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 China decreased 0.40 percent in May 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 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 Netherlands increased 3.90 percent in March 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.

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.

Yelp Dataset JSON Each file is composed of a single object type, one JSON-object per-line.

Take a look at some examples to get you started: https://github.com/Yelp/dataset-examples.

Note: the follow examples contain inline comments, which are technically not valid JSON. This is done here to simplify the documentation and explaining the structure, the JSON files you download will not contain any comments and will be fully valid JSON.

business.json Contains business data including location data, attributes, and categories.

{ // string, 22 character unique string business id "business_id": "tnhfDv5Il8EaGSXZGiuQGg",

// string, the business's name
"name": "Garaje",

// string, the full address of the business
"address": "475 3rd St",

// string, the city
"city": "San Francisco",

// string, 2 character state code, if applicable
"state": "CA",

// string, the postal code
"postal code": "94107",

// float, latitude
"latitude": 37.7817529521,

// float, longitude
"longitude": -122.39612197,

// float, star rating, rounded to half-stars
"stars": 4.5,

// integer, number of reviews
"review_count": 1198,

// integer, 0 or 1 for closed or open, respectively
"is_open": 1,

// object, business attributes to values. note: some attribute values might be objects
"attributes": {
  "RestaurantsTakeOut": true,
  "BusinessParking": {
    "garage": false,
    "street": true,
    "validated": false,
    "lot": false,
    "valet": false
  },
},

// an array of strings of business categories
"categories": [
  "Mexican",
  "Burgers",
  "Gastropubs"
],

// an object of key day to value hours, hours are using a 24hr clock
"hours": {
  "Monday": "10:00-21:00",
  "Tuesday": "10:00-21:00",
  "Friday": "10:00-21:00",
  "Wednesday": "10:00-21:00",
  "Thursday": "10:00-21:00",
  "Sunday": "11:00-18:00",
  "Saturday": "10:00-21:00"
}

} review.json Contains full review text data including the user_id that wrote the review and the business_id the review is written for.

{ // string, 22 character unique review id "review_id": "zdSx_SD6obEhz9VrW9uAWA",

// string, 22 character unique user id, maps to the user in user.json
"user_id": "Ha3iJu77CxlrFm-vQRs_8g",

// string, 22 character business id, maps to business in business.json
"business_id": "tnhfDv5Il8EaGSXZGiuQGg",

// integer, star rating
"stars": 4,

// string, date formatted YYYY-MM-DD
"date": "2016-03-09",

// string, the review itself
"text": "Great place to hang out after work: the prices are decent, and the ambience is fun. It's a bit loud, but very lively. The staff is friendly, and the food is good. They have a good selection of drinks.",

// integer, number of useful votes received
"useful": 0,

// integer, number of funny votes received
"funny": 0,

// integer, number of cool votes received
"cool": 0

} user.json User data including the user's friend mapping and all the metadata associated with the user.

{ // string, 22 character unique user id, maps to the user in user.json "user_id": "Ha3iJu77CxlrFm-vQRs_8g",

// string, the user's first name
"name": "Sebastien",

// integer, the number of reviews they've written
"review_count": 56,

// string, when the user joined Yelp, formatted like YYYY-MM-DD
"yelping_since": "2011-01-01",

// array of strings, an array of the user's friend as user_ids
"friends": [
  "wqoXYLWmpkEH0YvTmHBsJQ",
  "KUXLLiJGrjtSsapmxmpvTA",
  "6e9rJKQC3n0RSKyHLViL-Q"
],

// integer, number of useful votes sent by the user
"useful": 21,

// integer, number of funny votes sent by the user
"funny": 88,

// integer, number of cool votes sent by the user
"cool": 15,

// integer, number of fans the user has
"fans": 1032,

// array of integers, the years the user was elite
"elite": [
  2012,
  2013
],

// float, average rating of all reviews
"average_stars": 4.31,

// integer, number of hot compliments received by the user
"compliment_hot": 339,

// integer, number of more compliments received by the user
"compliment_more": 668,

// integer, number of profile compliments received by the user
"compliment_profile": 42,

// integer, number of cute compliments received by the user
"compliment_cute": 62,

// integer, number of list compliments received by the user
"compliment_list": 37,

// integer, number of note compliments received by the user
"compliment_note": 356,

// integer, number of plain compliments received by the user
"compliment_plain": 68,

// integer, number of coo...

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.

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

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.

Abstract

Access to up-to-date socio-economic data is a widespread challenge in Pacific Island Countries. To increase data availability and promote evidence-based policymaking, the Pacific Observatory provides innovative solutions and data sources to complement existing survey data and analysis. One of these data sources is a series of High Frequency Phone Surveys (HFPS), which began in 2020 as a way to monitor the socio-economic impacts of the COVID-19 Pandemic, and since 2023 has grown into a series of continuous surveys for socio-economic monitoring. See https://www.worldbank.org/en/country/pacificislands/brief/the-pacific-observatory for further details.

In Fiji, monthly HFPS data collection commenced in February 2024 on topics including employment, income, food security, health, food prices, assets and well-being. Fieldwork took place in rounds roughly one month in length in a panel method, where each household was only recontacted at least thirty days after the previous interview. Each month has approximately 700 households in the sample and is representative of urban and rural areas and divisions. This dataset contains combined monthly survey data between February and October 2024. There is one date file for household level data with a unique household ID, and a separate file for individual level data within each household data, that can be matched to the household file using the household ID, and which also has a unique individual ID within the household data which can be used to track individuals over time within households

Geographic coverage

Urban and rural areas of Fiji.

Analysis unit

Household, invidiual.

Kind of data

Sample survey data [ssd]

Sampling procedure

The initial sample was drawn through Random Digit Dialing (RDD) with geographic stratification. As an objective of the survey was to measure changes in household economic wellbeing over time, the HFPS sought to contact a consistent number of households across each division month to month. It had a probability-based weighted design, with a proportionate stratification to achieve geographical representation. A panel was established from the outset, where in each subsequent round after February 2024, the survey firm would first attempt to contact all households from the previous month and then attempt to contact households from earlier months that had dropped out. After previous numbers were exhausted, RDD with geographic stratification was used for replacement households. This dataset includes 4,120 completed interviews with 1,360 unique households.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire, which can be found in the External Resources of this documentation, is available in English, with iTaukei translation available. There were few changes to the questionnaire across the survey months, with some sections only asked in some rounds, such as the digital governance module in rounds 3 and 4. The survey instrument consists of the following modules, with notes in parentheses on dates of collection for questions which were not collected consistently across the whole survey period: - Basic information, - Household roster, - Access to Services and Shocks (additional questions on water disruption were asked since April 2024) - Subjective well-being - Food insecurity experience scale (FIES) - Views on the economy and government (some questions were added since May 2024) - Household income - Labor - Agriculture - Medical service utilization - Climate migration (April 2024) - Digital government services (May and June 2024)

Cleaning operations

The raw data were cleaned by the World Bank team using STATA. This included formatting and correcting errors identified through the survey's monitoring and quality control process. The data are presented in two datasets: a household dataset and an individual dataset. The individual dataset contains information on individual demographics and labor market outcomes of all household members aged 15 and above, and the household data set contains information about household demographics, food security, household income, agriculture activities, social protection, subjective well-being, access to services, shocks, and perceptions. The household identifier (panel_hid) is available in both the household dataset and the individual dataset. The individual identifier (panel_indid) can be found in the individual dataset.

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.

Abstract

Access to up-to-date socio-economic data is a widespread challenge in Solomon Islands and other Pacific Island Countries. To increase data availability and promote evidence-based policymaking, the Pacific Observatory provides innovative solutions and data sources to complement existing survey data and analysis. One of these data sources is a series of High Frequency Phone Surveys (HFPS), which began in 2020 as a way to monitor the socio-economic impacts of the COVID-19 Pandemic, and since 2023 has grown into a series of continuous surveys for socio-economic monitoring. See https://www.worldbank.org/en/country/pacificislands/brief/the-pacific-observatory for further details.

For Solmon Islands, after five rounds of data collection from 2020-2020, in April 2023 a monthly HFPS data collection commenced and continued for 18 months (ending September 2024) –on topics including employment, income, food security, health, food prices, assets and well-being. Fieldwork took place in two non-consecutive weeks of each month. Data for April 2023-December 2023 were a repeated cross section, while January 2024 established the first month of a panel, the was continued to September 2024. Each month has approximately 550 households in the sample and is representative of urban and rural areas, but is not representative at the province level. This dataset contains combined monthly survey data for all months of the continuous HFPS in Solomon Islands. There is one date file for household level data with a unique household ID. and a separate file for individual level data within each household data, that can be matched to the household file using the household ID, and which also has a unique individual ID within the household data which can be used to track individuals over time within households, where the data is panel data.

Geographic coverage

Urban and rural areas of Solomon Islands.

Analysis unit

Household, individual.

Kind of data

Sample survey data [ssd]

Sampling procedure

The initial sample was drawn through Random Digit Dialing (RDD) with geographic stratification. As an objective of the survey was to measure changes in household economic wellbeing over time, the HFPS sought to contact a consistent number of households across each province month to month. This was initially a repeated cross section from April 2023-Dec 2023. The initial sample was drawn from information provided by a major phone service provider in Solomon Islands, covering all the provinces in the country. It had a probability-based weighted design, with a proportionate stratification to achieve geographical representation. The geographical distribution compared to the 2019 Census is listed below for the first month of the HFPS monthly survey:

Choiseul : Census: 4.3%, HFPS: 5.2% Western : Census: 14.4%, HFPS: 13.7% Isabel : Census: 4.8%, HFPS: 4.7% Central : Census: 3.6%, HFPS: 5.2% Ren Bell : Census: 0.6%, HFPS: 1.4% Guadalcanal: Census: 19.8%, HFPS: 21.1% Malaita : Census: 23.1%, HFPS: 18.7% Makira : Census: 5.6%, HFPS: 5.6% Temotu: Census: 3.0%, HFPS: 3% Honiara: Census: 20.7%, HFPS: 21.3%

Source: Census of Population and Housing 2019

Note: The values in the HFPS column represent the proportion of survey participants residing in each province, based on the raw HFPS data from April.

In April 2023, the geographic distribution of World Bank HFPS participants was generally similar to that of the census data at the province level, though within provinces, areas with less mobile phone connectivity are likely to be underrepresented. One indication of this is that urban areas constituted 38.2 percent of the survey sample, which is a slight overrepresentation, compared to 32.5 percent in the Census 2019.

A monthly panel was established in January 2024, that is ongoing as of March 2025. In each subsequent month after January 2024, the survey firm would first attempt to contact all households from the previous month and then attempt to contact households from earlier months that had dropped out. After previous numbers were exhausted, RDD with geographic stratification was used for replacement households. Across all months of the survey a total of, 9,926 interviews were completed.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire, which can be found in the External Resources of this documentation, is available in English, with Solomons Pijin translation. There were few changes to the questionnaire across the survey months, but some sections were only introduced in 2024, namely energy access questions and questions to inform the baseline data of the Solomon Islands Government Integrated Economic Development and Climate Resilience (IEDCR) project.

Cleaning operations

The raw data were cleaned by the World Bank team using STATA. This included formatting and correcting errors identified through the survey’s monitoring and quality control process. The data are presented in two datasets: a household dataset and an individual dataset. The total number of observations is 9,926 in the household dataset and 62,054 in the individual dataset. The individual dataset contains information on individual demographics and labor market outcomes of all household members aged 15 and above, and the household data set contains information about household demographics, education, food security, food prices, household income, agriculture activities, social protection, access to services, and durable asset ownership. The household identifier (hhid) is available in both the household dataset and the individual dataset. The individual identifier (id_member) can be found in the individual dataset.

Cost of food in Singapore increased 1.10 percent in May of 2025 over the same month in the previous year. This dataset provides the latest reported value for - Singapore 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.

Cost of food in Germany increased 2.80 percent in February 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.

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.

Cost of food in Nigeria increased 21.14 percent in May of 2025 over the same month in the previous year. This dataset provides - Nigeria Food Inflation - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.