supply chain
2. Estimating the economic impact of a potential global food crisis
3. Developing policies to mitigate the impact of a potential global food crisis

Acknowledgements

We would like to thank the Department of Agriculture for their data on food prices, meat prices, dairy prices, cereal prices, oil prices, and sugar prices. This dataset is of utmost importance to researchers as it will help inform their work on finding solutions to this potential crisis

License

See the dataset description for more information.

Columns

File: FAOFP1990_2022.csv

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 .

Cost of food in Canada increased 3.40 percent in October 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.

Description

This dataset contains Countries, Commodities, and Markets data, sourced from the World Food Programme Price Database. The volume of data means that the actual Food Prices data is in country-level datasets. 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.

Acknowledgements & Source

Compiled by the World Food Program and distributed by HDX.

Image Source

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The USDA, Economic Research Service (ERS) Food Price Outlook (FPO) provides data on food prices and forecasts annual food price changes up to 18 months in the future. On a monthly basis, ERS forecasts the annual percentage change in food prices for the current year and, beginning in July each year, for the following year. These forecasts are primarily based on the U.S. Department of Labor, Bureau of Labor Statistics’ (BLS) Consumer Price Index (CPI) and Producer Price Index (PPI) data.

This dataset contains the following records from the Food Price Outlook: 1. Changes in Consumer Price Indexes, 2020 through 2023 2. Annual percent changes in selected Consumer Price Indexes, 1974 through 2021

The data examines the historical change in the Consumer Price Index (CPI) for food in the United States. 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. This includes different categories of food such as meat and dairy products, fruits and vegetables, as well as the overall cost of Food at home compared to Food away from home. The CPI for food measures the changes in the retail prices of food items only.

For more information on this dataset visit: ers.usda.gov

Food Price Index in World decreased to 126.40 Index Points in October from 128.50 Index Points in September of 2025. This dataset includes a chart with historical data for World Food Price Index.

The online price changes for a selection of food and drink products from several large UK retailers. These data are experimental estimates developed to deliver timely indicators to help better understand real time economic activity and social change in the UK.

The dataset contains aggregated weekly sales and product-level information for 200 grocery products sold in 10 stores over a period of 52 weeks (a full year). The dataset captures product sales, promotions, store traffic, and other related metrics, along with information on the complementary, substitute, and unrelated product sales.

The dataset is structured in the following columns: 1) Product_ID: Description: Unique identifier for each product in the store's inventory. Type: Integer (ranging from 101 to 300 for a total of 200 products).

2) Product_Category: Description: The product's category in the store. Type: Categorical variable with categories like: Beverages, Dairy, Snacks, Vegetables, Packaged Food, Frozen Foods, Cereals, Breads, Confectionery, Household

3) Week: Description: The week number in the year, ranging from 1 to 52 (52 weeks in total for the year).

4) Promotion_Flag: Description: Indicates whether the product was on promotion for that week. 0: No promotion 1: Promotion (with price discount)

5) Price: Description: The regular price of the product (before any discount). Type: Float (between 1.5 and 6.5)

6) Promoted_Price: Description: The discounted price of the product when it's on promotion. Type: Float (lower than regular price if the product is promoted).

7) Total_Sales_Volume: Description: The total number of units of the product sold during the week. Type: Integer (ranging from 1,000 to 6,000 units per week).

8) Total_Sales_Revenue: Description: The total revenue generated from the sales of the product during the week. Type: Float (calculated as Total Sales Volume × Promoted Price).

9) Store_Traffic: Description: The total number of customers visiting the store during the given week. Type: Integer (between 12,000 and 17,000 visits per week). Note: This value is consistent across all products for the week, as store traffic is not product-specific but depends on the store's overall footfall.

10) Promoted_Complementary_Sales: Description: The sales volume of complementary products during the week for products on promotion. Type: Float (calculated based on the relationship between the promoted product and its complementary items). Note: Complementary items are products that are typically bought together with the promoted product.

11) Non_Promoted_Complementary_Sales: Description: The sales volume of complementary products during the week for products that are not on promotion. Type: Float

12) Promoted_Substitute_Sales: Description: The sales volume of substitute products during the week for products on promotion. Type: Float (substitute items are products that could serve as alternatives to the promoted product).

13) Non_Promoted_Substitute_Sales: Description: The sales volume of substitute products during the week for products that are not on promotion. Type: Float

14) Promoted_Unrelated_Sales: Description: The sales volume of unrelated products during the week for products on promotion. Type: Float (unrelated items are products that are not closely linked to the promoted product).

15) Non_Promoted_Unrelated_Sales: Description: The sales volume of unrelated products during the week for products that are not on promotion. Type: Float

16) Store_Profit: Description: The profit generated by the store during the week. Type: Integer (between 4,000 and 6,000 for each week, consistent across products in the same week).

17) Weekday_Indicator: Description: Indicates whether the sales occurred during a Weekday or Weekend (based on the week’s sequence of days). Type: Categorical, with two possible values: Weekday: Monday through Friday Weekend: Saturday and Sunday Note: The distribution of weekdays and weekends is consistent across all products in a given week, with 5 weekdays and 2 weekend days.

Key Insights & Assumptions: 1) Weekday/Weekend Traffic & Sales: Weekday sales are typically higher than weekend sales, though weekends tend to see a spike in store traffic due to increased customer visits. This is reflected in the Weekday_Indicator column.

2) Promotion Effects: Products on promotion tend to have a lower price and higher sales volume, contributing to changes in retailer profit and possibly store traffic. Sales of complementary products also tend to increase when the promoted product is in high demand, while substitute products often experience a decline during promotion periods.

3) Product Relationships: Complementary products generally see an increase in sales when the promoted product is sold. Conversely, substitute products might experience a dip in sales, especially when a cheaper version or the promoted product is available.

4) Store Traffic & Profit: The store traffic and store profit are constants for each week, but the product-specific sales (both promoted and non-promoted) affect retailer profits based on the prices, volumes, and promotions applied.

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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

Cost of food in China decreased 2.90 percent in October of 2025 over the same month in the previous year. This dataset provides - China Food Inflation - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

Geographic coverage notes

The data cover the following areas: Afghanistan, Armenia, Bangladesh, Burkina Faso, Burundi, Cameroon, Central African Republic, Chad, Congo, Dem. Rep., Congo, Rep., Gambia, The, Guinea, Guinea-Bissau, Haiti, Indonesia, Iraq, Kenya, Lao PDR, Lebanon, Liberia, Libya, Malawi, Mali, Mauritania, Mozambique, Myanmar, Niger, Nigeria, Philippines, Senegal, Somalia, South Sudan, Sri Lanka, Sudan, Syrian Arab Republic, Yemen, Rep.

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.

Monthly average retail prices for food, household supplies, personal care items, cigarettes and gasoline. Prices are presented for the current month and previous four months. Prices are in Canadian current dollars.

Monthly average retail prices for selected products, for Canada, provinces, Whitehorse and Yellowknife. 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 India decreased 5.02 percent in October 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.

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.

Summary of Commodity Price Relationships Across Data Sources.

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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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.