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.

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.

Food Price Index in World increased to 128 Index Points in June from 127.30 Index Points in May of 2025. This dataset includes a chart with historical data for World Food Price Index.

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 China decreased 0.30 percent in June 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.

Cost of food in Netherlands increased 4.40 percent in June of 2025 over the same month in the previous year. This dataset provides - Netherlands Food Inflation - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Abstract

Key components of the WFSO database cover the prevalence of severe food insecurity, including estimates for countries lacking official data, population sizes of the severely food insecure, and required safety net financing. Data is presented in a user-friendly format.

WFSO data primarily relies on hunger and malnutrition data from the State of Food Security and Nutrition in the World (SOFI) report, led by the Food and agriculture Organization (FAO) in collaboration with multiple UN agencies. WFSO complements SOFI data by providing estimates for unreported countries. Historical estimates are produced with a machine learning model leveraging World Development Indicators (WDI) for global coverage.

Financing needs for safety nets are calculated similarly to past approaches by the International Development Association (IDA) to assess food insecurity response needs (IDA (2020) and IDA (2021)). Preliminary estimates and projections rely on the same model and incorporate International Monetary Fund (IMF)'s World Economic Outlook (WEO) growth and inflation forecasts. WEO data reflects the IMF's expert analysis from various sources, including government agencies, central banks, and international organizations.

Minor gaps in WDI data inflation data are replaced with unofficial WEO estimates. Minor inflation data gaps not covered by both, are replaced with unofficial inflation estimates from the World Bank's Real Time Food Prices (RTFP) data.

The WFSO is updated three times a year, coinciding with IMF's WEO and SOFI releases. It provides food security projections that align with economic forecasts, aiding policymakers in integrating food security into economic planning.

The WFSO database serves various purposes, aiding World Bank economists and researchers in economic analysis, policy recommendations, and the assessment of global financing needs to address food insecurity.

Additionally, the WFSO enhances transparency in global food security data by tracking regional and global figures and breaking them down by individual countries. Historical estimates support research and long-term trend assessments, especially in the context of relating outlooks to past food security crises.

Geographic coverage

World

Geographic coverage notes

191 countries and territories mutually included by the World Bank's WDI and IMF's WEO databases. The country coverage is based on mutual inclusion in both the World Bank World Development Indicators database and the International Monetary Fund’s World Economic Outlook database. Some countries and territories may not be covered. Every attempt is made to provide comprehensive coverage. To produce complete historical predictions, missing data in the WDI are completed with unofficial data from the WEO and the World Bank's RTFP data when inflation data is not available in either database. Final gaps in the WDI and WEO are interpolated using a Kernel-based pattern-matching algorithm. See background documentation for equations.

Analysis unit

Country

Kind of data

Process-produced data [pro]

this is the Zomato dataset that has excellent features and is best for EDA and analysis for good practice you can use this dataset. below is the explanation of each feature. Restaurant ID 9551 non-null int64

1 Restaurant Name --> name of the Restaurant 2 Country Code --> code of the country Each country has their unique code 3 City --> city in which the restaurant located 4 Address --> address of the restaurant
5 Locality --> famous location around the restaurant
6 Locality Verbose --> It is also similar to location 7 Longitude --> geographic coordinate that specifies the east-west position
8 Latitude --> latitude is a coordinate that specifies the north-south position
9 Cuisines --> name of Cuisines
10 Average Cost for two --> cost if two-person book a table or order food 11 Currency --> transaction currency type
12 Has Table booking --> Is table booking available
13 Has Online delivery --> is the restaurant accept online delivery
14 Is delivering now --> It tells at the current time the restaurant is taking orders 15 Switch to the order menu --> not a useable feature you can drop it 16 Price range --> price range of food and booking
17 Aggregate rating --> The average rating based on multiple ratings or reviews
18 Rating colour --> It is a feature that shows ratings using colour 19 Rating text --> rating text like good poor, best, not rated etc.
20 Votes --> number of votes or ratings 21 Country --> name of the country in which the restaurant located 22 Restaurant ID --> the id of the restaurant provided by Zomato

Measures of monthly UK inflation data including CPIH, CPI and RPI. These tables complement the consumer price inflation time series dataset.

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.

Annual indexes for major components and special aggregates of the Consumer Price Index (CPI), for Canada, provinces, Whitehorse, Yellowknife and Iqaluit. Data are presented for the last five years. The base year for the index is 2002=100.

Cost of food in Japan increased 6.50 percent in May of 2025 over the same month in the previous year. This dataset provides the latest reported value for - Japan 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 South Africa increased 4.80 percent in May of 2025 over the same month in the previous year. This dataset provides the latest reported value for - South Africa Food Inflation - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Abstract

The purpose of this second round of data collection was to understand food insecurity in conflict affected regions. Armed conflict can have a detrimental effect on food security. This might be due to for example reduced agricultural production, or price increases due to malfunctioning markets. Food insecurity might be permanent, such that a household living below the poverty line has a constant struggle to acquire food from the market or produce food for their own use. In situations such as armed conflict, also better endowed households might be temporarily food insecure. In this report, we find that food insecurity is a major concern in all the three regions studied:

• The mean household in all the three regions is “highly food insecure”.

• North East of Nigeria is the most food insecure of the three regions.

• Reducing meals or portion size is the most important coping strategy in all three regions.

• Food prices are the most important source of food insecurity in all three regions.

• A large majority of households rely on the market as the main source of food in all regions. Price concerns should therefore be taken very seriously by policy makers.

• Households in all three regions do not report there being an inadequate supply of food in the market.

Geographic coverage

Zones, States and Local Government Areas (LGAs).

Analysis unit

• Individuals

• Households

• Communities

Universe

The survey covered all household members. The questionnaire was administered to only one respondent per household - most often a male household head.

Kind of data

Sample survey data [ssd]

Sampling procedure

In the three conflict affected geographical zones comprising of 16 states of Nigeria, households from LGS's that had high conflict exposure were oversampled chosen for a pilot sample, conducted before the telephone surveys. These LGS's were chosen based on the following criteria: The oversampled LGS's needed to have over 10 conflict events during 2012-14 recorded in the Armed Conflict Location & Event Data Project (ACLED) database.

The first round of the telephone survey (which took place after the pilot) first attempted to reach 742 households from the GHS panel, of which 529 could be reached and interviewed. The rest did not have phone numbers or functioning phone numbers (only 2.7 percent refused to answer). In order to increase the sample size to a level that was considered adequate for the survey, an additional 288 replacement households were included in the sample also from the GHS panel. Out of these replacement households 188 could be interviewed. Therefore altogether 1030 households were attempted to be reached, with a final sample size of 717 completed interviews.

Conflict affected areas were oversampled in order to have a large enough sample of households that in fact experienced conflict events in order to shed light on the type of events that have happened. A random sample of the zones might have given too small sample of conflict affected households and therefore restricted the analysis of the various types of conflict events. Due to the oversampling however, the sample drawn was not representative at the level of the geographical zone, as is the case in the GHS. Therefore in the analysis we use sampling weights that adjust for the propensity of being in a conflict affected LGA in order to ensure that the sample is representative at the level of the geographical zone.

During the second round of the survey 582 of the 717 households were re-interviewed on food security related issues (only the 717 were attempted to be reached). Of the 582 households 147 in the North East, 219 in North Central, and 216 in South South were interviewed. The attrition rates in our sample from round one to round two are hence 16 percent, 21 percent, and 19 percent for North East, North Central and South South, respectively. The attrition from the conflict survey round was mostly due to not being able to reach the respondents possibly due to non-functioning phone numbers. Only 3 percent of respondents refused to answer.

Similar telephone-based surveys are being conducted in six countries in Sub-Saharan Africa under the World Bank project "Listening to Africa". As a comparison, a mobile phone survey in Tanzania (see Croke et al. 2012 for details), had a high drop-out rate between the very first rounds from 550 to 458 respondents, but very low attrition for the subsequent rounds for the 458 respondents, who could reliably be reached by a mobile phone. In light of this reference point and also considering the fact that the households interviewed live in conflict affected regions, our attrition rates seem to be within reasonable limits.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire is divided into 9 sections including a household roster. Information on food insecurity (the coping strategy index, CSI), food and market access, water quality, employment, income, employment and assets were collected.

Cleaning operations

Data was analyzed using descriptive statistics in Stata 15. All data analysis was tracked using comprehensive do files to ensure reproducibility. All statistics presented in this report have been adjusted with probability weights, when possible, to be representative at the level of the geopolitical zone.

Demographics for each geopolitical zone were analyzed based on the complete GHS 2016 dataset.

Response rate

The first round of the telephone survey (which took place after the pilot), first attempted to reach 742 households from the GHS panel, of which 529 could be reached and interviewed. The rest did not have phone numbers or functioning phone numbers (only 2.7 percent refused to answer). In order to increase the sample size to a level that was considered adequate for the survey, an additional 288 replacement households were included in the sample also from the GHS panel. Out of these replacement households, 188 could be interviewed. Therefore altogether 1030 households were attempted to be reached, with a final sample size of 717 completed interviews.

The response rate is 96%.

Data appraisal

Limitations

• Recall Bias

In the pilot data collection, respondents were asked to report on conflict events that had taken place in their family and their community over the last six years. This extremely long recall period must be considered when drawing inferences from the data. People are likely to under-report less severe (and therefore less memorable) events, particularly those that happened to community members in larger communities. Respondents are also more likely to recall events that happened to family members than those that happened to community members. Other biases may also be at play - for example, those who have been most highly affected by conflict over the last six years may have moved to another community. These factors demonstrate the importance of implementing a regular data collection schedule, which would allow for more accurate data to be collected.

• Sampling Bias

The GHS is a panel survey taking place over multiple rounds through a period of time. Therefore, households that are more mobile or households that are nomadic are less likely to be represented in this sample. This may be particularly relevant in circumstances where nomadic groups are named as perpetrators of conflict events.

• Power Dynamics There are some disadvantages to the phone system, and for this reason, it should be supplemented by additional types of data collection wherever possible. In a mobile phone survey, the respondent is the person who owns a mobile phone. In many areas, particularly those highly affected by poverty and those located in rural areas, only one family member owns a mobile phone. This is generally the household head, who is most likely male. Furthermore, in many of these communities, women are not allowed to have access to mobile phones and are forbidden from speaking to outsiders, which can prohibit mobile phone-based data collection.

• Gender Dynamics The questionnaire was administered to only one respondent per household - most often a male household head. This means that crimes that carry a stigma, especially sexual violence, are less likely to be reported. In this dataset, no sexual assault was reported despite data collected elsewhere that indicate that rape was used as a weapon by Boko Haram and elsewhere. This also means that violence that affects members of the household with less power (such as women, children, and employees), is less likely to be reported. This may be particularly important when considering violence not related to ongoing external conflict, such as domestic violence.

Core consumer prices in the United States increased 2.80 percent in May of 2025 over the same month in the previous year. This dataset provides - United States Core Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Cost of food in Egypt increased 6.90 percent in June of 2025 over the same month in the previous year. This dataset provides the latest reported value for - Egypt 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 Germany increased 2.70 percent in June of 2025 over the same month in the previous year. This dataset provides the latest reported value for - Germany 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 Indonesia increased 3.69 percent in January of 2025 over the same month in the previous year. This dataset provides the latest reported value for - Indonesia Food Inflation - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Wheat fell to 545.50 USd/Bu on July 11, 2025, down 1.62% from the previous day. Over the past month, Wheat's price has risen 3.61%, but it is still 0.95% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Wheat - values, historical data, forecasts and news - updated on July of 2025.